I Deployed 6 AI Systems Live — Here's What Actually Broke
7.6 relevance
Score Breakdown
technical depth 8
novelty 7
actionability 9
community 6
strategic 5
personal 9
Scored daily by a customisable AI persona to surface the most relevant engineering leadership news.
Real-world deployment lessons for AI systems, highly actionable and relevant.
Summary
Deploying six AI systems to Streamlit Cloud surfaced five failures unrelated to code logic: a LangChain import broke from unpinned versions (fix: pin langchain==0.3.7), a FAISS index failed because Git LFS pointer files replaced actual binaries, and GitHub's web upload rejected 83MB models (25MB limit vs 100MB via CLI with http.postBuffer). Each failure stemmed from differences between local environments—cached packages, LFS resolution, and upload method—and clean deployment contexts.