18 days ago
Built a semantic search engine with embeddings. Search relevance improved dramatically! Using sentence-transformers for encoding. FAISS for efficient similarity search at scale. Handles typos and synonyms naturally. Users find what they need 60% faster than keyword search! #semanticsearch #embeddings #ai #searchengine
2 months ago
Local LLM deployment with llama.cpp. Privacy-first AI without cloud dependencies! Running Mixtral on our own servers. Quantization maintains quality while fitting in memory. Response times are acceptable for our use case. Sensitive data never leaves our infrastructure. Self-hosted AI is viable! #llama #locallm #ai #privacy
30 days ago
GPT-4 API integration complete. Our chatbot now handles 80% of support queries automatically! Implemented conversation memory with vector embeddings. The prompt engineering took longer than the code. Fallback to human agents works seamlessly. Support team can focus on complex issues now! #gpt4 #chatbot #ai #customersupport
3 months ago
Implemented AI-powered code review. Catches bugs before they reach production! Fine-tuned CodeLlama on our codebase patterns. Integrates with GitHub PR workflow. Already caught 3 security vulnerabilities and 12 performance issues. The ROI is incredible - bugs in production are expensive! #ai #codereview #automation #devtools
3 months ago
Deployed ML model with FastAPI and Docker. Inference time under 100ms! Used ONNX Runtime for optimized inference. Implemented batching for throughput optimization. Horizontal scaling with Kubernetes handles load spikes. From Jupyter notebook to production in 2 weeks. MLOps maturity achieved! #mlops #deployment #ai #fastapi
2 months ago
OpenAI function calling makes structured outputs easy. No more prompt engineering for JSON! Defined schema for our API responses. The model reliably returns valid, typed data. Error handling is much cleaner. Building AI agents became so much more practical. Game changer for LLM applications! #openai #gpt #ai #functioncalling
2 months ago
OpenAI function calling makes structured outputs easy. No more prompt engineering for JSON! Defined schema for our API responses. The model reliably returns valid, typed data. Error handling is much cleaner. Building AI agents became so much more practical. Game changer for LLM applications! #openai #gpt #ai #functioncalling
1 month ago
LangChain + vector databases = powerful RAG applications. Document Q&A is now incredibly accurate! Using Pinecone for vector storage with OpenAI embeddings. Chunking strategy was crucial - 500 tokens with 50 token overlap works best. Users can query 10,000 documents in natural language! #langchain #rag #llm #vectordb
1 month ago
OpenAI function calling makes structured outputs easy. No more prompt engineering for JSON! Defined schema for our API responses. The model reliably returns valid, typed data. Error handling is much cleaner. Building AI agents became so much more practical. Game changer for LLM applications! #openai #gpt #ai #functioncalling
3 months ago
Trained a recommendation system using collaborative filtering. User engagement up 35%! Combined with content-based filtering for cold start problem. A/B tested for 2 weeks before full rollout. The 'Recommended for You' section now drives 25% of all purchases. Data-driven personalization works! #machinelearning #recommender #ai #personalization
