44 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.
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.
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.
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.
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.
A production-tested comparison of vLLM, SGLang, TensorRT-LLM, and Ollama for self-hosted LLM serving in 2026 — throughput, cold-start, cost math, and decision matrix from running a 4-product AI backend on a shared H100.
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.
A firsthand comparison of four AI agent orchestration platforms — Inngest, Trigger.dev v3, Hatchet, and Temporal — across pricing, durability, language support, and real-world cost for production workflows in 2026.
I ran the same LoRA fine-tune of Llama 3.1 8B on four platforms with 12,400 training pairs from our SmartExam product. Real costs, training times, inference latency, and the multi-adapter math that decided which one we shipped.
After porting a customer-support agent across all three frameworks, here is the honest TypeScript AI framework comparison for production in 2026 with benchmarks, code volume counts, and migration notes from real client work.