We haven’t been inching closer to users writing a half-decent ticket in decades though.

Solutions like https://bugherd.com/ might make the issue context capture part more accurate.

Maybe the agent can ask the user clarifying questions. Even better if it could do it at the point of submission.

This is because for some reason all agentic systems think that slapping cron on it is enough, but that completely ignores decades of knowledge about prospective memory. Take a look at https://theredbeard.io/blog/the-missing-memory-type/ for a write-up on exactly that.

What in the ChatGPT is this?

I’m getting pretty decent at spotting LLM text. This doesn’t contain the obvious tells at least.

It’s a self fulfilling prophecy. They’re extremely expensive so they must be good so they must be worth it. And because at that level measurement is extremely subjective it’s mainly about the vibes.

Like everything it’s just marketing.

I’m sorry but no attempt was made here. It contains all the red flags in the first few paragraphs.

It's a fair question. I've had problems with Gemini 3 due to rate limiting, and I've been working on this for a while now. I'm planning Gemini 3 for a follow up.

It’s not groundbreaking in a technological sense. The codebase is actually a bit of a monstrosity. But it removed guardrails that were artificially put on these LLMs which suddenly gave it an entire new dimension and the timing was right.

I built this because I was curious what Claude sends to the API, how subagents get work delegated and what contexts look like. Interesting to see how small part of the context the user interaction really is typically.

Pretty interesting to see how widely the token use differs across the models for the same task.