I'm curious: could you give me an example of code that AI can't help with?
I ask because I've worked across different domains: V8 bytecode optimizations, HPC at Sandia (differential equations on 50k nodes, adaptive mesh refinement heuristics), resource allocation and admission control for CI systems, custom network UDP network stack for mobile apps https://neumob.com/. In every case in my memory, the AI coding tools of today would have been useful.
You say your work is "very specific" and AI is "too stupid" for it. This just makes me very curious what does that look like concretely? What programming task exists that can't be decomposed into smaller problems?
My experience as an engineer is that I'm already just applying known solutions that researchers figured out. That's the job. Every problem I've encountered in my professional life was solvable - you decompose it, you research up an algorithm (or an approximation), you implement it. Sometimes the textbook says the math is "graduate-level" but you just... read it and it's tractable. You linearize, you approximate, you use penalty barrier methods. Not an theoretically optimal solution, but it gets the job done.
I don't see a structural difference between "turning JSON into pretty HTML" and using OR-tools to schedule workers for a department store. Both are decomposable problems. Both are solvable. The latter just has more domain jargon.
So I'm asking: what's the concrete example? What code would you write that's supposedly beyond this?
I frequently see this kind of comment in AI threads that there is more sophisticated kinds of AI proof programming out there.
Let me try to clarify another way. Are you claiming that say 50% of the total economic activity is beyond AI? or is some sort of niche role that only contributes 3% to GDP? Because its very different if this "difficult" job is everywhere or only in a few small locations.
Did you played Assassin's Creed Valhalla? In it there is a board game called Orlog. Go and make that game to be multiplayer so you can play with your spouse/son/friend. Come back to me once you're done and we see then how much time it took you.
Or remake the Gwent board game that is in Witcher 3.
Make either of that mobile game so you can enjoy in the same room with the person you love. Also make sure you can make multiple decks (for Gwent) / multiple starting God (for Orlog) and you just select the start and hit "ready to play" (or whatever). You'll know what I mean once you understand either of these games.
Good luck with having any of them made in one session and not breaking the big picture in million of pieces and you keep the big picture in your head.
I'm trying to understand where our viewpoints differ, because I suspect we have fundamentally different mental models about where programming difficulty actually lives.
It sounds like you believe the hard part is decomposing problems - breaking them into subproblems, managing the "big picture," keeping the architecture in your head. That this is where experience and skill matter.
My mental model is the opposite: I see problem decomposition as the easy part - that's just reasoning about structure. You just keep peeling the onion until you hit algorithmically irreducible units. The hard part was always at the leaf nodes of that tree.
Why I think decomposition is straightforward:
People switch jobs and industries constantly. You move from one company to another, one domain to another, and you're productive quickly. How is that possible if decomposition requires deep domain expertise?
I think it's because decomposition is just observing how things fit together in reality. The structure reveals itself when you look at the problem.
Where I think the actual skill lived:
The leaf nodes. Not chipping away until you are left with "this is a min-cut problem" - anyone off the street can do that. The hard part was:
- Searching for the right algorithm/approach for your specific constraints
- Translating that solution into your project's specific variables, coordinate system, and bookkeeping
Those two things - search and translation - are precisely what AI excels at.
What I think AI changed:
I could walk into any building on Stanford campus right now, tap a random person (no CS required!) on the shoulder, and they could solve those leaf problems using AI tools. It no longer requires years of experience and learned skills.
I think this explains our different views: If you believe the skill is in decomposition (reasoning about structure), then AI hasn't changed much. But if the skill was always in search and translation at the leaf nodes (my view), then AI has eliminated the core barrier that required job-specific expertise.
Does this capture where we disagree? Am I understanding your position correctly?
Challenge accepted. If you get back to me I'll livestream this on Saturday.
I want to be crystal clear about what I'm claiming
My Claim:
AI assistance has effectively eliminated specialized job skills as a barrier. Anyone can now accomplish what previously required domain expertise, in comparable time to what a pre-AI professional would take.
Specifically:
- I've never written a game. I've never used a browser rigid body physics library. Never written a WebGL scene with Three.js before. Zero experience. So I should fail write.
- I think I could recreate the full 3D scene, hand meshes, rigging, materials, lighting - everything you see in that screenshot - using AI assistance
- I'm not going to do ALL of that in a couple hours, because even a professional game developer couldn't do it all from scratch in a couple hours. They would have assets, physics engine, rendering engine, textures, etc all because they were creating Orlog inside a larger game that provides all these affordances.
