Production RAG pipeline with FAISS vector search, semantic chunking, and LLM observability. Sub-5...

Production RAG pipeline with FAISS vector search, semantic chunking, and LLM observability. Sub-5...Production RAG pipeline with FAISS vector search, semantic chunking, and LLM observability. Sub-5...

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Emmanuel Nwanguma

• Jun 11

Production RAG pipeline with FAISS vector search, semantic chunking, and LLM observability. Sub-500ms retrieval on 100K+ documents. FastAPI backend deployed on Render

Data Engineering AI Development AI Engineer FastAPI LangChain Python

Ivanna Lysyshak

• Jul 29

I know building healthy relationships is hard. That's why I built Partnero AI - to make it easier. When you feel lost, overwhelmed, or frustrated, you can jump into the app and track your emotions. You discover hidden patterns and find solutions. You connect with your partner to grow together. Let me know what you think of the app design and what you would add or change. P.S. I will share my progress in the comments below. Flowstep project here: Link

AI Engineer UX Design Claude Flowstep Visual Studio Code flowstepchallenge Product Design appdesign

Rahul Singh Negi

• Jul 30

hope it helps people build a better relationships . The main feature is mood tracking could avoid lot of fights😂

Sunday Moses

• Jul 29

🚨 “It works on my machine.” The developer's most dangerous sentence 😂

Your app works perfectly locally, then suddenly breaks in production.

Before blaming the code, check:

🔹 Environment variables 🔹 Node.js versions 🔹 Database connection 🔹 API URLs 🔹 CORS configuration 🔹 Production build settings

Local success ≠ production readiness.

Always test your application in an environment as close to production as possible.

What’s the funniest “works locally, breaks in production” bug you've faced? 👇

devtips Node.js nextjs AI Development DevOps Software Development JavaScript Next.js

Afolabi Samuel

• Jul 30

🚀 🚀 🚀

daniel linus

• Jul 29

This Windsurf AI project had all the right features but suffered from slow performance, broken components, and deployment failures. Cleaned the codebase, fixed backend integration, optimized loading speed, and delivered a scalable web application built for real users.