In the last year I’ve shipped a couple of small OSS tools that I almost certainly would not have finished without AI‑assisted “vibe coding”. Everything I build now flows through AI, but in a slightly different way than just chatting with an LLM. I rarely use standalone ChatGPT/Gemini/Claude; almost all of it happens inside GitHub with Copilot and agents wired into my repos.
The big shift was treating GitHub as the interface for almost all of my work, not just code. I have repos for things like hiring, application review, financial reviews, and other operational workflows. There are “master” repos with folders and sub‑folders, and each folder has specific context plus instructions that the AI agent should follow when operating in that scope, essentially global rules and sub‑rules for each area of work.
Because of that structure, AI speeds up more than just the typing of code. Idea → spec → implementation → iteration all compress into much tighter loops. Tasks that would have taken me weeks end‑to‑end are now usually a couple of days, sometimes hours. Subjectively that’s where the 10–20x feeling comes from, even though it’s hard to measure precisely.
On the team side we’ve largely replaced traditional stand‑ups with AI‑mediated updates. KPIs and goals live in these repos, and progress/achievements are logged and summarized via AI, which makes updates more quantitative and easier to search back through. It’s starting to feel less like “AI helps me with code” and more like “AI is the main operating system for how our team works.”
Happy to share more about the repo/folder structure or what has/hasn’t worked if anyone’s curious.