Community
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Deployed my first ML model! Lessons learned
After 4 months on the MLOps track, I finally deployed a sentiment analysis model serving 10K requests/day. Here are the 7 things I wish I knew before starting...
Study group forming: Reinforcement Learning (Sundays 3PM UTC)
Looking for 5-8 people to work through the RL course together. We'll meet weekly on Sundays, discuss concepts, and work on implementations. Intermediate Python required.
Weekly Challenge Solutions Thread - Build a Better Chatbot
Congrats to all participants! Let's discuss different approaches. I used a hybrid RAG approach with re-ranking and got 91.2 on the evaluation. Post your solutions and let's learn from each other.
New to AI - where should I start?
I'm a computer science student (junior year) and I want to break into AI/ML. I know Python well and have taken a linear algebra course. What track should I follow?
Help: Transformer attention weights all look uniform
I'm implementing multi-head attention from scratch and my attention weights are coming out nearly uniform across all positions. I've checked my scaling factor (sqrt(d_k)) and it seems correct. What am I missing?
Paper Discussion: Scaling Monosemanticity
This week we're discussing Anthropic's paper on scaling monosemanticity. Key findings: they found interpretable features in Claude that correspond to real-world concepts. Discussion questions inside.
My journey from bootcamp to ML Engineer in 8 months
Eight months ago I was a web developer with no ML experience. Today I accepted an offer as an ML Engineer at a Series B startup. Here's exactly what I did, what worked, and what I'd do differently...
Is fine-tuning dead? RAG vs fine-tuning debate
I've been seeing a lot of discussion about whether RAG has made fine-tuning obsolete for most use cases. In my experience building production LLM applications, I've found that RAG works great for knowledge-intensive tasks, but fine-tuning is still essential for style, format, and reasoning improvements. What's your take?