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.
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.
This jumps to proof assistants and barely mentions fuzzing. I've found that with a bit of guidance, Claude is pretty good at suggesting interesting properties to test and writing property tests to verify that invariants hold.
With Visual Studio and Copilot I like the fact that runs a comment and then can read the output back and then automatically continues based on the error message let's say there's a compilation error or a failed test case, It reads it and then feeds that back into the system automatically. Once the plan is satisfied, it marks it as completed
Running all shorts of tests (e2e, API, unit) and for web apps using the claude extension with chrome to trigger web ui actions and observe the result. The last part helps a lot with frontend development.
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.
Most of my feedback that can be automated is done either by this or by fuzzing. Would love to hear about other optimisations y'all have found.
I’ve been wondering why I can’t use it to generate electricity.