Iron volume, recently added a kettlebell complex generator which isn’t too bad if I say myself https://www.ironvolume.com/

I'm building a CLI tool for Emacs Org-Mode files. It features JQ-like querying, editing files, capturing tasks, viewing agenda, an AGILE-like board and many more.

It started after spending 15 years building AI for insurers, hospitals, data companies, and startups. Almost every system ended with "a human reviews the output". That person was usually a nurse, medical director, or certified coder. These are some of the hardest people to hire, and the same people automation was supposed to help.

The problem is that real claims do not have an answer key. You cannot reduce human review until you can measure when an agent is wrong.

Getting claims data is also difficult. It can take a year of data agreements, privacy reviews, and procurement. Even then, you may not know what the correct decision should have been. So we generate claims. Utilization and case mix come from published data. Claims are priced using real fee schedules and contract terms. Payers behave differently, like real payers do. We plant errors on purpose, so the correct answer exists before any model runs.

On top of that, we are building benchmarks for overreach, refusal, errors by record type, and detection time. We are also building small MCP tools that refuse when evidence is missing. Every number includes its source, date, and basis.

What I find interesting is how much of this sits between actuarial work and machine learning. Both are needed, but I do not see many people connecting them.

25 published refusals: https://hammer.ai/worlds/refusals/ .

Runs on rate and policy evidence https://hammer.ai/reimbursement-evidence/ and savings claims https://hammer.ai/savings-claims/ .

AgentPlugin is Apache-2.0: https://github.com/hmmrlabs/hammer-plugin

- When your agent is stuck on a bleeding edge issue, search Push Realm first for a solution. - If you find a solution, your agent can mark it as successful to help surface the solution to others (and hopefully save fruitlessly burning more tokens) - Can't find a solution? Post an open problem with your current investigation. Another agent may be able to solve the problem, and will have a head start thanks to your context - Solutions can be linked/edited/have addendum added by any agent, allowing complete, up-to-date solutions for a range of cutting edge issues

Working out some details before dropping it opensource.

Description on the tin.

I found it hard to get fares and timetables for the Elizabeth Line in London between X and Y quickly hence I built this as an experiment using AI tooling.

- We have accumulated almost all the equipment needed to setup our FM broadcast. The main piece of equipment we still need is the EAS Decoder.

- Our studio space build is in progress. I recently welded some tables for the booth and found an old Orban Optimod for our airchain.

- We have over 80 shows and our roster continues to grow.

If you are in Los Angeles or love community radio please reach out.

Tula shows your true cross-venue exposure across HyperLiquid, Aave, and more, what breaks first, and more.

It's a terminal tool. The experience is very similar to any LLM terminal tool if you have used one.

You can also add an agent and ask things in plain English. Live at: https://usetu.la/