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Comment by Neywiny | Hacker News Reader

I keep running into this. It's nice seeing others here struggle. I guess when all your doing is one-shot simple trivial tasks who cares. I've found they're great for that. But once I need to do real work, everybody makes their own esoteric abandonware that kinda works but kinda doesn't. Stars aren't a perfect indicator, but I haven't seen anything over a few hundred for these things I'm finding on GitHub. Same with downloads of plugins. It was very isolating feeling like I'm the only one not enamored by the state of this.

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Same here. I have a feeling that once the AI hype cycle blows over to a degree (not that it will go away, just calm down), we'll be left with an enhanced version of pre-boom development. LLMs are hitting a dead end. I'm not convinced they can be bandaid-fixed to become AGI or SI or whatever the cool new trendy term is. They're good at many things, but sustaining a lean and directed software project's architecture over long periods of time doesn't seem to be one of them. That still remains to be further proven over time, but I've already seen plenty of examples of this.

Also, "esoteric abandonware" is a great way to put it. I keep looking for solutions to problems, finding dead projects that haven't been committed to in months, they have all the indicators of AI slopification (the primary one for me being emoji-filled READMEs). I don't think these types of projects will be around for the long-haul. These projects are just as much about community and people as they are about code. And people are much less likely to care about a short-lived project that doesn't have the backing of people that intuitively understand the internals of it. It's like building on sand.

Personally, I've decided that allowing the "agents" (these buzzwords are quickly becoming pet peeves of mine) to write the code for me is a bad idea, because the faster the LLM constructs architecture than I can, the less I'm absorbing the details of it, and the less likely I am to intuit obvious shortcomings. They do help me work much faster than before though, because I still use them like a research/learning assistant chatbot. Learning what I'm doing wrong faster, while still being the one putting the pieces together, is very great.

A couple of suggestions from the other side of this:

* Hold on to that isolating feeling. Those of us who feel AI is an incredible accelerator also feel just as isolated as you do. We wonder why you aren't seeing what we are seeing - and that isn't criticism, we just genuinely don't understand where the gap is and what would help other people jump across.

* I wouldn't bother looking at GitHub, or using that as any kind of measure. Personally getting away from all dependencies is my goal, and avoids a lot of supply chain issues.

* I don't use any plugins or external skills. I do use a ton of self built local MCP tools though, and it drives much of my workflow. That's me though.

* You sometimes need to work up to it. I've had periods of hitting a wall this year with LLMs. Once I end up building a framework, and the framework is in place, everything flows. The models can learn from looking at your adjacent projects, and they just fly. That's for coding, but it's similar for solving other problems... record everything, give it a ton of context, etc.

* If the software "kinda works kinda doesn't", then someone isn't doing enough testing. You should be dogfooding your software every day. Use your AIs to help you find edge cases, sure, but they're alien minds. Your human mind will sometimes think in ways they don't, but that your customers will. Sometimes we write software for AI to use, sure, but if you're writing for humans, you still should add some human mind thinking just to double check the model's work. "That's right, it goes in the square hole."

It's okay to struggle, some of us have been building toolkits over the last two years that make AI work so much easier. But don't get sucked into thinking "it doesn't work", especially in the Opus 5.5 era. On my side of the fence, these last two weeks have been the most intense and productive in a long time.

Lastly: some of us have tips we will not share. Some is slight selfishness - if others haven't discovered what the models can do, that is a small moat. But there are also some tips you just have to find on your own. I could tell you playing The Witness is some of the best training you could have for working with AI, and many will think that makes no sense at all. But I think a handful will nod and quietly understand, the lesson can't be taught, it must be learned.

Sorry for the long ramble, but I genuinely hope some of that helps.

I mean that's the problem though. I'm not paid to spend 2 years fine tuning a framework. I have things that need to get done, accurately and precisely. And to me, to my work, the models just suck without intense prompting. The harnesses are simultaneously too open and restrictive, and annoying. The terms are too overloaded. The models fight with me on highly domain specific knowledge that it's long since compressed or quantized away, until I fill the context with the documentation. It's all a nightmare to get up the learning curve.

I'm not blaming you, but it's incredible how much open source garbage there is and people spend years making actually good stuff that actually works and nobody's publishing it. So to me, and I'm trying, but to me your point is hearsay at best. You may as well have told me it took 2 seconds and you've always had a perfect time with it. It's all meaningless to me.

