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𓀐 @n84FPf~?: A general for vibe coding, agentic engineering, coding agents, and shipping code with LLMs. You use Git — right, anon? ## What “vibe coding” is, and how to do it https://simonwillison.net/2025/Mar/19/vibe-coding/ https://simonwillison.net/2025/Mar/11/using-llms-for-code/ ## Related generals >>>/g/lmg/ ---- ## Frontier models using fully-general tooling — start here if you have $20 or so https://claude.com/product/claude-code — generally better currently https://developers.openai.com/codex/cli https://antigravity.google/pricing ## Near-the-old-frontier models for code https://www.deepseek.com/en/harness/ https://opencode.ai/go ## Best free tier https://opencode.ai/zen ---- ## Prompting https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview https://developers.openai.com/api/docs/guides/latest-model ## Skills https://github.com/mattpocock/skills — /grill-with-docs is a favorite https://github.com/Vuk97/forward-implementation-first — do less redundant bookkeeping ## Other editors / terminal agents / coding agents https://osaurus.ai/ https://pi.dev/ ## Is our AIs unlearning? https://aistupidlevel.info/ ## Will there be a codex reset? https://codex-resets.com/ ## What we’ve done https://vcg.gitgud.site ## Previous thread (Cross-thread)

has anyone solved the complexityslop problem that all these LLMs are affected by? the minute i stop carefully reviewing code, all models I've tried add vastly unnecessary amounts of abstractions and scaffolding that make the resulting application harder for the agent to work with in the future while providing no gain for the project. i know that my code reviews are the bottleneck here and i know i need to review less to get more throughput out of these models, but i don't know how to be less hands-on while also making sure the AI doesn't take a massive shit on the codebase

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