13 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 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 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.
Choosing the wrong embedding model is the most expensive mistake in RAG. Here is a side-by-side comparison of OpenAI text-embedding-3-large, Voyage voyage-3-large, Cohere embed-v4, and Jina embeddings-v3 with real pricing math, latency, multilingual, and a clear decision matrix from production RAG experience.
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.
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.
A production comparison of Intercom Fin, Zendesk AI Agent, and self-hosted Chatwoot plus Dify in 2026. Real pricing, resolution rates from a working deployment, and a clear decision framework for engineering and support leaders.
Learn how to use Midjourney in 2025 with this practical guide covering setup, prompt writing, styles, parameters, and techniques for better AI art generation.
Mistral AI launched Forge for enterprise custom AI model training. I break down what it does, who is using it (ASML, ESA, Ericsson), and how it compares to OpenAI, Google, and AWS offerings.
Anthropic made 1M context generally available for Claude Opus 4.6 and Sonnet 4.6 at standard pricing — no premium, no beta header. MRCR v2 score of 78.3% leads frontier models.
A 3-person marketing agency in Austin produces more content than teams five times their size using AI. I embedded with them for two weeks to learn exactly how their system works.