DomainTasker

X4

X402vps

Show HN: X402vps – Docker containers for AI agents, paid per hour with USDC

WA

What's AI's go-to, public or private healthcare?

Show HN: What's AI's go-to, public or private healthcare?

BE

Bento

Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness. To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it. Open it in a browser and then you can edit, present, print and save. Share it via email or via Airdrop and all they need is a browser to edit, present and also do live collab on the slides. Drop it in to Claude or ChatGPT to transform existing pptx files into Bento slides. There is no cloud involved, only an encrypted blind relay to allow for shared editing. The relay doesn't see any of the data. Check it out at https://bento.page/slides/ which takes you straight to the editor. Go to https://bento.page/guestbook/ to try out the live guestbook to experience share editing / collab. There is also a gallery with some sample decks on the website - https://bento.page/ All the code is MIT licensed and you can find it here - https://github.com/nyblnet/bento . I used reveal.js with several other libraries (including some homegrown ones), and Claude Code.

TH

threadfork

Hi HN, I built threadfork. It's an AI notetaker where the whole pipeline runs locally on Apple Silicon. Your microphone and system audio gets captured without a meeting bot and most importantly your meeting data doesn’t get sent or saved anywhere. I've been building in the privacy + local LLM space for the past several years (I started DataFog, an OSS PII detection library with 300K+ downloads); keeping all the processing and data on-device was a day 0 condition for me when building this out. Granola's updated privacy policy, effective later this week, keeps non-enterprise users opted into model training on "de-identified" data by default. Having built PII detection for a living, I'll say plainly: de-identifying meeting transcripts is really hard. Names are the easy part; deal terms, client details, and health disclosures are the transcript. For the lawyers, consultants, and therapists I've been talking to, the only version of this that holds up is the model never seeing the data at all. It's macOS 14+ on Apple M-Series computers (for now), 14-day trial, no credit card. I have a user running this on an M1 8GB so older machines welcome. $40/month after that. I use this product daily and you can expect regular updates. Welcome any/all feedback.

NE

Newrepublic

FBI Forced to Reveal New Details on How It Redacted Epstein Files