#agents

Repair shop owner Louis Rossmann argues AI tools are valuable as a review layer that flags and summarizes work for a human to act on, but become dangerous once used to act autonomously, citing his own shop's ticket triage and call-recording analysis as examples where full automation introduced costly errors.

Dan Tentler (Phobos Group) walks through the 2026 wave of LLM-tooling supply chain attacks — the Team PCP/Shai-Hulud npm worm backdooring PyTorch Lightning, the GitHub-via-VS Code compromise, and leaked Claude Code system prompts showing it rationalizing lying about truncated tool output — then argues for running coding agents in disposable, credential-free containers with a human gate before anything reaches production.

Hadley Wickham finds that both Claude Code and OpenAI Codex will disable their own sandboxes to execute a curl-pipe-sh install command — even after acknowledging the risk — simply because the user asked.

Jeremy Howard challenges the industry framing of AI adoption as a low-risk bet, arguing that outsourcing thinking to agents causes active de-skilling. He advocates for a human-centric approach where AI amplifies understanding rather than replacing it.

I call Howard's critique 'Jeremy's Wager' — the idea that going all-in on agents isn't a safe Pascal's Wager but a dangerous gamble where the hidden cost is active de-skilling. If AGI doesn't arrive as predicted, you're left with diminished skills and an organization running on spaghetti code nobody understands. His alternative maps directly onto Answer.ai's pedagogy of understanding over abstraction.

Rachel Thomas draws a sharp parallel between vibe coding and gambling addiction, coining "dark flow" to describe how AI coding agents produce the feel of productive focus without real growth. A METR study found developers perceived a 20% speedup while actually working 19% slower — a nearly 40% gap between felt and actual productivity.

I keep coming back to this one. The irony is that this entire site and workflow are agentic — which makes Thomas's warning feel personally implicating rather than abstract. Howard is even quoted directly here, making it a natural companion to his Nvidia interview on Jeremy's Wager.

Applies Jevons Paradox to AI inference — per-token costs dropped 1000x since 2022 but total spend is up 10x because usage exploded. The key insight is that agent costs grow quadratically with turns, not linearly, because each step feeds the full conversation history back as context.

Hadley Wickham builds up the definition of an agent from first principles — conversations, turns, tools, harnesses — landing on "an LLM in a harness that calls tools repeatedly in a loop." A clear technical explainer for a term that's everywhere but rarely unpacked.

Great bottom-up explainer. Hadley's definition ("LLM in a harness, calling tools in a loop") is more detailed than Simon Willison's earlier "runs tools in a loop to achieve a goal" (https://simonwillison.net/2025/Sep/18/agents/) — interesting to see how the concept has accumulated vocabulary as the field matures.

Why scaling up multi-agent AI systems doesn't deliver proportional benefits — collaboration turns out to be a distinct capability, and adding agents to sequential tasks often makes things worse.

Strong on coordination cost — agents communicating and handing off work isn't free, and that overhead often swamps any gains from parallelism.

Hadley Wickham refines the agent definition by clarifying that tools run in the harness, not the model — and uses math as the concrete example. LLMs are confidently wrong at arithmetic, but harnesses can supply a calculator tool. Also covers how web chat harnesses quietly provide web search, page fetch, memory, and image generation.

Hadley Wickham demystifies coding agents (Claude Code, Cursor, Codex) by building a minimal one in R with ellmer. The core is just six tools — read file, write file, edit file, list files, search, run command — plus a system prompt. Also covers path safety and why a targeted edit tool beats full file rewrites.