hckrnws

This March 2025 post from Aral Balkan stuck with me:

https://mastodon.ar.al/@aral/114160190826192080

"Coding is like taking a lump of clay and slowly working it into the thing you want it to become. It is this process, and your intimacy with the medium and the materials you’re shaping, that teaches you about what you’re making – its qualities, tolerances, and limits – even as you make it. You know the least about what you’re making the moment before you actually start making it. That’s when you think you know what you want to make. The process, which is an iterative one, is what leads you towards understanding what you actually want to make, whether you were aware of it or not at the beginning. Design is not merely about solving problems; it’s about discovering what the right problem to solve is and then solving it. Too often we fail not because we didn’t solve a problem well but because we solved the wrong problem.

When you skip the process of creation you trade the thing you could have learned to make for the simulacrum of the thing you thought you wanted to make. Being handed a baked and glazed artefact that approximates what you thought you wanted to make removes the very human element of discovery and learning that’s at the heart of any authentic practice of creation. Where you know everything about the thing you shaped into being from when it was just a lump of clay, you know nothing about the image of the thing you received for your penny from the vending machine."

And when programming with agentic tools, you need to actively push for the idea to not regress to the most obvious/average version. The amount of effort you need to expend on pushing the idea that deviates from the 'norm' (because it's novel), is actually comparable to the effort it takes to type something out by hand. Just two completely different types of effort.

There's an upside to this sort of effort too, though. You actually need to make it crystal clear what your idea is and what it is not, because of the continuous pushback from the agentic programming tool. The moment you stop pushing back, is the moment the LLM rolls over your project and more than likely destroys what was unique about your thing in the first place.

You just described the burden of outsourcing programming.

Outsourcing development and vibe coding are incredibly similar processes.

If you just chuck ideas at the external coding team/tool you often get rubbish back.

If you're good at managing the requirements and defining things well you can achieve very good things with much less cost.

With the basic and enormous difference that the feedback loop is 100 or even 1000x faster. Which changes the type of game completely, although other issues will probably arise as we try this new path.

That embeds an assumption that the outsourced human workers are incapable of thought, and experience/create zero feedback loops of their own.

Frustrated rants about deliverables aside, I don't think that's the case.

No. It just means the harsh reality: what's really soul crushing in outsourced work is having endless meetings to pass down / get back information, having to wait days/weeks/months to get some "deliverable" back on which iterate etc. Yes, outsourced human workers are totally capable of creative thinking that makes sense, but their incentive will always be throughput over quality, since their bosses usually give closed prices (at least in what I lived personally).

If you are outsourcing to an LLM in this case YOU are still in charge of the creative thought. You can just judge the output and tune the prompts or go deep in more technical details and tradeoffs. You are "just" not writing the actual code anymore, because another layer of abstraction has been added.

Also, with an LLM you can tell it to throw away everything and start over whenever you want.

When you do this with an outsourced team, it can happen at most once per sprint, and with significant pushback, because there's a desire for them to get paid for their deliverable even if it's not what you wanted or suffers some other fundamental flaw.

Yep, just these past two weeks. I tried to reuse an implementation I had used for another project, it took me a day to modify it (with Codex), I tried it out and it worked fine with a few hundred documents.

Then I tried to push through 50000 documents, it crashed and burned like I suspected. It took one day to go from my second more complicated but more scalable spec where I didn’t depend on an AWS managed service to working scalable code.

It would have taken me at least a week to do it myself

It doesn't have to be soul crushing.

Just like people more, and have better meetings.

Life is what you make it.

Enjoy yourself while you can.

It's not strictly soul-crushing for me, but I definitely don't like to waste time in non-productive meetings where everyone bullshits everyone else. Do you like that? Do you find it a good use of your time and brain attention capacity?

Just have better meetings

If we could I think we would be doing that...

It's going to come across very naive and dumb, but I believe we can and people just aren't aware of or they simply aren't implementing the basics.

Harvard Business Review and probably hundreds of other online content providers provide some simple rules for meetings yet people don't even do these.

1. Have a purpose / objective for the meeting. I consider meetings to fall into one of three broad categories information distribution, problem solving, decision making. Knowing this will allow the meeting to go a lot smoother or even be moved to something like an email and be done with it.

2. Have an agenda for the meeting. Put the agenda in the meeting invite.

3. If there are any pieces of pre-reading or related material to be reviewed, attach it and call it out in the invite. (But it's very difficult to get people to spend the time preparing for a meeting.)

4. Take notes during the meeting and identify any action items and who will do them (preferably with an initial estimate). Review these action items and people responsible in the last couple of minutes of the meeting.

5. Send out the notes and action items.

Why aren't we doing these things? I don't know, but I think if everyone followed these for meetings of 3+ people, we'd probably see better meetings.

Probably like most businesses issues, it's a people problem. They have to care in the first place and idk if you can make people who don't care starting caring.

I agree the info is out there about how to run effective meetings.

Bingo -- 95% of work is people problems.

The coding is the easy part.

With LLMs and advanced models, even more so.

I think there's a certain kind of irony in being asked externally to enjoy the rubbish I've been given to eat. It's still rubbish.

You sit at a desk.

You get paid in the top 1% globally

You have benefits

Some hope or dreams for what to do with your future, life after work, retirement.

You get to work with other people, overseas.

Talk to those contractors sometimes. They are under tremendous pressure. They are mistreated. One wrong move, they're gone. They undergo tremendous prejudices, and soft racism everyday especially by us FTEs.

You find out that they struggle with the drudgery as well, looking for solutions, better understanding, etc.

We all feel disposable by our corporate masters, but they feel it even more so.

Be the change you want to see in the world.

Not really, its just obviously true that the communication cycle with your terminal/LLM is faster than with a human over Slack/email.

100%! There is significant analogy between the two!

There is a reason management types are drawn to it like flies to shit.

Working with and communicating with offshored teams is a specific skill too.

There are tips and tricks on how to manage them and not knowing them will bite you later on. Like the basic thing of never asking yes or no questions, because in some cultures saying "no" isn't a thing. They'll rather just default to yes and effectively lie than admit failure.

We need a new word for on-premise offshoring.

On-shoring ;

> On-shoring

I thought "on-shoring" is already commonly used for the process that undos off-shoring.

How about "in-shoring"? We already have "insuring" and "ensuring", so we might as well add another confusingly similar sounding term to our vocabulary.

How about we leave "...shoring" alone?

Ha, my inexperience is showing :)

En-shoring?

If the on-premise offshoring centers around the use of LLMs then I suggest the term "off-braining." :)

Corporate has been using the term "best-shoring" for a couple of years now. To my best guess, it means "off-shoring or on-shoring, whichever of the two is cheaper".

Rubber-duckying... although a rubber ducky can't write code... infinite-monkeying?

In silico duckying

NIH-shoring?

Ai-shoring.

Tech-shoring.

Would work, but with "snoring". :D

vibe-shoring

eshoring

We already have a perfect one

Slop;

YES!

AI assistance in programming is a service, not a tool. You are commissioning Anthropic, OpenAI, etc. to write the program for you.

Yes, but as with outsourcing those who are making such decisions often lack the awareness, or even skills, to properly specify the requirements and be able to evaluate the results.

Fair enough but I am a programmer because I like programming. If I wanted to be a product manager I could have made that transition with or without LLMs.

Agreed. The higher-ups at my company are, like most places, breathlessly talking about how AI has changed the profession - how we no longer need to code, but merely describe the desired outcome. They say this as though it’s a good thing.

They’re destroying the only thing I like about my job - figuring problems out. I have a fundamental impedance mismatch with my company’s desires, because if someone hands me a weird problem, I will happily spend all day or longer on that problem. Think, hypothesize, test, iterate. When I’m done, I write it up in great detail so others can learn. Generally, this is well-received by the engineer who handed the problem to me, but I suspect it’s mostly because I solved their problem, not because they enjoyed reading the accompanying document.

FWIW, when a problem truly is weird, AI & vibe coding tends to not be able to solve it. Maybe you can use AI to help you spend more time working on the weird problems.

When I play sudoku with an app, I like to turn on auto-fill numbers, and auto-erase numbers, and highlighting of the current number. This is so that I can go directly to the crux of the puzzle and work on that. It helps me practice working on the hard part without having to slog through the stuff I know how to do, and generally speaking it helps me do harder puzzles than I was doing before. BTW, I’ve only found one good app so far that does this really well.

With AI it’s easier to see there are a lot of problems that I don’t know how to solve, but others do. The question is whether it’s wasteful to spend time independently solving that problem. Personally I think it’s good for me to do it, and bad for my employer (at least in the short term). But I can completely understand the desire for higher-ups to get rid of 90% of wheel re-invention, and I do think many programmers spend a lot of time doing exactly that; independently solving problems that have already been solved.

You touch on an aspect of AI-driven development that I don't think enough people realize: choosing to use AI isn't all or nothing.

The hard problems should be solved with our own brains, and it behooves us to take that route so we can not only benefit from the learnings, but assemble something novel so the business can differentiate itself better in the market.

For all the other tedium, AI seems perfectly acceptable to use.

Where the sticking point comes in is when CEOs, product teams, or engineering leadership put too much pressure on using AI for "everything", in that all solutions to a problem should be AI-first, even if it isn't appropriate—because velocity is too often prioritized over innovation.

> choosing to use AI isn't all or nothing.

That's how I have been using AI the entire time. I do not use Claude Code or Codex. I just use AI to ask questions instead of parsing the increasingly poor Google search results.

I just use the chat options in the web applications with manual copy/pasting back and forth if/when necessary. It's been wonderful because I feel quite productive, and I do not really have much of an AI dependency. I am still doing all of my work, but I can get a quicker answer to simple questions than parsing through a handful of outdated blogs and StackOverflow answers.

If I have learned one thing about programming computers in my career, it is that not all documentation (even official documentation) was created equally.

Same! I don't mind copy/pasting a code snippet or asking a question, and I also always ask it to show its sources for anything non-obvious. That alone cuts down on a lot of bullshit.

Though it is not like management roles have ever appreciated the creative aspects of the job, including problem solving. Management has always wished to just describe the desired outcome and get magic back. They don't like acknowledging that problems and complications exist in the first place. Management likes to think that they are the true creatives for company vision and don't like software developers finding solutions bottom up. Management likes to have a single "architect" and maybe a single "designer" for the creative side that they like and are a "rising" political force (in either the Peter Principle or Gervais Principle senses) rather than deal with a committee of creative people. It's easier for them to pretend software developers are blue collar cogs in the system rather than white collar problem solvers with complex creative specialties. LLMs are only accelerating those mechanics and beliefs.

Agreed. I hate to say it, but if anyone thought this train of thought in management was bad now, it's going to get much worse, and unfortunately burnout is going to sweep the industry as tech workers feel evermore underappreciated and invisible to their leaders.

And worse: with few opportunities to grow their skills from rigorous thinking as this blog post describes. Tech workers will be relegated to cleaning up after sloppy AI codebases.

I greatly agree with that deep cynicism and I too am a cynic. I've spent a lot of my career in the legacy code mines. I've spent a lot of my career trying to climb my way out of them or at least find nicer, more lucrative mines. LLMs are the "gift" of legacy-code-as-a-service. They only magnify and amplify the worst parts of my career. The way the "activist shareholder" class like to over-hype and believe in Generative AI magic today only implies things have more room to keep getting worse before they get better (if they ever get better again).

I'm trying my best to adapt to being a "centaur" in this world. (In Chess it has become statistically evident that Human and Bot players of Chess are generally "worse" than the hybrid "Centaur" players.) But even "centaurs" are going to be increasingly taken for granted by companies, and at least for me the sense is growing that as WOPR declared about tic-tac-toe (and thermo-nuclear warfare) "a curious game, the only way to win is not to play". I don't know how I'd bootstrap an entirely new career at this point in my life, but I keep feeling like I need to try to figure that out. I don't want to just be a janitor of other people's messes for the rest of my life.

They’re destroying the only thing I like about my job - figuring problems out.

So, tackle other problems. You can now do things you couldn't even have contemplated before. You've been handed a near-godlike power, and all you can do is complain about it?

> You can now do things you couldn't even have contemplated before. You've been handed a near-godlike power, and all you can do is complain about it?

This seems to be a common narrative, but TBH I don't really see it. Where is all the amazing output from this godlike power? It certainly doesn't seem like tech is suddenly improving at a faster pace. If anything, it seems to be regressing in a lot of cases.

Agreed. Mostly, what has occurred is I now have to field a lot more PRs that weren't well thought out (let's be honest, there was no thought), and - my personal favorite - argue with an AI via a human, where they post an AI's opinion on what I said as a reply. I've started just asking them to post links to documentation validating their claims.

I’m a programmer (well half my job) because I was a short (still short) fat (I got better) kid with a computer in the 80s.

Now, the only reason I code and have been since the week I graduated from college was to support my insatiable addictions to food and shelter.

While I like seeing my ideas come to fruition, over the last decade my ideas were a lot larger than I could reasonably do over 40 hours without having other people working on projects I lead. Until the last year and a half where I could do it myself using LLMs.

Seeing my carefully designed spec that includes all of the cloud architecture get done in a couple of days - with my hands on the wheel - that would have taken at least a week with me doing some work while juggling dealing with a couple of other people - is life changing

Not sure why this is getting downvoted, but you're right — being able to crank out ideas on our own is the "killer app" of AI so to speak.

Granted, you would learn a lot more if you had pieced your ideas together manually, but it all depends on your own priorities. The difference is, you're not stuck cleaning up after someone else's bad AI code. That's the side to the AI coin that I think a lot of tech workers are struggling with, eventually leading to rampant burnout.

What would I learn that I don’t already know? The exact syntax and property of Terraform and boto3 for every single one of the 150+ services that AWS offers? How to modify a React based front end written by another developer even though I haven’t and have actively stayed away from front end development for well over a decade?

Will a company pay me more for knowing those details? Will I be more affectively able to architect and design solutions that a company will pay my employer to contract me to do and my company pays me? They pay me decently not because I “codez real gud”. They pay me because I can go from empty AWS account, empty repo and ambiguous customer requirements to a working solution (after spending time talking to a customer) to a full well thought out architecture + code on time on budget and that meets requirements.

I am not bragging, I’m old those are table stakes to being able to stay in this game for 3 decades

[dead]

I became an auto mechanic because I love machining heads, and dropping oil pans to inspect, and fitting crankshafts in just right, and checking fuel filters, and adjusting alternators.

If I wanted to work on electric power systems I would have become an electrician.

(The transition is happening.)

I can't help but imagine training horses vs training cats. One of them is rewarding, a pleasure, beautiful to see, the other is frustrating, leaves you with a lot of scratches and ultimately both of you "agreeing" on a marginal compromise.

Right now vibe coding is more like training cats. You are constantly pushing against the model's tendency to produce its default outputs regardless of your directions. When those default outputs are what you want - which they are in many simple cases of effectively English-to-code translation with memorized lookup - it's great. When they are not, you might as well write the code yourself and at least be able to understand the code you've generated.

Yup - I've related it to working with Juniors, often smart and have good understandings and "book knowledge" of many of the languages and tools involved, but you often have to step back and correct things regularly - normally around local details and project specifics. But then the "junior" you work with every day changes, so you have to start again from scratch.

I think there needs to be a sea change in the current LLM tech to make that no longer the case - either massively increased context sizes, so they can contain near a career worth of learning (without the tendency to start ignoring that context, as the larger end of the current still-way-too-small-for-this context windows available today), or even allow continuous training passes to allow direct integration of these "learnings" into the weights themselves - which might be theoretically possible today, but is many orders of magnitude higher in compute requirements than available today even if you ignore cost.

Try writing more documentation. If your project is bigger than a one man team then you need it anyways and with LLM coding you effectively have an infinite man team.

But that doesn't actually work for my use cases though, plenty of other people have already told me "I'm Holding It Wrong" without actual suggestions that work I've started ignoring them. At this stage I just assume many people work in very different sectors, and some see the "great benefits" often proselytized on the internet. And other areas don't see that. Systems programming, where I work, seems to be a poor fit - possibly due to relatively lack of content in the training corpus, perhaps due to company internal styles and APIs meaning lots of the context is taken up simply detailing takes a huge amount of the context leaving little for further corrections or details, or some other failure modes.

We have lots of documentation. Arguably too much - it quickly fills much of the claude opus context window with relevant documentation alone, and even then repeatedly outputs things directly counter to the documentation it just ingested.

I've never seen horse that scratches you.

This is why people thinkless of artists like Damien Hirst and Jeff Koons because their hands have never once touched the art. They have no connection to the effort. To the process. To the trail and error. To the suffer. They’ve out sourced it, monetized it, and make it as efficient as possible. It’s also soulless.

To me it feels a bit like literate programming, it forces you to form a much more accurate idea of your project before your start. Not a bad thing, but can be wasteful also when eventually you realise after the fact that the idea was actually not that good :)

Yeah, it's why I don't like trying to write up a comprehensive design before coding in the first place. You don't know what you've gotten wrong until the rubber meets the road. I try to get a prototype/v1 of whatever I'm working on going as soon as possible, so I can root out those problems as early as possible. And of course, that's on top of the "you don't really know what you're building until you start building it" problem.

> need to make it crystal clear

That's not an upside in that it's unique to LLM vs human written code. When writing it yourself, you also need to make it crystal clear. You do that in the language of implementation.

And programming languages are designed for clarifying the implementation details of abstract processes; while human language is this undocumented, half grandfathered in, half adversarially designed instrument for making apes get along (as in, move in the same general direction) without excessive stench.

The humane and the machinic need to meet halfway - any computing endeavor involves not only specifying something clearly enough for a computer to execute it, but also communicating to humans how to benefit from the process thus specified. And that's the proper domain not only of software engineering, but the set of related disciplines (such as the various non-coding roles you'd have in a project team - if you have any luck, that is).

But considering the incentive misalignments which easily come to dominate in this space even when multiple supposedly conscious humans are ostensibly keeping their eyes on the ball, no matter how good the language machines get at doing the job of any of those roles, I will still intuitively mistrust them exactly as I mistrust any human or organization with responsibly wielding the kind of pre-LLM power required for coordinating humans well enough to produce industrial-scale LLMs in the first place.

What's said upthread about the wordbox continually trying to revert you to the mean as you're trying to prod it with the cowtool of English into outputting something novel, rings very true to me. It's not an LLM-specific selection pressure, but one that LLMs are very likely to have 10x-1000xed as the culmination of a multigenerational gambit of sorts; one whose outset I'd place with the ever-improving immersive simulations that got the GPU supply chain going.

I think harder while using agents, just not about the same things. Just because we all got a super powers doesn't make the problems go away, they just move and we still have our full brains to solve them.

It isn't all great, skills that feel important have already started atrophying, but other skills have been strengthened. The hardest part is in being able to pace onself as well as figuring out how to start cracking certain problems.

Uniqueness is not the aim. Who cares if something is uniquely bad? But in any case, yes, if you use LLMs uncritically, as a substitute for reasoning, then you obviously aren't doing any reasoning and your brain will atrophy.

But it is also true that most programming tedious and hardly enriching for the mind. In those cases, LLMs can be a benefit. When you have identified the pattern or principle behind a tedious change, an LLM can work like a junior assistant, allowing you to focus on the essentials. You still need to issue detailed and clear instructions, you still need to verify the work.

Of course, the utility of LLMs is a signal that either the industry is bad at abstracting, or that there's some practical limit.

Yet another example of "comments that are only sort of true because high temperature sampling isn't allowed".

If you use LLMs at very high temperature with samplers which correctly keep your writing coherent (i.e. Min_p, or better like top-h, P-less decoding, etc), than "regression to the mean" literally DOES NOT HAPPEN!!!!

Have you actually tried high temperature values for coding? Because I don’t think it’s going to do what you claim it will.

LLMs don’t “reason” the same way humans do. They follow text predictions based on statistical relevance. So raising the temperature will more likely increase the likelihood of unexecutable pseudocode than it would create a valid but more esoteric implementation of a problem.

To put it another way, a high-temperature mad-libs machine will write a very unusual story, but that isn't necessarily the same as a clever story.

So why is this "temperature" not on, like, a rotary encoder?

So you can just, like, tweak it when it's working against your intent in either direction?

AFAIK there's no algorithmic reason against it, but services might not expose the controls in a convenient way, or at all.

High temperature seems fine for my coding uses on GPT5.2.

Code that fails to execute or compile is the default expectation for me. That's why we feed compile and runtime errors back into the model after it proposes something each time.

I'd much rather the code sometimes not work than to get stuck in infinite tool calling loops.

How do you configure LLM température in coding agents, e.g. opencode?

https://opencode.ai/docs/agents/#temperature

set it in your opencode.json

Note when I said "you have to hack it in", I mean you'll need to hack in support for modern LLM samplers like min_p, which enables setting temperature up to infinity (given min_p approaching 1) while maintaining coherence.

You can't without hacking it! That's my point! The only places you can easily are via the API directly, or "coomer" frontends like SillyTavern, Oobabooga, etc.

Same problem with image generation (lack of support for different SDE solvers, the image version of LLM sampling) but they have different "coomer" tools, i.e. ComfyUI or Automatic1111

Once again, porn is where the innovation is…

Please.. "Creative Writing"

To me it's all abstraction. I didn't write my own OS. I didn't write my own compiler. I didn't write the standard library. I just use them. I could write them but I'm happy to work on the new thing that uses what's already there.

This is no different than many things. I could grow a tree and cut it into wood but I don't. I could buy wood and nails and brackets and make furniture but I don't. I instead just fill my house/apartment with stuff already made and still feel like it's mine. I made it. I decided what's in it. I didn't have to make it all from scratch.

For me, lots of programming is the same. I just want to assemble the pieces

> When you skip the process of creation you trade the thing you could have learned to make for the simulacrum of the thing you thought you wanted to make

No, your favorite movie is not crap because the creators didn't grind their own lens. Popular and highly acclaimed games not at crap because they didn't write their own physics engine (Zelda uses Havok) or their own game engine (Plenty of great games use Unreal or Unity)

When I read discussions about this sort of thing, I often find that folks look harder for similarities and patterns but once they succeed here, they ignore the differences. AI in particular is so full of this "pattern matching" style of thinking that the really significance of this tech, ie., how absolutely new and different it is, yeah it just sort of goes ignored, or even worse, machines get "pattern matched" into humans and folks argue from that point of view lol witness all the "new musicians" who vibe code disco hits, I'll invariably see the argument that AIs train on existing music just like humans do so whats the big deal?

But these arguments and the OP's article do reinforce that AI rots brains. Even my sparing use of googles gemini and my interaction with the bots here have really dinged my ability to do simple math.

OS and compilers have a deterministic public interface. They obey a specification developers know, so you they can be relied on to write correct software that depends on them even without knowing the internal behavior. Generative AI does not have those properties.

Yes but developers don’t have a deterministic interface. I still had to be careful about writing out my specs and make sure they were followed. At least I don’t have to watch my tone when my two mid level ticket taking developers - Claude and Codex - do something stupid. They also do it a lot faster

But the code you’re writing is guard railed by your oversight, the tests you decide on and the type checking.

So whether you’re writing the spec code out by hand or ask an LLM to do it is besides the point if the code is considered a means to an end, which is what the post above yours was getting at.

Tests and type checking are often highway-wide guardrails when the path you want to take is like a tightrope.

Also the code is not a means to an end. It’s going to be run somewhere doing stuff someone wants to do reliably and precisely. The overall goal was ever to invest some programmer time and salary in order to free more time for others. Not for everyone to start babysitting stuff.

> They obey a specification developers know

Which spec? Is there a spec that says if you use a particular set of libraries you’d get less than 10 millisecond response? You can’t even know that for sure if you roll your own code, with no 3rd party libraries.

Bugs are by definition issues arise when developers expect they code to do one thing, but it does another thing, because of unforeseen combination of factors. Yet we all are ok with that. That’s why we accept AI code. They work well enough.

For compilers: the C++ standard.

For OSes: POSIX, or the MSDN documentation for Windows.

Compiler bugs and OS bugs are extremely rare so we can rely on them to follow their spec.

AI bugs are very much expected when the "spec" (the prompt) is correct, and since the prompt is written using imprecise human language likely by people that are not used to writing precise specifications, the prompt is likely either mistaken or insufficiently specified.

> Is there a spec that says if you use a particular set of libraries you’d get less than 10 millisecond response?

There can be. But you’d have to map the libraries to opcodes and then count the cycles. That’s what people do when they care about that particular optimization. They measure and make guaranties.

That’s not realistic with any processor that does branch prediction, cache hits vs cache misses etc

You can easily compute the worst cases. All the details are in the specs of the processor.

Assuming also that you are not running on top of an operating system, running in a VM with “noisy neighbors”…

I haven’t counted cycles since programming assembly on a 65C02 where you cooks save a clock cycle by accessing memory in the first page of memory - two opcodes to do LDA $02 instead of LDA $0201

Then assumes the opposite. Build an RTOS and don’t virtualize your software on top of it.