- But I could do it in the same timeframe a professional would have taken pre-AI
My interpretation of your challenge:
You're claiming that writing the multiplayer networking and state management for a turn-based dice game is beyond what AI can help a -- what you called me "run of the mill coder camp wanna be programmer" -- accomplish in a reasonable timeframe. That even with a simple 2D UI, I lack the fundamental programming skills to write the multiplayer networking code and manage state transitions properly.
So here's what I'll build:
A multiplayer Orlog game with:
- Full game logic implementing all Orlog rules and mechanics
- two players can connect and play together
- observers can join and watch
- game state properly synced managed across clients.
- Real dice physics simulation (because otherwise the game feels boring and unsatisfying - I'll grant you that point). But I'll have a server pick the dice roll values to avoid cheating. (Easiest trick in the book, just run the physics offscreen first, find the face that lands up, remap the textures, replay on screen this time), but use a library for simple rigid body physics engine because you couldn't write one from scratch in 3 hours either.
- Visual approach: Simple 2D/indie game cartoon style UI, with dice rolling in a separate physics area (just compositing dice roll app at bottom of screen, results animate onto 2D board, reality is they are totally separate systems)
What I need from you:
1. Is this the right interpretation? You're claiming the networking/state management is beyond what AI can help me accomplish?
2. Time predictions:
- How long would a competent game developer take to build what I've described?
- How long will it take me?
3. At what point do I prove my point? What's the minimum deliverable you'd accept as having completed the challenge?
Are you willing to make concrete, falsifiable predictions about this specific challenge?
Go ahead and do it. Put the code at your convenience on github. I don't care about your livestream, you can do it whenever. What I want is an app that I can install on my smartphone, Android, and that I can have my son do it as well and we play together. I am giving you a generous one week, should be more than enough for your "AI can do it easier". I did it in a month, and I put like 120 hours during that time. You get close to those 120 hours or over, you fail this "challenge accepted". Have fun.
I actually agree with everything you said, and I see I failed to communicate my idea that's exactly why I'm so upset.
You said "the only exception here is learning" - and that exception was my hobby. Programming simple things wasn't work for me. It was entertainment. It was what I did for fun on weekends.
Reading a blog post about writing a toy database or a parser combinator library and then spending a Saturday afternoon implementing it myself. that was like going to an amusement park. It was a few hours of enjoyable, bounded exploration. I could follow my curiosity, learn something new, and have fun doing it.
And you're right: if an LLM can solve it with the same quality, it's not a problem worthy of human effort. I agree with that logic. I've internalized it from years in the industry, from working with AI, from learning to make decisions about what to spend time on.
But here's what's been lost: that logic has closed the amusement park. All those simple, fun learning projects now feel stupid. When I see those blog posts now, my gut reaction is "why would I waste time on that? That's one prompt away." The feeling that it's "not worthy" has completely drained the joy out of it.
I can't turn off that instinct anymore. I know those 200 lines of code are trivial. I know AI can generate them. And so doing it myself feels like I'm deliberately choosing to be inefficient, like I'm LARPing at being a programmer instead of actually learning something valuable.
The problem isn't that I disagree with you. The problem is that I agree with you so completely that I can no longer have fun. The only "worthy" problems left are the hard ones AI can't do. But those require months of serious investment, not a casual Saturday afternoon.
I genuinely do not understand this. You can totally still do that for learning purposes.
The only thing you cannot do anymore is show off such projects. The portfolio of mini-tutorials is definitely a bygone concept. I actually like that part of how the culture has changed.
Another interesting challenge is to set yourself up to outperform the LLM. Golf with it. LLM can do a parser? Okay, I'll make a faster one instead. Less lines of code. There's tons of learning opportunities in that.
> The only "worthy" problems left are the hard ones
That's not true. There are also unexplored problems which the AI doesn't have enough training data to be useful.
> The feeling that it's "not worthy" has completely drained the joy out of it.
It was never "worthy". With the proliferation of free, quality, open source software, what's now a prompt away, has been a github repo away for a long time. It's just that, before, you chose to ignore the existence of github repos and enjoy your hobby. Now you're choosing to not ignore the AI.
