u/snowman-london

Myrmex Hive is a decentralised, secure, and geeky agent orchestration framework built on top of the Model Context Protocol (MCP). It is designed to securely monitor, query, and manage distributed edge servers, Docker hosts, and Kubernetes nodes without exposing any ingress ports on your target systems.

https://github.com/olafkfreund/myrmex-hive

https://myrmex-hive.freundcloud.com

I did this using LLM's but they work, so no need to wait for the update. Use them at your own risk. Here they are anyway: https://github.com/olafkfreund/nocatalia-v5-plugins

Hi made a low effort ( with help from claude) google workspace client for the console. It gives you: calendar, task, people, mail, meet, drive, docs and much much more using the gog cli. Written in python. Easy to use and almost perfect. If you wanna try it or help me make it better? Please do.

Find it here: https://github.com/olafkfreund/gogmail

The part of agentic dev that gets demoed is "watch the agent write code." The part that actually decides whether you'd run it on a real repo is everything around that: can you govern what it plans, and can you trust what it tests?

I spent the last few months building the pipeline around those two problems instead of around code generation. Four services, each useful on its own, each handing off on a loop I call PARR (Prepare · Act · Reflect ·Review) :

- Prepare — PFactory. Planning layer in front of the coding agent. Grounds a plan in real org context (Kubernetes, cloud, Backstage), runs architecture/security/feasibility gates where every verdict is cited, and waits for human approval before emitting GitHub issues.

- Act — AIFactory. Spec-first. Coder implements in an isolated git worktree; nothing touches main until you merge.

- Reflect — TFactory. Generates tests across lanes and grades each on a 5-signal verdict (coverage delta, stability reruns, mutation kills, lint, semantic relevance), then posts a ranked triage to the PR.

- Review — CFactory. Control tower threading plan → code → test by GitHub issue number, with a copilot that explains state and proposes human-confirmed actions.

Two design choices this sub might find interesting:

1. Correlation by GitHub issue number is the whole spine.

2. The handback loop — failing tests route a correction request back to the coder agent (bounded closed loop, not a human re-prompting).

Honest about rough edges: each service runs well alone, but the full cross-service handoff is still being wired up. Solo-built, multi-provider, copilot is advise-and-confirm — never acts without a click.

Disclosure: my own project. Guided tours (real screenshots) for all four:

https://factory.freundcloud.com/#products

For people building multi-agent systems: how are you handling the verify/govern half? Curious if anyone else does a test→fix handback loop, and how you keep it from looping forever.