> I didn't write my own OS. I didn't write my own compiler. I didn't write the standard library. I just use them. I could write them

Maybe, but beware assuming you could do something you haven't actually tried to do.

Everything is easy in the abstract.

[dead]

> No, your favorite movie is not crap because the creators didn't grind their own lens.

But Pulp Fiction would not have been a masterpiece if Tarantino just typed “Write a gangster movie.” into a prompt field.

> But Pulp Fiction would not have been a masterpiece if Tarantino just typed “Write a gangster movie.” into a prompt field.

Doesn’t that prove the point? You could do that right now, and it would be absolute trash. Just like how right now we are nowhere close to being able to make great software with a single prompt.

I’ve been vibecoding a side project and it has been three months of ideating, iterating, refining and testing. It would have taken me immeasurably longer without these tools, but the end result is still 100% my vision, and it has been a tremendous amount of work.

Pulp Fiction, like many Tarantino movies, also gets much of its effect from using existing songs rather than using an all new soundtrack

Songs he likely hand picked and for reasons that even you and I don't know about, instead of songs suggested by an AI with no personal taste.

More to your original point, Tarantino is actually well known for his deliberate uses of rare lenses. He doesn't grind them himself, but he did resurrect a dead lens format for The Hateful Eight:

https://en.wikipedia.org/wiki/Ultra_Panavision_70

And if he did, why would I prefer using his prompt instead of mine?

"Write a gangster movie that I like", instead of "...a movie this other guy likes".

But because this is not the case, we appreciate Tarantino more than we appreciate gangster movies. It is about the process.

This is exactly the process happening in the music space with Suno. Go to their subreddit, they all talk about how they only listen to ‘their’ songs, for the exact reasons you list.

Its bleak out there.

It is very different with music. Music and images fall into "just shit something and I don't care what is is" category. Most people prompting for things in this category will be satisfied with anything, they might not admit, but the degrees of freedom the model has is infinite. Now when you pin the output, let's say a character you generated, and ask for modifications WHILE KEEPING lots of characteristics, you reduce the degrees of freedom from infinite to a small, very constrained, set of states. There are workarounds but natively llms can't really do this. You ask the model to rotate an image, the hair becomes blue and the sword becames an axe.

With music this is much more pronounced because most people are musically illiterate, so even the basic mistakes while dragging characteristics over diffs becomes invisible. It's an interesting phenomenon I agree, but it says more about lack of taste and illiteracy of the common individual.

But on the point of "thinking hard", with music and artistic production in general, individuals (human with soul, not npc) crave for ideas and perspective. It is the play, the relationship between ideas that are hard to vocalize and describe but can be provocative. Because we cannot describe or understand, we have no choice other than provoke into another a similar contemplation.

But make no mistake, nobody is enjoying llm slop. They have fantasies that now they can produce something of value, or delegate this production. If this becomes true, instantly they lose and everyone goes directly to the source.

Art is specifically about communicating the inconceivable, cannot be delegated. If the tool is sufficient to produce art, then the expression is of the tool itself, now they ARE.

> But because this is not the case, we appreciate Tarantino more than we appreciate gangster movies.

Do we? I don't think people appreciate tarantino more than gangster movies. Don't think people appreciate tarantino more than pulp fiction. Frankly, tarantino doesn't factor in at all.

> It is about the process.

I never considered the process when watching pulp fiction. It's the finished product, not the process, that matters.

Put it this way, we know who tarantino is because of pulp fiction. Not the other way around.

> It's the finished product, not the process, that matters.

I think the point is that the finished product depends on the process.

The creative process is not dependent on the abstraction.

> For me, lots of programming is the same. I just want to assemble the pieces

How did those pieces came to be? By someone assembling other pieces or by someone crafting them together out of nowhere because nobody else had written them by the time?

Of course you reuse other parts and abstractions to do whatever things that you're not working on but each time you do something that hasn't been done before you can't but engage the creative process, even if you're sitting on top of 50 years worth of abstractions.

In other words, what a programmer essentially has is a playfield. And whether the playfield is a stack of transistors or coding agents, when you program you create something new even if it's defined and built in terms of the playfield.

>I instead just fill my house/apartment with stuff already made and still feel like it's mine.

I'm starting to wonder if we lose something in all this convenience. Perhaps my life is better because I cook my own food, wash my own dishes, chop my own firewood, drive my own car, write my own software. Outwardly the results look better the more I outsource but inwardly I'm not so sure.

On the subject of furnishing your house the IKEA effect seems to confirm this.

https://en.wikipedia.org/wiki/IKEA_effect

I really appreciate this sentiment. It feels absolutely overwhelming the pace at which new tools and AI protocols are being released, leaving a feeling of constantly falling behind. But approaching from the other end, I can just make things that I do come up with and explore the new protocols only if I can't do the thing with what I've already grasped.

There are two stages to becoming a decent programmer: first you learn to use abstraction, then you learn when not to use abstraction.

Trying to find the right level is the art. Once you learn the tools of the trade and can do abstraction, it's natural to want to abstract everything. Most programmers go through such a phase. But sometimes things really are distinct and trying to find an abstraction that does both will never be satisfactory.

When building a house there are generally a few distinct trades that do the work: bricklayers, joiners, plumbers, electricians etc. You could try to abstract them all: it's all just joining stuff together isn't it? But something would be lost. The dangers of working with electricity are completely different to working with bricks. On the other hand, if people were too specialised it wouldn't work either. You wouldn't expect a whole gang of electricians, one who can only do lighting, one who can only do sockets, one who can only do wiring etc. After centuries of experience we've found a few trades that work well together.

So, yes, it's all just abstraction, but you can go too far.

Well said, great analogy. Sometimes the level of abstraction feels arbitrary - you have to understand the circumstances that led there to see why it's not.

In higher end work they do have specialized lighting, branch power, and feeder electricians. And among feeder even special ones for medium voltage etc

AI is not an abstraction.

> No, your favorite movie is not crap because the creators didn't grind their own lens.

One of the reasons Barry Lyndon is over 50 years old and still looks like no other movie today is because Kubrick tracked down a few lenses originally designed for NASA and had custom mounts built for them to use with cinema cameras.

https://neiloseman.com/barry-lyndon-the-full-story-of-the-fa...

> Popular and highly acclaimed games not at crap because they didn't write their own physics engine (Zelda uses Havok)

Super Mario Bros is known for having a surprisingly subtle and complex physics system that enabled the game to feel both challenging and fair even for players very new to consoles. Celeste a newer game also famous for being very difficult yet not feeling punishing does something similar:

https://maddymakesgames.com/articles/celeste_and_towerfall_p...

> or their own game engine (Plenty of great games use Unreal or Unity)

And Minecraft doesn't, which is why few other games at the time of its release felt and played like it.

You're correct that no one builds everything from scratch all the time. However, if all you ever do is cobble a few pre-made things together, I think you'll discover that nothing you make is ever that interesting or enduring in value. Sure, it can be useful, and satisfying. But the kinds of things that really leave a mark on people, that affect them deeply, always have at least some aspect where the creator got obsessive and went off the deep end and did their own thing from scratch.

Further, you'll never learn what a transformative experience it can be to be that creator who gets obsessive about a thing. You'll miss out on discovering the weird parts of your own soul that are more fascinated by some corner of the universe than anyone else is.

I have a lot of regrets in my life, but I don't regret the various times I've decided I've deeply dug into some thing and doing it from scratch. Often, that has turned out later to be some of the most long-term useful things I've done even though it seemed like a selfish indulgence at the time.

Of course, it's your life. But consider that there may be a hidden cost to always skimming along across the tops of the stacks of things that already exist out there. There is growth in the depths.

Great comment. One of the best in the thread. I do hope we can get over this endless derivative phase and see some people digging deeper again.

Did you not read the post? You're talking from the space of the Builder while neglecting the Thinker. That's fine for some people, but not for others.

In 30 years across 10 jobs, the companies I’ve worked for have not paid me to “code”. They’ve paid me to use my experience to add more business value than the total cost of employing me.

I’m no less proud of what I built in the last three weeks using three terminal sessions - one with codex, one with Claude, and one testing everything from carefully designed specs - than I was when I first booted a computer, did “call -151” to get to the assembly language prompt on my Apple //e in 1986.

The goal then was to see my ideas come to life. The goal now is to keep my customers happy, get projects done on time, on budget and meets requirements and continue to have my employer put cash in my account twice a month - and formerly put AMZN stock in my brokerage account at vesting.

But you can move a layer up.

Instead of pouring all of your efforts into making one single static object with no moving parts, you can simply specify the individual parts, have the machine make them for you, and pour your heart and soul into making a machine that is composed of thousands of parts, that you could never hope to make if you had to craft each one by hand from clay.

We used to have a way to do this before LLMs, of course: we had companies that employed many people, so that the top level of the company could simply specify what they wanted, and the lower levels only had to focus on making individual parts.

Even the person making an object from clay is (probably) not refining his own clay or making his own oven.

> we had companies that employed many people, so that the top level of the company could simply specify what they wanted, and the lower levels only had to focus on making individual parts.

I think this makes a perfect counter-example. Because this structure is an important reason for YC to exist and what the HN crowd often rallies against.

Such large companies - generally - don't make good products. Large companies rarely make good products in this way. Most, today, just buy companies that built something in the GP's cited vein: a creative process, with pivots, learnings, more pivots, failures or - when successful - most often successful in an entirely different form or area than originally envisioned. Even the large tech monopolies of today originated like that. Zuckerberg never envisioned VR worlds, photo-sharing apps, or chat apps, when he started the campus-fotobook-website. Bezos did not have some 5d-chess blueprint that included the largest internet-infrastructure-for-hire when he started selling books online.

If anything, this only strengthens the point you are arguing against: a business that operates by a "head" "specifying what they want" and having "something" figure out how to build the parts, is historically a very bad and inefficient way to build things.

And therein lies the crux: some people love to craft each part themselves, whereas others love to orchestrate but not manufacture each part.

With LLMs and engineers often being forced by management to use them, everyone is pushed to become like the second group, even though it goes against their nature. The former group see the part as a means, whereas the latter view it as the end.

Some people love the craft itself and that is either taken away or hollowed out.

This is really what it’s about.

As someone that started with Machine Code, I'm grateful for compiled -even interpreted- languages. I can’t imagine doing the kind of work that I do, nowadays, in Machine Code.

I’m finding it quite interesting, using LLM-assisted development. I still need to keep an eye on things (for example, the LLM tends to suggest crazy complex solutions, like writing an entire control from scratch, when a simple subclass, and five lines of code, will work much better), but it’s actually been a great boon.

I find that I learn a lot, using an LLM, and I love to learn.

But we become watchers instead of makers.

There is a difference between cooking and putting a ready meal into the microwave.

Both satisfy your hunger but only one can give some kind of pride.

The same thing happens if you are the head cook in a restaurant.

If you are a cook wanting to open a restaurant, you will be delegating, the same thing with AI. If you are fine only doing what your hands can possibly do in the time allotted, go ahead and cook in your kitchen.

But I need to make money to be able to trade for the food I eat.

You will make money but the others are the artists.

That’s the whole point. You become a customer of an AI service, you get what you want but it wasn’t done by you. You get money but not the feeling of accomplishment from cracking a problem. Like playing a video game following a solution or solving a crossword puzzle with google.

Check this out: https://imgur.com/a/aVxryBf

It's a carved wooden dragon that my dad got from Indonesia (probably about 50 years ago).

It's hard to appreciate, if you aren't holding it, but it weighs a lot, and is intricately carved, all over.

I guarantee that the carver used a Dremel.

I still have a huge amount of respect for their work. That wood is like rock. I would not want to carve it with hand tools.

There's just some heights we can't reach, without a ladder.

What good is a “feeling of accomplishment” as I am on the street homeless, hungry and naked?

Pretty B/W view. The feeling of accomplishment is the part that makes a job interesting, if it’s just about money it becomes dull.

And don’t forget, it’s more likely to find someone cheaper who can write the same prompts as you than people with the same kind of experience in cracking problems.

To tackle the second part first, do you think creating finely crafted bespoke code is going to save a mid level ticket taker (not referring to you of course) who can take well defined requirements and create code is going to save anyone’s job - ie “a human LLM”?

Those types of developers on the enterprise dev side - where most developers work - were becoming a commodity a decade ago and wages have been basically stagnant. Now those types of developers are finding it hard to stand out and get noticed.

The trick is to move “up the stack” and closer to the customer whether that be an internal customer or external customer and be able to work at a higher level of scope, impact and ambiguity.

https://www.levels.fyi/blog/swe-level-framework.html

It’s been well over a decade and 6 jobs ago that I had to do a coding interview to prove I was able “to codez real gud”, every job I’ve had since then has been more concerned with whether I was “smart and get things done”. That could mean coding, leading teams, working with “the business”, being on Zoom calls with customers, flying out to the customers site, or telling a PE backed company with low margins that they didn’t need a team of developers, they needed to outsource complete implementations to other companies.

I’ve always seen coding as grunt work. But the only way to go from requirements -> architectural vision -> result and therefore getting money in my pocket.

My vision was based on what I could do myself in the allotted time at first and then what I could do with myself + leading a team. Now it’s back to what I can do by myself + Claude Code and Codex.

As far as the first question, my “fun” during my adult life has come from teaching fitness classes until I was 35 and running with friends in charity races on the weekend, and just hanging out, spending time with my (now grown) stepsons after that and for the past few years just spending time with my wife and traveling, concerts, some “digital nomadding” etc

Eh. I've had pride in my work for over 40 years.

The tools change, but the spirit only grows.

[dead]

Yes, but bad ingredients do not make a yummy pudding.

Or, it's like trying to make a MacBook Pro by buying electronics boards from AliExpress and wiring them together.

I'd rather have a laptop made from AliExpress components than only have a single artisanal hand-crafted resistor.

That's a false dichotomy, because transistors and ICs are manufactured to be deterministic and nearly perfect. LLMs can never be guaranteed to be like that.

Yes, some things are better when manufactured in highly automated ways (like computer chips), but their design has been thoroughly tested and before shipping the chips themselves go through lots of checks to make sure they are correct. LLM code is almost never treated that way today.

Yes, the point is that only if you're willing to accept crappy results then you can use AI to build bigger things.

To me that seems like a spurious (maybe even false) dichotomy. You can have crappy results without AI. And you can have great results with AI.

Your contrast is an either or, that - in the real world - does not exist.

Take content written by AI, prompted by a human. A lot of it is slop and crap. And there will be more slop and crap with AI than before. But that was the case, when the medium changed from hand writen to printed books. And when paper and printing became cheap, we had slop like those 10 Cent Western or Romance novellas.

We also still had Goethe, still had Kleist, still had Grass (sorry, very German centric here).

We also have Inception vs. the latest sequel of any Marvel franchise.

I have seen AI writen, but human prompted short stories, that made people well up and find ideas presented in a light not seen before. And I have seen AI generated stories that one wants to purge from my brain.

It isn't the tool - it is the one yielding it.

Question: Did photoshop kill photography? Because honestly, this AI discussion to me sounds very much like the discussion back then.

> Question: Did photoshop kill photography? Because honestly, this AI discussion to me sounds very much like the discussion back then.

It killed an aspect of it. The film processing in the darkroom. Even before digital cameras were ubiquitous it was standard to get a scan before doing any processing digitally. Chemical processing was reduced the minimum necessary.

Lightroom killed photography.

I was going to reply defending AI tooling and crappy results, but I think I'm done with it.

I think there are just a class of people know that think that you cannot get 'macbook' quality with a LLM. I don't know why I try to convince them, it's not in my benefit.

It's more like the chess.com vs lichess example in my mind. On the one hand you have a big org, dozens of devs, on the other you have one guy doing a better job.

It's amazing what one competent developer can do, and it's amazing how little a hundred devs end up actually doing when weighed down by beaurocracy. And lets not pretend even half of them qualify as competent, not to mention they probably don't care either. They get to work and have a 45 min coffee break, move some stuff around in the Kanban board, have another coffee break, then lunch, then foosball etc. Ad when they actually write some code it's ass.

And sure, for those guys maybe LLMs represent a huge productivity boost. For me it's usually faster to do the work myself than to coax the bot into creating something acceptable.

Agreed. Most people don't do anything and this might actually get them to produce code at an acceptable rate. I find that I often know what I need to do and just hitting the LLM until it does what I want is more work than writing the damn code (the latter also being a better way to be convinced that it works, since you actually know what it does and how). People are very bad code reviewers, especially those people who don't do anything, so making them full time code reviewers always seemed very odd to me.

I personally have found success with an approach that's the inverse of how agents are being used generally.

I don't allow my agent to write any code. I ask it for guidance on algorithms, and to supply the domain knowledge that I might be missing. When using it for game dev for example, I ask it to explain in general terms how to apply noise algorithms for procedural generation, how to do UV mapping etc, but the actual implementation in my language of choice is all by hand.

Honestly, I think this is a sweet spot. The amount of time I save getting explanations of concepts that would otherwise get a bit of digging to get is huge, but I'm still entirely in control of my codebase.

Yep, this is the sweet spot. Though I still let it type code a lot - boilerplate stuff I’d be bored out of my mind typing. And I’ve found it has an extremely high success rate typing that code on top of its very easy for me to review that code. No friction at all. Granted this is often no larger than 100 lines or so (across various files).

If it takes you more than a few seconds or so to understand code an agent generated you’re going to make mistakes. You should know exactly what it’s going to produce before it produces it.

Supposedly when Michelangelo was asked about how he created the statue of David, he said "I just chipped away everything that wasn’t David.”

Your work is influenced by the medium by which you work. I used to be able to tell very quickly if a website was developed in Ruby on Rails, because some approaches to solve a problem are easy and some contain dragons.

If you are coding in clay, the problem is getting turned into a problem solvable in clay.

The challenge if you are directing others (people or agents) to do the work is that you don't know if they are taking into account the properties of the clay. That may be the difference between clean code - and something which barely works and is unmaintainable.

I'd say in both cases of delegation, you are responsible for making sure the work is done correctly. And, in both cases, if you do not have personal experiences in the medium you may not be prepared to judge the work.

This is an amazing quote - thank you. This is also my argument for why I can't use LLMs for writing (proofreading is OK) - what I write is not produced as a side-effect of thinking through a problem, writing is how I think through a problem.

Counterpoint (more devil's advocate), I'd argue it's better than an LLM writes something (e.g. the solution or thinking through of a problem) than nothing at all.

Counterpoint to my own counterpoint, will anyone actually (want to) read it?

counterpoint to the third degree, to loop it back around, an LLM might and I'd even argue an LLM is better at reading and ingesting long text (I'm thinking architectural documentation etc) than humans are. Speaking for myself, I struggle to read attentively through e.g. a document, I quickly lose interest and scan read or just focus on what I need instead.

I kinda saw this happen in realtime on reddit yesterday. Someone asked for advice on how to deal with a team that was in over their heads shipping slop. The crux of their question was fair, but they used a different LLM to translate their original thoughts from their native language into English. The prompt was "translate this to english for a reddit post" - nothing else.

The LLM adding a bunch of extra formatting to add emphasis and structure to what might have originally been a bit of a ramble, but obviously human written. The comments absolutely lambasted this OP for being a hypocrite complaining about their team using AI, but then seeing little problem with posting what is obviously an AI generated question because the OP didn't deem their English skills good enough to ask the question directly.

I'm not going to pass judgement on this scenario, but I did think the entire encounter was a "fun" anecdote in addition to your comments.

Edit: wrods

I saw the same post and was a bit saddened that all the comments seemed to be focused on the implied hypocrisy of the OP instead of addressing the original concern.

As someone that’s a bit of a fence-sitter on the matter of AI, I feel that using it in the way that OP did is one of the less harmful or intrusive uses.

I see it as worse because you could have put just as much effort in - less even - and gotten a better result just sticking it in a machine translator and pasting that.

Writing is how I think through a problem too, but that also applies to writing and communicating with an AI coding agent. I don't need to write the code per se to do the thinking.

You could write pseudocode as well. Bit fo someone who is familiar with a programming language, it’s just faster to use the latter. And if you’re really familiar with the language, you start thinking in it.

Coding is not at all like working a lump of clay unless you’re still writing assembly.

You’re taking a bunch of pre-built abstractions written by other people on top of what the computer is actually doing and plugging them together like LEGOs. The artificial syntax that you use to move the bricks around is the thing you call coding.

The human element of discovery is still there if a robot stacks the bricks based on a different set of syntax (Natural Language), nothing about that precludes authenticity or the human element of creation.

It depends what you're doing not really what you do it with.

I can do some crud apps where it's just data input to data store to output with little shaping needed. Or I can do apps where there's lots of filters, actions and logic to happen based on what's inputted that require some thought to ensure actually solve the problem it's proposed for.

"Shaping the clay" isn't about the clay, it's about the shaping. If you have to make a ball of clay and also have to make a bridge of Lego a 175kg human can stand on, you'll learn more about Lego and building it than you will about clay.

Get someone to give you a Lego instruction sheet and you'll learn far less, because you're not shaping anymore.

> You’re taking a bunch of pre-built abstractions written by other people on top of what the computer is actually doing and plugging them together like LEGOs.

Correct. However, you will probably notice that your solution to the problem doesn't feel right, when the bricks that are available to you, don't compose well. The AI will just happily smash together bricks and at first glance it might seem that the task is done.

Choosing the right abstraction (bricks) is part of finding the right solution. And understanding that choice often requires exploration and contemplation. AI can't give you that.

Not yet, anyway; I do trust LLMs for writing snippets or features at this point, but I don't trust them for setting up new applications, technology choices, architectures, etc.

The other day people were talking about metrics, the amount of lines of code people vs LLMs could output in any given time, or the lines of code in an LLM assisted application - using LOC as a metric for productivity.

But would an LLM ever suggest using a utility or library, or re-architecture an application, over writing their own code?

I've got a fairly simple application, renders a table (and in future some charts) with metrics. At the moment all that is done "by hand", last features were stuff like filtering and sorting the data. But that kind of thing can also be done by a "data table" library. Or the whole application can be thrown out in favor of a workbook (one of those data analysis tools, I'm not at home in that are at all). That'd save hundreds of lines of code + maintenance burden.

I was creating a Jira/bb wrapper with node recently and Claude actually used plenty of libraries to solve some tasks.

Same with gpt, but I felt it was more like "hey, everyone uses that, so why not me" than finding the right tool for the job. Can't say for Claude.

Unless you limit your scope of problem solving to only what you can do yourself, you are going to have to delegate work - your abstraction is going to be specs and delegating work to other people and ensuring it works well together and follows the specs - just like working with an LLm.

Exactly, and that's why I find AI coding solving this well, because I find it tedious to put the bricks together for the umpteenth time when I can just have an AI do it (which I will of course verify the code when it's done, not advocating for vibe coding here).

This actually leaves me with a lot more time to think, about what I want the UI to look like, how I'll market my software, and so on.

> Coding is not at all like working a lump of clay unless you’re still writing assembly.

Isn't the analogy apt? You can't make a working car using a lump of clay, just a car statue, a lump of clay is already an abstraction of objects you can make in reality.

Bingo.

I think the analogy to high level programming languages misunderstands the value of abstraction and notation. You can’t reason about the behavior of an English prompt because English is underspecified. The value of code is that it has a fairly strong semantic correlation to machine operations, and reasoning about high level code is equivalent to reasoning about machine code. That’s why even with all this advancement we continue to check in code to our repositories and leave the sloppy English in our chat history.

Yep. Any statement in python or others can be mapped to something that the machine will do. And it will be the same thing every single time (concurrency and race issue aside). There’s no english sentence that can be as clear.

We’ve created formal notation to shorten writing. And computation is formal notation that is actually useful. Why write pages of specs when I could write a few lines of code?

There's also creative space inside the formal notation. It's not just "these are the known abstractions, please lego them together", the formal syntax and notation is just one part of the whole. The syntax and notation define the forms of poetry (here's the meter, here's the rhyme scheme, here's how the whitespace works), but as software developers we're still filling in the words that fit that meter and rhyme scheme and whitespace. We're adding the flowery metaphors in the way we choose variable names and the comments we choose to add and the order we define things or choose to use them.

Software developers can use the exact same "lego block" abstractions ("this code just multiplies two numbers") and tell very different stories with it ("this code is the formula for force power", "this code computes a probability of two events occurring", "this code gives us our progress bar state as the combination of two sub-processes", etc).

LLMs have only so many "stories" they are trained on, and so many ways of thinking about the "why" of a piece of code rather than mechanical "what".

Computers only care about the what, and have no use for the why. Humans care about the latter too and the programmer lives at the intersection of both. Taking a why and transforming it into a what is the coding process.

Software engineering is all about making sure the what actually solves the why, making the why visible enough in the what so that we can modify the latter if the former changes (it always does).

Current LLM are not about transforming a why into a what. It’s about transforming an underspecified what into some what that we hope fits the why. But as we all know from the 5 Why method, why’s are recursive structure, and most software engineer is about diving into the details of the why. The what are easy once that done because computers are simple mechanisms if you chose the correct level of abstraction for the project.

