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Earlier quoted context omitted.
I presume via Goose via MCP in Claude Code: > I also have a local mcp which runs Goose and o3.
Ah, I skimmed the docs for Goose but I couldn't figure out exactly what it is that it does , which is a common issue for docs. For example: https://block.github.io/goose/docs/category/tutorials/ I just want to see an example workflow before I set this up in CI or build a custom extension to it!
I don't think "correct by construction" means what OP thinks it means.
Okay, what is he actually building with this? I have a problem where half the times I see people talking about their AI workflow, I can't tell if they are talking about some kind of dream workflow that they have, or something they're actually using productively
I keep coming to the same conclusion, which basically is: if I had an LLM write it for me, I just don't care about it. There are 2 projects out of the maybe 50 or so that are LLM generated, and even for those two I cared enough to make changes myself without an LLM. The rest just sit there because one day I thought huh wouldn't it be neat if, and then realized actually I cared more about having that thought than havi…
In my case it’s more like developing a mindset building a framework than to push feature after feature. I would think it’s like that for most companies. You can get an unpolished version of most apps easily, but polishing takes 3-5x the time.
Lets not talk about development robustness, backend security etc etc. Like AI has just way too many slippages for me in these cases.
However I would still consider myself a heavy AI user, but I mainly use it to discuss plans,(what google used to be) or to check it if I’ve forgotten anything.
For most features in my app I’m faster typing it out exactly the way I want it. (with a bit of auto-complete) The whole brain-coordination works better.
I guess long talk, but you’re not alone trust your instinct. You don’t seem narrow minded.
> When something goes wrong, I don’t hand-patch the generated code. I don’t argue with claude. Instead, I adjust the plan, the prompts, or the agent mix so the next run is correct by construction. I don't think "correct by construction" means what OP thinks it means.
> When something goes wrong, I don’t hand-patch the generated code. I don’t argue with claude. Instead, I adjust the plan, the prompts, or the agent mix so the next run is correct by construction. I don't think "correct by construction" means what OP thinks it means.
For example, an agent working on the dashboard for the Documents portion of my project has a completely different idea from the agent working on the dashboard for the Design portion of my project. The design consistency is not there, not just visually, but architecturally. Database schema and API ideas are inconsistent, for example. Even on the same input things are wildly different. It seems that if it can be different, it will be different.
You start to update instruction files to get things consistent, but then these end up being thousands of lines on a large project just to get the foundations right, eating into the context window.
I think ultimately we might need smaller language models trained on certain rules & schemas only, instead of on the universe of ideas that a prompt could result in. Small language models are likely the correct path.
I'd love to see more specifics here, that is, how Claude and o3 talk to each other, an example session, etc.
It's wild to see in action when it's unprompted.
For planning, I usually do a trip out to Gemini to check our work, offer ideas, research, and ratings of completeness. The iterations seem to be helpful, at least to me.
Everyone in these sorta threads asks for "proofs" and I don't really know what to offer. It's like 4 cents for a second opinion on what claude's planning has cooked up, and the detailed response has been interesting.
I loaded 10 bucks onto OpenRouter last month and I think I've pulled it down by like 50 cents. Meanwhile I'm on Claude Max @ $200/mo and GPT Plus for another $20. The OpenRouter stuff seems like less than couch change.
$0.02 :D
Earlier quoted context omitted.
Ah, I skimmed the docs for Goose but I couldn't figure out exactly what it is that it does , which is a common issue for docs. For example: https://block.github.io/goose/docs/category/tutorials/ I just want to see an example workflow before I set this up in CI or build a custom extension to it!
Classic Steve Klabnik comment.
The issue I'm facing with multiple agents working on separate work trees is that each independent agent tends to have completely different ideas on absolutely every detail, leading to inconsistent user experience. For example, an agent working on the dashboard for the Documents portion of my project has a completely different idea from the agent working on the dashboard for the Design portion of my project. The desig…
> The design consistency is not there, not just visually, but architecturally.
Seniors always gonna have to senior. Doesn't matter if the coders are AI or humans. You have to make sure you provide enough structures for the agents to move in roughly the same direction while allowing enough flexibility that you're not better off just writing the code.
I talked about a similar, but slightly simpler workflow in my post on "Vibe Specs".
https://lukebechtel.com/blog/vibe-speccing
I use these rules in all my codebases now. They essentially cause the AI to do two things differently:
(1) ask me questions first (2) Create a `spec.md` doc, before writing any code.
Seems not too dissimilar from yours, but I limit it to a single LLM