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This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass The problem I wanted to solve My screen time is embarrassing, and I know I'm not alone. Every outdoor app I tried made it worse: you go outside to escape the screen, and then the app keeps you on the screen —...
TL;DR Most self-improvement in LLM systems happens around the model: better prompts, better agent scaffolds, better tool use. Model-level RSI is different. It changes the weights of the model itself, without a human handing it new labels.
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Twelve chapters in, this project has a domain model, a persistence layer, a service layer, an exception handler and something close to a hundred tests, and yet there is no entry point to fire up everything. There is nothing to start. No main method, no application context, no datasource.
Running a language model on your own machine has obvious appeal: your data stays local, there's no per-token bill and you can work offline. But the choice usually starts in the wrong place, with a parameter count or a leaderboard. A model that excels at code completion may be a poor choice for ...