I posted Million Short 14 years ago to HN and the search engine has been largely the same since then. Now working on a pretty major overhaul: Launching our own decent sized independent index along with some (I think) cool & useful features.

The search requires a paid account currently although I plan to post a Show HN soon that won't require any account or payment.

Here: https://bookofrevenue.com

To my surprise, I think it's the first open-source revenue analytics for Stripe.

I worked at Stripe building both revenue recognition and analytics, and I always wanted to build an open-source version of those. Finally, I had the time to do it.

Bonus: Revenue is not MRR nor payments: https://medium.com/@tanin47/revenue-is-not-mrr-nor-payments-...

Parliament of Owls, Pounce of Kittens, Murder of Crows - the terms of venery are for animals.

But what about other things?

What do you call a group of Roses, or Snowflakes, or Lawyers?

Well, I couldn't figure it out, so I put it to a vote.

Simple ELO scoring, Endless mode is always on, and there is a Daily Contest for you to compete in (Wordle for wordsmiths, I guess).

I'd love to hear any and all feedback on it.

https://venery.palaich.net/vote

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.

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.

[1]: https://github.com/utkarsh261/pho

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

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