That '[free opt-out] 080…' number tacked onto the end of a spam text — can you make your Mac dial it, listen, press the keys, and handle it on its own? Without spending a cent on cloud APIs, on a single M1 MacBook Air. A log of automating the whole chain: dial → real-time IVR audio capture → Korean transcription → menu decision → DTMF input → hang up. Call audio captured via a Core Audio process tap, transcribed live with mlx-whisper large-v3-turbo, wired together as a perceive-decide-act loop. Centered on where I got stuck and what I took away from it.

The Anthropic Institute published its own numbers in a piece called 'When AI Builds Itself.' Claude writes 80%+ of Anthropic's production code, and per-engineer code output is up roughly 8× in two years. The far-future story of recursive self-improvement (RSI) isn't the point. The real message is in what's already happened — writing code has become nearly free, and the bottleneck moved to judging what's right. And uncomfortably, even that last refuge of judgment sits on the same curve.

On June 9, Anthropic shipped a 'Mythos-class' model that sits above Opus. The public version is Fable 5; the same model with its safety classifiers removed is Mythos 5. But read the official prompting guide and something is off — there's almost nothing about getting it to do more, and most of it is about getting it to do less and to translate itself back into human language. Blog #17 said to drop step-by-step instructions; Fable 5 goes one step further. Here's what changed and how to actually use it.

How do you engineer an LLM agent harness for a domain where wrong answers are expensive? Using Korea's ISMS-P (102 controls) as the vehicle, Claude Code reviews and audits through a domain-experts → interviewer → auditor pipeline. 10 reusable harness patterns + honest limits.

Public domain novel characters as AI agents. RAG + cognitive engine over the original text -- talk to Sherlock Holmes.

A native macOS meeting notes app. Real-time recording, automatic transcription, and AI-powered summaries -- all processed locally with on-device STT and LLM.