> And so doing it myself feels like I'm deliberately choosing to be inefficient
People have plenty of hobbies that are not the most "efficient" way to solve a problem. There are planes, but some people ride bikes across continents. Some walk.
LLMs exist, you can choose to what level you use them. Maybe you need to detox for a weekend or two.
I think you're misunderstanding my point. I'm not saying I don't know how to use planning modes or iterate on solutions.
Yes, you still decompose problems. But what's the decomposition for? To create sub-problems small enough that the AI can solve them in one shot. That's literally what planning mode does - help you break things down into AI-solvable chunks.
You might say "that's not real thinking, that's just implementation details." Look who came up the the plan in the first place << It's the AI! Plan mode is partial automation of the thinking there too (improving every month)
Claude Code debugs something, it's automating a chain of reasoning: "This error message means execution reached this file. That implies this variable has this value. I can test this theory by sending this HTTP request. The logs show X, so my theory was wrong. Let me try Y instead."
> When I stop the production line to say "wait, let me understand what's happening here," the implicit response is: "Why are you holding up progress? It mostly works. Just ship it. It's not your code anyway."
This is not a technical problem or an AI problem, it’s a cultural problem where you work
We have the opposite - I expect all of our devs to understand and be responsible for AI generated code
To get it done correctly, that's always what it's been about.
I don't feel that code I write without assistance is mine, or some kind of achievement to be proud of, or something that inflates my own sense of how smart I am. So when some of the process is replaced by AI, there isn't anything in me that can be hurt by that, none of this is mine and it never was.
It's gold plating because no one wants or needs my little tool. If I spent that same hour iterating on the output format to be more user friendly while at work, I would be taken out behind the woodshed and shot. It's pure wasted effort.
I'm working on Happy Coder, an open source Codex and Claude Code native mobile app (plus a web app).
Happy lets you spawn and control multiple Codex/Claude Code sessions in parallel. Happy Coder runs on your hardware, works from your phone and desktop, costs nothing, End to End encrypted, and permissive MIT License.
Happy Coder is a unix style "do one thing well" project.
The goal is zero workflow disruption. I want to be able to run CLI coding agents on any internet connected computer, and control them with my phone. Happy has a command line wrapper for Codex and Claude Code that let you start a session in your terminal, and then continue it from your phone with real time sync. So type in your terminal and see it on the phone, type into your phone and see it in your terminal. So you can switch back and forth.
There is an optional voice agent some contributors have been hacking on that lets you talk to the voice agent first, and the voice agent then writes prompts for Codex/Claude Code and answers questions about what the coding agent running on your computer is doing/did. The voice agent feature is pretty neat, but in my opinion needs a bit more iteration, so any ideas or help would be awesome.
I'm working on Happy Coder, an open source Codex and Claude Code native mobile app (plus a web app).
Happy lets you spawn and control multiple Codex/Claude Code sessions in parallel. Happy Coder runs on your hardware, works from your phone and desktop, costs nothing, End to End encrypted, and permissive MIT License.
Happy Coder is a unix style "do one thing well" project.
The goal is zero workflow disruption. I want to be able to run CLI coding agents on any internet connected computer, and control them with my phone. Happy has a command line wrapper for Codex and Claude Code that let you start a session in your terminal, and then continue it from your phone with real time sync. So type in your terminal and see it on the phone, type into your phone and see it in your terminal. So you can switch back and forth.
There is an optional voice agent some contributors have been hacking on that lets you talk to the voice agent first, and the voice agent then writes prompts for Codex/Claude Code and answers questions about what the coding agent running on your computer is doing/did. The voice agent feature is pretty neat, but in my opinion needs a bit more iteration, so any ideas or help would be awesome.
Counterpoint: don't blame the founder for leading with driving use cases. It's audience selection bias, not the founder being reckless. He's showing what gets traction, not necessarily what he thinks people should actually do
I'm contributing to a similar open source coding tool [1] and I see the same skewed reaction: voice control of "whatever" while driving gets 5-10x the clicks of any other demo.
There's a logical reason so many people think of voice control while driving. It's not because they're reckless.
It reflects the hierarchy of needs. People with long commutes (often younger, lower-paid engineers living further out) spend 2+ hours driving daily.
This is their biggest time sink, so of course they think about making it productive. When you're living far out for cheap housing and hear "coding while driving", its easy to think: finally, a way to get ahead without choosing between career growth and seeing my family.
