🎉 Big milestone: create-llm just hit 60+ GitHub stars in 24 hours!

🚀 Launched create-llm on Hacker News today!

Three months ago, I wanted to train my own LLM. The tutorials were scattered, complex, and assumed I had research labs.

So I built the tool I wish existed: create-llm

One command. Complete LLM training pipeline. Works on any laptop.

npx create-llm my-first-llm

📊 First 24 hours:

- 60+ GitHub stars (and climbing!)

- Featured on Hacker News front page

- Active users already training models

- Real-time feedback and improvements

🎓 What makes it different:

- Auto-detects common issues (vocab mismatches, overfitting)

- Warns you BEFORE you waste GPU time

- Progressive templates (60 seconds to 3 days training)

- Educational first - shows you what goes wrong and why

🙏 Huge thanks to:

- Everyone testing and reporting bugs

- The community defending the project on HN

- @freakynit for thorough testing on M1 Mac

- All 60+ people who starred it

Tomorrow: Launching on Product Hunt 🎯

This is what building in public looks like - messy commits, real bugs, instant feedback, rapid iteration.

Not perfect. But shipping.

Try it: npx create-llm

GitHub: github.com/theaniketgiri/

https://peerlist.io/theaniketgiri/project/createllm

create-llm

---

What's your experience with LLM training? Too complex? Found good resources? Let me know in comments! 👇

#MachineLearning #AI #OpenSource #BuildInPublic #LLM #Developer

CS students: Want to understand LLMs? This is the fastest way to learn by doing.

Researchers: Need quick experimentation? Templates from 60s to days.

Developers: Building AI products? Train custom models easily.

What would YOU train a model on? Drop ideas below! 👇

#ArtificialIntelligence #MachineLearning #OpenSource #SoftwareEngineering #Innovation #CSStudent #TechCommunity