88 articles matching your search.
A working engineer's comparison of the five vector databases teams shortlist in 2026 — real benchmark numbers, pricing at scale, index-type tradeoffs, and a decision matrix for production RAG.
A hands-on comparison of the four small language models I tested in production builds during 2026 — benchmarks, memory footprints, licensing traps, and what broke on real phones.
Why bigger context windows won't save your AI agent, and the four context-engineering techniques that do: compaction, structured note-taking, just-in-time retrieval, and sub-agents.
An honest, hands-on 2026 comparison of the four web-data tools every RAG team weighs: Firecrawl, Jina Reader, Crawl4AI, and ScrapingBee. Pricing traps, anti-bot strength, and when each one actually wins.
A hands-on comparison of the three AI agent authentication platforms I evaluated for our own stack — plus where WorkOS and Merge fit, and which to pick for each scenario.
A production-tested comparison of fixed-size, recursive, semantic, late chunking, and contextual retrieval for RAG — with 2026 benchmarks and the strategy I actually deploy.
Benchmark headlines say 94%, but production text-to-SQL fails silently on complex joins. Here's where it actually breaks in 2026 and the semantic-layer architecture that fixes it.
GraphRAG promises smarter retrieval, but it can cost 40x more to index. Here is a production breakdown of GraphRAG vs vector RAG vs hybrid, with real 2026 cost, latency, and a decision matrix.
After shipping three agent rewrites of ContentForge AI Studio in 18 months, here is what LangGraph, CrewAI, OpenAI Agents SDK, and AutoGen v2 actually feel like in production — with token costs, latency numbers, and the pitfalls each one steers you into by default.
After 90 days running production traffic on ServiceBot AI Helpdesk, here is my hands-on comparison of four STT APIs — Whisper, Deepgram Nova-3, AssemblyAI Universal-2, and Speechmatics Ursa 3 — with WER benchmarks on real Indonesian-English call audio, latency measurements at p95, and the hidden add-on stack that destroys budgets.
Hands-on comparison of the 4 LLM red teaming tools I shipped to production across 6 AI products at Warung Digital — what each catches, what it costs, and the kill-chain stack that found 91 severity-high vulnerabilities in 4 months.
Five rerankers tested for production RAG in 2026 - Cohere 3.5, Voyage 2.5, Jina v3, Mixedbread mxbai-large-v2, and FlashRank. BEIR scores, latency, cost, and the call I made for our aggregator stack.