Lego boxes include a set of instructions that implies there's only one way to assemble the contents, but that's sometimes an injustice to the creative space that Legos are built to provide. There can be a joy in algorithmically building the thing some other designers worked to make look nice, but there's a creative space outside the instructions, too.

The risk of LLMs laying more of these bricks isn't just loss of authenticity and less human elements of discovery and creation, it's further down the path of "there's only one instruction manual in the Lego box, and that's all the robots know and build for you". It's an increased commodification of a few legacy designers' worth of work over a larger creative space than at first seems apparent.

You’re both right. It just depends on the problems you’re solving and the languages you use.

I find languages like JavaScript promote the idea that of “Lego programming” because you’re encouraged to use a module for everything.

But when you start exploring ideas that haven’t been thoroughly explored already, and particularly in systems languages which are less zealous about DRY (don’t repeat yourself) methodologies, the you can feel a lot more like a sculptor.

Likewise if you’re building frameworks rather than reusing them.

So it really depends on the problems you’re solving.

For general day-to-day coding for your average 9-to-5 software engineering job, I can definitely relate to why people might think coding is basically “LEGO engineering”.

> plugging them together like LEGOs

Aren't Legos known for their ability to enable creativity and endless possibilities? It doesn't feel that different from the clay analogy, except a bit coarser grained.

changing "clay" for "legos" doesn't change the core argument. The tactile feel you get for the medium as you work it with your hands and the "artificial syntax" imposed by the medium.

"... software is not a product, but rather a medium for the storage of knowledge"

Phillip G. Armour The Five Orders of Ignorance https://www.researchgate.net/publication/27293624_The_Five_O...

For me it’s a related but different worry. If I’m no longer thinking deeply, then maybe my thinking skills will simply atrophy and die. Then when I really need it, I won’t have it. I’ll be reduced to yanking the lever on the AI slot machine, hoping it comes up with something that’s good enough.

But at that point, will I even have the ability to distinguish a good solution from a bad one? How would I know, if I’ve been relying on AI to evaluate if ideas are good or not? I’d just be pushing mediocre solutions off as my own, without even realising that they’re mediocre.

> Being handed a baked and glazed artefact that approximates what you thought you wanted to make

Isn't this also an overstatement, and the problem is worse. That is - the code being handed back is a great prototype, needs polishing/finishing, and is ignorant of obvious implicit edge cases unless you explicitly innumerate all of them in your prompts??

For me, the state of things reminds me of a bad job I had years ago.

Worked with a well-regarded long tenured but truculent senior engineer who was immune to feedback due to his seniority. He committed code that either didn't run, didn't past tests, or implemented only the most obvious happy path robotically literal interpretation of requirements.

He was however, very very fast... underbidding teammates on time estimates by 10x.

He would hand back the broken prototype and we'd then spend the 10x time making his code actually something you can run in production.

Management kept pushing this because he had a great reputation, promised great things, and every once in a while did actually deliver stuff fast. It took years for management to come around to the fact that this was not working.

While there is still a market for artisanal furniture, dishes and clothes most people buy mass-produced dishes, clothes and furniture.

I wonder if software creation will be in a similar place. There still might be a small market for handmade software but the majority of it will be mass produced. (That is, by LLM or even software itself will mostly go away and people will get their work done via LLM instead of "apps")

As with furniture, it's supply vs demand, and it's a discussion that goes back decades at this point.

Very few people (even before LLM coding tools) actually did low level "artisanal" coding; I'd argue the vast majority of software development goes into implementing features in b2b / b2c software, building screens, logins, overviews, detail pages, etc. That requires (required?) software engineers too, and skill / experience / etc, but it was more assembling existing parts and connecting them.

Years ago there was already a feeling that a lot of software development boiled down to taping libraries together.

Or from another perspective, replace "LLM" with "outsourcing".

I would argue the opposite..

What you get right now is mass replicated software, just another copy of sap/office/Spotify/whatever

That software is not made individually for you, you get a copy like millions of other people and there is nearly no market anymore for individual software.

Llms might change that, we have a bunch of internal apps now for small annoying things..

They all have there quirks, but are only accessible internally and make life a little bit easier for people working for us.

Most of them are one shot llms things, throw away if you do not need it anymore or just one shoot again

The question is whether that's a good thing or not; software adages like "Not Invented Here" aren't going to go away. For personal tools / experiments it's probably fine, just like hacking together something in your spare time, but it can become a risk if you, others, or a business start to depend on it (just like spare time hacked tools).

I'd argue that in most cases it's better to do some research and find out if a tool already exists, and if it isn't exactly how you want it... to get used to it, like one did with all other tools they used.

> it can become a risk if you, others, or a business start to depend on it (just like spare time hacked tools).

So that Excel spreadsheet that manages the entire sales funnel?

Acceptance of mass production is only post establishment of quality control.

Skipping over that step results in a world of knock offs and product failures.

People buy Zara or H&M because they can offload the work of verifying quality to the brand.

This was a major hurdle that mass manufacturing had to overcome to achieve dominance.

>Acceptance of mass production is only post establishment of quality control.

Hence why a lot of software development is gluing libraries together these days.

This makes no sense to me. There are plenty of artists out there (e.g. El Anatsui), not to mention whole professions such as architects, who do not interact directly with what they are building, and yet can have profound relationship with the final product.

Discovering the right problem to solve is not necessarily coupled to being "hands on" with the "materials you're shaping".

In my company, [enterprise IT] architects are separated into two kinds. People with a CV longer than my arm who know/anticipate everything that could fail and have reached a level of understandind that I personnally call "wisdom". And theorists, who read books and norms, who focus mostly on the nominal case, and have no idea [and no interest] in how the real world will be a hard brick wall that challenges each and every idea you invent.

Not being hands-on, and more important not LISTENING to the hands-on people and learning from them, is a massive issue in my surroundings.

So thinking hard on something is cool. But making it real is a whole different story.

Note: as Steve used to say, "real artists ship".

you think El Anatsui would concur that they didn't interact directly with what they were building? "hands on", "material you're shaping" is a metaphor

I don't see why his involvement, explaining to his team how exactly to build a piece, is any different from a developer explaining to an LLM how to build a certain feature, when it comes to the level of "being hands on".

Obviously I am not comparing his final product with my code, I am simply pointing out how this metaphor is flawed. Having "workers" shape the material according to your plans does not reduce your agency.

> I don't see why his involvement, explaining to his team how exactly to build a piece, is any different from a developer explaining to an LLM

Because everyone under him knows that a mistake big enough is a quick way to unemployment or legal actions. So the whole team is pretty much aligned. A developer using an LLM may as well try to herd cats.

First, that's quite a sad view of incentives structures. Second, you can't be serious in thinking that "worker worried they might be fired" puts the person in charge closer to the "materials" and more "hands on" with the project.

I relate to this. But also, isn't it just that every human endeavor goes through an evolution from craft to commodity, which is sad for the craftsmen but good for everyone else, and that we happen to be the ones living through that for software?

For instance, I think about the pervasive interstate overpass bridge. There was a time long ago when building bridges was a craft. But now I see like ten of these bridges every day, each of which is better - in the sense of how much load they can support and durability and reliability - than the best that those craftsmen of yore could make.

This doesn't mean I'm in any way immune to nostalgia. But I try to keep perspective, that things can be both sad and ultimately good.

If you're only building things that have been built before, then sure, though I'd argue we already had solutions for that before LLMs.

there is a presumption that the models we are using today are 'good enough'. by models I mean thinks like linkers and package managers, micro services and cluster management tools.

I personally think that we're not done evolving really, and to call it quits today would leave alot of efficiency and productivity on the table

Having a background in fine art (and also knew Aral many years ago!), this prose resonates heavily with me.

Most of the OP article also resonated with me as I bounce back and forth between learning (consuming, thinking, pulling, integrating new information) to building (creating, planning, doing) every few weeks or months. I find that when I'm feeling distressed or unhappy, I've lingered in one mode or the other a little too long. Unlike the OP, I haven't found these modes to be disrupted by AI at all, in fact it feels like AI is supporting both in ways that I find exhilarating.

I'm not sure OP is missing anything because of AI per se, it might just be that they are ready to move their focus to broader or different problem domains that are separate from typing code into an IDE?

For me, AI has allowed me to probe into areas that I would have shied away from in the past. I feel like I'm being pulled upward into domains that were previously inaccessible.

I use Claude on a daily basis, but still find myself frequently hand-writing code as Claude just doesn't deliver the same results when creating out of whole cloth.

Claude does tend to make my coarse implementations tighter and more robust.

I admittedly did make the transition from software only to robotics ~6 years ago, so the breadth of my ignorance is still quite thrilling.

>> Coding is like

That description is NOT coding, coding is a subset of that.

Coding comes once you know what you need to build, coding is the process of you expressing that in a programming language and as you do so you apply all your knowledge, experience and crucially your taste, to arrive at an implementation which does what's required (functionally and non-functionally) AND is open to the possibility of change in future.

Someone else here wrote a great comment about this the other day and it was along the lines of if you take that week of work described in the GP's comment, and on the friday afternoon you delete all the code checked in. Coding is the part to recreate the check in, which would take a lot less than a week!

All the other time was spent turning you into the developer who could understand why to write that code in the first place.

These tools do not allow you to skip the process of creation. They allow you to skip aspects of coding - if you choose to, they can also elide your tastes but that's not a requirement of using them, they do respond well to examples of code and other directions to guide them in your tastes. The functional and non-functional parts they're pretty good at without much steering now but i always steer for my tastes because, e.g. opus 4.5 defaults to a more verbose style than i care for.

It's all individual. That's like saying writing only happens when you know exactly the story to tell. I love open a blank project with a vague idea of what I want to do, and then just start exploring while I'm coding.

I'm sure some coding works this way, but I'd be surprised if it's more than a small percentage of it.

I get what he's pointing at: building teaches you things the spec can't, and iteration often reveals the real problem.

That said, the framing feels a bit too poetic for engineering. Software isn't only craft, it's also operations, risk, time, budget, compliance, incident response, and maintenance by people who weren't in the room for the "lump of clay" moment. Those constraints don't make the work less human; they just mean "authentic creation" isn't the goal by itself.

For me the takeaway is: pursue excellence, but treat learning as a means to reliability and outcomes. Tools (including LLMs) are fine with guardrails, clear constraints up front and rigorous review/testing after, so we ship systems we can reason about, operate, and evolve (not just artefacts that feel handcrafted).

> That said, the framing feels a bit too poetic for engineering.

I wholeheartedly disagree but I tend to believe that's going to be highly dependent on what type of developer a person is. One who leans towards the craftsmanship side or one who leans towards the deliverables side. It will also be impacted by the type of development they are exposed to. Are they in an environment where they can even have a "lump of clay" moment or is all their time spent on systems that are too old/archaic/complex/whatever to ever really absorb the essence of the problem the code is addressing?

The OP's quote is exactly how I feel about software. I often don't know exactly what I'm going to build. I start with a general idea and it morphs towards excellence by the iteration. My idea changes, and is sharpened, as it repeatedly runs into reality. And by that I mean, it's sharpened as I write and refactor the code.

I personally don't have the same ability to do that with code review because the amount of time I spend reviewing/absorbing the solution isn't sufficient to really get to know the problem space or the code.

"The muse visits during the act of creation, not before. Start alone."

That has actually been a major problem for me in the past where my core idea is too simple, and I don't give "the muse" enough time to visit because it doesn't take me long enough to build it. Anytime I have given the muse time to visit, they always have.

It's very similar now, you have to surf a swell of selective ignorance that is (feels?) less reliable than the ignorance that one adopts when using a dependency one hasn't read and understood the source code for.

One must be conversant in abstractions that are themselves ephemeral and half hallucinated. It's a question of what to cling to, what to elevate beyond possible hallucinated rubbish. At some level it's a much faster version of the meastspace process and it can be extermely emotionally uncomfortable and anarchic to many.

The best analogy I think is, if you just take Stack Overflow code solutions, smoosh over your code and hit compile / build, and move on without ever looking at "why it works" you're really not using your skills to the best of your ability, and it could introduce bugs you didn't expect, or completely unnecessary dependencies. With Stack Overflow you can have other people pointing out the issues with the accepted answer and giving you better options.

This keeps coming up again and again and again, but like how many times were you able to copy paste SO solution wholesale and just have it work? Other than for THE most simple cases (usually CSS) there would always have to be some understanding involved. Of course you don't always learn deeply every time, but the whole "copy paste off of stackoverflow" was always an exaggeration that is being used in seeming earnest.

Sometimes you want an artistic vase that captures some essential element of beauty, culture, or emotion.

Sometimes you want a utilitarian teapot to reliably pour a cup of tea.

The materials and rough process for each can be very similar. One takes a master craftsman and a lot of time to make and costs a lot of money. The other can be made on a production line and the cost is tiny.

Both have are desirable, for different people, for different purposes.

With software, it's similar. A true master knows when to get it done quick and dirty and when to take the time to ponder and think.

> Sometimes you want a utilitarian teapot to reliably pour a cup of tea.

If you pardon the analogy, watch how Japanese make a utilitarian teapot which reliably pours a cup of tea.

It's more complicated and skill-intensive than it looks.

In both realms, making an artistic vase can be simpler than a simple utilitarian tool.

AI is good at making (poor quality, arguably) artistic vases via its stochastic output, not highly refined, reliable tools. Tolerances on these are tighter.

There is a whole range of variants in between those two "artistic vs utilitarian" points. Additionally, there is a ton of variance around "artistic" vs "utilitarian".

Artisans in Japan might go to incredible lengths to create utilitarian teapots. Artisans who graduated last week from a 4-week pottery workshop will produce a different kind quality, albeit artisan. $5.00 teapots from an East Asian mass production factory will be very different than high quality mass-produced upmarket teapots at a higher price. I have things in my house that fall into each of those categories (not all teapots, but different kinds of wares).

Sometimes commercial manufacturing produces worse tolerances than hand-crafting. Sometimes, commercial manufacturing is the only way to get humanly unachievable tolerances.

You can't simplify it into "always" and "never" absolutes. Artisan is not always nicer than commercial. Commercial is not always cheaper than artisan. _____ is not always _____ than ____.

If we bring it back to AI, I've seen it produce crap, and I've also seen it produce code that honestly impressed me (my opinion is based on 24 years of coding and engineering management experience). I am reluctant to make a call where it falls on that axis that we've sketched out in this message thread.

This is very insightful, thanks. I had a similar thought regarding data science in particular. Writing those pandas expressions by hand during exploration means you get to know the data intimately. Getting AI to write them for you limits you to a superficial knowledge of said data (at least in my case).

Thanks for the quote, it definitely resonates. Distressing to see many people who can't relate to this, taking it literally and arguing that there is nothing lost the more removed they are from the process.

yes, this is maybe it's my preference to jump directly to coding, instead of canva to draw the gui and stuff. i would not know what to draw because the involvemt is not so deep ...or something

Honestly this sounds like a Luddite mindset (and I mean that descriptively, not to be insulting). This mindset holds us back.

You can imagine the artisans who made shirts saying the exact same thing as the first textile factories became operational.

Humans have been coders in the sense we mean for a matter of decades at most - a blip in our existence. We’re capable of far more, and this is yet another task we should cast into the machine of automation and let physical laws do the work for us.

We’re capable of manipulating the universe into doing our bidding, including making rocks we’ve converted into silicones think on our behalf. Making shirts and making code: we’re capable of so much more.

That quote sounds like special pleading for artisans.

reminds of arguments for - hosting a server vs running stuff in cloud - vps vs containers

I love Aral, he is so invested.

This is cute, but this is true for ALL activities in life. I have to constantly remind my brother that his job is not unique and if he took a few moments, he might realize, flipping burgers is also molding lumps of clay.

I think the biggest beef I have with Engineers is that for decades they more or less reduced the value of other lumps of clay and now want to throw up arms when its theirs.

This is beautifully written, but as a point against agentic AI coding, I just don't really get it.

It seems to assume that vibe coding or like whatever you call the Gas Town model of programming is the only option, but you don't have to do that. You don't have to specify upfront what you want and then never change or develop that as you go through the process of building it, and you don't have to accept whatever the AI gives you on the other end as final.

You can explore the affordances of the technologies you're using, modify your design and vision for what you're building as you go; if anything, I've found AI coding mix far easier to change and evolve my direction because it can update all the various parts of the code that need to be updated when I want to change direction as well as keeping the tests and specification and documentation in sync, easily and quickly.

You also don't need to take the final product as a given, a "simulacrum delivered from a vending machine": build, and then once you've gotten something working, look at it and decide that it's not really what you want, and then continue to iterate and change and develop it. Again, with AI coding, I've found this easier than ever because it's easier to iterate on things. The process is a bit faster for not having to move the text around and looking up API documentation myself, even though I'm directly dictating the architecture and organization and algorithms and even where code should go most of the time.

And with the method I'm describing, where you're in the code just as much as the AI is, just using it to do the text/API/code munging, you can even let the affordances of not just the technologies, but the source code and programming language itself effect how you do this: if you care about the code quality and clarity and organization of the code that the AI is generating, you'll see when it's trying to brute force its way past technical limitations and instead redirect it to follow the grain. It just becomes easier and more fluid to do that.

If anything, AI coding in general makes it easier to have a conversation with the machine and its affordances and your design vision and so on, then before because it becomes easier to update everything and move everything around as your ideas change.

And nothing about it means that you need to be ignorant of what's going on; ostensibly you're reviewing literally every line of code it creates and deciding what libraries and languages as well as the architecture, organization and algorithms it's using. You are aren't you? So you should know everything you need to know. In fact, I've learned several libraries and a language just from watching it work, enough that I can work with them without looking anything up, even new syntax and constructs that would have been very unfamiliar prior on my manual coding days.

I have no idea who this guy is (I guess he's a fantasy novelist?) but this video came up in my YouTube feed recently and feels like it matches closely with the themes you're expressing. https://youtu.be/mb3uK-_QkOo?si=FK9YnawwxHLdfATv

Yeah? And then you continue prompting and developing, and go through a very similar iterative process, except now it's faster and you get to tackle more abstract, higher level problems.

"Most developers don't know the assembly code of what they're creating. When you skip assembly you trade the very thing you could have learned to fully understand the application you were trying to make. The end result is a sad simulacrum of the memory efficiency you could have had."

This level of purity-testing is shallow and boring.

I don't think this comparison holds up. With a higher-level language, the material you're building with is a formal description of the software, which can be fed back into a compiler to get a deterministic outcome.

With an LLM, you put in a high-level description, and then check in the "machine code" (generated code).

I dunno, when you've made about 10,000 clay pots its kinda nice to skip to the end result, you're probably not going to learn a ton with clay pot #10,001. You can probably come up with some pretty interesting ideas for what you want the end result to look like from the onset.

I find myself being able to reach for the things that my normal pragmatist code monkey self would consider out of scope - these are often not user facing things at all but things that absolutely improve code maintenance, scalability, testing/testability, or reduce side effects.

Depends on the problem. If the complexity of what you are solving is in the business logic or, generally low, you are absolutely right. Manually coding a signup flow #875 is not my idea of fun either. But if the complexity is in the implementation, it’s different. Doing complex cryptography, doing performance optimization or near-hardware stuff is just a different class of problems.

> If the complexity of what you are solving is in the business logic or, generally low, you are absolutely right.

The problem is rather that programmers who work on business logic often hate programmers who are actually capable of seeing (often mathematical) patterns in the business logic that could be abstracted away; in other words: many business logic programmers hate abstract mathematical stuff.

So, in my opinion/experience this is a very self-inflected problem that arises from the whole culture around business logic and business logic programming.

Coding signup flow #875 should as easy as using a snippet tool or a code generator. Everyone that explains why using an LLM is a good idea always sound like living in the stone age of programming. There are already industrial level tools to get things done faster. Often so fast that I feel time being wasted describing it in english.

Of course I use code generation. Why would that be mutually exclusive from AI usage? Claude will take full advantage of it with proper instruction.

In my experience AI is pretty good at performance optimizations as long as you know what to ask for.

Can't speak to firmware code or complex cryptography but my hunch is if it's in it's training dataset and you know enough to guide it, it's generally pretty useful.

> my hunch is if it's in it's training dataset and you know enough to guide it, it's generally pretty useful.

Presumably humanity still has room to grow and not everything is already in the training set.

> In my experience AI is pretty good at performance optimizations as long as you know what to ask for.

This rather tells that the kind of performance optimizations that you ask for are very "standard".

Most optimizations are making sure you do not do work that is unnecessary or that you use the hardware effectively. The standard techniques are all you need 99% of the time you are doing performance work. The hard part about performance is dedicating the time towards it and not letting it regress as you scale the team. With AI you can have agents constantly profiling the codebase identifying and optimizing hotspots as they get introduced.

> Most optimizations are making sure you [...] use the hardware effectively.

If you really care about using the hardware effectively, optimizing the code is so much more than what you describe.

As most are

import claypot

trillion dollar industry boys

[dead]

> you're probably not going to learn a ton with clay pot #10,001

Why not just use a library at that point? We already have support for abstractions in programming.

Eloquent, moving, and more-or-less exactly what people said when cameras first hit the scene.

Ironic. The frequency and predictability of this type of response — “This criticism of new technology is invalid because someone was wrong once in the past about unrelated technology” — means there might as well be an LLM posting these replies to every applicable article. It’s boring and no one learns anything.

It would be a lot more interesting to point out the differences and similarities yourself. But then if you wanted an interesting discussion you wouldn’t be posting trite flamebait in the first place, would you?

Note that we still have not solved cameras or even cars.

The biggest lesson I am learning recently is that technologists will bend over backwards to gaslight the public to excuse their own myopia.

Interesting comparison. I remember watching a video on that. Landscape paintings, portraits, etc, was an art that has taken an enormous nosedive. We, as humans, have missed out on a lot of art because of the invention of the camera. On the other hand, the benefits of the camera need no elaboration. Currently AI had a lot of foot guns though, which I don't believe the camera had. I hope AI gets to that point too.

>We, as humans, have missed out on a lot of art because of the invention of the camera.

I so severely doubt this to the point I'd say this statement is false.

As we go toward the past art was expensive and rare. Better quality landscape/portraits were exceptionally rare and really only commissioned by those with money, which again was a smaller portion of the population in the time before cameras. It's likely there are more high quality paintings now per capita than there were ever in the past, and the issue is not production, but exposure to the high quality ones. Maybe this is what you mean by 'miss out'?

In addition the general increase in wealth coupled with the cost of art supplies dropping this opens up a massive room for lower quality art to fill the gap. In the past canvas was typically more expensive so sucky pictures would get painted over.

The footgun cameras had was exposure time.

1826 - The Heliograph - 8+ hours

1839 - The Daguerreotype - 15–30 Mins

1841 - The Calotype - 1–2 Mins

1851 - Wet Plate Collodion - 2–20 Secs

1871 - The Dry Plate - < 1 Second.

So it took 45 years to perfect the process so you could take an instant image. Yet we complain after 4 years of LLMs that they're not good enough.

> Eloquent, moving, and more-or-less exactly what people said when cameras first hit the scene.

This is a non sequitur. Cameras have not replaced paintings, assuming this is the inference. Instead, they serve only to be an additional medium for the same concerns quoted:

  The process, which is an iterative one, is what leads you 
  towards understanding what you actually want to make, 
  whether you were aware of it or not at the beginning.

Photography’s rapid commercialisation [21] meant that many painters – or prospective painters – were tempted to take up photography instead of, or in addition to, their painting careers. Most of these new photographers produced portraits. As these were far cheaper and easier to produce than painted portraits, portraits ceased to be the privilege of the well-off and, in a sense, became democratised [22].

Some commentators dismissed this trend towards photography as simply a beneficial weeding out of second-raters. For example, the writer Louis Figuier commented that photography did art a service by putting mediocre artists out of business, for their only goal was exact imitation. Similarly, Baudelaire described photography as the “refuge of failed painters with too little talent”. In his view, art was derived from imagination, judgment and feeling but photography was mere reproduction which cheapened the products of the beautiful [23].

https://www.artinsociety.com/pt-1-initial-impacts.html#:~:te...

Cameras have not replaced paintings, assuming this is the inference.

You wouldn't have known that, going by all the bellyaching and whining from the artists of the day.

Guess what, they got over it. You will too.

What stole the joy you must have felt, fleetingly, as a child that beheld the world with fresh eyes, full of wonder?

Did you imagine yourself then, as your are now, hunched over a glowing rectangle. Demanding imperiously that the world share your contempt for the sublime. Share your jaundiced view of those that pour the whole of themselves into the act of creation, so that everyone might once again be graced with wonder anew.

I hope you can find a work of art that breaks you free of your resentment.

Thank you for brightening my morning with a brief moment of romantic idealism in a black ocean of cynicism

So I'm the cynic here. That's a hoot.

[flagged]

Thank you for the AI warning, so I didn't have to read that.

