Comment by SyneRyder
3 hours ago
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 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.
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.