Show HN: iMessage is the best interface, so I built TypeScript SDK for it https://ift.tt/udo5HC6

Show HN: iMessage is the best interface, so I built TypeScript SDK for it I was building my personal agent, but I had to use Telegram, as it was the easiest platform to integrate. I wanted to build the harness and agent, not the infrastructure around these two, yet my UX was struggling. I stick to iMessage, and then I had to use another app to interact with my agent... So I spent a weekend on building a TypeScript SDK, that unifies how to interact with different iMessage providers (as there is no official way to use iMessage), so you can play around with them, without having to commit to one, nor with a need to rewrite half of the codebase to change the integration. It's open-source, you can check the repo here: https://ift.tt/Wf73Tp5 https://ift.tt/HvxZP2n July 18, 2026 at 03:47AM

Show HN: ride-recap, teaching a LLM my taste to automate cycling highlights https://ift.tt/YSdkzar

Show HN: ride-recap, teaching a LLM my taste to automate cycling highlights TL;DR: Turn hours of raw GoPro footage + a .fit file into a 60-second highlight reel with ride telemetry burned in. Every second of the ride is scanned by gemini-3.5-flash, as is the clip ranking + curation. The whole thing costs about $0.04 per ride and takes 10 minutes. Longer version: Road cycling has been my primary form of exercise, social outlet, therapy, wardrobe expense, and personality trait for almost a decade. I ride most weekends, usually out of Manhattan and up 9W. By the end of a ride I have hours of GoPro footage and one .fit file with per-second speed, power, heart rate, cadence, and GPS. Absolutely no one wants to watch 3 hours of being stuck behind Citibikes on West Side Highway. It's fun to look through past footage, identify the fun parts, and put together a narrative to remember. But since cycling is already time consuming, manually editing a highlight reel edit per ride is a nonstarter. So I built and open-sourced https://ift.tt/OULBHeA . I identify compelling moments from four separate sources: * Garmin telemetry via .fit file (speed, HR, power spikes, sprints, climbs) * Strava via API (popular segments) * Gemini vision scan + rating of individual frames * (optional) hand-labels via Streamlit app The fusion step has a LLM narrative pass pick 20 clips to best tell the story of the ride, boosting “cross-source agreements” (if a human label + telemetry + Strava + Gemini all agree that a clip is interesting), with greedy re-ranking and a crowding penalty to avoid clips too close to something already selected. Obvious in retrospect, but there’s no substitute other than looking at the clips Gemini selects, being highly opinionated about what should / should not be included, being specific enough about why, and repeating until you can’t think of anything more to improve. You cannot teach an LLM taste if you do not have taste yourself. https://ift.tt/JDfs96l July 18, 2026 at 02:41AM

Show HN: Hearth, an open-source local AI that runs your PC (files, apps, voice) https://ift.tt/oHSs54T

Show HN: Hearth, an open-source local AI that runs your PC (files, apps, voice) https://ift.tt/eWUFgr2 July 17, 2026 at 03:34AM

Show HN: Notvex – encrypted local-first notes with SQLCipher and XChaCha20 https://ift.tt/6E87zuA

Show HN: Notvex – encrypted local-first notes with SQLCipher and XChaCha20 Hi HN, Notvex is a local-first, encrypted note-taking that stores everything in a single .nvx file on your machine. No account, no server, no cloud dependency. The idea came about because I needed to store sensitive information—including security notes, private keys, and seed phrases—but I didn't want to rely on an external service that could leak my data in the event of a security breach or revoke my access if I wanted to use it tomorrow. I wanted something in between: the simplicity of a notes app with the security of a password manager, but fully offline and self-contained. So I started creating this app, which meets that need. The vault is a single .nvx file you own and control. SQLCipher (AES-256) encrypts the database at rest, XChaCha20-Poly1305 encrypts each note individually, and keys are derived with Argon2id, so the file is completely opaque without your password. It also supports an optional key file for two-factor protection and a BIP39 recovery phrase as a fallback. I'd like to share this project with HW in case you find it useful, and get some feedback from the community. THX :) https://ift.tt/Sfm2xTX July 17, 2026 at 02:01AM

Show HN: Deadwire-HTTPD A triple-threaded x86-64 assembly static server https://ift.tt/a9U4GWY

Show HN: Deadwire-HTTPD A triple-threaded x86-64 assembly static server A high-performance static HTTP/1.0 server driven by a bespoke triple-thread architecture (Supervisor, Acceptor, Engine) delivering nanosecond-level processing using raw x86-64 assembly and pure Linux syscalls. https://ift.tt/ladK3NX July 16, 2026 at 10:51PM

Show HN: Skillful, stop maintaining the same AI workflow in five places https://ift.tt/tKp7SiY

Show HN: Skillful, stop maintaining the same AI workflow in five places Hey all, Over the past year I noticed I was maintaining the same AI workflows in multiple AI tools such as Claude code and Codex. Whenever I improved one workflow, I had to remember everywhere else I'd copied it and most of the time I didn't... so I quickly went the "dotfiles" route where I symlinked everything together. Then I thought, this works for me since I'm used to my CLI but others might not have such a good time. Had a few talks with people, had some discussions, and in the end I ended up building Skillful. Instead of treating prompts or skills as something that lives inside a single AI tool, Skillful treats them as ordinary folders on disk. A skill consists of a SKILL.md or AGENT.md together with whatever else it needs, examples, scripts, screenshots, documentation, etc. The application lets you: - manage everything from one local library - install the same workflow into multiple AI tools using links instead of copies - Sync your library to multiple computers with Github - import public GitHub repositories containing skills - detect and repair broken installs Everything stays on YOUR machine. There are NO accounts, NO telemetry, and NO backend. It's essentially a GUI around a filesystem with symlinks, because I wanted the files to remain useful even if the application disappeared one day. I also started an "awesome" repository with reusable skill packs that anyone can publish or contribute to: https://ift.tt/O8ImYs3 I'd love feedback on a few things: Does the "one source, many AI tools" approach make sense for you too? Would you rather manage these as plain Git repositories? What AI tools should be supported next? If you already use reusable skills, how are you organising them today? Looking forward to hearing what you think! PS: I know that Vercel Labs also built `npx skills` whilst I was building Skillful, I think of this as an alternative :P https://skillful.md/ July 16, 2026 at 12:35AM

Show HN: AI-CLI – tiny C terminal assistant powered by local LLM https://ift.tt/cCkzTO4

Show HN: AI-CLI – tiny C terminal assistant powered by local LLM https://ift.tt/AkKEeWw July 15, 2026 at 12:46AM