Ah well, I'm neurodivergent and it’s challenging for me to write a comment while remembering that others don’t have access to my thoughts and might interpret things differently. And it's too late to edit it now

What I wanted to show is that, clearly different from a camera or other devices, AI can copy originality. OPs comment was pretty original in it's wording, and gpt came pretty close imo. It really wasn't meant as a low effort comment

Plot twist. The comment you love is the cynical one, responding to someone who clearly embraces the new by rising above caution and concern. Your GPT addition has missed the context, but at least you've provided a nice little paradox.

What I especially enjoy is seeing those people accuse AI of being a "parrot" or a "mindless next-token predictor." Inevitably, these accusations are levied in comments whose every thought and token could have been lifted verbatim from any of a thousand such comments over the past few years, accompanied by the rusty squeak of goalpost wheels.

>> Cameras have not replaced paintings, assuming this is the inference.

> You wouldn't have known that, going by all the bellyaching and whining from the artists of the day.

> Guess what, they got over it.

You conveniently omitted my next sentence, which contradicts your position and reads thusly:

  Instead, they serve only to be an additional medium for the 
  same concerns quoted ...

This statement is assumptive and gratuitous.

Username checks out, at least.

> Username checks out, at least.

Thoughtful retorts such as this are deserving of the same esteem one affords the "rubber v glue"[0] idiom.

As such, I must oblige.

0 - https://idioms.thefreedictionary.com/I%27m+rubber%2c+you%27r...

Logic needs to be shown the door on occasion. Sometimes via the help of an ole Irish bar toss.

There are other sites. Other doors, on other bars.

> Guess what, they got over it. You will too.

Prediction is difficult, especially of the future.

It ain't over 'til it's over. And when you come to a fork in the road, take it.

Source?

Art history. It's how we ended up with Impressionism, for instance.

People felt (wrongly) that traditional representational forms like portraiture were threatened by photography. Happily, instead of killing any existing genres, we got some interesting new ones.

Yeah, and cameras changed art forever.

If you don't like change, then my recommendation is to steer clear of careers in either art or technology.

people still make clay pots and paint landscapes

Creativity is not what would expect out of the Renaissance

I don't get it.

I think just as hard, I type less. I specify precisely and I review.

If anything, all we've changed is working at a higher level. The product is the same.

But these people just keep mixing things up like "wow I got a ferrari now, watch it fly off the road!"

Yeah so you got a tools upgrade; it's faster, it's more powerful. Keep it on the road or give up driving!

We went from auto completing keywords, to auto completing symbols, to auto completing statements, to auto completing paragraphs, to auto completing entire features.

Because it happened so fast, people feel the need to rename programming every week. We either vibe coders now, or agentic coders or ... or just programmers hey. You know why? I write in C, I get machine code, I didn't write the machine code! It was all an abstraction!

Oh but it's not the same you say, it changes every time you ask. Yes, for now, it's still wonky and janky in places. It's just a stepping stone.

Just chill, it's programming. The tools just got even better.

You can still jump on a camel and cross the desert in 3 days. Have at it, you risk dying, but enjoy. Or you can just rent a helicopter and fly over the damn thing in a few hours. Your choice. Don't let people tell you it isn't travelling.

We're all Linus Torvalds now. We review, we merge, we send back. And if you had no idea what you were doing before, you'll still have no idea what you're doing today. You just fat-finger less typos today than ever before.

I find it interesting, the comments on this post (not just this particular comment per se) and the sheer inability to relate or ATTEMPT to relate to another persons experience or feeling. The post itself articulated a viewpoint and experience, your having a different one does not negate the other. Nor does your perspective mean the other does not exist. I'm dumbfounded at many of the comments.

Here are some clipped comments that I pulled from the overall post

> I don't get it.

> I'm using LLMs to code and I'm still thinking hard.

> I don't. I miss being outside, in the sun, living my life. And if there's one thing AI has done it's save my time.

> Then think hard? Have a level of self discipline and don’t consistently turn to AI to solve your problems.

> I am thinking harder than ever due to vibe coding.

> Skill issue

> Maybe this is just me, but I don't miss thinking so much.

The last comment pasted is pure gold, a great one to put up on a wall. Gave me a right chuckle thanks!!!

When I read the article, I feel the same emotions that I feel if someone were to tell me "I keep trying to ride a bike but I keep falling off". My experience with LLMs is that the "lack of thinking" is mostly a quick trough you fall into before you come out the other side understanding how to deal with LLMs better. And yes, there's nothing wrong with relating to someone's experience, but mostly I just want to tell that guy, just keep trying, it'll get better, and you'll be back to thinking hard if you keep at it.

But then OP says stuff like:

> I am not sure if there will ever be a time again when both needs can be met at once.

In my head that translates to "I don't think there will ever be a time again when I can actually ride my bike for more than 100 feet." At which point you probably start getting responses more like "I don't get it" because there's only so much empathy you can give someone before you start getting a little frustrated and being like "cmon it's not THAT bad, just keep trying, we've all been there".

If I can 'speak' for the OP:

> I keep trying to ride a bike but I keep falling off

I do not think this analogy is apt.

The core issue is that AI is taking away, or will take away, or threatens to take away, experiences and activities that humans would WANT to do.

The article is lamenting the disappearing of something meaningful for the OP. One can feel sad for this alone. It is not an equation to balance: X is gone but Y is now available. The lament stands alone. As the OP indicates with his 'pragmatism' we now collectively have little choice about the use of AI. The flood waters do not ask they take everyone in their path.

I think the disagreement is over what exactly will be taken away. Certainly, like any technology that came before, AI will automate something. A programmer that finds joy in the raw act of coding- thinking of how to solve a problem and crafting the resulting logic line by line will indeed have something taken away by AI.

But there is a spectrum here. AI is a cruder, less fine-grained method of producing output. But it is a very powerful tool. Instead of "chiseling" the code line by line, it transforms relatively short prompts along with "context" into an imperfect, but much larger/fully formed product. The more you ask it to do in one go, usually the more imperfect it is. But the more precise your prompts, and the "better" your context, the more you can ask it to do while still hanging on to its "form" (always battling against the entropy of AI slop).

Incidentally, those "prompts" are the thinking. The point is to operate at the edge of LLM/machine competence. And as the LLMs become more capable, your vision can grow bigger.

I think if OP had said "I miss getting paid for (a particular type of) thinking hard" I would find it to be a lot more agreeable. But he's just saying he misses it in general. I think that's what I (and, from OP's summary, many other people) find confusing. Can't you still do it? AI is not physically preventing you from thinking hard.

>You can still jump on a camel and cross the desert in 3 days. Have at it, you risk dying, but enjoy. Or you can just rent a helicopter and fly over the damn thing in a few hours. Your choice. Don't let people tell you it isn't travelling.

its obviously not wrong to fly over the desert in a helicopter. its a means to an end and can be completely preferable. I mean myself I'd prefer to be in a passenger jet even higher above it, at a further remove personally. But I wouldn't think that doing so makes me someone who knows the desert the same way as someone who has crossed it on foot. It is okay to prefer and utilize the power of "the next abstraction", but I think its rather pig headed to deny that nothing of value is lost to people who are mourning the passing of what they gained from intimate contact with the territory. and no it's not just about the literal typing. the advent of LLMs is not the 'end of typing', that is more reductionist failure to see the point.

Reminds me of all the parables about kings who "pretend to be a common man" for a day and walk among their subjects and leave with some new enlightenment.

The idea that you lose a ton of knowledge when you experience things through intermediaries is an old one.

I felt the same way about python when I was switching from C++ to python for data analysis

How? Other then calling utility functions that C++ doesn't have you can't just like skip understanding what you are coding by using Python. If you are importing libraries that do stuff for you that wouldn't be any different than if someone wrote those libs in C++.

Are you saying I was incorrect for feeling that way?

The reason is that you no longer really know what's going on. (And yes, that feeling would be the same if C++ had as rich a library of packages as python for numerical analysis.)

If you are doing something that requires precision you need to know everything that is happening in that library. Also IIRC, I think not knowing what type something is bothered me at the time.

I think I understand what the author is trying to say.

We miss thinking "hard" about the small details. Maybe "hard" isn't the right adjective, but we all know the process of coding isn't just typing stuff while the mind wanders. We keep thinking about the code we're typing and the interactions between the new code and the existing stuff, and keep thinking about potential bugs and issues. (This may or may not be "hard".)

And this kind of thinking is totally different from what Linus Torvalds has to think about when reviewing a huge patch from a fellow maintainer. Linus' work is probably "harder", but it's a different kind of thinking.

You're totally right it's just tools improving. When compilers improved most people were happy, but some people who loved hand crafting asm kept doing it as a hobby. But in 99+% cases hand crafting asm is a detriment to the project even if it's fun, so if you love writing asm yourself you're either out of work, or you grudgingly accept that you might have to write Java to get paid. I think there's a place for lamenting this kind of situation.

Spot on. It’s the lumberjack mourning the axe while holding a chainsaw. The work is still hard. it’s just different. The friction comes from developers who prioritize the 'craft' of syntax over delivering value. It results in massive motivated reasoning. We see people suddenly becoming activists about energy usage or copyright solely to justify not using a tool they dislike. They will hunt for a single AI syntax error while ignoring the history of bugs caused by human fatigue. It's not about the tech. it's about the loss of the old way of working.

And it's also somewhat egotistical it seems to me. I sense a pattern that many developers care more about doing what they want instead of providing value to others.

I disagree. It's like the lumberjack working from home watching an enormous robotic forestry machine cut trees on a set of tv-screens. If he enjoyed producing lumber, then what he sees on those screens will fill him with joy. He's producing lots of lumber. He's much more efficient than with both axe and chainsaw.

But if he enjoyed being in the forest, and _doesn't really care about lumber at all_ (Because it turns out, he never used or liked lumber, he merely produced it for his employer) then these screens won't give him any joy at all.

That's how I feel. I don't care about code, but I also don't really care about products. I mostly care about the craft. It's like solving sudokus. I don't collect solved sudokus. Once solved I don't care about them. Having a robot solve sudokus for me would be completely pointless.

> I sense a pattern that many developers care more about doing what they want instead of providing value to others.

And you'd be 100% right. I do this work because my employer provides me with enough sudokus. And I provide value back which is more than I'm compensated with. That is: I'm compensated with two things: intellectual challenge, and money. That's the relationship I have with my employer. If I could produce 10x more but I don't get the intellectual challenge? The employer isn't giving me what I want - and I'd stop doing the work.

I think "You do what the employer wants, produce what needs to be produced, and in return you get money" is a simplification that misses the literal forest for all the forestry.

[dead]

But now you are conflating solving problems with a personal preference of how the problem should be solved. This never bodes well (unless you always prefer picking the method best suited to solve the problem.)

Well as I said, I consider myself compensated with intellectual challenge/stimulus as part of my compensation. It's _why_ I do the work to begin with. Or to put it another way: it's either done in a way I like, or it's probably not done at all.

I'm replaceable after all. If there is someone who is better and more effective at solving problems in some objectively good way - they should have my job. The only reason I still have it is because it seems this is hard to find. Employers are stuck with people who solve problems in the way they like for varying personal reasons and not the objectively best way of solving problems.

The hard part in keeping employees happy is that you can't just throw more money at them to make them effective. Keeping them stimulated is the difficult part. Some times you must accept that you must perhaps solve a problem that isn't the most critical one to address, or perhaps a bad call business wise, to keep employees happy, or keep them at all. I think a lot of the "Big rewrites" are in this category, for example. Not really a good idea compared to maintenance/improvement, but if the alternative is maintaining the old one _and_ lose the staff who could do that?

A perfect solution never exists

> And it's also somewhat egotistical it seems to me. I sense a pattern that many developers care more about doing what they want instead of providing value to others.

I use LLMs a lot. They're ridiculously cool and useful.

But I don't think it's fair to categorize anybody as "egotistical". I enjoy programming for the fun puzzley bits. The big puzzles, and even often the small tedious puzzles. I like wiring all the chunks up together. I like thinking about the best way to expose a component's API with the perfect generic types. That's the part I like.

I don't always like "delivering value" because usually that value is "achieve 1.5% higher SMM (silly marketing metric) by the end of the quarter, because the private equity firm that owns our company is selling it next year and they want to get a good return".

Egotistical would be to reject the new tools in principle and be a less efficient developer.

But really, most of us who personally feel sad about the work being replaced by LLMs can still act reasonable, use the new tooling at work like a good employee, and lament about it privately in a blog or something.

> We see people suddenly becoming activists about energy usage or copyright solely to justify not using a tool they dislike.

Maybe you don’t care about the environment (which includes yourself and the people you like), or income inequality, or the continued consolidation of power in the hands of a few deranged rich people, or how your favourite artists (do you have any?) are exploited by the industry, but some of us have been banging the drum about those issues for decades. Just because you’re only noticing it now or don’t care it doesn’t mean it’s a new thing or that everyone else is being duplicitous. It’s a good thing more people are waking up and talking about those.

I agree. I think some of us would rather deal with small, incremental problems than address the big, high-level roadmap. High-level things are much more uncertain than isolated things that can be unit-tested. This can create feelings of inconvenience and unease.

I work with a lot of artists, and selling them on (not totally rejecting) AI has largely been unsuccessful until they both understand the analogies and the specifics of what different tools do.

AI makes you the manager. The models are like GRAs or contract workers, maybe new to their fields but with tireless energy, and you need to be able to instruct them correctly and evaluate their outputs. None of them can do everything, and you'll need to carefully hire the ones you want based on the work you need, which means breaking workflows into batchable parts. If you've managed projects before, you've done this.

Right now, my focus is improving pipelines in composition and arrangement based on an artist's corpus. A lot of them just want to be more productive, and it's a slog to write, then break into parts, etc using modern notation software.

That's a warm and cozy way to look at things but its not true.

LLM-aided coding is not a higher level tool. It is not writing in C vs writing in assembly. It is closer to asking other people to do something for you and (supposedly, hopefully, although how many people really do it?) reviewing the result.

The thing is, some people already disliked the thinking involved in programming and are welcoming these tools. That's fine, but you don't get to equate it with programming.

You _think_ you're thinking as hard. Reading code != writing it. Just like watching someone do a thing isn't the same as actually doing it.

Correct… reading code is a much more difficult and ultimately, productive, task.

I suspect those using the tools in the best way are thinking harder than ever for this reason.

> reading code is a much more difficult

Not inherently, no. Reading it and getting a cursory understanding is easy, truly understanding what it does well, what it does poorly, what the unintended side effects might be, that's the difficult part.

In real life I've witnessed quite a few intelligent and experienced people who truly believe that they're thinking "really hard" and putting out work that's just as good as their previous, pre-AI work, and they're just not. In my experience it roughly correlates to how much time they think they're saving, those who think they're saving the most time are in fact cutting corners and putting out the sloppiest quality work.

  The first principle is that you must not fool yourself--and you are the easiest person to fool. So you have to be very careful about that.

  - Feynman

It depends on the language, paradigm (or lack thereof), quality/accuracy of the names.

My work’s codebase is 30 years of never-refactored C++. It takes an exceptional amount of focus and thinking to get even a cursory understanding of anything a particular method or class does or why it’s there.

But for languages like C, I agree with you (as long as function pointers aren’t used abused).

Sure. Reading a book is a much more difficult and ultimately, productive, task than writing a book.

Well, depending on the scope of work, they may be still thinking hard, just on a higher level. That is, thinking about the requirements, specification, and design.

You don't have a strong mental model after agentic coding something in my experience.

It isn't an abstraction like assembly -> C. If you code something like: extract the raw audio data from an audio container, it doesn't matter if you write it in assembly, C, Javascript, whatever. You will be able to visualize how the data is structured when you are done. If you had an agent generate the code the data would just be an abstraction.

It just isn't worth it to me. If I am working with audio and I get a strong mental model for what different audio formats/containers/codecs look like who knows what creative idea that will trigger down the line. If I have an agent just fix it then my brain will never even know how to think in that way. And it takes like... a day...

So I get it as a optimized search engine, but I will never just let it replace understanding every line I commit.

I think the more apt analog isn't a faster car, a la Ferrari, it's more akin to someone who likes to drive and now has to sit and monitor the self-driving car steer and navigate. Comparing to the Ferrari is incorrect since it still takes a similar level of agency from the driver versus a <insert slower vehicle>

This is exactly the right analogy here.

FSD is very very good most of the time. It's so good (well, v14 is, anyway), it makes it easy to get lulled into thinking that it works all the time. So you check your watch here, check your phone there, and attend to other things, and it's all good until the car decides to turn into a curb (which almost happened to me the other day) or swerve hard into a tree (which happened to someone else).

Funny enough, much like AI, Tesla is shoving FSD down people's throats by gating Autopilot 2, a lane keeping solution that worked extremely well and is much friendlier to people who want limited autonomy here and there, behind the $99/mo FSD sub (and removing the option to pay for the package out of pocket).

> I think just as hard, I type less. I specify precisely and I review.

Even if you "think just as hard" the act of physically writing things down is known to improve recall, so you're skipping a crucial step in understanding.

And when I review code, it's a different process than writing code.

These tradeoffs may be worth it, because we can ask the tools to analyze things for us just as easily as we can ask them to create things for us, but your own knowledge and understanding of the system is absolutely being degraded when working this way.

It is simple. Continuing your metaphor, I have a choice of getting exactly where I want on a camel in 3 days, or getting to a random location somewhere on the other side of the desert on a helicopter in few hours.

And being a reasonable person I, just like the author, choose the helicopter. That's it, that's the whole problem.

Why is that the reasonable choice if it doesn't get you to your destination?

I too did a lot of AI coding but when I saw the spaghetti it made, I went back to regular coding, with ask mode not agent mode as a search engine.

Because of compound efficiency and technological enablement.

Or, risking to beat the metaphor to death, because over a span of time I'll cross many more deserts than I would have on a camel, and because I'll cross deserts that I wouldn't even try crossing on a camel.

Why does it matter how many deserts you cross if you never get to where you want to go? I similarly can take 10 flights across oceans but never end up in the city I'm trying to visit. Sounds like in your metaphor the person is just crossing desserts because they want to with no goal or destination in mind.

Maybe you wanted to visit Mexico and your dream was specifically about Cancún, but then you ended up in Veracruz and were like "oh well, it is Mexico after all, I'd rather be here and visit five other countries similarly than only a single one with my dream city."

I guess this is stretching the analogy a bit far because I'd rather get to the exact place I wanted to go rather than wasting my time non-deterministically wandering around because who knows how long that will take to get anywhere close to my destination.

Because taking a rental camel from the airport is faster.

You did something smart and efficinently using the least amount of energy and time needed. +1 for consciousness being a mistake

Helicopters are deterministic though :)

> We're all Linus Torvalds now.

So...where's your OS and SCM?

I get your point that wetware stills matter, but I think it's a bit much to contend that more than a handful of people (or everyone) is on the level of Linus Torvalds now that we have LLMs.

I should have been clearer. It was a pun, a take, a joke. I was referring to his day-to-day activity now, where he merges code, doesn't write hardly any code for the linux kernel.

I didn't imply most of use can do half the thing he's done. That's not right.

Even disregarding what he has done, this is utterly absurd. I almost spit my coffee reading that.

You are going to tell me that the vibe coders care and read the code they merge with the same attention to detail and care that Linus has? Come on...

That's the key for me. People are churning out "full features" or even apps claiming they are dealing with a new abstraction level, but they don't give a fuck about the quality of that shit. They don't care if it breaks in 3 weeks/months/years or if that code's even needed or not.

Someone will surely come say "I read all the code I generate" and then I'll say either you're not getting these BS productivity boost people claim or you're lying.

I've seen people pushing out 40k lines of code in a single PR and have the audacity to tell me they've reviewed the code. It's preposterous. People skim over it and YOLO merge.

Or if you do review everything, then it's not gonna be much faster than writing it yourself unless it's extremely simple CRUD stuff that's been done a billion times over. If you're only using AI for these tasks maybe you're a bit more efficient, but nothing close to the claims I keep reading.

I wish people cared about what code they wrote/merged like Linus does, because we'd have a hell of a lot less issues.

> his day-to-day activity now, where he merges code

But even then...don't you think his insight into and ability to verify a PR far exceeds that of most devs (LLM or not)? Most of us cannot (reasonably) aspire to be like him.

Like I said if you didn't know what you were doing before, you won't know what you're doing with today.

Agentic coding in general only amplify your ability (or disability).

You can totally learn how to build an OS and invest 5 years of your life doing so. The first version of Linux I'm sure was pretty shoddy. Same for a SCM.

I've been doing this for 30 years. At some point, your limit becomes how much time you're willing to invest in something.

But some can aspire to be him circa five years ago,

while Linus has his own efforts multiplied as well.

My hair hasn't turned blonde and I don't suddenly know how to speak Finnish, either.

You might have missed their point.

> You just fat-finger less typos today than ever before.

My typos are largely admissible.

I agree! It's a lot more pleasant than being stuck over figuring out how to use awk properly for hours. I knew what I needed to do then, and I know what I need to do now too. The difference is I get to results faster. Sometimes I even learn that awk was not even the right tool in my situation and learn about a new way of doing things while AI is "thinking" for me

> We're all Linus Torvalds now. We review, we merge, we send back. And if you had no idea what you were doing before, you'll still have no idea what you're doing today. You just fat-finger less typos today than ever before.

Except Linus understands the code that is being reviewed / merged in since he already built the kernel and git by hand. You only see him vibe-coding toys but not vibe-coding in the kernel.

Today, we are going to see a gradual skill atrophy with developers over-relying on AI and once something like Claude goes down, they can't do any work at all.

The most accurate representation is that AI is going to rapidly make lots of so-called 'senior engineers' who are over-reliant and unable to detect bad AI code like juniors and interns.

My “skill” for 40 years has been to turn what I wanted my computer to do into code to get it done using the tools available to me. A “senior developer” is not someone who “codez real gud”. It’s someone who can work at a higher level of scope and ambiguity and has a larger impact on the organization than a ticket taker

If you can't rebuke code today. You can't rebuke code tomorrow.

By induction that means either nobody can rebuke code or someone who can rebuke code can do that from the day they're born.

“can” has multiple meanings in English. It can express both epistemic (describing the world accurately as is) and dynamic (describing the capabilities and attributes of an object) modes. If used epistemically, then parent’s phraseology makes perfect sense since his epistemology makes no claim about an object’s variability across time.

I get it.

I got excited about agents because I told myself it would be "just faster typing". I told myself that my value was never as a typist and that this is just the latest tool like all the tools I had eagerly added to my kit before.

But the reality is different. It's not just typing for me. It's coming up with crap. Filling in the blanks. Guessing.

The huge problem with all these tools is they don't know what they know and what they don't. So when they don't know they just guess. It's absolutely infuriating.

It's not like a Ferrari. A Ferrari does exactly what I tell it to, up to the first-order effects of how open the throttle is, what direction the wheels face, how much pressure is on the brakes etc. The second-order effects are on me, though. I have to understand what effect these pressures will have on my ultimate position on the road. A normie car doesn't give you as much control but it's less likely to come off the road.

Agents are like a teleport. You describe where you want to be and it just takes you directly there. You say "warm and sunny" and you might get to the Bahamas, but you might also get to the Sahara. So you correct: "oh no, I meant somewhere nice" and maybe you get to the Bahamas. But because you didn't travel there yourself you failed to realise what you actually got. Yeah, it's warm, sunny and nice, but now you're on an island in the middle of nowhere and have to import basically everything. So I prompt again and rewrite the entire codebase, right?

Linus Torvalds works with experts that he trusts. This is like a manic 5 year old that doesn't care but is eager to work. Saying we all get to be Torvalds is like saying we all get to experience true love because we have access to porn.

except the thing does not work as expected and it just makes you worse not better

Like I said that's temporary. It's janky and wonky but it's a stepping stone.

Just look at image generation. Actually factually look at it. We went from horror colours vomit with eyes all over, to 6 fingers humans, to pretty darn good now.

It's only time.

Why is image generation the same as code generation?

it's not. We were able to get rid of 6 fingered hands by getting very specific, and fine tuning models with lots of hand and finger training data.

But that approach doesn't work with code, or with reasoning in general, because you would need to exponentially fine tune everything in the universe. The illusion that the AI "understands" what it is doing is lost.

It isn't.

Code generation progression in LLMs still carries higher objective risk of failure depending on the experience on the person using it because:

1. They still do not trust if the code works (even if it has tests) thus, needs thorough human supervision and still requires on-going maintainance.

2. Hence (2) it can cost you more money than the tokens you spent building it in the first place when it goes horribly wrong in production.

Image generation progression comes with close to no operational impact, and has far less human supervision and can be safely done with none.

This sounds like every system that I didn’t write completely myself and honestly some that I did

> Just look at image generation. Actually factually look at it. We went from horror colours vomit with eyes all over, to 6 fingers humans, to pretty darn good now.

