You've used some LLM, but do you understand how they actually work? We bridge the gap between popular AI articles and dense research papers, giving you the technical depth needed to understand and implement production AI systems.
No reviews yetBe the first to leave a review for LMPath
Wispr Flow: Dictation That Works EverywhereStop typing. Start speaking. 4x faster.
Promoted
Maker
📌
Hey Product Hunt! 👋
After 15+ years building startups and watching AI go from "academic curiosity" to "holy shit this changes everything," I got tired of the gap between buzzword-heavy AI content and actual understanding.
What LMPath is (and isn't):
- ✅ Technical depth without PhD-level math intimidation
- ✅ Built from foundational research papers (Attention Is All You Need, GPT-3, etc.)
- ✅ Explains HOW language models work, not just what they can do
- ❌ NOT another ChatGPT wrapper or surface-level business guide
- ❌ NOT a comprehensive ML course (there are great ones already)
What's already working:
- 📍 Auto-progress tracking - picks up exactly where you left off
- 🌙 Dark mode - because who codes in light mode anyway
- 📱 Mobile-responsive - learn on commute, finish on desktop
- 🍞 Smart breadcrumbs - never lose your place in complex topics
- 📊 Reading progress indicators - see how far through each chapter
Current limitations I'm honest about:
- Only 2 courses live (Intro to LMs + RAG Systems)
- No interactive coding environments yet
- Missing some advanced topics like training dynamics
- Built by one person (me), so updates take time
What's coming soon:
- 🔧 Hands-on coding labs - actual implementations, not just theory
- 💬 Command system - ask questions directly within lessons
- 📚 More courses - Transformers deep-dive, fine-tuning, embeddings
- 🎯 Practice problems - reinforce concepts with real scenarios
The goal isn't to replace existing resources but fill the gap between "AI is magic" and "here's a 50-page math proof." If you've ever wondered how ChatGPT actually generates text or why RAG systems work, this might click for you.
Built this because I needed to understand these systems to make good technical decisions. Maybe it'll help others facing the same challenge.
Feedback welcome - especially if something doesn't make sense. That's usually where the best improvements come from.