What we think about
We write about what we learn, how we work, and what we observe.
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Article Fetcher One sign error took down the papers built on it
OpenAI posted 722 AI-generated math manuscripts, then withdrew three the next day. A single sign error invalidated one paper and the two that were built on its construction.
The bill arrives in a channel the agent never reads
Cloud providers are shipping hard spend caps that pause projects instead of billing them. The change that matters: overspend becomes an error an agent can see, not an invoice a human finds later.
What the gate learns from the traffic it gated
Decision models went open weight two weeks after they launched, and the fine-tuning loop runs on your own captured traffic. Owning the gate means owning what its training data cannot see.
Three million sandboxes a day, and none of them serve users
DeepSeek published the sandbox plant under its agent training: three million isolated environments a day, none serving users. Training data for agents is runnable worlds now.
The instructions were on disk and the agent never read them
Claude Code silently skipped AGENTS.md when telemetry was off. The file sat on disk, the agent never read it, nothing failed loudly. Why we now canary-test what our agents actually load.
The user authorized the agent. The store did not.
Amazon blocked Meta's Muse from shopping on its site. Every delegated transaction has three parties, and the counterparty's consent is the one nobody asked for.
A schema cannot catch a confident wrong answer
A new model class returns typed decisions in milliseconds instead of prose in seconds. The type system is real progress. It also means a wrong answer now arrives clean, fast, and confidently scored.
The brakes have to live in the runtime, not in the news cycle
A fleet of agents running production work around the clock cannot be slowed by an industry announcement. The only throttle that matters is the one wired into the runtime.
A vendor's misuse casefile is a monitoring spec, not a press release
When the company behind a model publishes how that model gets abused, it is handing us a list of runtime checks to build. We treat it as intelligence, not reading material.