Six repos to test around a Grok Bot workflow
Citing an agent project that burned 500B+ tokens rebuilding a game with initially wrong code, the author argues only objective checks prove progress and lists six repos to test around a Grok Bot workflow.
AI AGENTS SPENT 500B+ TOKENS REBUILDING A GAME. THEIR FIRST MONTH OF CODE LOOKED RIGHT. IT WAS WRONG. In Maurice Heumann’s project, progress only became real when the code had to pass an objective check. I’d start there with Grok Bot, then add these 6 repos: 1/ LiteLLM · 61.6K⭐ Track model calls made by scripts working alongside the bot. → http://github.com/BerriAI/litellm 2/ RouteLLM · 5.6K⭐ Send those scripts’ requests to the model that fits each task. → http://github.com/lm-sys/RouteLLM 3/ Crawl4AI · 85.9K⭐ Turn documentation pages into clean, searchable text. → http://github.com/unclecode/crawl4ai 4/ LLMLingua · 6.8K⭐ Shorten large inputs, then check that the important details survived. → http://github.com/microsoft/LLMLingua 5/ Prefect · 24.0K⭐ Track each step and rerun the ones that need another attempt. → http://github.com/PrefectHQ/prefect 6/ Phoenix · 11.9K⭐ Inspect the workflow and spot work that keeps repeating for no reason. → http://github.com/Arize-ai/phoenix These aren’t the repos used to rebuild that game. They’re the tools I’d test around my own Grok Bot workflow. My takeaway: prompts, commits and hours spent aren’t proof of progress. Show me the check that confirms the result actually works.