behat

Daughter loves getting these in the mail, and this paragraph is absolutely right!

>> That year, Parton explained that as well as improving child literacy, “it’s the fact they get recognised. They get this little book with their little name on it in the mail, and they feel special. They start taking pride in themselves, and they know that somebody out there is thinking of [them].”

Appreciate the detail in this and the previous post on creating internal benchmarks!

Have you all attempted finetuning smaller OSS models on your repos for coding?

well, at least they are not leaving to be MTS at Anthropic.

Thanks for writing this, and look forward to the one on procedural memory.

Seems like teams are encoding procedural knowledge in skills repositories, and I wonder if there’s additional utility from an auto created procedural memory layer

>> Superlog scans your codebase and infrastructure to add new alerts, metrics and dashboards, preventing tricky failure modes and observability decay.

This is interesting, and my prior belief here has been that this automates a one time set up, and perhaps a quarterly clean-up or reactive monitoring changes that people do today. Curious what your experience has been - do teams accept these ongoing maintenance PRs at a good rate?

For full disclosure / context: we work in a related space - investigation agents for production issues.

This is a very interesting read on failure modes of AI agents in prod.

Curious about this section on the system prompt change: >> After multiple weeks of internal testing and no regressions in the set of evaluations we ran, we felt confident about the change and shipped it alongside Opus 4.7 on April 16. As part of this investigation, we ran more ablations (removing lines from the system prompt to understand the impact of each line) using a broader set of evaluations. One of these evaluations showed a 3% drop for both Opus 4.6 and 4.7. We immediately reverted the prompt as part of the April 20 release.

Curious what helped catch in the later eval vs. initial ones. Was it that the initial testing was online A/B comparison of aggregate metrics, or that the dataset was not broad enough?

Do you mean how we connect to internal data? Today, you can connect any API endpoint to Relvy, so if you have internal business data / dashboards that you look at while debugging, Relvy can do the same if there's an API for it.

Most of our deployments are self-hosted, in which case the data stays locally (your chosen LLM provider exempted), if that's what you are asking.

Nice to see you here, Will! I’d generally recommend using open telemetry for instrumentation so that you keep the option of switching between telemetry vendors.

Re: runbooks, yeah even larger teams don’t have good ones to begin with. Relvy helps debug without runbooks as well - it might take longer to explore, but once you are happy with a particular investigation path the AI took, you can save it as a runbook for more deterministic future executions.

> They want extension to their agent. If a project tells me I have to use their interface or agentic setup, it's 95% not going to happen

Yes, there’s definitely friction there. It may be that the right form factor is that you trigger Relvy’s debugging agent via Claude code / Cursor .

Our early users are heavy on needing to look at the raw data to be able to review the AI RCA, so a standalone set up makes sense. Also, the dominant usage pattern is background agentic execution triggered by alerts, and not manual.