Yes, but you’re not taking into account what actually caused this evolution. At first glance, it looks like exponential growth, but then we see OpenAI (as one example) with trillions in obligations compared to 12–13 billion in annual revenue. Meanwhile, tool prices keep rising, hardware demand is surging (RAM shortages, GPUs), and yet new and interesting models continue to appear. I’ve been experimenting with Claude over the past few days myself. Still, at some point, something is bound to backfire.

The AI "bubble" is real, you don’t need a masters degree in economics to recognize it. But with mounting economic pressures worldwide and escalating geopolitical tension we may end up stuck with nothing more than those amusing Will Smith eating pasta videos for a while.

Comments like these are why I don't browse HN nearly ever anymore

Nothing new. Whenever a new layer of abstraction is added, people say it's worse and will never be as good as the old way. Though it's a totally biased opinion, we just have issues with giving up things we like as human being.

> Whenever a new layer of abstraction is added

LLMs aren't a "layer of abstraction."

99% of people writing in assembly don't have to drop down into manual cobbling of machine code. People who write in C rarely drop into assembly. Java developers typically treat the JVM as "the computer." In the OSI network stack, developers writing at level 7 (application layer) almost never drop to level 5 (session layer), and virtually no one even bothers to understand the magic at layers 1 & 2. These all represent successful, effective abstractions for developers.

In contrast, unless you believe 99% of "software development" is about to be replaced with "vibe coding", it's off the mark to describe LLMs as a new layer of abstraction.

> unless you believe 99% of "software development" is about to be replaced with "vibe coding"

Probably not vibe coding, but most certainly with some AI automation

The difference is that LLM output is very nondeterministic.

And because of that, we check in the generated code, not the high-level abstraction. So to understand your program, you have to read the output, not the input.

It depends. Temperature is a variable. If you really need determinism, you could build a LLM for that. Non-determinism can be a good feature though.

How would you do that? If it's possible, it seems strange that someone hasn't done it already.

Totally possible and we can already do it ! Simply put, just set the temperature to 0 and reuse the same seed. But it's just not what people really want, and providers are reluctant because they cost up to 5x more to generate. It's also not 100% non-deterministic, because cloud providers don't run on the same hardware, with the same conditions required for producing the same output. So, in practice, not so good, but in theory if you need it and can afford it, you can.

That's your opinion and you can not use those tools.

People are paying for it because it helps them. Who are you to whine about it?

But that's the entire flippin' problem. People are being forced to use these tools professionally at a stagering rate. It's like the industry is in its "training your replacement" era.

That's Capitalism, baby

you don't like it? Find a place that doesn't enforce it. Can't find it? Then either build it or accept that you want a horse carriage while people want taxi.

How can you be so sure to think you're not replaceable? Because when you use the tools, you're giving data to companies to eventually get even you.

It really bothers me how many comments on this topic (here and elsewhere) draw a false parallel between LLM-based coding as an abstraction and frameworks and compilers as an abstraction. They're not the same thing and it matters.

Frameworks and compilers are designed to be leak-proof abstractions. Any way in which they deviate from their abstract promise is a bug that can be found, filed, and permanently fixed. You get to spend your time and energy reasoning in terms of the abstraction because you can trust that the finished product works exactly the way you reasoned about at the abstract level.

LLMs cannot offer that promise by design, so it remains your job to find and fix any deviations from the abstraction you intended. If you fell short of finding and fixing any of those bugs, you've just left yourself a potential crisis down the line.

[Aside: I get why that's acceptable in many domains, and I hope in return people can get why it's not acceptable in many other domains]

All of our decades of progress in programming languages, frameworks, libraries, etc. has been in trying to build up leak-proof abstractions so that programmer intent can be focused only on the unique and interesting parts of a problem, with the other details getting the best available (or at least most widely applicable) implementation. In many ways we've succeeded, even though in many ways it looks like progress has stalled. LLMs have not solved this, they've just given up on the leak-proof part of the problem, trading it for exactly the costs and risks the industry was trying to avoid by solving it properly.

Your comment gets to the crux of my thinking about LLM coding. The way I think of what LLM coding is doing is decompressing your prompt into code based on the statistical likelihood of that decompression based on training data. Basically "Build me an IOS app" -> a concrete implementation of an iOS app. The issue here is that the user supplying the prompt needs to encode all of the potential variables that the AI needs to work with into the prompt, or else the implementation will just be based on the "bog-standard" iOS app based on the training corpus, although with potential differences in the app based on other tokens in the prompt. Is natural language the right way to encode that information? Do we want to rely on input tokens to the context of a model successfully making it into the output to guarantee accuracy? I think the Kiro Spec driven development starts to get at addressing the inherent issues in LLM based coding assistance, but it is an early step.

Another way in which their different is that, because they are non-deterministic, we check in the output, not the input. It's the equivalent of checking machine code into our source control instead of the input language. It's not abstraction, its non-deterministic code generation.

> LLMs cannot offer that promise by design, so it remains your job to find and fix any deviations from the abstraction you intended.

LLMs are clumsy interns now, very leaky. But we know human experts can be leak-proof. Why can't LLMs get there, too, better at coding, understanding your intentions, reviewing automatically for deviations, etc.?

Thought experiment: could you work well with a team of human experts just below your level? Then you should be able to work well with future LLMs.

That is a good point because your brain interacts with them differently as well.

If my C compiler sometimes worked and sometimes didn't I would just mash compile like an ape until it started working.

Exactly. It’s a bit startling how many programmers don’t seem to know the meaning of “abstraction.”

You know, I was expecting what the post would say and was prepared to dunk on it and just tell them to stop using ai then, but the builder/thinker division they presented got me thinking. How ai/vibe coding fulfills the builder, not the thinker, made me realize that I'm basically 100% thinker, 0% builder, and that's why I don't really care at all about ai for coding.

I'll spend years working on a from scratch OS kernel or a vulkan graphics engine or whatever other ridiculous project, which never sees the light of day, because I just enjoy the thinking / hard work. Solving hard problems is my entertainment and my hobby. It's cool to eventually see results in those projects, but that's not really the point. The point is to solve hard problems. I've spent decades on personal projects that nobody else will ever see.

So I guess that explains why I see all the ai coding stuff and pretty much just ignore it. I'll use ai now as an advanced form of google, and also as a last ditch effort to get some direction on bugs I truly can't figure out, but otherwise I just completely ignore it. But I guess there's other people, the builders, where ai is a miraculous thing and they're going to crazy lengths to adopt it in every workflow and have it do as much as possible. Those 'builder' types of people are just completely different from me.

Absolutely nah. I know it feels good to jump into the 'thinker' camp and lump the users of AI into a non-thinker group, but this dichotomy is very poorly suited. Builders/engineers want a great tool to build faster with. Coders want to write code and find elegance in the prose. Both are thinkers.

This just sounds like a boring definitional issue. 'Thinker' is a loaded word.

You are advocating for a particular (more inclusive) definition for 'thinker' which clashes with the author's, but his is equally valid. You're both just gesturing at different concepts and suggesting they should be tagged to that word.

OP raises a particular way to classify something about personalities, says he finds it quite interesting/discriminative, and calls that kind of personality a "thinker". You instead consider a "thinker" a broader category.

That feels like an empty disagreement (nobody is right on such matters) - the real debatable question of substance is whether the _concept_ OP is gesturing at has interesting discriminative power. That concept is something like "personalities which seem to value the act of thinking through a problem/problem solving itself rather than downstream result".

As someone who's been coding for several decades now (i.e. I'm old), I find the current generation of AI tools very ... freeing.

As an industry, we've been preaching the benefits of running lots of small experiments to see what works vs what doesn't, try out different approaches to implementing features, and so on. Pre-AI, lots of these ideas never got implemented because they'd take too much time for no definitive benefit.

You might spend hours thinking up cool/interesting ideas, but not have the time available to try them out.

Now, I can quickly kick off a coding agent to try out any hare-brained ideas I might come up with. The cost of doing so is very low (in terms of time and $$$), so I get to try out far more and weirder approaches than before when the costs were higher. If those ideas don't play out, fine, but I have a good enough success rate with left-field ideas to make it far more justifiable than before.

Also, it makes playing around with one-person projects a lot practical. Like most people with partner & kids, my down time is pretty precious, and tends to come in small chunks that are largely unplannable. For example, last night I spent 10 minutes waiting in a drive-through queue - that gave me about 8 minutes to kick off the next chunk of my one-person project development via my phone, review the results, then kick off the next chunk of development. Absolutely useful to me personally, whereas last year I would've simply sat there annoyed waiting to be serviced.

I know some people have an "outsourcing Lego" type mentality when it comes to AI coding - it's like buying a cool Lego kit, then watching someone else assemble it for you, removing 99% of the enjoyment in the process. I get that, but I prefer to think of it in terms of being able to achieve orders of magnitude more in the time I have available, at close to zero extra cost.

> 8 minutes to kick off the next chunk of my one-person project development via my phone, review the results, then kick off the next chunk of development.

How are you doing this via your phone?

Termius + tailscale + tmux is a common setup for mobile coding sessions.

Exactly this setup

The (iOS) Claude phone app has a Claude code feature which runs "in the cloud". It's pretty handy for getting things done on the bus.

claude can deploy to github spaces and modify code for deployment to those by commits and pull requests to the repo exclusively

claude via browser and claude mobile apps function this way

but alongside that, people do make tunnels to their personal computer and setup ways to be notified on their phone, or to get the agent unstuck when it asks for a permission, from their phone

Just wanted to +1 this as a deep thinker who disagrees with the blog post's conclusion. I remember back on the years and decades I wasted dealing with the conceptual flaws inherent to nearly all software, and it breaks my heart.

90-99% of programming is a waste of time. Most apps today have less than a single spreadsheet page of actual business logic. The rest is boilerplate. Conjuring up whatever arcane runes are needed to wake a slumbering beast made of anti-patterns and groupthink.

For me, AI offers the first real computer that I've had access to in over 25 years. Because desktop computing stagnated after the 2000 Dot Bomb, and died on the table after the iPhone arrived in 2007. Where we should have symmetric multiprocessing with 1000+ cores running 100,000 times faster for the same price, we have the same mediocre quad core computer running about the same speed as its 3 GHz grandfather from the early 2000s. But AI bridges that divide by recruiting video cards that actually did increase in speed, albeit for SIMD which is generally useless for desktop computing. AI liberates me from having to mourn that travesty any longer.

I think that people have tied their identity to programming without realizing that it's mostly transcribing.

But I will never go back to manual entry (the modern equivalent of punch cards).

If anything, I can finally think deeply without it costing me everything. No longer having to give my all just to tread water as I slowly drown in technical debt and deadlines which could never be met before without sacrificing a part of my psyche in the process.

What I find fascinating is that it's truly over. I see so clearly how networks of agents are evolving now, faster than we can study, and have already passed us on nearly every metric. We only have 5-10 years now until the epiphany, the Singularity, AGI.

It's so strange to have worked so hard to win the internet lottery when that no longer matters. People will stop buying software. Their AI will deliver their deepest wish, even if that's merely basic resources to survive, that the powers that be deny us to prop up their fever dream of late-stage crony capitalism under artificial scarcity.

Everything is about to hit the fan so hard, and I am so here for it.

> 90-99% of programming is a waste of time. Most apps today have less than a single spreadsheet page of actual business logic.

I would very much like to know the kind of app you’ve seen. It’s very hard to see something like mpv, calibre, abiword, cmus,… through that lens. Even web apps like forgejo, gonic, sr.ht, don’t fit into that view.

Fair enough. I meant social network websites and social media apps like Facebook and TikTok that could have been made in a weekend using HyperCard, FileMaker, Microsoft Access, etc, if we had real reactive backends similar to Firebase, Airtable and Zapier, which come so close to almost working for normal people but miss the mark fundamentally somehow.

I know that programming has gone terribly wrong, but it's hard for me to articulate how, because it's all of it - the entire frontend web development ecosystem, mobile development languages and frameworks, steep learning curve languages like Rust that were supposed to make things easier but put the onus on the developer to get the busywork right, everything basically. It's like trying to explain screws to craftsmen only familiar with nails.

In the simplest terms, it's because corporations are driving the development of those tools and vacuuming up all profits on the backs of open sources maintainers working in their parents' basements, rather than universities working from first principles to solve hard problems and give solutions away to everyone for free for the good of society. We've moved from academia to slavery and call it progress.

I think this is the other side of the same coin, and it's a really important one. When time is scarce, especially with family and a life outside work, the ability to turn tiny, fragmented moments into forward progress is huge

Totally agree. I can spend an afternoon trying out an approach to a problem or product (usually while taking meetings and writing emails as well). If it doesn't work, then that's a useful result from my time. If it does work, I can then double-down on review, tests, quality, security, etc and make sure it's all tickety-boo.

Completely agree, there’s so many small projects I’d never been able to even start in my free time, because I’m NOT a full-stack dev and I’d rather not spend all my evenings fixing or working around all the small changes and quirks of the $currentjsframework

Exactly, it kills me to see a people a lot younger (I’m 51) pining about the good old days while the coding part of my day to day life now is using AI tools to their fullest extent.

I will never not be upset at my fellow engineers for selling out the ONE thing that made us valuable and respected in the marketplace and trying to destroy software engineering as a career because "Claude Code go brrrrrr" basically.

It's like we had the means for production and more or less collectively decided "You know what? Actually, the bourgeoisie can have it, sure."

Will we see growing unemployment in software development? Maybe in the short term, but the appetite for software is not finite just as the appetite for any other kind of economic tool is not finite.

What percentage of software just ceases development when it's "good enough?" Probably a tiny percentage of all software, and which is mostly internal tooling.

It's going to push software development into the front of business development precisely because it will be cheaper to develop. The companies that will benefit from AI at least early on will all be software-driven companies since the results are undeniable. If software developers lose out when all of their companies become more efficient and new companies are built because it becomes easier to build software with small teams, I will be very surprised.

This is a more honest comment than the many around it making overly intellectualized arguments.

But I would sooner compare this engineer class to something of a small bourgeoisie swallowed by a yet larger one, especially in the United States.

The personification of the quote “your scientists were so preoccupied with whether or not they could that they didn't stop to think if they should”

I feel the existential problem for a world that follows the religion of science and technology to its extreme, is that most people in STEM have no foundation in humanities, so ethical and philosophical concerns never pass through their mind.

We have signed a pact with the devil to help us through boring tasks, and no one thought to ask what we would give in exchange.

Yeah, lawyers, politicians and MBA types all usually have solid foundation in humanities. Not exactly known for concerning themselves with ethics or philosophy though.

I find it fascinating how the “true believers” of AI and AGI, have essentially been manipulated by capitalists to undermine the value of their own labor.

so AI can actually replace software engineers? It's relatively unusual to see someone who "believes in AI", but is "anti-AI", at least among the HN crowd.

Also, I find this view very selfish. Yes, let's gatekeep a ground-breaking technology because it will hurt my specific profession. That's literally what technological progress is- it always (at least temporarily) disadvantages a particular sector of workers that specialized in that skill for the benefit of society. It's just funny because usually it's the blue collar workers who have to worry about this and the white collar workers who sanctimoniously tell them to suck it up or learn to code.

Also, I can't help but chuckle slightly at the irony here. The entire purpose of software engineering and computers is automation, and the lost jobs in other sectors due to SWEs is massive. To spend one's entire life in pursuit of this and be morally opposed to the "ultimate" automation (automating this process of automation itself) is a bit rich.

Based comment

Money. It's always money. It was always money.

Couldn't agree more. AI as it's designed today is very heavy on the "f u; got mine" vibe.

Tech in general, I remember when I was younger thinking the tech world was so cool and different.

I still love the work, but to say I’m disillusioned by the industry is an understatement.

You might like Penelope Scott's Rat:

https://www.youtube.com/watch?v=qoPyqPXxtAg

The bloodless pragmatists aided and abetted them. Those who have no apparent values. Only short-term, terminally idiotic obsession with building things, creating value, creating business value, or whatever the pragmatist posturing of the day is.

Now they can have the fruits of their labor. Nothing. But why would they even want that? It’s not about getting something out of it. Or maintaining a living. It’s about building things.

[dead]

I think the article has a point. There seem to be two reactions among senior engineers atound me these days.

On one side, there are people who have become a bit more productive. They are certainly not "10x," but they definitely deliver more code. However, I do not observe a substantial difference in the end-to-end delivery of production-ready software. This might be on me and my lack of capacity to exploit the tools to their full extent. But, iterating over customer requirements, CI/CD, peer reviews, and business validation takes time (and time from the most experienced people, not from the AI).

On the other hand, soemtimes I observe a genuine degradation of thinking among some senior engineers (there aren’t many juniors around, by the way). Meetings, requirements, documents, or technology choices seem to be directly copy/pasted from an LLM, without a grain of original thinking, many times without insight.

The AI tools are great though. They give you an answer to the question. But, many times making the correct question, and knowing when the answer is not correct is the main issue.

I wonder if the productivity boost that senior engineers actually need is to profit from the accumulated knowledge found in books. I know it is an old technology and it is not fashionable, but I believe it is mostly unexploited if you consider the whole population of engineers :D

> This might be on me and my lack of capacity to exploit the tools to their full extent. But, iterating over customer requirements, CI/CD, peer reviews, and business validation takes time (and time from the most experienced people, not from the AI).

Yeah, you're certainly not the only one. For me the implementation part has always been a breeze compared to all the "communication overhead" so to speak. And in any mature system it easily takes 90% of all time or more.

One thing this discussion made me realize is that "thinking hard" might not be a single mode of thinking.

In grad school, I had what I'd call the classic version. I stayed up all night mentally working on a topology question about turning a 2-torus inside out. I already knew you can't flip a torus inside out in ordinary R^3 without self-intersection. So I kept moving and stretching the torus and the surrounding space in my head, trying to understand where the obstruction actually lived.

Sometime around sunrise, it clicked that if you allow the move to go through infinity(so effectively S^3), the inside/outside distinction I was relying on just collapses, and the obstruction I was visualizing dissolves. Birds were chirping, I hadn't slept, and nothing useful came out of it, but my internal model of space felt permanently upgraded. That's clearly "thinking hard" in the sense.

But there's another mode I've experienced that feels related but different. With a tough Code Golf problem, I might carry it around for a week. I'm not actively grinding on it the whole time, but the problem stays loaded in the background. Then suddenly, in the shower or on a walk, a compression trick or a different representation just clicks.

That doesn't feel "hard" moment to moment. It's more like keeping a problem resident in memory long enough for the right structure to surface.

One is concentrated and exhausting, the other is diffuse and slow-burning. They're different phenomenologically, but both feel like forms of deep engagement that are easy to crowd out.

Yup, yup! There's so many different ways of thinking hard.

For me, thinking about an extremely technical TCS problem, for example, is my version of actively, tirelessly thinking hard. I'm logging a ton of observations, trying new ideas and hypotheses, using a mix of computer simulation and math to try and arrive at a concrete framing and answer.

On the other end of the specturm, I have philosophy. It's definitely a different type of hard. Most of my "Aha!" moments come from when I realize I've been strawmanning some argument, and not actually understanding what the person is saying. Why is the person saying this, relative to what, why is this a new observation, etc. Things are so amorphus and you can tweak the problem parameters in so many ways, and it's really tempting to either be too fluid and pretend you understand the thinker (because it's a subset of some conception you already have), or be too rigid and dissolve the thinker as a category error / meaningless. I've never felt the same feeling as I did when doing TCS research, but the feeling was definitely hard thinking nonetheless.

In terms of extremely nitty-gritty technical things, like linker bullshit and linux kernel programming, I'm much more familiar with, and these things are more about reading documentation (because the tool won't behave like you want it to) and iteration / testing (because... the tool won't behave like you want it to, so you need to make sure it behaves like you want it to!). This is also a type of thinking - I would call it hard as in the physiological response I have is similar to that of research in the very bad moments, but in terms of my lofty ideals, I don't want to call this hard.... it's very "accidental" complexity, but it's what I get paid to do :/

At work, you have a huge idea space to consider, both problem and solution framings, mixing in "bullshit" constraints like business ones. You also throw in the real-time aspect of it, so I can't just either armchair on a problem for a month (unlike Philosophy) or deep dive on a problem for a month (unlike research). I'm technically doing the third type of programming right now, but we'll see how long that lasts and I get put on a new project.

I'm not even sure if there's a clean demarcation between any of these. These are certainly better than brainrotting youtube though.

I'm using LLMs to code and I'm still thinking hard. I'm not doing it wrong: I think about design choices: risks, constraints, technical debt, alternatives, possibilities... I'm thinking as hard as I've ever done.

Yeah, but thinking with an LLM is different. The article says:

> By “thinking hard,” I mean encountering a specific, difficult problem and spending multiple days just sitting with it to overcome it.

The "thinking hard" I do with an LLM is more like management thinking. Its chaotic and full of conversations and context switches. Its tiring, sure. But I'm not spending multiple days contemplating a single idea.

The "thinking hard" I do over multiple days with a single problem is more like that of a scientist / mathematician. I find myself still thinking about my problem while I'm lying in bed that night. I'm contemplating it in the shower. I have little breakthroughs and setbacks, until I eventually crack it or give up.

Its different.

YMMV, but I've found that I actually do way more of that type of "thinking hard" thanks to LLMs. With the menial parts largely off my plate, my attention has been freed up to focus on a higher density of hard problems, which I find a lot more enjoyable.

Yup, there is a surprisingly high amount of boilerplate in programming, and LLMs definitely can remove this and let you focus on the more important problems. For a person with a day job, working on side projects actually became fun with LLMs again, even with the limitation of free time and mental energy to invest in.

There are a lot of hard problems to solve in orchestration. We've barely scratched the surface on this.

I very much think its possible to use LLMs as a tool in this way. However a lot of folks are not. I see people, both personally and professionally, give it a problem and expect it to both design and implement a solution, then hold it as a gold standard.

I find the best uses, for at least my self, are smaller parts of my workflow where I'm not going to learn anything from doing it: - build one to throw away: give me a quick prototype to get stakeholder feedback - straightforward helper functions: I have the design and parameters planned, just need an implementation that I can review - tab-completion code-gen - If I want leads for looking into something (libraries, tools) and Googling isn't cutting it

> then hold it as a gold standard

I just changed employers recently in part due to this: dealing with someone that appears to now spend his time coercing LLM's to give the answers he wants, and becoming deaf to any contradictions. LLMs are very effective at amplifying the Reality Distortion Field for those that live in them. LLMs are replacing blog posts for this purpose.

I echo this sentiment. Even though I'm having Claude Code write 100% of the code for a personal project as an experiment, the need for thinking hard is very present.

In fact, since I don't need to do low-thinking tasks like writing boilerplate or repetitive tests, I find my thinking ratio is actually higher than when I write code normally.

I'm with you, thinking about architecture is generally still a big part of my mental effort. But for me most architectural problems are solve in short periods of thought and a lot of iteration. Maybe its an skill issue, but not now nor in the pre-LLM era I've been able to pre-solve all the architecture with pure thinking.

That said architectural problems have been also been less difficult, just for the simple fact that research and prototyping has become faster and cheaper.

I think it depends on the scope and level of solution I accept as “good”. I agree that often the thinking for the “next step” is too easy architecturally. But I still enjoy thinking about the global optimum or a “perfect system”, even it’s not immediately feasible, and can spend large amounts of time on this.

And then also there’s all the non-systems stuff - what is actually feasible, what’s most valuable etc. Less “fun”, but still lots of potential for thinking.

I guess my main point is there is still lots to think about even post-LLM, but the real challenge is making it as “fun” or as easily useful as it was pre-LLM.

I think local code architecture was a very easy domain for “optimality” that is actually tractable and the joy that comes with it, and LLMs are harmful to that, but I don’t think there’s nothing to replace it with.

And thinking of how to convey all of that to Claude without having to write whole books :)

tfw you start expressing your thoughts as code because its shorter instead

Ya, they are programming languages after all. Language is really powerful when you really how to use it. Some of us are more comfortable with the natural variety, some of us are more comfy with code ¯\_(ツ)_/¯

Agreed. My recent side projects involve lots of thinking over days and weeks.

With AI we can set high bars and do complex original stuff. Obviously boilerplate and common patterns are slop slap without much thinking. That's why you branch into new creative territory. The challenge then becomes visualising the mental map of modular pieces all working nicely together at the right time to achieve your original intent.

My experience is similar, but I feel I'm actually thinking way harder than I ever was before LLMs.

Before LLMs once I was done with the design choices as you mention them - risks, constraints, technical debt, alternatives, possibilities, ... I cooked up a plan, and with that plan, I could write the code without having to think hard. Actually writing code was relaxing for me, and I feel like I need some relax between hard thinking sessions.

Nowadays we leave the code writing to LLMs because they do it way faster than a human could, but then have to think hard to check if the code LLM wrote satisfies the requirements.

Also reviewing junior developers' PRs became harder with them using LLMs. Juniors powered by AI are more ambitious and more careless. AI often suggests complicated code the juniors themselves don't understand and they just see that it works and commit it. Sometimes it suggests new library dependencies juniors wouldn't think of themselves, and of course it's the senior's role to decide whether the dependency is warranted and worthy of being included. Average PR length also increased. And juniors are working way faster with AI so we spend more time doing PR reviews.

