| ▲ | qazxcvbnmlp 6 hours ago | ||||||||||||||||
My mental model is that ai coding tools are machines that can take a set of constraints and turn them into a piece of code. The better you get at having it give its self those constraints accurately, the higher level task you can focus on. Eg compiler errors, unit tests, mcp, etc. Ive heard of these; but havent tried them yet. https://github.com/hmans/beans https://github.com/steveyegge/gastown Right now i spent a lot of “back pressure” on fitting the scope of the task into something that will fit in one context window (ie the useful computation, not the raw token count). I suspect we will see a large breakthrough when someone finally figures out a good system for having the llm do this. | |||||||||||||||||
| ▲ | AnonyX387 3 hours ago | parent [-] | ||||||||||||||||
> Right now i spent a lot of “back pressure” on fitting the scope of the task into something that will fit in one context window (ie the useful computation, not the raw token count). I suspect we will see a large breakthrough when someone finally figures out a good system for having the llm do this. I've found https://github.com/obra/superpowers very helpful for breaking the work up into logical chunks a subagent can handle. | |||||||||||||||||
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