29 articles matching your search.
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.
A production engineer's comparison of the four leading AI agent memory layers in 2026 — Mem0, Zep, Letta, and Cognee — with real benchmark numbers, token costs, and pricing.
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 hands-on comparison of GPTCache, Redis LangCache, Upstash, and Canopy for semantic caching, with real hit rates, costs, and threshold-tuning lessons from production.
A hands-on 2026 comparison of DSPy, TextGrad, and GEPA for automatic prompt optimization — what each one optimizes, the published benchmarks, real production costs, and a decision matrix from running all three on live AI products.
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.
I shipped LLM batch APIs across three production AI products in 2026 and saved $2,800/month. Here is the head-to-head on OpenAI, Anthropic, and Vertex AI batch — discount math, real turnaround times, and when batch is the wrong answer.
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.
After shipping streaming for 6 production AI apps, I learned SSE, WebSocket, and polling each win different battles. Here is when to pick which, with real numbers from our Hostinger stack.
I tested four production LLM guardrail stacks across six AI products I shipped. Honest comparison of Lakera, NeMo Guardrails, Guardrails AI, and Pillar Security — latency, pricing, and what I actually run in production.
A working engineer's view of the four libraries that actually solve the malformed-JSON problem in production AI: Instructor, BAML, Outlines, and Pydantic AI. Real benchmark numbers from 1.4M monthly LLM calls.