Again, I think its just an off-the-cuff reaction, not actually what people will do. Just like people try your app and tell you its amazing but then never pay. Doing stuff while driving just sounds nice until you know... you think about it for 3 seconds and yeah, its bad idea.
"Don't anthropomorphize the founder, they're just responding to market pressures" is not the defense that you think it is. For the same reason you don't want to do business with Oracle (see the great rant from which I borrowed this phrasing [1]) you don't want to do business with someone who will advertise their product for unethical use cases - they'll cross other ethical boundaries too involving screwing you over.
Let me rephrase this. I am feeling empathy for the Blue founder because I am in a similar position to show voice control to people and the overwhelming response is "cool, I'll use it while driving". And similar to people here, I don't like that conclusion. So I think it feels bad for the Blue founder to have people pile onto the driving detail that he likely already agrees with meanwhile people ignore the hardware accomplishment.
To me the point of voice control is walking. I'm thinking of Einstein, Darwin, Thoreau. They believed that physical activity helped stimulate his mind and spark creative thought.
You walk, you think, and occasionally you say something.
But in practice people have been quick to jump to conclusions that surprise me. Such as "the point is to try to kill people".
So I'm merely working backwards to figure out why people want to jump in a car first thing.
And the charitable explanation is the people are just not thinking to carefully and are just excited by seeing a computer respond to voice with a low enough error rate.
I'm all for applying the principle of charity when it comes to "someone just heard of this and that was their first idea", and even with "there are maybe some responsible use cases involving driving".
But the blue founders here don't fall into the first category, and they made a 2 minute ad advertising their product that doesn't fall into the second category either. Charity only stretches so far.
I'm not a startup investor, but I'm quite serious when I say that ad would give me serious doubts about doing business with this founder for purely practical reasons (on top of other ethical ones). That doesn't mean the product is bad, but a business is more than a product.
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HN definitely does have a dog piling problem where one issue is picked out by the community and discussed ad nauseam to exclusion of all else. To be honest between the magnitude of this issue and the fairly significant discussion downthread about other topics I'm not particularly feeling that it is a problem in this thread though.
Driving while drunk is a major crime if you get caught these days. People still do it anyway. People who are caught have to get a device installed into their car that they have to blow into to prove they're not intoxicated. They still drive intoxicated. They get sent to jail and they still get behind the wheel. Talking with your phone because driving is boring as shit is fairly harmless because people are going to do dumb ass shit regardless. The better answer is to promote comma.ai and similar ADAS like blue cruise, because I think that'll lead to fewer accidents. We have the technology to take a lot of the human out of the equation. People are dying. Why are we waiting?
Talking with your phone because driving is boring as shit is fairly harmless
It really isn't. Driving while distracted by a phone conversation is comparable in safety to driving while heavily intoxicated or while barely awake. This is not a hypothetical. There is actual experimental research behind it and the evidence is consistent and overwhelming. There is no justification and no defence. It's not a debate. It kills people.
Everyone is right in saying that those drivers should take public transport instead if they want to work or call or play a game while they are on the move. And yes - maybe in time there will be self-driving options that will make the whole issue obsolete. But right now anyone driving but not paying full attention is a danger to themselves and others and this is not something we should condone or attempt to justify. It kills people.
> A reverse-centaur is a machine that is assisted by a human being, who is expected to work at the machine’s pace.
This exactly describes the attitude of a PM I work with who makes abundant use of ChatGPT to generate PRDs.
We get so much crap for not keeping up with the flood of requirements. "Why don't you just plug my specs into Claude Code, review it, just tell Claude what needs to be fixed?" Its exhausting.
I really do feel like a reverse-centaur. I'm genuinely expected to work at the pace of this rube goldberg bullshit machine this PM has rigged up.
Hey, you mentioned needing a web UI for your agent.
I contribute to Happy Coder, an open source agent client (mobile app, desktop app, and web app). The project is just a UI layer for existing agents. Adding Codex specific UI and plumbing last week was a 2,600 line diff that took a contributor 3 evenings. And it should be even less plumbing for the next agent.
I'm looking for developers to try integrating their agents and tell me what's broken or awkward. I'd appreciate feedback on where the abstractions leak or what's missing. Even with the friction of using someone else's codebase, it could be less work than starting from zero.