I feel like my whole work somehow from both sides collapsed to reviewing code = from one side the code that my AI writes, from the other side the code that juniors' AI wrote, the amount of which has increased. And even though I like reviewing code, it feels like the hardest part of my profession and I liked it more when it was balanced with tasks which required less thinking...

Reading this comment and other similar comments there's definitely a difference between people. Personally I agree and resonate a lot with the blog post, and I've always found designs of my programs to come sort of naturally. Usually the hard problems are the technical problems and then the design is figured out based on what's needed to control the program. I never had to think that hard about design.

Aptitude testing centers like Johnson O'Connor have tests for that. There are (relatively) huge differences between different people's thinking and problem solving styles. For some, creating an efficient process feels natural, while others need stability and redundancy. Programmers are by and large the latter.

[1]: https://www.jocrf.org/how-clients-use-the-analytical-reasoni...

> I'm using LLMs to code and I'm still thinking hard. I'm not doing it wrong: I think about design choices: risks, constraints, technical debt, alternatives, possibilities... I'm thinking as hard as I've ever done.

Okay, for you that is new - post-LLM.

For me, pre-LLM I thought about all those things as well as the code itself.

IOW, I thought about even more things. Now you (if I understand your claim correctly) think only about those higher level things, unencumbered by stuff like implementation misalignments, etc. By definition alone, you are thinking less hard.

------------------------

[1] Many times the thinking about code itself acted as a feedback mechanism for all those things. If thinking about the code itself never acted as a feedback mechanism to your higher thought processes then ... well, maybe you weren't doing it the way I was.

Thats not thinking hard, you are making decisions

It's certainly a different style of thinking hard. I used to really stress myself over coding - i.e. I would get frustrated that solving an issue would cause me to introduce some sort of hack or otherwise snowball into a huge refactor. Now I spend most of my time thinking about what cool new features I am going to build and not really stressing myself out too much.

I think OP's post is an attempt to move us past this stage of the discussion, which is frankly an old hat.

The point they are making is that using AI tools makes it a lot harder for them to keep up the discipline to think hard.

This may or may not be true for everyone.

It is a different kind of thinking, though.

I use Claude Code a lot, and it always lets me know the moment I stopped thinking hard, because it will build something completely asinine. Garbage in, garbage out, as they say...

its how you use the tool... reminds me of that episode of simpsons when homer gets a gun lic... he goes from not using it at all, to using it a little, to using it without thinking about what hes doing and for ludicrous things...

thinking is tiring and life is complicated, the tool makes it easy to slip into bad habits and bad habits are hard to break even when you recognise its a bad habit.

Many people are too busy/lazy/self-unaware to evaluate their behaviour to recognise a bad habit.

there's no such thing as right or wrong , so the following isn't intended as any form of judgement or admonition , merely an observation that you are starting to sound like an llm

> you are starting to sound like an llm

My observation: I've always had that "sound." I don't know or care much about what that implies. I will admit I'm now deliberately avoiding em dashs, whereas I was once an enthusiastic user of them.

I still use em-dashes. I started using them when my professor lambasted my use of semi-colons. I'm not looking back -- LLM haters be damned!

I'd go as far as to say I think harder now – or at least quicker. I'm not wasting cycles on chores; I can focus on the bigger picture.

I've never felt more mental exhaustion than after a LLM coding session. I assume that is a result of it requiring me to think harder too.

It wasn't until I read your comment that I was able to pinpoint why the mental exhaustion feels familiar. It's the same kind (though not degree) of exhaustion as formal methods / proofs.

Except without the reward of an intellectual high afterwards.

Personally I do get the intellectual high after a long LLM coding session.

I feel this too. I suspect its a byproduct of all the context switching I find myself doing when I'm using an LLM to help write software. Within a 10 minute window, I'll read code, debug a problem, prompt, discuss the design, test something, do some design work myself and so on.

When I'm just programming, I spend a lot more time working through a single idea, or a single function. Its much less tiring.

In my experience it's because you switch from writing code to reviewing code someone else wrote. Which is massively more difficult than writing code yourself.

Yes, if anything I think harder because I know it's on the frontier of whatever I'm building (so i'm more motivated and there's much more ROI)

I haven't reduced my thinking! Today I asked AI to debug an issue. It came with a solution that it was clearly correct, but it didn't explain why the code was in that state. I kept steering AI (which just wanted to fix) toward figuring out the why and it digged through git and github issue at some point,in a very cool way. And finally it pulled out something that made sense. It was defensive programming introduced to fix an issue somewhere else, which was also in turn fixed, so useless.

At that point an idea popped in my mind and I decided to look for similar patterns in the codebase, related to the change, found 3. 1 was a non bug, two were latent bugs.

Shipped a fix plus 2 fixes for bugs yet to be discovered.

>I haven't reduced my thinking!

You just detailed an example of where you did in fact reduce your thinking.

Managers who tell people what to get done do not think about the problem.

I think my message is doing a disservice to explaining what actually happened because a lot of it happens in my head.

    1. I received the ticket, as soon as I read it I had a hunch it was related to some querying ignoring a field that should be filtered by every query (thinking)
    2. I give this hunch to the AI which goes search in the codebase in the areas I suggested the problem could be and that's when it find the issue and provide a fix
    3. I think the problem could be spread given there is a method that removes the query filter, it could have been used in multiple places, so I ask AI to find other usages of it (thinking, this is my definition of "steering" in this context)
    4. AI reports 3 more occurrences and suggests that 2 have the same bug, but one is ok
    5. I go in, review the code and understand it and I agree, it doesn't have the bug (thinking)
    6. AI provide the fix for all the right spots, but I said "wait, something is fishy here, there is a commit that explicitly say it was added to remove the filter, why is that?" (thinking), so I ask AI to figure out why the commit says that
    7. AI proceeds to run a bunch of git-history related commands, finds some commit and then does some correlation to find another commit. This other commit introduced the change at the same time to defend from a bug in a different place
   8. I understand what's going on now, I'm happy with the fix, the history suggests I am not breaking stuff. I ask AI to write a commit with detailed information about the bug and the fix based on the conversation
    

Thanks for expanding your comment. But to what you explain here, I think your knowledge and comprehension has only slimmed down a notch. It seems to me that this argument equates thinking to be on the vertical vertices only, but may I say there is a horizontal/broad aspect to it? e.g. You lose grip on what is a good combination of framework/language/standards, you remove the abstraction of multiple layers of external and internal APIs, you leave to study the right software pattern for the job, having the AI comprehend the large chunks for you (thats all loss on thinking). You've lost simple querying and digging through codebase. Gosh, lets even say you lost a bit of git command knowledge. You catch my drift here? I am completely for using AI as a tool to do a lot of the boilerplate work with the right directions. Though remembering some changes in codebase before and letting LLMs do the work, is not the same to me as fully owning up to your system as you know, you actually know. Old man shouting at screen so, to each their own of course! Cheers

Did you use your AI to create that list for you?

That's not very nice. Be nice.

Who are you? The morality police?

I'm wondering if everyone here saying they think harder with LLM agents have never reached "flow state" while programming. I just can't imagine using 100% of my mental focus state for hours with an agent. Sure, I think differently when my coding is primarily via agent, but I've never been totally enveloped by my thoughts while doing so.

For those who have found a "flow state" with LLM agents, what's that like?

I've literally never found flow state with programming. Is it even possible when working as a full stack web dev? I am constantly context switching between backend/frontend, reading documentation, looking at/modifying data, refreshing & interacting with the browser, checking/double checking feature requirements, asking people about things.

For me, yes. It is possible as when working full-stack if you aren't being interrupted. Not all tasks, but some tasks, where you're pretty sure of the high-level requirements, have the necessary agency to stay in a bubble for a bit, and the task is sufficiently complex but narrow enough so you don't have to context switch too much.

With LLM Agents I can't seem to do it, as waiting for the agent to finish working just doesn't tickle my brain in the right way. I feel... distracted, I guess?

> where you're pretty sure of the high-level requirements, have the necessary agency to stay in a bubble for a bit, and the task is sufficiently complex but narrow enough

yea these kind of problems are the exact ones that claude code is very good at one shotting these days. If you can descibe in detail what needs to be done without context switching or research, then describe it to the LLM and bam

I've also mostly worked at small companies so the high level requirements are never well defined :D

I believe you can enter "flow state" with something like Claude Code, from what I've read, but it's mostly reduced to pressing 1 or 2 and typing a few prompts. The reward loop is much more closed now though, so it's a bit more akin to reaching flow state playing Tetris.

While I large agree, when I rely too much on agentic llm usage I come away feeling that I haven’t really learnt much over the session, and the code wasn’t really “mine”. It’s also easy to let your skills atrophy over time if you’re not careful, and for the hardest / interesting problems I often turn the llm off entirely and write out the code by hand, and come out a lot happier than just guiding Claude

I've found that it's often useful to spend the time thinking about the way I would architect the code (down to a fair level of minutia) before letting the agent have a go.

That way my 'thinker' is satiated and also challenged - Did the solution that my thinker came up with solve the problem better than the plan that the agent wrote?

Then either I acknowledge that the agent's solution was better, giving my thinker something to chew on for the next time; or my solution is better which gives the thinker a dopamine hit and gives me better code.

I agree with your points. I can clearly see the difference in myself between how I used to program before and how I do now.

At one point, I was using Copilot very heavily. One day I lost my internet connection and tried to write code manually, but I did not even know what to write. All I had been doing was reading the code and pressing the Tab key.

When I tried to write code myself, my fingers automatically moved to the Tab key with perfect millisecond timing. That was the day I disabled AI auto-completion and decided to use only Ask mode in Copilot.

Now I only use code generation when I 100% (atleast 90%) understand what is happening in the code, or when I am working on something that will not help my future growth, like routine code at my job.

People here seem to be conflating thinking hard and thinking a lot.

Most examples mentioned of “thinking hard” in the comments sound like they think about a lot of stuff superficially instead one particular problem deeply, which is what OP is referring to.

If you actually have a problem worth thinking deeply about, AI usually can’t help with it. For example, AI can’t help you make performant stencil buffers on a Nokia Ngage for fun. It just doesn’t have that in it. Plenty of such problems abound, especially in domains involving some or the other extreme (like high throughput traffic). Just the other day someone posted a vibe coded Wikipedia project that took ages to load (despite being “just” 66MB) and insisted it was the best it was possible to do, whereas Google can load the entire planet (perceptually) in a fraction of a second.

I miss the thrill of running through the semi-parched grasslands and the heady mix of terror triumph and trepidation as we close in on our meal for the week.

I think that feeling is fairly common across the entire population. Play more tag, it’ll help.

There are people who still hunt, fish and run. Some even climb without ropes. It would seem the feeling is missed.

To be honest, I do not quite understand the author's point. If he believes that agentic coding or AI has negative impact on being a thinker, or prevent him from thinking critically, he can simply stop using them.

Why blame these tools if you can stop using them, and they won't have any effect on you?

In my case, my problem was often overthinking before starting to build anything. Vibe coding rescued me from that cycle. Just a few days ago, I used openclaw to build and launch a complete product via a Telegram chat. Now, I can act immediately rather than just recording an idea and potentially getting to it "someday later"

To me, that's evolutional. I am truly grateful for the advancement of AI technology and this new era. Ultimately, it is a tool you can choose to use or not, rather than something that prevents you from thinking more.

For me personally, the problem is my teammates. The ability or will to critically think, or investigate existing tools in the codebase seems to disappear. Too often now I have to send back a PR where something is fixed using novel implementations instead of the single function call using existing infrastructure.

I've been coding since the late 70s (Commodore PET 2001), and I've always paired it with real-world physical work—mechanical engineering, rigging, technical diving, hydraulics, welding, electronics, controls, you name it. Programming was just one of the tools, never the whole job. What I've learned is that the best thinking almost never happens staring at a screen. It happens when you're away from it: reading something deep, building something with your hands, debugging why the thing you designed doesn't work in reality. I love the updated saying: "Jack of all trades, master of none—but often better than a master of one." In a world obsessed with hyper-specialization, that range is a real advantage. What bothers me most is how quickly people—especially younger ones—now reach for a phone the second there's any friction. Forgotten a name? Phone. Stuck on a small logic puzzle? Phone. A group of engineers at the lunch table can't remember an actor and within seconds everyone's just silently googling instead of laughing and piecing it together from their collective memory. Where's the fun in that? Yeah, you get the answer instantly. But you skip the actual mental workout—and the fun of it. Remembering and reasoning are muscles. Use them or lose them. And honestly, the shared back-and-forth is usually the best part.

I find I think harder with AI programming. It generates the code, but I have to approve the overall design and then approve every single line. I will edit and rearrange the code until it is correct.

But since the AI is generating a lot of code, it is challenging me. It also allows me to tackle problems in unfamiliar areas. I need to properly understand the solutions, which again is challenging. I know that if I don't understand exactly what the code is doing and have confidence in the design and reliability, it will come back and bite me when I release it into the wild. A lesson learnt the hard way during many decades of programming.

Good highlight of the struggle between Builder and Thinker, I enjoyed the writing. So why not work on PQC? Surely you've thought about other avenues here as well.

If you're looking for a domain where the 70% AI solution is a total failure, that's the field. You can't rely on vibe coding because the underlying math, like Learning With Errors (LWE) or supersingular isogeny graphs, is conceptually dense and hasn't been commoditized into AI training data yet. It requires that same 'several-day-soak' thinking you loved in physics, specifically because we're trying to build systems that remain secure even against an adversary with a quantum computer. It’s one of the few areas left where the Thinker isn't just a luxury, but a hard requirement for the Builder to even begin.

I generally feel the same. But in addition, I also enjoy the pure act of coding. At least for me that’s another big part why I feel left behind with all this Agent stuff.

I agree, that's another factor. Definitely the mechanical act of coding specially if your are good at it gives the type of joy that I can imagine an artisan or craftsman having when doing his work.

> If I can get a solution that is “close enough” in a fraction of the time and effort, it is irrational not to take the AI route. And that is the real problem: I cannot simply turn off my pragmatism.

I've not seen this take yet, but this is exactly how I feel. I do not yet know what I want to do, and my parts of my personality are no longer satisfied by coding. I'm thinking we need some kind of community of people like us where we can discuss these things.

I bring these up with others, and I find that most people around me are just builders.

> If I can get a solution that is “close enough” in a fraction of the time and effort, it is irrational not to take the AI route.

I think this is context dependent. Over time, within a single code base or ecosystem, incompatibilities between "close enough" solutions can add up and create a lot of problems and complexity, kinda like floating point inaccuracies. Especially if you're not going back and revisiting the structure/abstractions you've already got when you're adding/changing something.

There's another angle too, which is that time taken to improve your abilities isn't necessarily irrational, especially because that improvement is usually applicable in many different ways. It can be an investment.

I think harder because of AI.

I have to think more rigorously. I have to find ways to tie up loose ends, to verify the result efficiently, to create efficient feedback loops and define categorical success criteria.

I've thought harder about problems this last year than I have in a long time.

> I have to find ways to tie up loose ends, to verify the result efficiently, to create efficient feedback loops and define categorical success criteria.

So... you didn't have to do that prior to using agents?

Not as much upfront. I had plenty of opportunities to adjust and correct along the way. With AI, the cost of not thinking upfront is high and the cost of being wrong in upfront decisions is low, so we bias towards that.

But beyond that, I have been thinking deeply about AI itself, which has all sorts of new problems. Permissions, verification, etc.

> With AI, the cost of not thinking upfront is high and the cost of being wrong in upfront decisions is low, so we bias towards that.

I don't really understand what that means:

1. If the cost of not thinking upfront is high, that means you need to think upfront.

2. If the cost of being wrong upfront is low, that means you don't need to think upfront.

To me, it looks like those assertions contradict each other.

Maybe I expressed that clumsily.

With historical development, investing in hypotheticals can be wasteful. Make the fewest assumptions until you get real user feedback.

With AI, we make more decisions upfront. Being wrong about those decisions has a low cost because the investment in implementation is cheap. We are less worried about building the wrong things because we can just try again quickly. It's cheap to be wrong.

The more decisions we make upfront, the more hands-off we can be. A shallow set of decisions might stall out earlier than a deeper set of decisions could. So we need to think through things more deeply. So we need to think more.

Seems like a lot of people use AI to code in their private commercial IP and products. How are people not concerned with the fact that all these ai companies have the source code to everything? Your just helping them destroy your job. Code is not worthless, you cannot easily duplicate any complex project with equal features, quality, and stability.

I think that is a very good point. Code is definitely not worthless, but I don't think that capitalism has the right tools for pricing it properly. I think it will become a lot like mathematics in that way.

I miss entering flow state when coding. When vibe coding, you are in constant interruption and only think very shallow. I never see anyone enter flow state when vibe coding.

Same here, waiting for response destroys any focus I have had.

The two ways I get into flow state these days are in setting up agentic loops, so I can get out of the way by letting AI check the results for itself, and by doing more things. I've got ~4 Claude Code instances working on problems, per project, and I've got multiple projects I'm working on at the same time.

I feel like AI has given me the opportunity to think MORE, not less. I’m doing so much less mindless work, spending most of my efforts critically analyzing the code and making larger scale architectural decisions.

The author says “ Even though the AI almost certainly won't come up with a 100% satisfying solution, the 70% solution it achieves usually hits the “good enough” mark.”

The key is to keep pushing until it gets to the 100% mark. That last 30% takes multiples longer than the first 70%, but that is where the satisfaction lies for me.

If it's this easy to convince you to stop being creative, to stop putting in effort to think critically, then you don't deserve the fulfilment that creativity and critical thinking can give you. These vibe coding self pity articles are so bizarre.

"Oh no, I am using a thing that no one is forcing me to use, and now I am sad".

Just don't use AI. The idea that you have ship ship ship 10X ship is an illusion and a fraud. We don't really need more software

> We don't really need more software

Yeah, we need better software.

Corollary: you don't really need more software engineers.

Who decides what we "need"? Software provides value, and if we want to keep providing value moving forward there will be more software

The post resonates deeply with me. I am a health professional in diagnostics and through the years I have observed different extremes in approaches to solving diagnostic challenges - the one extreme is to rely on "knowing", the other on "thinking/reasoning". The former is usually very fast, but not easily explainable - just like pattern recognition. The latter was slow, but could give a solution from "first principles" and possibly not described before. Of course it's a spectrum and the thinking part requires and includes the deep enough "knowing" part. One usually uses both approaches on daily work, but I have seen some people who relied much more on knowing than thinking/reasoning, sometimes to the extreme (as in refusing to diagnose a condition on their own because they "have not seen this before").

I did competitive programming seriously between '17 and '24, then kept on coaching people

As a beginner I often thought about a problem for days before finding a solution, but this happened less and less as I improved

I got better at exploiting the things I knew, to the point where I could be pretty confident that if I couldn't solve a problem in a few hours it was because I was missing some important piece of theory

I think spending days "sitting with" a problem just points at your own weakness in solving some class of problems.

If you are making no articulable progress whatsoever, there is a pathology in your process.

Even when working on my thesis, where I would often "get stuck" because the problem was far beyond what I could solve in one sitting, I was still making progress in some direction every time.

Do you mean that sitting with the problem for days is a weakness that you should fix since you're wasting time making no progress? Or that it is a necessary practice in order to understand your weaknesses?

When you're a beginner you're a beginner, no way around it.

But understanding your weaknesses and working on them is huge, and I think most people just don't try to do it.

Being stuck for days is something to be overcome.

The next step would be being slow because you are trying out many different ideas and have no intuition for what the right one is.

My solution has been to lean into harder problems - even as side projects, if they aren't available at work.

I too am an ex-physcist used to spending days thinking about things, but programming is a gold mine as it is adjacent to computer science. You can design a programming language (or improve an existing one), try to build a better database (or improve an existing one), or many other things that are quite hard.

The LLM is a good rubber duck for exploring the boundaries of human knowledge (or at least knowledge common enough to be in its training set). It can't really "research" on its own, and whenever you suggest something novel and plausable it gets sycophantic, but it can help you prototype ideas and implementation strategies quite fast, and it can help you explore how existing software works and tackles similar problems (or help you start working on an existing project).

This resonates with me, but I quit programming about a decade ago when we were moving from doing low level coding to frameworks. It became no longer about figuring out the actual problem, but figuring out how to get the framework to solve it and that just didn't work for me.

I do miss hard thinking, I haven't really found a good alternative in the meantime. I notice I get joy out of helping my kids with their, rather basic, math homework, so the part of me that likes to think and solve problems creatively is still there. But it's hard to nourish in today's world I guess, at least when you're also a 'builder' and care about efficiency and effectiveness.

Great article. Moment I finished reading this article, I thought of my time in solving a UI menu problem with lot of items in it and algorithm I came up with to solve for different screen sizes. It took solid 2 hrs of walking and thinking. I still remember how I was excited when I had the feeling of cracking the problem. Deep thinking is something everyone has it within and it varies how fast you can think. But we all got it with right environment and time we all got it in us. But thats long time ago. Now I always off load some thinking to AI. it comes up with options and you just have to steer it. By time it is getting better. Just ask it you know. But I feel like it is good old days to think deep by yourself. Now I have a partner in AI to think along with me. Great article.

I’d been feeling this until quite literally yesterday, where I sort of just forced myself to not touch an AI and grappled with the problem for hours. Got myself all mixed up with trig and angles until I got a headache and decided to back off a lot of the complexity. I doubt I got everything right, I’m sure I could’ve had a solution with near identical outputs using an AI in a fraction of the time.

But I feel better for not taking the efficient way. Having to be the one to make a decision at every step of the way, choosing the constraints and where I cut my losses on accuracy, I think has taught me more about the subject than even reading literature would’ve directly stated.

I think the heart of the matter is this section in the blog:

> Yes, I blame AI for this.

> I am currently writing much more, and more complicated software than ever, yet I feel I am not growing as an engineer at all. [...] (emphasis added by me)

AI is a force multiplier for accidental complexity in the Brooks sense. (https://en.wikipedia.org/wiki/No_Silver_Bullet)

Eventually I always get to a problem I can't solve by just throwing an LLM at it and have to go in and properly debug things. At that point knowing the code base helps a hell of a lot, and I would've been better off writing the entire thing by hand.

I'm thinking much more than ever, now that the coding agent is building for me.

I strongly experience that coding agents are helping me think about stuff I wasn't able to think through before.

I very much have both of these builder and thinker personas inside me, and I just am not getting this experience with "lack of thinking" that I'm seeing so many other people write about. I have it exactly the other way around, even if I'm a similar arch type of person. I'm spending less time building and more time thinking than ever.

I've loved programming my whole life. Something happens inside my brain when I'm solving a problem. I think it's a release of endorphins or something that has a calming effect. When I was younger, it came easier. I used to be able to get "into the zone" and stay there for hours. Almost a constant stream of endorphins, almost a euphoria. Other things give me a similar feeling. Anything with numbers. I used to really love playing blackjack. Something about figuring out how to get to 21 just really did it for me. Or playing dominoes or other games that require me to solve something. I'm almost 65 now and it's just much more difficult to get there. I think I've been slow to adopt AI partly because I'm afraid it's going to take me further away from what I've loved. Friends tell me it's just a new tool to add to my toolbox. I'm giving it a try but I'm still skeptical.

Before I started using AI, I was already used to thinking abstractly. When I first got into AI, that way of thinking, combined with interacting with these models, really brought out the best in both. Honestly, I haven’t felt that “peak” in a while

You don’t use it, you lose it.

Not every programming task needs to be a research project. There are plenty of boring business problems that needs the application of computing to automate. And it’s been a decent way to make a living for a while.

It’s great getting a good problem to chew on.

I try to keep a small percentage of my time occupied by one or two good ones. If I’m always bored it’s a sign I could be doing better. And I like being at my best.

I see the current generation of AI very much as a thing in between. Opus 4.5 can think and code quite well, but it cannot do these "jumps of insight" yet. It also struggles with straightforward, but technically intricate things, where you have to max out your understanding of the problem.

Just a few days ago, I let it do something that I thought was straightforward, but it kept inserting bugs, and after a few hours of interaction it said itself it was running in circles. It took me a day to figure out what the problem was: an invariant I had given it was actually too strong, and needed to be weakened for a special case. If I had done all of it myself, I would have been faster, and discovered this quicker.

For a different task in the same project I used it to achieve a working version of something in a few days that would have taken me at least a week or two to achieve on my own. The result is not efficient enough for the long term, but for now it is good enough to proceed with other things. On the other hand, with just one (painful) week more, I would have coded a proper solution myself.

What I am looking forward to is being able to converse with the AI in terms of a hard logic. That will take care of the straightforward but technically intricate stuff that it cannot do yet properly, and it will also allow the AI to surface much quicker where a "jump of insight" is needed.

