AI Gateway for Workers AI is now one argument on env.AI.run. What it logs instantly, why caching stays off until you ask, and the 60-second TTL floor.
Ads for AI agents are live. TIME serves crawlers a 42 KB markdown site with sponsored blocks; humans and Googlebot get 1.2 MB of HTML. I measured all of it.
Run agent tool calls in parallel by swapping the loop for a DAG planner: ten round trips become two levels, plus the cap, budget and critic on top.
Agent harness design decided a benchmark: OpenAI's ARC-AGI-3 score went 13.3% → 38.3% with zero model changes. What that means for your agent loop.
Cloudflare Wallets and x402 explained: how AI agents get a spending identity, how HTTP 402 payments work, and what breaks when your agent holds a budget.
Production-grade AI agents need more than a working demo. 353,000 signed up to learn agents; 6,000 shipped a capstone. Here's exactly what closes the gap.
Agent-to-human delegation is the pattern nobody designed for: your AI messaging a coworker. Why it lands badly, and the handoff rules that fix it.
Explorative modeling factors the training loop instead of generation: sample K guesses, train only on the best. 4.1x FLOP efficiency, 1.43 FID on ImageNet.
Build a RAG chatbot in Next.js with the AI SDK: embed the query, search pgvector, stream a grounded answer with citations, and stop hallucinations.
Vercel AI SDK in production: streaming, tool-calling, aborting generations, error retry UX, rate limiting, and cost control — the layer every tutorial skips.
The ChatGPT Apps SDK explained: how apps in ChatGPT work, why it's built on MCP, who the launch partners are, and how developers build and submit apps.
How to build enterprise-grade AI agents for free in 2026: a hands-on MaxKB + local LLM guide to RAG precision, security, and $0 API cost.
Agentic browsing in PageSpeed Insights grades your site for AI agents, not just humans. What the category checks, why Google added it, how to pass it.
Only 23% of autonomous AI agents reach production in 2026. The demo-to-production gap, why agents fail, and the playbook the winners actually use.
Build a RAG pipeline from scratch: chunking, embeddings, retrieval, reranking, grounded generation, and the production patterns that decide whether it works.
Deploy an MCP server on Cloudflare Workers: wrangler.toml, the run_worker_first model, routing /mcp, local testing, and going live on the free tier.
How to build an MCP server, step by step: JSON-RPC 2.0, the Streamable HTTP transport, typed tools, and agent discovery — from a real one I shipped.
RAG vs fine-tuning for LLMs in 2026: a practical decision framework covering architecture tradeoffs, cost, latency, and when to use each in production.