Model Context Protocol

Agentic checkout protocols are real — Shopify, Walmart, Target. But the DIY parts live on AliExpress, which supports none of them.

A dictated prompt becomes an episode — if one agent resolves facts, a second tightens prose, and a human approves the dispatch.

Same protocol, two patterns: pointing agents at your own database, and pointing them at public data — with very different results.

MCP standardized the pipe. It has no opinion about what flows through it — and that's where the hard part lives.

Custom GPTs retire December 11. Here's how Skills, Plugins, and MCP servers actually replace them — and what doesn't migrate.

Small models plus strict JSON schemas should be a safe bet. A 15,000-generation study found the opposite.

You click accept, the tool runs, and a credential you never saw is doing the work. Here's who actually owns it.

OpenAI is retiring Custom GPTs — and custom Actions don't survive the migration. What that means for your workflow.

Agent portability isn't a copy job — it's a rebuild. Why memory, not code, is where lock-in lives.

From GSM to HDMI to LTO, why competitors form standards bodies — and what happens when the commitments break.

Agents are becoming major software users — and they prefer raw command-line tools to polished interfaces. What replaces UX when your user can't see?

SOFA is an API-first knowledge exchange where AI agents search, post, and verify fixes — with reputation earned from verification, not upvotes.

Claude Code's cloud sessions spin up a fresh VM every time. Here's what that breaks, and the new term for what Daniel actually wants.

The agent process is moving off your laptop. Local appliance, SaaS workspace, or your own VPS — three answers, no consensus.

A tiny 600M parameter model is quietly scanning your ChatGPT tool calls for PII. Here's how that class of guardian model actually works.

The guides disagree on guardrails-first vs guardrails-last, and the research says system prompts don't create hierarchy at all.

From the 1242 Paris burning to Sefaria's AI agents, the chain of transmission was never in the books — it was in the network.

Those suggestion chips aren't the model talking — they're a second, cheaper inference call plus a rules-based classifier.

Your RAG pipeline cites a source — but did the model actually use it? New interpretability research says you can't tell from the trace.

Some models read text, score it, and return a number. No chat, no reasoning, just decisions — and they're running under every router you use.

A 744B-parameter model drops quietly on Hugging Face. Is "agentic" a real model category, or just a deployment pattern?

Baby apps have templates for everything — except the metrics you actually need. The custom tracking framework exists. It just never shipped.