🚀 70 Days → 800 GitHub Stars (Cold Start)
I wanted to write this down as a milestone.
I started my open-source project a little over two months ago, with no prior audience, no ads, no funding. Pure cold start.
Today, the repo just crossed 800 stars in 70 days. 🌟
🔑 The secret?
It wasn’t hype, it wasn’t marketing.
It was Problem Map — a set of text-based diagnostics that pin down why AI systems fail, and how to fix them without touching your infra.
Developers immediately recognized it as real, because the same 16+ errors keep showing up in RAG, OCR, vector search, and semantic reasoning pipelines. Once they saw the fixes work, the stars came naturally.
📖 Why it matters
- Authenticity: every issue in the map comes from real-world users debugging their own systems.
- Reproducibility: anyone can open the MIT-licensed repo and test it in 60 seconds.
- Scalability: the same fixes apply across GPT, Claude, Gemini, Mistral, Ollama, Grok, etc.
This wasn’t just “content.” It was something developers could use immediately — and that made all the difference.
🛠️ Try it yourself
Repo link (MIT License, free forever):
👉 Problem Map — WFGY
Would you like me to also draft a shorter, Twitter-thread style recap (like “70 days, 800 stars, cold start — here’s what worked”) so you can cross-post to X for reach?
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