Hacker News

https://www.macrumors.com/2026/07/10/apple-sues-openai/

https://x.com/__eknight__/status/2075643450196971805, https://xcancel.com/__eknight__/status/2075643450196971805

Prompt: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98...

https://www.nyc.gov/mayors-office/news/2026/07/mayor-mamdani...

Over the years I often thought that there should be a calculator for Algebra that works this way... something where you can drag terms around and cancel & distribute with gestures, but most importantly enter your own problems. It should also do more kinds of problems than DragonBox allowed. So I finally decided to build it.

https://dicroce.github.io/wyrm/home.html

Here's a video showing it: https://www.youtube.com/watch?v=_STbS4zvIlU. If you'd rather just play with it: there's a limited in-browser demo (real engine, a few example equations, no download) on the landing page — https://dicroce.github.io/wyrm/home.html.

The app can be found on iOS (https://apps.apple.com/us/app/wyrm-math/id6782342042) and as of this week on Google Play (https://play.google.com/store/apps/details?id=com.dicroce.wy...).

I also decided to open source the underlying math engine so others could build on it: https://github.com/dicroce/wyrm_math. My goal for the engine btw is to build it all the way up to Calculus.

Monetization is deliberately boring: the engine is free (MIT), and the polished gesture app is $4.99 once. No subscriptions, ads, accounts, or analytics.

I'd love feedback on the engine design — especially from anyone who's worked on CAS or proof-assistant-adjacent problems. And if you played DragonBox as a kid and wished it went further: this is for you!

I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks.

I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and analyze the cost and compare this spend to other models, then show you how much you could potentially save by switching those calls to a cheaper model.

When you run analyze you get an offline estimate priced against LiteLLM and gated by LMArena tiers. The general savings bands come from the research published by RouteLLM; but you can confirm this yourself using 2 commands --measure (shows the prompt-response output side by side) and --judge (a model chosen to do the comparisons). These send a sample of the prompts from the logs to the candidate models - either the default choice or set by you. This call goes directly to the model provider (never through me) as any normal LLM call would, and the response is shown and judged to either be better or worse or a tie.

It's deliberately small, because I tend to over complicate/think things sometimes: analyze + capture + a few commands, doing three jobs. Cost, quality visibility, routing recommendation.

Nothing is hosted. capture is an optional local proxy on your own machine, and there's no endpoint in the path of your data. You can confirm this by checking the source.

I included a demo so you can check out the output. It has a synthetic 56k call log (a month's worth) showing how costs can drop from $549.46 to $343.91 a month. A 37.4% saving.

Try it:

  uvx frugon analyze --demo

or

  uv tool install frugon

Then point it at your own logs.

All feedback is welcome, especially any on the routing/quality logic, or anything else, good or bad.

It’s been 10 years since we had the last leap second and it looks like we will get the first negative one soonish. Are systems ready for that?