I've been working on my own esoteric abandonware for about a year now. Iterating on it, but it needs some experience on when to prompt it for what.

The thing is, a year ago the models started to be good enough that i cut my dev time down by 50%, and i started to use 30% of my time on improving my tools; and still feel like a win overall. Exhausting it is though.

The big chasm i saw grow in the early months of this year are primarily between the people who had and took the time/opportunity to toy with workflows, and the people who didn't.

So to your problems i do not recognize them:

Give the harness their own user account and launch it with no exec limits. Domain specific knowledge needs their own markdown files. AGENTS.md has important rules, and references to a well-kept set of source-of-truth Markdown documents. You need to remember what is in the context. Avoid compression at almost all costs; You want to be able to restart a session without it being too much of a setback (i.e. keep Markdown docs).

But it all boils down to the ability to: spot a workflow issue, think it can be improved, and then just improving it.

Every once in a while I go to my abandonware and strip out the things I thought would work but didn't.

I can see vague parallels between The Witness and AI prompting/engineering, but I'm not sure I'd describe it as training beyond the simple concept of teaching vs learning. Maybe I'm just in the category of people that think that makes no sense at all? (I completed the primary game and got about halfway through the secondary game)

I'll admit the parallels are vague, and I may be unhelpfully throwing people off on a side quest there. At least you'll understand my reasons for being vague, and how telling someone "the answer" actually prevents the learning. The "aha" moment is the important mental shift.

What I was thinking of here - if you remember the statue near the mountain, and it reaches out. There's also other statues in the area. But somewhere around that area, there are also trees. And if you look at the trees from the right place... the statues, the trees, they're doing the same thing. Why are trees doing the same thing?! Pondering that led in interesting directions.

The Witness was so multilayered, in ways that unexpectedly rewired my mind. My suggestion is that the rewiring that can occur from The Witness, is also a useful mental shift when interacting with AI. I think many of the people I see going fastest and most fluidly with AI have had that mental shift.

On a different note, where I can be more direct. Prompting is not just "the prompt". The entire environment is the prompt. Everything about the folder the AI finds itself in, adjacent files it's allowed to look at, everything it sees and encounters as it progresses, they each steer the model slightly. So where there used to be "prompt engineering" and "context engineering", there's also "environment engineering". The environment is the context.

And that perspective may bring you back to The Witness again.

> At least you'll understand my reasons for being vague, and how telling someone "the answer" actually prevents the learning

I'm gonna be real with you, my dude, this 100% sounds like you wanting to feel clever and superior rather than actually being that way.

I, too, love me some hard puzzle games. Baba Is You does some wild things with the mechanics that make you feel super clever when you figure it out. However, games are deliberately designed to give you those moments. That's why they're fun. They're an experience.

Waving your hands around vaguely and going "you must experience the maaaaaaaagic of aaaaaiiiiiiiiii" does not make you sound like a great and powerful wizard with secret knowledge, it kinda just makes you sound like that kid who took yugioh way too seriously and believed in the heart of the cards.

I don't get FOMO when I hear people talk about their super secret prompting techniques, because if it was actually smart, they'd want me to know lol. So far I'm batting 1.000 on that conjecture.

Upvoted :) I agree, I wrote this just before going to sleep, and spent parts of today thinking it wouldn't land, and that I would regret it. I guess I'll have to own looking silly on the internet, once again :)

On The Witness though, if you think it's a puzzle game, we didn't experience it the same way. Maybe I should just leave that one there.

I agree, this was terribly unhelpful for someone struggling with AI. My prompting is no different to Boris Cherny's advice a couple of days ago on X: "Talk to Claude the way you would a coworker. There's no secret to prompting." I remain confused at why some people think AI doesn't work, but I guess I'm not helping them solve it.

I'm also in the boat that says things should be better, but instead of improving the agent I decided to try to implement the agent management (in: https://beolis.com).

It still leverages agents (people are used to them already) but can deal with executing it on a different vm, dealing with git worktrees, etc (not that coding agents themselves cannot be improved, but I think the sw development infrastructure around it has a lot to improve too).

A perfect polished artifact is not important. Every person can just vibe their own good enough solution. And that’s fine.

We live in a society though

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