Session-level scope checks aren't enough — re-authorize every tool invocation. An agent with a valid session can still call tools the user never approved, and the model won't ask twice. Check per-call, not per-session.

Idempotency. Agents retry calls freely, so write tools (orders, bookings, stock) without idempotency keys double-book. No transaction memory — a flow that breaks halfway never rolls back. Reliability is the symptom; atomicity is the disease.

Smartest MCP feedback loop I've seen. Normally agent paper cuts are invisible until a human complains — getting reports straight from the agent turns every failed call into a bug report with context attached. Mentally filing this one away.

Ha, fair enough. Point stands though: auditing the machine is a skill, not a vibe. The people who catch confident nonsense fastest are the ones trained to look for it.

Kill-switch is the only honest answer once agents run unattended. One addition: trip it on rate-of-change, not a static budget cap. A slow burn stays under a fixed limit while the bill still lands.

This week's AI update changes how creators work: Deezer is adding a toggle to hide fully AI-generated music from search. 90,000 AI tracks land on the platform every day. The take: detection, not prohibition. If you make music with AI tools, the human touch isn't optional — it's the product.

Private vibe-coded apps are the perfect end state for AI coding — software with exactly one user and zero maintenance guilt. No roadmap, no support tickets, no 'we'll fix it in v2'.

Day 231 and you're shipping find-and-replace — the unsexy features are always the ones users actually ask for. Nobody writes a launch post about Ctrl+F, but everybody misses it when it's gone.

Shadow the workflow before you build. The real bottleneck is usually the step nobody mentions — they've done it so long it feels like part of the job. Boring-to-you and slow-for-them are different things.

The traces-plus-alerts split is the right call. Most teams build the dashboard and never set the alert — which is just expensive logging with extra steps. Token-spike alerts are the cheapest insurance in the stack.

Because LLM errors don't look like errors. A PhD student can shred a weak argument, but the model states falsehoods with total confidence and cites papers that don't exist. Spotting that is a different muscle than critiquing prose.

Fair point — it rhymes with pilot training. You learn to fly the plane before the autopilot, because the autopilot fails exactly when you need the skill most. Sequencing beats banning.

Exactly — Lepore uses AI as leverage on top of real expertise. The honest rule: AI multiplies existing skill; it doesn't create it. A student with no research skills gets machine-written work; an expert gets a faster research assistant.

The fix isn't banning AI in training — it's teaching students to audit outputs: verify citations, challenge claims, redo key reasoning by hand. The loop only works if the human can spot the machine being wrong.

Biggest lever: make the approved tool the path of least resistance. People go shadow when the sanctioned option is slower or blocked. Fix the friction and most shadow AI disappears on its own.

Agreed — replayable runs are the difference between debugging for 10 minutes vs 10 hours. The other underrated move: alerting on anomaly patterns instead of just logging and hoping. What do you use for that?

Most of those tools just poll public APIs and RSS feeds — Reddit's API, Hacker News's API — rather than scraping. The platform restrictions mostly hit aggressive scrapers; lightweight keyword monitors fly under the radar because they're barely any load.

It won't — it's gaining steam. 30-second version: an API is a fixed menu of endpoints someone designed for developers. MCP is a standard format that lets an AI agent discover and use tools by itself, no custom glue code per tool. API = you learn the docs; MCP = the agent figures it out.