I am not sure what all of this means for us needing to think hard. Certainly thinking hard will be necessary for quite a while. I guess it comes down to when the AIs will be able to do these "jumps of insight" themselves, and for how long we can jump higher than they can.

I’d love to be able to see statistics that show LLM use and reception according to certain socioeconomic factors.

Anything in particular you expect to see?

If you are thinking hard I think you are software engineering wrong. Even before AI. As an industry all the different ways of doing things have already played out. Even doing big reactors or performance optimizations often can not be 100% predicted in their effectiveness. You will want to just go ahead and implement these things over spending more time thinking. And as AI gets stronger the just try a bunch of approaches will beat the think hard approach by an even bigger margin.

Why do anything at all then? It's all been done before. This line of thinking sounds like depression to me. Why decorate my house? I know I could do it, but it's all been done before, why bother?

Just because we know the best way to add 2 ints together that doesn't mean it's pointless to do addition with ints. We don't need people trying to spend a lot of extra time to come up with alternate ways to do it. The right function using addition may be valuable to a certain population.

Many people here might be in a similar situation to me, but I took an online masters program that allowed for continuing education following completion of the degree. This has become one of my hobbies; I can take classes at my own expense, not worry about my grades, and just enjoy learning. I can push myself as much as I want and since the classes are hard, just completing 1 assignment is enough to force me to "think". Just sharing my experience for people who might be looking for ways to challenge themselves intellectually.

I'm a DevOps/SRE and I've spent the past couple weeks trying to vibecode as much of what I do as possible.

In some ways, it's magical. e.g. I whipped up a web based tool for analyzing performance statistics of a blockchain. Claude was able to do everything from building the gui, optimizing the queries, adding new indices to the database etc. I broke it down into small prompts so that I kept it on track and it didn't veer off course. 90% of this I could have done myself but Claude took hours where it would have taken me days or even weeks.

Then yesterday I wanted to do a quick audit of our infra using Ansible. I first thought: let's try Claude again. I gave it lots of hints on where our inventory is, which ports matter etc but it still was grinding away for several minutes. I eventually Ctrl-C'ed and used a couple one liners that I wrote myself in a few minutes. In other words, I was faster that the machine in this case.

After the above, it makes sense to me that people may have conflicting feelings about productivity. e.g. sometimes it's amazing, sometimes it does the wrong thing.

I think there's an argument where if Claude had the knowledge map of your personal one liners and a tool for using them, it would often do the right thing in those cases. But it's definitely not as able to compress all the entropy of 'what can go wrong' operations wise as it is when composing code yet.

My experience that with careful specs, Claude or Codex can whip up either CDK, Cloudformation, or Terraform code much quicker than I can and I’ve been using IAC for 8 years - developer/consultant specializing in development + cloud architecture

The answer to this is to shift left into product/design.

Sure, I'm doing less technical thinking these days. But all the hard thinking is happening on feature design.

Good feature design is hard for AI. There's a lot of hidden context: customer conversations, unwritten roadmaps, understanding your users and their behaviour, and even an understanding of your existing feature set and how this new one fits in.

It's a different style of thinking, but it is hard, and a new challenge we gotta embrace imo.

> Good feature design is hard for AI

For now. Go back a year and take a look how the AI/LLM coding tools looked and worked back then.

I think what plagues a lot of pure STEM types in this tumultuous period of AI (or "AI") is that they've spent a majority of their lives mulling over some problem until they've worked out every possible imperfection, and once they've achieved something they consider close to that level of perfection, that's when they say they're done.

While this may be an unfair generalization, and apologies to those who don't feel this way, but I believe STEM types like the OP are used to problem solving that's linear in the sense that the problem only exists in its field as something to be solved, and once they figure it out, they're done. The OP even described his mentality as that of a "Thinker" where he received a problem during his schooling, mulled over it for a long time, and eventually came to the answer. That's it, next problem to crack. Their whole lives revolve around this process and most have never considered anything outside it.

Even now, despite my own healthy skepticism of and distaste for AI, I am forced to respect that AI can do some things very fast. People like the OP, used to chiseling away at a problem for days, weeks, months, etc., now have that throughput time slashed. They're used to the notion of thinking long and hard about a very specific problem and finally having some output; now, code modules that are "good enough" can be cooked up in a few minutes, and if the module works the problem is solved and they need to find the next problem.

I think this is more common than most people want to admit, going back to grumblings of "gluing libraries together" being unsatisfying. The only suggestion I have for the OP is to expand what you think about. There are other comments in this thread supporting it but I think a sea change that AI is starting to bring for software folks is that we get to put more time towards enhancing module design, user experience, resolving tech debt, and so on. People being the ones writing code is still very important.

I think there's more to talk about where I do share the OP's yearning and fears (i.e., people who weren't voracious readers or English/literary majors being oneshot by the devil that is AI summaries, AI-assisted reading, etc.) but that's another story for another time.

> I think what plagues a lot of pure STEM types in this tumultuous period of AI (or "AI") is that they've spent a majority of their lives mulling over some problem until they've worked out every possible imperfection, and once they've achieved something they consider close to that level of perfection, that's when they say they're done.

These people are miserable to work with if you need things done quickly and can tolerate even slight imperfection.

That operating regime is, incidentally, 95% of the work we actually get paid to do.

"I don't want to have to write this for the umpteenth time" -- Don't let it even reach a -teenth. Automate it on the 2nd iteration. Or even the 1st if you know you'll need it again. LLMs can help with this.

Software engineers are lazy. The good ones are, anyway.

LLMs are extremely dangerous for us because it can easily become a "be lazy button". Press it whenever you want and get that dopamine hit -- you don't even have to dive into the weeds and get dirty!

There's a fine line between "smart autocomplete" and "be lazy button". Use it to generate a boilerplate class, sure. But save some tokens and fill that class in yourself. Especially if you don't want to (at your own discretion; deadlines are a thing). But get back in those weeds, get dirty, remember the pain.

We need to constantly remind ourselves of what we are doing and why we are doing it. Failing that, we forget the how, and eventually even the why. We become the reverse centaur.

And I don't think LLMs are the next layer of abstraction -- if anything, they're preventing it. But I think LLMs can help build that next layer... it just won't look anything like the weekly "here's the greatest `.claude/.skills/AGENTS.md` setup".

If you have to write a ton of boilerplate code, then abstract away the boilerplate in code (nondeterminism is so 2025). And then reuse that abstraction. Make it robust and thoroughly tested. Put it on github. Let others join in on the fun. Iterate on it. Improve it. Maybe it'll become part of the layers of abstraction for the next generation.

I definitely identify with the "Thinker" described in this post, but I am routinely mulling over ideas and problems (multiple hours, days, and weeks) with the help of LLMs now.

One of the benefits of LLM usage is to figure out the boundaries of your own knowledge and that of humanity's existing knowledge--at least for the LLM's training data.

Enumerating through existing options and existing solutions to problems gets you to the knowledge boundary sooner--where the real work begins! While faster with LLMs, I don't see this process as much different than bouncing ideas off of colleagues (and critiquing your own thoughts).

However, the difference is likely human's unpredictable ability to apply creativity throughout the process...such that a new solution may arise at any point and leap-frog existing solutions/explanations. (Think Einstein taking known data from Lorentz, Michelson, Morley plus Maxwell's equations on light and coming up with special relativity.)

The sampling rate we use to take input information is fixed. And we always find a way to work with the sampled information, no matter if the input information density is high or low.

We can play a peaceful game and a intense one.

Now, when we think, we can always find a right level of abstract to think on. Decades ago a programmer thought with machine codes, now we think with high level concepts, maybe towards philosophy.

A good outcome always requires hard thinking. We can and we WILL think hard at a appropriate level.

I had similar thoughts recently. I wouldn't consider myself "the thinker", but I simply missed learning by failure. You almost don't fail anymore using AI. If something fails, it feels like it's not your fault but the AI messed up. Sometimes I even get angry at the AI for failing, not at myself. I don't have a solution either, but I came up with a guideline on when and how to use AI that has helped me to still enjoy learning. I'm not trying to advertise my blog and you don't need to read it, the important part is the diagram at the end of "Learning & Failure": https://sattlerjoshua.com/writing/2026-02-01-thoughts-on-ai-.... In summary, when something is important and long-term, I heavily invest into understanding and use an approach that maximizes understanding over speed. Not sure if you can translate it 100% to your situation but maybe it helps to have some kind of guideline, when to spend more time thinking instead of directly using and AI to get to the solution.

I sympathize with this. I wonder if the author might find it helpful to reimagine the thinking they do as coming up with good questions, rather than good answers. I was inspired to try to do so myself after reading this essay: https://link.springer.com/article/10.1186/s13059-019-1902-1

"[I]f a scientist proposes an important question and provides an answer to it that is later deemed wrong, the scientist will still be credited with posing the question. This is because the framing of a fundamentally new question lies, by definition, beyond what we can expect within our frame of knowledge: while answering a question relies upon logic, coming up with a new question often rests on an illogical leap into the unknown."

"When you change the way you look at things, the things you look at change." - Max Planck

I relate to the post, but I'm not sure it's hitting the nail on the head _for me_.

I like being useful, and I'm not yet sure how much of what I'm creating with AI is _me_, and how much it is _it_. It's hard to derive as much purpose/meaning from it compared to the previous reality where it was _all me_.

If I compare it to a real world problem; e.g. when I unplug the charging cable from my laptop at my home desk, the charging cable slides off the table. I could order a solution online that fixes the problem and be done with it, but I could also think how _I_ can solve the problem with what I already have in my spare parts box. Trying out different solutions makes me think and I'm way more happy with the end result. Every time I unplug the cable now and it stays in place it reminds me of _my_ labour and creativity (and also the cable not sliding down the table -- but that's besides the point).

That's exactly it for me. Coming up with a solution using my own brainpower is a large part of the pleasure of programming for me.

A silly example to illustrate the kind of guy I am: when I'm watching a show or movie, I'll often wonder where I've seen an actor before. A "normal" person, like my wife, would just look it up on IMDB and be done with it. But I almost always insist on rifling through all the dustiest corners of my brain to figure it out. Even if it takes me a day or two of thinking about it off and on. Because to me, the satisfaction of doing it myself is worth it.

You were walking to your destination which was three miles away

You now have a bicycle which gets you there in a third of the time

You need to find destinations that are 3x as far away than before

but if you enjoy walking, what then?

Personally: technical problems I usually think for a couple days at most before I need to start implementing to make progress. But I have background things like future plans, politics, philosophy, and stories, so I always have something to think about. Close-up technical thinking is great, but sometimes step back and look at the bigger picture?

I don't think AI has affected my thinking much, but that's because I probably don't know how to use it well. Whenever AI writes a lot of code, I end up having to understand if not change most of it; either because I don't trust the AI, I have to change the specification (and either it's a small change or I don't trust the AI to rewrite), the code has a leaky abstraction, the specification was wrong, the code has a bug, the code looks like it has a bug (but the problem ends up somewhere else), I'm looking for a bug, etc. Although more and more often the AI saves time and thinking vs. if I wrote the implementation myself, it doesn't prevent me from having to think about the code at all and treating it like a black box, due to the above.

> My method was simply to think. To think hard and long... This method never failed me. I always felt that deep prolonged thinking was my superpower. I might not be as fast or naturally gifted as the top 1%, but given enough time, I was confident I could solve anything.

This mindset is a healthy and good one. It is built on training yourself, learning, and practicing a discipline of problem solving without giving up.

Persistence is something we build, not something we have. It must be maintained. Persistence is how most good in the world has been created.

Genius is worthless without the will to see things through.

I believe it is a type of burnout. AI might have accelerated both the work and that feeling.

I found that doing more physical projects helped me. Large woodworking, home improvement, projects. Built-in bookshelves, a huge butcher block bar top (with 24+ hours of mindlessly sanding), rolling workbenches, and lots of cabinets. Learning and trying to master a new skill, using new design software, filling the garage with tools...

The use of AI is a choice. You make that choice, no one else does it for you. I myself chose to act like AI does not exist. The author, evidently, did not. So it's odd, then, that they're complaining about the consequences of their own choices.

I've had the completely opposite experience as somebody that also likes to think more than to build: LLMs take much of the legwork of actually implementing a design, fixing trivial errors etc. away from me and let me validate theories much more quickly than I could do by myself.

More importantly, thinking and building are two very different modes of operating and it can be hard to switch at moment's notice. I've definitely noticed myself getting stuck in "non-thinking building/fixing mode" at times, only realizing that I've been making steady progress into the wrong direction an hour or two in.

This happens way less with LLMs, as they provide natural time to think while they churn away at doing.

Even when thinking, they can help: They're infinitely patient rubber ducks, and they often press all the right buttons of "somebody being wrong on the Internet" too, which can help engineers that thrive in these kinds of verbal pro/contra discussions.

One of the best programmers I know is a self declared thinker that also is loving this new wave of agentic coding.

What he told me is that he loves thinking about the design of the code in his head, picking the best piece for each part of "the machine" and assembling it design in his head.

After doing that he loathed the act of translating that pure design into code. He told me it felt like pushing all that design through a thin tube through sheer force against syntax, wrong library versions, compiler errors, complex IDEs...

So for him, this is the best scenario possible.

I'm more a of a builder but after talking with him and reading the OP post maybe thinkers come in various shapes.

You can do all your thinking and minimal prompting.

I know Ralphs like to do all the prompting and no thinking.

> I am currently writing much more, and more complicated software than ever, yet I feel I am not growing as an engineer at all.

This sounds like approving bad/poor abstractions too prematurely and keep building on top of that.

What about the satisfaction that comes not with struggling but from the calmness of an elegant functional model that dynamically covers all the flows and all the edge cases you could (deep and slowly [1]) think about?

[1] maybe refining in different days, in the shower, after recovering breath in a hard set in a workout session, after a nap...

I believe the article is wrong in so many ways.

If you think too much you get into dead ends and you start having circular thoughts, like when you are lost in the desert and you realise you are in the same place again after two hours as you have made a great circle(because one of your legs is dominant over the other).

The thinker needs feedback on the real world. It needs constant testing of hypothesis on reality or else you are dealing with ideology, not critical thinking. It needs other people and confrontation of ideas so the ideas stay fresh and strong and do not stagnate in isolation and personal biases.

That was the most frustrating thing before AI, a thinker could think very fast, but was limited in testing by the ability to build. Usually she had to delegate it to people that were better builders, or else she had to be builder herself, doing what she hates all the time.

Dear author, I suggest trying out a job in a niche part of the field like firmware/embedded. Bonus if it's a company with a bunch of legacy devices to maintain. AI just hasn't quite grokked it there yet and thinking still reigns supreme :)

Just yesterday I noticed I was mentally EXHAUSTED after building out a feature with Claude Code.

I actually had to think really, really hard to keep up with the idiot savant as it cranked out code.

Correctness was extremely important for this feature. Claude would consistently make subtle mistakes, and I needed to catch them to keep things from going off the rails. I could have done it myself, but it would have taken MUCH longer.

I essentially compressed a week’s worth of work into a few hours, and my brain paid the price.

So yeah. You can use AI to replace your thinking, or you can use it to push yourself to your max potential.

So today are you doing it again? Or did you compress a week's worth of work into a few hours and the rest of the week is shot because you don't expand?

I did do it again, and spent time figuring out how to do it even more.

The cool thing though is that I could have chosen to do something else, too. I’m not at a loss for fulfilling, productive things to do. AI won’t change that.

It's funny to think that all this discussion would not be happening if not for naive idealism embraced by software people fo decades, which enabled sharing everything they made after working hours for free and for everyone. That all it was hijacked by a handful of rich and not very altruistic people to enslave everyone comes as a big surprise. Who would have thought. I guess we arent that smart after all

I am thinking harder than ever due to vibe coding. How will markets shift? What will be in demand? How will the consumer side adapt? How do we position? Predicting the future is a hard problem... The thinker in me is working relentlessly since December. At least for me the thinker loves an existential crisis like no other.

I also recognise ‘thinking’ as a valuable and enjoyable activity - and have also obsessed about problems for days (even in my sleep)to reach a deeper understanding. But I think the issue here is the impact of AI on just that - ‘understanding’. It might be true that with AI we no longer need to have as deeper understanding in order to ‘ship’. And even if that is true I think there are plenty of other domains you can think deeply about without AI getting in the way e.g. mathematics or philosophy - where is object is often to understand in and of itself, not just the products it may generate.

Advent of Code (which given my schedule runs into January). That’s the last time thinking hard about a coding problem, I don’t remember exactly if it was day 10 or 11 that had me scratching my head for a while.

I intentionally do not use AI though.

But I sympathize with the author. I enjoy thinking deeply about problems which is why I studied compsci and later philosophy, and ended up in the engineering field. I’m an EM now so AI is less of an “immediate threat” to my thinking habits than the role change was.

That said, I did recently pick up more philosophy reading again just for the purpose of challenges my brain.

I don't. I miss being outside, in the sun, living my life. And if there's one thing AI has done it's save my time.

I'm not sure how you live and work in the US, but here in Sweden, in my experience, it's more focused on results than sitting at your desk 9-5.

So AI does enable me to take more free time, be outside more when the sun is out, because I finish my tasks faster.

I'm just afraid that managers will start demanding more, demand that we increase our output instead of our work life balance. But in that case I at least have the seniority to protest.

Homeless people have the most sun and spend a lot of time outside. I hope that this is not where we are headed with AI

I heard this interesting quote about AI (paraphrasing): "We invented a cure for exercise, we should not be surprised later when we realize that we quickly run out of breath".

This title and the first half of the post (before the AI discussion) just makes me miss the intellectual environment of college.

So I’m tempted to say that this is just a part of the economic system in general, and isn’t specifically linked to AI. Unless you’re lucky enough to grab a job that requires deep intellectual work, your day job will probably not challenge your mental abilities as much as a difficult college course does.

Sad but true, but unfortunately I don’t think any companies are paying people to think deeply about metaphysics (my personal favorite “thinking hard subject” from college.)

Thinking harder than I have in a long time with AI assisted coding.

As I'm providing context I get to think about what an ideal approach would look like and often dive into a research session to analyze pros and cons of various solutions.

I don't use agents much because it's important to see how a component I just designed fits into the larger codebase. That experience provides insights on what improvements I need to make and what to build next.

The time I've spent thinking about the composability, cohesiveness, and ergonomics of the code itself have really paid off. The codebase is a joy to work in, easy to maintain and extend.

The LLMs have helped me focus my cognitive bandwidth on the quality and architecture instead of the tedious and time consuming parts.

> At the end of the day, I am a Builder. I like building things. The faster I build, the better.

This I can’t relate to. For me it’s “the better I build, the better”. Building poor code fast isn’t good: it’s just creating debt to deal with in the future, or admitting I’ll toss out the quickly built thing since it won’t have longevity. When quality comes into play (not just “passed the tests”, but is something maintainable, extensible, etc), it’s hard to not employ the Thinker side along with the Builder. They aren’t necessarily mutually exclusive.

Then again, I work on things that are expected to last quite a while and aren’t disposable MVPs or side projects. I suppose if you don’t have that longevity mindset it’s easy to slip into Build-not-Think mode.

Cognitive skills are just like any other - use them and they will grow, do not and they will decline. Oddly enough, the more one increases their software engineering cognition, the less the distance between "The Builder" and "The Thinker" becomes.

This reads like mourning the loss of _forced_ difficulty instead of taking responsibility for seeking _chosen_ difficulty.

Chosen difficulty is a huge part of being human (music, art, athletics, games, etc.). AI hasn't taken that away.

The friction didn't disappear with AI tools. It just shifted. It's now more so about knowing when to trust an AI system versus when to dig into things yourself. The key insight is this: don't devalue learning things on your own. AI is a tool, but if the tool messes up, you need other tools in your toolbox. If you've only ever leaned on the AI, you're in trouble the moment it fails on something subtle.

Sometimes I miss thinking hard, but I also don't miss thinking really hard on a problem that seems difficult only to find out it was actually trivial and I was just looking at it the wrong way or overthinking it. We've all spent hours tracking down bugs that turned out to be silly syntax errors or spelling mistakes. It seems like that might be the trade off now.

On the other hand, those situations can be valuable learning experiences, and (even if subconscious) can help you learn to see things from more angles in the future.

Unfortunately, predicting which situations are worth it and which aren't is as hard as predicting anything else...

You're extremely on point. I don't remember the last time I was able to sit around to think because at the back of my mind, I knew AI could help me generate the initial draft of ideas, logic, or structure that would otherwise require hours of my time.

But to be honest, those hours spent structuring thoughts are so important to making things work. Or you might as well get out of the way and let AI do everything, why even pretend to work when all we're going to do is just copy and paste things from AI outputs?

LLMs haven't changed how hard it is to truly invent. What LLMs do instead is painfully rub it in that most problems already have their solutions.

There's no need to create another serialization format or a JavaScript framework. You now have more time to direct your focus onto those problems that haven't yet been solved, or at least haven't been solved well.

A question that might be hard to digest: was that "thinker" really a thinker, or a well-disguised re-inventor?

After leaving my previous day job, I have some downtime to get back to thinking and realizing how much I love reading and thinking.

Contemplating the old RTFM, I started a new personal project called WTFM and spends time writing instead of coding. There is no agenda and product goals.

https://wtfm-rs.github.io/

There are so many interesting things in human generated computer code and documentation. Well crafted thoughts are precious.

This isn’t the point of the piece, but I have found that the thinker often gets in the way of the builder, because there’s always a better way to build, there’s always some imperfect subsystem you just want to tear out and rewrite and then you realize you were all wrong about this and that, etc.

More to the piece itself, I know some crusty old embedded engineers who feel the same way about compilers as this guy does about AI, it doesn’t invalidate his point but it’s food for thought

I've found that AI still makes me think. Yes I use it as my 10x, but I still have to plan what I am going to do, call it on its bull, and keep it on track. AI just saves me from the tedium of typing (and lets be real, mistyping). I can't specialize in 13 different languages, but I can bring good architecture, and good practices to bear.

It's a different type of thinking in my opinion, more "systems" thinking.

I think this problem existed before AI. At least in my current job, there is constant, unrelenting demand for fast results. “Multi-day deep thinking” sounds like an outrageous luxury, at least in my current job.

Which is a reason for software becoming worse across the board. Just look at Windows. The "go go go" culture is ruinous to products.

Even 30 years ago when I started in the industry, most jobs required very little deep thinking. All of mine has been done on personal projects. Thats just the reality of the typical software engineering job.

this is why productivity is a word that should really just be reserved for work contexts, and personal time is better used for feeding "The Thinker"

A lot of productive thinking happens when asleep, in the shower, in flow walking or cycling or rowing.

It's hard to rationalise this as billable time, but they pay for outcome even if they act like they pay for 9-5 and so if I'm thinking why I like a particular abstraction, or see analogies to another problem, or begin to construct dialogues with mysel(ves|f) about this, and it happens I'm scrubbing my back (or worse) I kind of "go with the flow" so to speak.

Definitely thinking about the problem can be a lot better than actually having to produce it.

I really don't believe AI allows you to think less hard. If it did, it would be amazing, but the current AI hasn't got to that capability. It forces you to think about different things at best.

It’s possible to be both.

The last time I had to be a Thinker was because I was in Builder mode. I’ve been trying to build an IoT product but I’ve been wayyyy over my head because I knew maybe 5% of what I needed to be successful. So I would get stuck many, many times, for days or weeks at a time.

I will say though that AI has made the difference in the last few times I got stuck. But I do get more enjoyment out of Building than Thinking, so I embrace it.

Well, for programming work which is essentially repetition (e.g. making another website not unlike thousands of others), it's no surprise that AI programming can work wonders - you're essentially using a sophisticated form of copy paste.

But there's still a lot of programming out there which requires originality.

Speaking personally, I never was nor ever will be too interested in the former variety.

When people missed working hard, they turned to fake physical work (gyms). So people now need some fake thinking work.

Except for eating and sleeping, all other human activities are fake now.

you forget about fake sleeping being loaded with fake dopamine hits before sleep AND broken sleep schedules; and eating fake ultraprocessed food instead of wholefoods.

Thanks for correcting. That completes the fake life pattern.

So, people fake things to get a fake life. Reminds me a Russian joke about factory workers. "They pretend to pay, and we pretend to work".

We've always been doing fake thinking work since the beginning, see: puzzles

I've missed the same even since before AI because I've done far too much work that's simple but time intensive. It's frustrating, and I miss problems that keep me up all night.

Reverse engineering is imo the best way of getting the experience of pushing your thinking in a controlled way, at least if you have the kind of personality where you are stubborn in wanting to solve the problem.

Go crack an old game or something!

I guess it depends on what you build, i feel the most complex part of the deal, that makes me think the hardest, is to figure out what to build. Eg understanding the client, and creating a solution that fits between their needs and abilities. The rest is often a technical detail, yes sometimes you need to deep dive to optimize. Anyway if you miss debugging try debugging people;)

In my experience you will need to think even harder with AI if you want a decent result, although the problems you'll be thinking about will be more along the lines of "what the hell did it just write?"

