introducing spawn numbers. we gave Savi a new job: your game’s data scientist. she can figure out why players leave, help fix it, and check whether they’re having fun for longer.

I have not seen near enough excitement about MCP Events developers.openai.com/plugin… Right now, the only way most people's agents can wake up and do something is either A) cron, or B) you decide to message it Event-driven triggers are a huge deal

As it stands now, here is my hottest take: A software factory is actually an applied automation practice in the actual product. It's not some external thing. It's a product pattern where the product builds the product inception style. Any external system or dependency should be internalized into the product to enable the product to build the product. If you can't build the product in the product, from the product, then you are missing the mark. I don't know how to explain this more clearly at this stage. It's a bit mind-bending, but if you understand meta programming, macros and understand the Factorio reference that a factory should build the factory so it can build the factory. It might be a little bit easier to follow along.... >Your product is the factory< ghuntley.com/rad

This is an extremely good watch. The things that felt novel/interesting to me: 1. Lock down your agents Humans tend to like 'sharp knife' abstractions - that are powerful, but you can cut yourself if your use them wrong. Lauren says agents perform much better in extremely locked-down environments. Abstractions are designed so they can't screw up, and lint rules enforce it. They built a whole internal framework (Dune) to keep the agent on track. That helps optimise agents that don't have a large context window to work productively in your codebase. 2. Create verification infrastructure To trust the results of any agent, you either need to sit and watch it OR have it provide evidence of its improvement. This has always made sense to me, but Lauren really pushes it hard here: - Invest in custom CLI's that let the agent drive the app and measure its performance - Make the app "factory ready" from the get-go - i.e. deployable to an environment where the agent can mess about with it 3. Feature Maps Lauren's software factory (what she calls an 'outer loop') often requires the agent to break down vague bug reports from users and to turn those into potential fixes. To aid that, they built a 'feature map' of all the main features in their application, which describe exactly how the app is supposed to function. This has become essential for helping the agent navigate the codebase, and figure out quickly how things are supposed to work. It's maintained along with the codebase, and kept in sync via automations. This is the kind of documentation I usually warn against. It goes stale quickly and can confuse agents if it's not kept up to date. But Lauren's team are using it as critical navigation infrastructure, and it makes it possible for agents to explore faster and better - even on a large codebase. So it sounds like navigation docs like this are worth it if they enable new behavior. Banger talk - watch the whole thing on 2x.

the only practical way to generate at scale is with an api endpoint & coding agents. i say this from lived experience. claude code, codex, opencode, hermes, openclaw, muse, recoup, grok bot. you need a file system + bash.

We're adding support for AGENTS.md to Claude Code. Starting today in version 2.1.277, if there is no CLAUDE.md in a folder, Claude will check for and use AGENTS.md. You can toggle this behavior in /config.