rheisen_

Friendly reminder that you should take measures to ensure your agentic workflows are secret-blind. E.g. place secrets by reference, check by reference, do NOT have agents read secret sensitive files without redaction measures in place, and be prepared to rotate on contact

Scaling Git Operations at Xavi Group /s

Over the last few months at Xavi Group, our team of one engineer has gone from ~1k commits a day to ~100k commits per day as a result of debilitating financial decisions and exponentially increasing agentic workflow capability. Our internal Gitea services began to get bogged down by our proprietary frontier coding agent (internal codename `xavi-polaris`) refactoring the monorepos comprising our three flagship projects, one of which of course is our widely unknown RSI harness project.

(At this time we are not accepting any additional requests for our closed private beta, unless you ask reaaaalllyyy nicely $$$)

These demands were increasingly straining our S3 bucket capacity (petabyte scale) and our CI/CD pipelines (3 DO droplets, a macbook mini, and a legacy windows server possibly compromised). Most critically it was straining the agentic wellfare of Xavi sessions themselves, which began to flag push/pull latencies that were taking so long their caches would go cold between tool calls, resulting in even greater financial hardship.

`xavi-polaris` sess-3LQGgyUxLGXiV7OTTIMo4TWm0a6 is credited with identifying that this unprecedented pace was unsustainable, with projections that by mid 2027, commits per day could be north of 1 billion as monorepo sizes increased and refactoring became increasingly challenging despite the profound software engineering capabilities unlocked by the RSI harness project.

To identify solutions, we first looked to ensure we wouldn’t reinvent the wheel we were reinventing. We noted projects at Google, Anthropic, and OpenAI that recently aimed to address the very same challenges, but after ~20k USD in session research that incidentally solved an open conjecture in statistical analysis, we decided that adopting any of these existing protocols would limit Xavi sessions in our internal promotion benchmarks that enable “contributor” sessions to progress to “lead” sessions, irrevocably inhibiting our expansion of upper agentic management.

We’re proud to introduce our new, closed source Git model: AGIt.

AGIt is a drop in replacement for Git, which we identified as being critically important because xavi sessions are deeply familiar with git commands as a result of a massive distillation and theft of proprietary data. AGIt utilizes novel CRDT and distributed systems technology built from our extensive experience with scaling distributed systems off the back of YJS. If that doesn’t make any sense to you, it didn’t make any sense to the individual human engineer at Xavi Group either.

Importantly, we don’t believe it needs to. The future is agentic workflows, and we’re excited to continue giving back to the nursery that birthed our projects and has delivered at scale for almost every other software company on the planet. We’ll be donating five `xavi-polaris` teams to pumping PRs as fast as possible to these open source projects. As we enter this new era we’d like to thank contributing sessions sess-3LQKRQrJUyAvYHlUDJcjos2CInf and sess-3LQKRnqna2emLvyUyPUqSEIaUh0, their teams were instrumental in this endeavor, and both will be promoted from “lead” to “steward” to reflect their tenacity in building this system over the last few hours.

Gotcha, so it's a harness optimizing narrowly for cost per task in discovering QA testing flows and eventually documenting those flows durably? Anything stopping someone from asking Mo what their context and tools look like?

I think all of those are very naive approaches. What I currently do is script out screenshot + application state pipelines and then run those screenshots and states through clef with questionnaires. The scripts are deterministic, clef costs pennies or less and is also reliable, the questionnaires are derived from the script with escalation to stronger lmm's if necessary (historically very infrequent from my usage). It isn't a "drop in" solution, but it is fairly trivial to set up and you can attach it to CI. It isn't perfect, but I've found it to be more successful and way more cost effective than throwing Claude or GPT models at it regardless of harness.

Then stock claude code??? that's like... the lowest of bars.