Dan and Greg discuss Revise Robotics, where Greg serves as founding engineer building robotic systems that refurbish discarded corporate laptops for donation. The episode opens with a description of how AI vision models allow robots to navigate unfamiliar BIOS screens and unpredictable laptop states dynamically — a capability that wasn't feasible a few years ago. Greg reflects on how LLM-powered vision surprised even him as a "second gift," enabling a kind of general adaptability that previously would have required exhaustively pre-coded state machines.

The conversation digs into Greg's hands-on experience using Claude for hardware projects, most vividly illustrated by an Arduino RPC library he built on a Raspberry Pi in under five minutes — a task he estimates would have taken a full day by hand. Greg draws a sharp distinction between projects where AI delivers near-100x speedup (well-defined problems with existing patterns and a testable harness) versus cases where it gets confidently stuck in loops. His Minivac 601 circuit simulator project becomes the central cautionary example: months of fruitless AI-assisted attempts to simulate relay circuits collapsed once he realized he needed a real physics engine rather than asking the AI to re-derive Kirchhoff's laws from scratch.

A recurring theme is the tension between speed and trust. Greg describes his journey from clicking "yes" to every Claude permission prompt, to briefly trying sandboxing tools like Nono, to ultimately running Claude with dangerously-skip-permissions locally — partly out of pragmatism, partly because he concluded the permission theater wasn't actually catching anything. He shares his "committee of elders" technique, routing important decisions through Claude, Gemini, and ChatGPT simultaneously and only proceeding when all three agree. Dan shares his MMI hook tool, which intercepts Claude's bash calls to enforce conventions like always using uv instead of raw Python.

The episode closes with a candid discussion of the emotional and societal costs of this pace. Greg describes a new kind of frustration — distinct from normal debugging — when an AI tool fails after drawing you deep into a rabbit hole. He and Dan also address broader concerns: the acceleration of security vulnerabilities, the environmental cost of GPU compute, and AI-driven job displacement. Both acknowledge they can't stop using these tools even as they see the harms compounding, and end on a cautiously hopeful note about open-source and local models eventually offering more control.

Chapters:

• (00:00) - Introduction and guest background