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caveman
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
Tell the llm to answer like a cavemen, if llm talk like cavemen, the answers become shorter and more compressed.
https://github.com/JuliusBrussee/caveman
It's for getting it to output shorter answers, but also could help with your burnout.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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I used an LLM to build this
https://github.com/dprkh/eventfs
It has good test coverage, mostly unit tests but also a number of end-to-end tests. I also made the LLM build benchmarks, which you can find at the bottom of the readme. It is obviously slow, but I thought that it is good enough to work. When I tried to write a 1 GiB file, I found that it broke down, and after writing half the file, the speed went to under one megabyte per second.
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https://github.com/gastownhall/gascity is certainly a choice. I enjoyed playing with gas town but it was a little too nondeterministic for production code, I think.
Directionally if what you're doing is straightforward it's an amazing experience to be able to slap in an epic planning document and wake up the next day to it being "done", with a big asterisk that done-ness is directly proportional to how good of a spec and how good of a model you were using.
That being said, these days if you use Fable, slap in an epic planning document, and ask it to run a workflow (be sure to specify that subagents should use, say, Sonnet, or wave goodbye to your wallet), it's almost as good as gastown/gascity but far more predictable.
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You have to iterate on the tests, review and validate them, just like any other code, and if you generate a whole project's tests all at once the quality is abysmal, of course. I've been using a lot of old school data-driven testing techniques, where the harness is just code I review, and the data itself is e.g. json files and drives the system.
I actually have a public (AGPL) example here: https://github.com/pgdogdev/pgdog/tree/main/integration/sql - pgdog is particularly testable since it is trying for complete transparency, so you have a perfect oracle in hand via base postgresql, but it demonstrates the concept at least.
