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.
Reads the SQLite db and shit. So burn your tokens on that.
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.
That said architectural problems have been also been less difficult, just for the simple fact that research and prototyping has become faster and cheaper.
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.
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
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.
When I'm just programming, I spend a lot more time working through a single idea, or a single function. Its much less tiring.
Except without the reward of an intellectual high afterwards.
[1]: https://www.jocrf.org/how-clients-use-the-analytical-reasoni...
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.
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.
> 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.
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.
Why solve a problem when you can import library / scale up / use managed kuberneted / etc.
The menu is great and the number of problems needing deep thought seems rare.
There might be deep thought problems on the requirements side of things but less often on the technical side.
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.
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.
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.
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.
7 months later waffling on it on and off with and without ai I finally cracked it.
Author is not wrong though, the number of times i hit this isnt as often since ai. I do miss the feeling though
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."
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.
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.
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.
I tried this with physics and philosophy. I think i want to do a mix of hard but meaningful. For academic fields like that its impossible for a regular person to do as a hobby. Might as well just do puzzles or something.
Just don't use it. That's always an option. Perhaps your builder doesn't actually benefit from an unlimited runway detached from the cost of effort.
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.
Please read up on his life. Mainlander is the most extreme/radical Philosophical Pessimist of them all. He wrote a whole book about how you should rationally kill yourself and then he killed himself shortly after.
https://en.wikipedia.org/wiki/Philipp_Mainl%C3%A4nder
https://dokumen.pub/the-philosophy-of-redemption-die-philoso...
Max Stirner and Mainlander would have been friends and are kindred spirits philosophically.
https://en.wikipedia.org/wiki/Bibliography_of_philosophical_...
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.)
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.
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 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.
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.
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.
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 didn't imply most of use can do half the thing he's done. That's not right.
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.
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.
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.
My typos are largely admissible.
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.
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...
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.
I use AI for the easy stuff.
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.
A few years before this wave of AI hit, I got promoted into a tech lead/architect role. All of my mental growth since then has been learning to navigate office politics and getting the 10k ft view way more often.
I was already telling myself "I miss thinking hard" years before this promotion. When I build stuff now, I do it with a much clearer purpose. I have sincerely tried the new tools, but I'm back to just using google search if anything at all.
All I did was prove to myself the bottleneck was never writing code, but deciding why I'm doing anything at all. If you want to think so hard you stay awake at night, try existential dread. It's an important developmental milestone you'd have been forced to confront anyway even 1000 years ago.
My point is, you might want to reconsider how much you blame AI.
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.
... OK I guess. I mean sorry but if that's revelation to you, that by using a skill less you hone it less, you were clearly NOT thinking hard BEFORE you started using AI. It sure didn't help but the problem didn't start then.
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.
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.
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
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.
1. Take a pen and paper.
2. Write down what we know.
3. Write down where we want to go.
4. Write down our methods of moving forward.
5. Make changes to 2, using 4, and see if we are getting closer to 3. And course correct based on that.
I still do it a lot. LLM's act as assist. Not as a wholesale replacement.
What?
Besibeta•2h ago
andsoitis•2h ago