Structured tutorials and reference knowledge—organized for learning and lookup
Current Situation Analysis Running Ollama in production is fundamentally different from running it on a developer laptop. The default ollama serve binary is a single-process, single-model router optimized for local development.
Current Situation Analysis Multi-document RAG is broken in production. Not because retrieval fails, but because context assembly fails. Most engineering teams treat multi-document retrieval as a volume problem: ingest more PDFs, increase chunk count, raise top-k, and pray the LLM synthesizes correc...
Current Situation Analysis Most engineering teams treat RAG evaluation as a batch analytics task. You spin up RAGAS or LangSmith, run a dataset of 500 queries once a week, and stare at a dashboard that says "Context Precision: 0.82". This approach fails in production for three reasons: 1.
Current Situation Analysis Most knowledge base indexing tutorials stop at split_text and vector_search. They show you how to dump chunks into Pinecone or pgvector and query with cosine similarity. This works for a 500-document demo.
Current Situation Analysis When I joined the AI infrastructure team at our FAANG-scale organization, our RAG pipeline was bleeding money and trust. We were processing 1.2M queries daily.
Current Situation Analysis Most React Native performance guides published between 2021 and 2023 are fundamentally misaligned with how modern mobile apps actually fail in production. They treat performance as a component-level problem, prescribing useMemo, useCallback, and React.
Current Situation Analysis Product Hunt launches are traffic earthquakes. Most engineering teams treat them as marketing events and bolt on manual CDN purges, aggressive auto-scaling policies, or static pre-warming scripts. This approach fails catastrophically when the leaderboard shifts.
Current Situation Analysis Most SwiftUI teams treat layout as a component composition problem. They nest VStack, HStack, and ZStack containers, chain .frame(), .padding(), and .offset() modifiers, and assume the renderer will resolve the geometry. This approach works for prototypes.
Current Situation Analysis Referral programs are deceptively simple on paper: user A shares a link, user B signs up, both get credits. In production, they are financial systems disguised as marketing features. When we inherited our legacy referral service at scale, it was a monolithic Express 4.
Current Situation Analysis We were serving Llama-3.1-8B-Instruct on four NVIDIA A10G instances behind a standard vLLM 0.6.4 deployment. The architecture looked clean: FastAPI 0.109.2 ingress, Redis 7.4 for rate limiting, and a synchronous request queue feeding into vLLM's AsyncLLMEngine.
Current Situation Analysis When we audited our email automation pipeline at scale, we found the same architectural debt most teams carry: a synchronous Promise.all loop wrapping a provider SDK, blindly pushing 10,000+ payloads per campaign.
Current Situation Analysis Most frontend performance guides stop at "use next/image" or "split your chunks." That's table stakes. If you're running a high-traffic application on Next.js 15 and React 19, your bottleneck isn't bundle size; it's the Render-Compute-Hydrate Tax.