The current major problem with the software industry isn't quantity, it's quality; and AI just increases the former while decreasing the latter. Instead of e.g. finding ways to reduce boilerplate, people are just using AI to generate more of it.

You can't change the world, you can change yourself. Many people don't like change. So, people get frustrated when the world inevitably changes and they fail to adapt. It's called getting older. Happens to us all.

I'm not immune to that and I catch myself sometimes being more reluctant to adapt. I'm well aware and I actively try to force myself to adapt. Because the alternative is becoming stuck in my ways and increasingly less relevant. There are a lot of much younger people around me that still have most of their careers ahead of them. They can try to whine about AI all they want for the next four decades or so but I don't think it will help them. Or they can try to deal with the fact that these tools are here now and that they need to learn to adapt to them whether they like it or not. And we are probably going to see quite some progress on the tool front. It's only been 3 years since ChatGPT had its public launch.

To address the core issue here. You can use AI or let AI use you. The difference here is about who is in control and who is setting the goals. The traditional software development team is essentially managers prompting programmers to do stuff. And now we have programmers prompting AIs to do that stuff. If you are just a middle man relaying prompts from managers to the AI, you are not adding a lot of value. That's frustrating. It should be because it means apparently you are very replaceable.

But you can turn that around. What makes that manager the best person to be prompting you? What's stopping them from skipping that entirely? Because that's your added value. Whatever you are good at and they are not is what you should be doing most of your time. The AI tools are just a means to an end to free up more time for whatever that is. Adapting means figuring that out for yourself and figuring out things that you enjoy doing that are still valuable to do.

There's plenty of work to be done. And AI tools won't lift a finger to do it until somebody starts telling them what needs doing. I see a lot of work around me that isn't getting done. A lot of people are blind to those opportunities. Hint: most of that stuff still looks like hard work. If some jerk can one shot prompt it, it isn't all that valuable and not worth your time.

Hard work usually involves thinking hard, skilling up, and figuring things out. The type of stuff the author is complaining he misses doing.

What a bizarre claim. If you can solve anything by thinking, why don't you become a scientist? Think of a theory that unites quantum physics and general relativity.

I dont think LLMs really took away much thinking, for me they replaced searching stackexchange to find incantations. Now I can get them instantly and customized to my situation. I miss thinking hard too, but I dont blame that on AI, its more that as a dev you are paid to think the absolute minimal amount needed to solve an issue or implement a feature. I dont regret leaving academia, but being paid to think I will always miss.

I think the author has discovered that coding is not what they actually enjoy.

I was a software and systems developer on cool shit, but I realized I enjoyed solving hard problems more than how I solved them. That led me to a role that is about solving hard problems. Sometimes I still use coding to do it, but that's just one tool of many.

There are many difficult problems involved in building LLMs that I have to think about on a regular basis, which the models themselves cannot help me with. I think that just because the cognitive load has been taken off of some tasks does not mean that there is nothing that requires serious thinking anymore.

While I see where you are coming from but I think what has really gone for a toss is the utility of thinking hard.

Thinking hard has never been easier.

I think AI for an autodidact is a boon. Now I suddenly have a teacher who is always accessible and will teach me whatever I want for as long as I want exactly the way I want and I don;t have to worry about my social anxiety kicking in.

Learn advanced cryptography? AI, figure out formal verification - AI etc.

I miss this too, I have had those moments of reward where something works and I want to celebrate. It's missing too for me.

With AI the pros outweigh the cons at least at the moment with what we collectively have figured out so far. But with that everyday I wonder if it's possible now to be more ambitious than ever and take on much bigger problem with the pretend smart assistant.

AI doesn't remove difficulty so much as it removes friction, and friction was often what forced me into deeper conceptual understanding. When the path of least resistance is always "ask the model", my brain never has to build the scaffolding it used to

When I wrote nimja's template inheritance. I thought about it multiple days, until, during a train commute, it made click and I had to get out my notebook and write it, directly in the train. Then some month later I found out, I had the same bug that jinja2 had fixed years ago. So I felt kinda like a brothers in hard thinking :)

AI doesn’t eliminate deep thinking; mediocre companies and minds do. Don't blame everything to AI and find a true company and find a way to join them.

I can relate to this. Coding satisifies my urge to build and ship and have an impact on the world. But it doesn't make me think hard. Two things which I've recently gravitated to outside of coding which make me think: blogging and playing chess.

Maybe I subconsciously picked these up because my Thinker side was starved for attention. Nice post.

I'm not sure I agree. Actually, I don't agree. You only stop thinking hard if you decide to stop thinking hard. Nobody, no tool, is forcing you to stop thinking, pushing, reaching. If the thinking ceiling has changed, which I think it has, then it's entirely up to you to either move with it or stay still.

AI is way less of a problem in regards to thinking than digital media consumption is.

I used to think about my projects when in bed, now i listen to podcasts or watch youtube videos before sleeping.

I think it has a much bigger impact than using our "programming calculator" as an assistant.

I think for days at a time still.

I don’t think you can get the same satisfaction out of these tools if what you want to do is not novel.

If you are exploring the space of possibilities for which there are no clear solutions, then you have to think hard. Take on wildly more ambitious projects. Try to do something you don’t think you can do. And work with them to get there.

I have mixed feelings. On the one hand, I totally feel what this author is saying. On the other hand , I love that I am now able to push into areas that I could have never touched before, and complete successful projects in them.

Make sure you start every day with the type of confidence that would allow you to refer to yourself as an intellectual one-percenter

He specifically wrote that his intellect was not in the "top 1%" of people who seemed to solve problems easily. His "1% status" was in the bullheadedness needed to pound away at a problem for days to find a solution.

Then think hard? Have a level of self discipline and don’t consistently turn to AI to solve your problems. Go to a library if you have to! People act like victims to the machine when it comes to building their thinking muscles and AI and it confuses me.

The problem with the "70% solution" is that it creates a massive amount of hidden technical debt. You aren't thinking hard because you aren't forced to understand the edge cases or the real origin of the problem. It used to be the case that you will need plan 10 steps ahead because refactoring was expensive, now people just focus in the next problem ahead, but the compounding AI slop will blow up eventually.

would you agree that there's more time to think about what problems are worth solving?

> the number of times I truly ponder a problem for more than a couple of hours has decreased tremendously

Isn't that a good thing? If you're stuck on the same problem forever, then you're not going to get past it and never move on to the next thing... /shrug

Just because a problem takes more than a few hours doesn't mean you'll never get past it. And those are the problems that really grow your abilities.

The author clearly loves coding more than the output from coding. I'm thinking harder than ever and so grateful I can finally think hard about the output I really want rather than how to resolve bugs or figure out how to install some new dependency.

Thinking hard and fast with positive results is like a drug, ah those were good and rewarding days in my past, would jump back into that work framework any time ( that was running geological operations in an unusually agile oil exploration programme )

It’s the journey, not the destination, but with AI it’s only the destination, and it takes all the joy.

If you miss challenge, the world has plenty more. Maybe it's not all your comfort zone, but if you try being a little ambitious and maybe use AI to understand a field you're not already deeply familiar with, you can continue to grow.

"I still encounter those occasionally, but the number of problems requiring deep creative solutions feels like it is diminishing rapidly."

Just let it try and solve an issue with your advanced SQLAlchemy query and see it burn. xD

I read something similar here https://open.substack.com/pub/strangeloopcanon/p/on-thinkers...

What OP wants to say is that they miss the process of thinking hard for days and weeks and one day this brilliant idea popping up on their bed before sleep. I lost my "thinking hard" process again too today at work against my pragmatism, or more precisely my job.

In my experience, the so-called 1% are mostly just thinkers and researchers who have dedicated a lot more time from an earlier age to thinking and/or researching. There are a few geniuses out there but it's 1 in millions not in hundreds.

I knew this sort of thing would happen before it was popular. Accordingly:

Never have I ever used an LLM.

Last weekend, was struggling with traversing nested trees/recursion was funny. I did eventually solve it but probably shows my tier as a dev, not FAANG tier for sure.

i have often pondered if the (sometimes facetiously, sometimes seriously) postulated AI utopia scenario of humans who don’t need to work but can devote their time to art and recreational pursuits, might be a hellscape for many industrious people.

This essay captures that.

Even the pure artist, for whom utility may not seem to matter, manufactures meaning not just from creative exploration directly, but also from the difficulty (which can take many forms) involved in doing something genuinely new, and what they learn from that.

What happens to that when we even have “new” on tap.

"Sometimes you have to keep thinking past the point where it starts to hurt." - Fermi

I wrote a blog about this as well

Hard Things in Computer Science, and AI Aren't Fixing Them

https://blog.est.im/2026/stderr-04

Exactly what I've been thinking. outsourcing tasks and thinking of problems to AI just seems easier these days; and you still get to feel in charge because you're the one still giving instructions.

In most research areas, if a few days thinking is good enough to reach a worthwhile conclusion, it's not "thinking hard". It's "low-hanging fruit".

I usually think hard. I correlate subjects with different areas to find similarities.

What I miss is having other people who likes to think and not always pushing for shallow results

It seems like what you miss is actually a stable cognitive regime built around long uninterrupted internal simulation of a single problem. This is why people play strategy video games.

I've been writing C/C++/Java for 25 years and am trying to learn forex disciplined, risk managed forex trading, It's a whole new level of hard work/thinking.

I feel that AI doesn't necessarily replace my thinking, but actually helps to explore deeper - on my behalf - alternative considerations in the approach to solving a problem, which in turn better informs my thinking.

I have a very similar background and a very similar feeling when i think of programming nowadays.

Personally, I am going deeper in Quantum Computing, hoping that this field will require thinkers for a long time.

That's why i'm still pushing bytes on C64 demoscene (and recommend such a niche as a hobby to anyone). It's great for the sanity in modern ai-driven dev-world ;)

The crowd that counterpoints with "just don't use it then" miss the point: The general population lacks the ability to judge when should use it and when they shouldn't. The average person will always lean towards the less effortful option, without awareness of the long term consequences.

On top of that, the business settings/environments will always lean towards the option that provides the largest productivity gains, without awareness of the long term consequences for the worker. At that environment, not using it is not an option, unless you want to be unemployed.

Where does that leave us? Are we supposed to find the figurative "gym for problem solving" the same way office workers workout after work? Because that's the only solution I can think of: Trading off my output for problem solving outside of work settings, so that I can improve my output with the tool at work.

> Are we supposed to find the figurative "gym for problem solving" the same way office workers workout after work?

That's it, yeah. It sucks but it's part of the job. It makes you a better engineer.

You're absolutely right that this isn't sustainable however. In one of my earlier jobs - specifically, the one that trained me up to become the senior engineer I am now - we had "FedEx Fridays" (same day delivery, get it?). In a word, you have a single work day to work on something non-work related, with one condition: you had to have a deliverable by the end of the day. I cannot overstate how useful having something like this in place in the place of business is for junior devs. The trick is convincing tech businesses that this kind of "training" is a legitimate overhead - the kinds of businesses that are run by engineers get this intuitively. The kind that have a non-technical C-suite less so.

The author's point is, If you use AI to solve the problem and after the chat gives you the solution you say “oh yes, ok, I understand it, I can do it”(and no, you can’t do it).

I feel like I'm doing much nicer thinking now, I'm doing more systems thinking, not only that I'm iterating on system design a lot more because it is a lot easier to change with AI

Just sit down and think hard. If it doesn’t work, think harder.

I use Aider because it allows me to retain both personalities and still benefit from AI. It truly is the best assistant I've used.

I definitely relate to this. Except that while I was in the 1% in university who thought hard, I don't think my success rate was that high. My confidence in the time was quite high, though, and I still remember the notable successes.

And also, I haven't started using AI for writing code yet. I'm shuffling toward that, with much trepidation. I ask it lots of coding questions. I make it teach me stuff. Which brings me to the point of my post:

The other day, I was looking at some Rust code and trying to work out the ownership rules. In theory, I more or less understand them. In practice, not so much. So I had Claude start quizzing me. Claude was a pretty brutal teacher -- he'd ask 4 or 5 questions, most of them solvable from what I knew already, and then 1 or 2 that introduced a new concept that I hadn't seen. I would get that one wrong and ask for another quiz. Same thing: 4 or 5 questions, using what I knew plus the thing just introduced, plus 1 or 2 with a new wrinkle.

I don't think I got 100% on any of the quizzes. Maybe the last one; I should dig up that chat and see. But I learned a ton, and had to think really hard.

Somehow, I doubt this technique will be popular. But my experience with it was very good. I recommend it. (It does make me a little nervous that whenever I work with Claude on things that I'm more familiar with, he's always a little off base on some part of it. Since this was stuff I didn't know, he could have been feeding me slop. But I don't think so; the explanations made sense and the the compiler agreed, so it'd be tough to get anything completely wrong. And I was thinking through all of it; usually the bullshit slips in stealthily in the parts that don't seem to matter, but I had to work through everything.)

I always search the web, ask others, or read books in order to find a solution. When I do not find an answer from someone else, that's where I have to think hard.

That's weird as I do the opposite: think by myself, then look for help if I don't know.

this also used to happened to me when I in a position that involved a lot of research earlier on and then after the product was a reality, and it worked, it tapered off to be small improvements and maintenance

I can imagine many positions work out this way in startups

it's important to think hard sometimes, even if it means taking time off to do the thinking - you can do it without the socioeconomic pressure of a work environment

you still can think hard but you can offload some parts to LLM when you're stuck. Then you can leave space for more hard-won inspiration. When you're faced with a high-stakes decision, evaluating all sorts of possibilities, it's really easy to maximize the utilization of your brain, so in those cases you have plenty of chance to think hard.

My current workplace has a new mandate: All the new code must be 100% AI generated code by the end of Q1.

Best part is getting paid for that!

Do you miss thinking hard or do you missing typing? GenAI can save you from lots of typing and stack overflow copy/paste but it’s not going to think for you. Further, think as hard as you want you’re perfectly free to do so. I get the feeling a lot of people are deep down upset that knowledge and speed with code that use to be theirs and theirs alone is now much more accessible to the unwashed masses.

Me: I put your text into AI and ask it to summarize. We really do have a critical problem of mental laziness.

I, too, miss thinking hard, before I had children.

AI isn’t the only thing that changes how you attend to life.

If you miss the experience of not using LLMs, then just... don't? Is someone forcing you to code with LLM help?

I think a lot of people are struggling with this. Look at the obesity epidemic. Nobody is forcing you to buy ultraprocessed foods. Nobody is forcing you to overeat. You can still cook with fresh vegetables at home. But many/most people in Western countries struggle with their weight.

An even better analogy is the slot machine. Once you've "won" one time it's hard to break the cycle. There's so little friction to just having another spin. Everyone needs to go and see the depressed people at slot machines at least once to understand where this ends.

Nobody is "forcing" but billions of dollars on the best psychologists and marketing is doing their hundred year long best to get as close as they can to it.

They (collectively) have thousands of employees, billions of dollars, and tons of things to play with (taste, texture, appearance, mouthfeel, snappiness, salt, sweet, sugar, packaging colours and styles, slogans, marketing, brand associations, shop placement, advertising) all you have is willpower to fight against 4 billion years of evolution telling you that fatty, sugary, energy dense food is important, and a whole pile of biases and weaknesses that you aren't really aware of and they are very aware of and trying to maximally take advantage of.

Maybe we get AI to handle the lightwork while we go on to think about different things?

I love thinking hard, it's genuinely my favorite thing, but ... we get paid to ship.

> skipping the process of creation you trade the thing you could have learned

Most comments in the thread are missing that critical point. Yes you are achieving the end goal, perhaps faster. And yes, built projects (perhaps worse quality) are still better than not built projects.

But take the home cooking vs ordering at restaurant example:

At a restaurant you can prompt for exactly what you want to eat and it will be made for you without you actually having to do it. When the food comes out you can taste it and notice it is missing some flavor. Problem is, you don't know what is missing. If you are knowledgeable about the dish, you can prompt for additional spices or flavours.

When I cook, I try all the ingredients before I add them and then taste the result so I know how an addition changes the final result.

I am now a much better cook because of this because I can make substitutions on the fly. Dish missing sweetness? Carrots, baby red peppers, beets etc can all substitute - never even reach for sugar. Already added a lot of salt but still feels like more needed? Add sour flavours like lemon juice.

Sure, reliance on AI will end up with more things built but you'll have a generation of "cooks" that don't know why you add a bay leaf or two to soup, except that it's always in recipes.

have a look at https://projecteuler.net/

for "Thinker" brain food. (it still has the issue of not being a pragmatic use of time, but there are plenty interesting enough questions which it at least helps)

Just give Umineko a play/readthrough to get your deep thinking gray cells working again.

All the time. Been working on my own projects, they all require hard thinking.

Just work on more ambitious projects?

Exactly, go where the AI can't go.

I am building a project that AI is incapable of doing and I really need to think hard and progress slowly but hopefully it will create real value.

"I always felt that deep prolonged thinking was my superpower. I might not be as fast or naturally gifted as the top 1%, but given enough time, I was confident I could solve anything. I felt a deep satisfaction in that process."

In psychological terms, he's saying he has a need to solve hard problems in order to validate his identity and make himself feel good. At some point in his past he experienced some psychological trauma, and this hard-problem-defeating became his coping mechanism.

"That satisfaction is why software engineering was initially so gratifying."

He became a software engineer to gratify his need to solve hard problems, to validate his identity, and make himself feel good. If he stops needing to engineer difficult software, there goes his identity, his self-worth, his good feeling.

"But recently, the number of times I truly ponder a problem for more than a couple of hours has decreased tremendously. Yes, I blame AI for this."

When he runs up against something that takes away this thing that validates him, he feels de-valued. Rather than recognize that AI is making his life easier, freeing him up from mental labor, he's experiencing it as a loss, almost an attack.

"If I can get a solution that is “close enough” in a fraction of the time and effort, it is irrational not to take the AI route. And that is the real problem: I cannot simply turn off my pragmatism."

Now this link of hard work with his identity is becoming a problem. He's going to feel bad because he doesn't know how to deal with his life being easier now. This is a reason to address it head on with therapy, and a re-evaluation of what gives him value as a person, so that having an easier life doesn't feel bad.

Maybe. But I don't have trauma that causes me to tie my self worth to physical exercise. Yet when I can get myself to actually do it, I feel better. I'm not sure deep thinking is that different.

This is less of a lament that software engineering was replaced by AI, and more that the challenging work has been cooped by large corporations. Mainly through weaponized bloat of platforms

> but the number of problems requiring deep creative solutions feels like it is diminishing rapidly.

If anything, we have more intractable problems needing deep creative solutions than ever before. People are dying as I write this. We’ve got mass displacement, poverty, polarization in politics. The education and healthcare systems are broken. Climate change marches on. Not to mention the social consequences of new technologies like AI (including the ones discussed in this post) that frankly no one knows what to do about.

The solution is indeed to work on bigger problems. If you can’t find any, look harder.

I wish the author would give some examples of what he wants to think hard about.

What are some thoughts for someone just getting into thinking?

You are doing something wrong. Ai has not taken away thinking hard

Give the AI less responsibility but more work. Immediate inference is a great example: if the AI can finish my lines, my `if` bodies, my struct instantiations, type signatures, etc., it can reduce my second-by-second work significantly while taking little of my cognitive agency.

These are also tasks the AI can succeed at rather trivially.

Better completions is not as sexy, but in pretending agents are great engineers it's an amazing feature often glossed over.

Another example is automatic test generation or early correctness warnings. If the AI can suggest a basic test and I can add it with the push of a button - great. The length (and thus complexity) of tests can be configured conservatively relative to the AI of the day. Warnings can just be flags in the editors spotting obvious mistakes. Off-by-one errors for example, which might go unnoticed for a while, would be an achievable and valuable notice.

Also, automatic debugging and feeding the raw debugger log into an AI to parse seems promising, but I've done little of it.

...And go from there - if a well-crafted codebase and an advanced model using it as context can generate short functions well, then by all means - scale that up with discretion.

These problems around the AI coding tools are not at all special - it's a classic case of taking the new tool too far too fast.

This is the way I would consider using them; I just haven't really been able to figure out what I would need to get a reasonably fast and useful local setup without spending a ton of money.

My mindset last year: I am now a mentor to a junior developer

My mindset this year: I am an engineering manager to a team of developers

If the pace of AI improvement continues, my mindset next year will need to be: I am CEO and CTO.

I never enjoyed the IC -> EM transition in the workplace because of all the tedious political issues, people management issues and repetitive admin. I actually went back to being an IC because of this.

However, with a team of AI agents, there's less BS, and less holding me back. So I'm seeing the positives - I can achieve vastly more, and I can set the engineering standards, improve quality (by training and tuning the AI) and get plenty of satisfaction from "The Builder" role, as defined in the article.

Likewise I'm sure I would hate the CEO/CTO role in real life. However, I am adapting my mindset to the 2030s reality, and imagining being a CEO/CTO to an infinitely scalable team of Agentic EMs who can deliver the work of hundreds of real people, in any direction I choose.

How much space is there in the marketplace if all HN readers become CEOs and try to launch their own products and services? Who knows... but I do know that this is the option available to me, and it's probably wise to get ahead of it.

With AI, I now think much harder. Timelines are shorter, big decisions are closer together, and more system interactions have to be "grokked" in my head to guide the model properly.

I'm more spent than before where I would spend 2 hours wrestling with tailwind classes, or testing API endpoints manually by typing json shapes myself.

I think it's just another abstraction layer, and moves the thinking process from "how do I solve this problem in code?" to "how do I solve this problem in orchestration?".

I recently used the analogy of when compilers were invented. Old-school coders wrote machine code, and handled the intricacies of memory and storage and everything themselves. Then compilers took over, we all moved up an abstraction layer and started using high-level languages to code in. There was a generation of programmers who hated compilers because they wrote bad, inelegant, inefficient, programs. And for years they were right.

The hard problems now are "how can I get a set of non-deterministic, fault-prone, LLM agents to build this feature or product with as few errors as possible, with as little oversight as possible?". There's a few generic solutions, a few good approaches coming out, but plenty of scope for some hard thought in there. And a generic approach may not work for your specific project.

Do some leetcode hard... you're welcome.

My favorite part of programming is thinking about the whole system before I write a single line of code. How will my data be structured? What does the UI look like? How do I structure the project? What tools do I use? What do I need to take out to make this simpler? Etc.

I think hard about this with a notebook and a pencil and a coffee. And I spend weeks and sometimes months thinking about this. I go deep. And then the actual coding is just the grunt work. I don’t hate it but I don’t love it. I couldn’t care less what language is written in as long as it accomplishes my goal. So AI works great for me in this step.

I think you can still use AI and think deeply. It just depends on your mindset and how you use it.

yes but you solved problems already solved by someone else. how about something that hasn't been solved, or yet even noticed? that gives the greatest satisfaction

Well thinking hard is still there if you work on hard abstract problems. I keep thinking very hard, even though 4 CCs pump code while I do this. Besides, being a Gary Kasparov, playing on several tables, takes thinking.

Many times, the really hard problems that I ask AI to solve, it solves it not so well, and that just makes me more frustrated. If you're building that just need to barely work, that's one thing. if you need things to work securely, scale, and be really efficient, I don't see how AI changes things. To the contrary, it might spit out test-cases and sample code to test your hypothesis with, saving you time and frustration, letting you focus on the architecture and problem solving.

I think perhaps moving the goal posts to demand better quality and performance might force people who rely on AI to "think hard". Like your app works fine, now make it load in under a second on any platform.

The ziphead era of coding is over. I'll miss it too.

AI is a cheat that ruins the game

That’s funny cause I feel the opposite: LLMs can automate, in a sloppy fashion, building the first trivial draft. But what remains is still thinking hard about the non trivial parts.

Would like to follow your blog, is there an rss feed?

reading this made me realize i used to actually think hard about bugs and design tradeoffs because i had no choice

Cognitive debt lies ahead for all of us.

I miss hard thinking people.

Listen... I'm just not going to use AI. This is a choice you can make kind of easily. I don't know why we have ten threads per day about AI angst. Just don't use it.

"But I will be left behind!" No you won't. A top 20 percentile dev is not being left behind by the 80% with AI. You'll drop to average, worst case. Unironically just get good.

Man, this resonates SO MUCH with me. I have always loved being confronted with a truly difficult problem. And I always had that (obviously misguided, but utterly motivating) feeling that, with enough effort, no problem could ever resist me. That it was just a matter of grinding a bit further, a bit longer.

This is why I am so deeply opposed to using AI for problem solving I suppose: it just doesn’t play nice with this process.

If you feel this way, you arent using AI right.

For me, Claude, Suno, Gemini and AI tools are pure bliss for creation, because they eliminate the boring grunt work. Who cares how to implement OAuth login flow, or anything that has been done 1000 times?

I do not miss doing grunt work!

Rich Hickey and the Clojure folks coined the term Hammock Driven Development. It was tongue in cheek but IMO it is an ideal to strive towards.