I keep seeing these proprietary clients charge $50/month for basically the same plumbing everyone needs. Selfishly I would like open source and free to win this category here.
Looks pretty neat, but I have a vision for my web client that I'm excited to execute on (it's a 3d map that shows all active agents with branching, it has high performance zoom/pan so you can visualize your entire swarm's activity at once, lots of nice visual flash), I just have a jammed launch schedule so I haven't been able to put the time towards the work yet, I'm trying to ship 3 products this week!
Speaking of which, if you're interested in a CLI that creates a comprehensive refactoring plan for agents, I'm going to drop https://github.com/sibyllinesoft/valknut tomorrow once I polish the github and create a product page for it on my site. It's helped me keep my agents on rails while refactoring, so that I can scale my AI workflows to larger codebases. It's available on crates as valknut-rs and brew as valknut.
I want to weigh in here because I see comments focusing on how these products are useless trash. I think that's missing the point.
This wealthy engineer mindset is too literal. The AI-generated photos and fake reviews aren't bugs. They're features. They let the poor American with $100 of disposable income pretend they found a way to get an Apple Watch for $11. Just for a few days, they get to believe it might be real. When it arrives and it's crap, they knew it would be. But they got to play the fantasy.
TEMU's tagline is "Shop like a billionaire." I want you to really think about that. Marketers test hundreds of combinations to find what resonates. TEMU probably has thousands of marketers. They've tested millions of possible hooks. Millions. And this is what won.
"Shop like a billionaire" is the message that brought new people in the door above all others. Now what about churn? That's not the tagline's job. Don't let your knowledge of what exactly TEMU does and how it functions conceal from you this signal of what many (not all!!) people want.
However I believe they're not scamming people. They're delivering exactly what they're selling, which is the experience of feeling like you could have nice things.
Twenty years ago you could go to a matinee movie for a dollar. Two hours of escapism for a dollar. That product doesn't exist anymore. Theaters decided to serve a different customer base. They went upmarket. But people still want cheap escapism. Now it's $1-3 on TEMU to get that same escape. You browse, you dream, you wait for the package. It's entertainment.
> TEMU's tagline is "Shop like a billionaire." I want you to really think about that. Marketers test hundreds of combinations to find what resonates. TEMU probably has thousands of marketers. They've tested millions of possible hooks. Millions. And this is what won. "Shop like a billionaire" is the message that resonated above all others.
Not saying you’re wrong, but I find “Shop like a billionaire” to be a deeply weird slogan.
I asked a friend who is probably right in the target audience (very low to no disposable income) how he reads that slogan. He said “never worry about the price”. It’s in line with the people it might try to attract, the ones for whom the price is of utmost importance, above all else.
Wire $1,000 to my bank account and I will wire $1,000,000 (or equivalent in financial experience product) back in just a few days. Only one condition - you have to explain what happened to you afterwards.
I ask because I've worked across different domains: V8 bytecode optimizations, HPC at Sandia (differential equations on 50k nodes, adaptive mesh refinement heuristics), resource allocation and admission control for CI systems, custom network UDP network stack for mobile apps https://neumob.com/. In every case in my memory, the AI coding tools of today would have been useful.
You say your work is "very specific" and AI is "too stupid" for it. This just makes me very curious what does that look like concretely? What programming task exists that can't be decomposed into smaller problems?
My experience as an engineer is that I'm already just applying known solutions that researchers figured out. That's the job. Every problem I've encountered in my professional life was solvable - you decompose it, you research up an algorithm (or an approximation), you implement it. Sometimes the textbook says the math is "graduate-level" but you just... read it and it's tractable. You linearize, you approximate, you use penalty barrier methods. Not an theoretically optimal solution, but it gets the job done.
I don't see a structural difference between "turning JSON into pretty HTML" and using OR-tools to schedule workers for a department store. Both are decomposable problems. Both are solvable. The latter just has more domain jargon.
So I'm asking: what's the concrete example? What code would you write that's supposedly beyond this?
I frequently see this kind of comment in AI threads that there is more sophisticated kinds of AI proof programming out there.
Let me try to clarify another way. Are you claiming that say 50% of the total economic activity is beyond AI? or is some sort of niche role that only contributes 3% to GDP? Because its very different if this "difficult" job is everywhere or only in a few small locations.