591–600 of 630 posts
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For Claude at least, the more recent guidance from Anthropic is to not yell at it. Just clear, calm, and concise instructions.
Sometimes I daydream about people screaming at their LLM as if it was a TV they were playing video games on.
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Notice that MOE isn’t different experts for different types of problems. It’s per token and not really connect to problem type. So if you send a python code then the first one in function can be one expert, second another expert and so on.
Can you back this up with documentation? I don't believe that this is the case.
I think the real value here isn’t “planning vs not planning,” it’s forcing the model to surface its assumptions before they harden into code. LLMs don’t usually fail at syntax. They fail at invisible assumptions about architecture, constraints, invariants, etc. A written plan becomes a debugging surface for those assumptions.
Sub agent also helps a lot in that regard. Have an agent do the planning, have an implementation agent do the code and have another one do the review. Clear responsabilities helps a lot. There also blue team / red team that works. The idea is always the same: help LLM to reason properly with less and more clear instructions.
After 6 months in production and 1100+ learned patterns: fewer moving parts, better debugging, more reliable output. Built a full production crawler this way — 26 extractors, 405 tests — without sub-agents. Orchestrator acts as gatekeeper that redispatches uncompleted work.
I think the real value here isn’t “planning vs not planning,” it’s forcing the model to surface its assumptions before they harden into code. LLMs don’t usually fail at syntax. They fail at invisible assumptions about architecture, constraints, invariants, etc. A written plan becomes a debugging surface for those assumptions.
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For the last few days I've been working on a personal project that's been on ice for at least 6 years. Back when I first thought of the project and started implementing it, it took maybe a couple weeks to eke out some minimally working code. This new version that I'm doing (from scratch with ChatGPT web) has a far more ambitious scope and is already at the "usable" point. Now I'm primarily solidifying things and incr…
This is a great come-back story. I have had a similar experience with a photoshop demake of mine. I recommend to try out Opencode with this approach, you might find it less tiring than ChatGPT web (yes it works with your ChatGPT Plus sub).
I agree with most of this, though I'm not sure it's radically different. I think most people who've been using CC in earnest for a while probably have a similar workflow? Prior to Claude 4 it was pretty much mandatory to define requirements and track implementation manually to manage context. It's still good, but since 4.5 release, it feels less important. CC basically works like this by default now, so unless you va…
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If you wouldn't mind sharing more about this in the future I'd love to read about it. I've been thinking about doing something like that myself because I'm one of those people who have tried countless apps but there's always a couple deal breakers that cause me to drop the app. I figured trying to agentically develop a planner app with the exact feature set I need would be an interesting and fun experiment.
same as you, I tried all sorts of apps. Todoist, habitica, fantastical, wunderlist, karakeep and more and more for all sorts of things. All are OK for most users, but none was GREAT for me. That's why I thought to give LLM's a proper spin. Get all the features I want, in the way I want. And anything I can come up with/see on the web over time, I can add myself within minutes instead of waiting for months for some 3rd…
Very cool and thanks for sharing.
Try OpenSpec and it'll do all this for you. SpecKit works too. I don't think there's a need to reinvent the wheel on this one, as this is spec-driven development.
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Making the model write a research file, then the plan and iterate on it by editing the plan file, then adding the todo list, then doing the implementation, and doing all that in a single conversation (instead of clearing contexts). There's nothing revolutionary, but yes, it's a workflow that's quite different from other posts I've seen, and especially from Boris' thread that was mentioned which is more like a collect…
> Making the model write a research file Having LLMs write their prompt files was something that became a thing the moment prompt files became a thing. > then the plan and iterate on it by editing the plan file, then adding the todo list, then doing the implementation, and doing all that in a single conversation (instead of clearing contexts). That's literally what planning mode is. Do yourself a favor and read the a…
I don't care about Visual Studio, I don't use it, but the page you've linked seems to describe yet another workflow (not very detailed).