So, the simulator only works if it knows what "correct" looks like and what kinds of failures to throw at the code, right? Who decided those two things? Was it the same agent that wrote the code? Were those human-written, or did they fall out of the formal specs?

You had three things that could each say "this is right", the end-to-end tests, the formal model, and how the old Go version behaved. When they disagreed, which one did you trust? Did the test ever turn out to be the thing that was wrong?

When the simulator caught something before release, was it usually the code that was wrong, or the definition of correct?

Feels like there's some really useful insights about best practices for coding with agents. I wonder if the Bun team used a similar approach if they still would have switched.

I’ve got a project right now, separate vector DB, Elasticsearch, graph store, all for an agent system.

When you say Antfly combines all three, what does that actually look like at query time? Can I write one query that does semantic similarity + full-text + graph traversal together, or is it more like three separate indexes that happen to live in the same binary?

Does it ship with a CLI that's actually good? I’m pivoting away from MCP. Like can I pipe stuff in, run queries, manage indexes from the terminal without needing to write a client? That matters more to me than the MCP server honestly.

And re: Termite + single binary, is the idea that I can just run `antfly swarm`, throw docs and images at it, and have a working local RAG setup with no API keys? If so, that might save me a lot of docker-compose work.

Who's actually running this distributed vs. single-node? Curious what the typical user experience looks like.