Context-aware Japanese furigana using Sudachi and ModernBERT
Show HN: Context-aware Japanese furigana using Sudachi and ModernBERT
LINQ CLI
Hey my name is Patrick, I’m a co-founder and CTO of Linq. We’re an API for sending and receiving iMessages (it does RCS/SMS too). It can do everything you can manually in iMessage (typing indicators, reactions, delivery emphasis, FindMy etc.) Our main customers are companies building conversational agents but we’re wanting to make it easier for developers to get started for free. To do that we built a CLI that lets you manage up to 20 contacts and gives you full API access for free. I’d love your feedback so we can keep improving it. Install via npm using: npm install -g @linqapp/cli Recently, I used the CLI to connect my Claude bot to WeWork & iMessage and haven’t had to use the WeWork app in a few weeks to book rooms. Github: https://github.com/linq-team/linq-cli Landing page: https://linqapp.com/cli Three constraints you should know about: 1. The free tier requires inbound-first (ie someone must text you before you text them) and has a limit of 20 contacts. This is to avoid spam. 2. The line is shared. This means a few other people will be using the same phone number as you, none of our paid production lines work this way. If you're testing enterprise grade our sandbox mirrors production, but has a 7 day time limit. The CLI is shared because there is a real infrastructure cost to us and we want to give this away for free. 3. We require an email to sign up. To avoid spam + our infrastructure cost. To be precise about "open source", it's the CLI. The whole client is in that repo, so you can read exactly what leaves your machine. The backend that delivers messages is closed.
Continue? Y/N: A 60-second game about AI agent permission fatigue
Show HN: Continue? Y/N: A 60-second game about AI agent permission fatigue
Open-Source AI Racing Harness
Hi I'm Dan from Elodin, making an open source real-time capable flight software simulation. For AI Grand Prix contestants, the wait for the Round 1 virtual qualifier simulation has been grueling. If you’re competing, check out our simulation harness to tide you over, built to match the published competition constraints and message format. It runs against real Betaflight, which we learned requires at least 1000 sensor samples per second to run real-time correctly. The competition warranted introducing a new feature to generate the camera sensor directly in the simulation loop. Typically people connect to Unreal or similar game engine to create a camera sensor, which works well but is very heavy. For the simple needs of this challenge, creating sample directly in the loop is very handy and easy to use. Happy to hear your feedback on this! While it's not fancy looking currently, it uses the Rust Bevy game engine, which should allow us to improve the visual fidelity quickly. We all should easily be able to shift our implementation to the published competition sim once it lands. Hope you enjoy and good luck!
Game Boy pixel pipeline explorer
I made a pixel pipeline explorer for the original Game Boy's Pixel Processing Unit (PPU). If you are implementing a Game Boy emulator or just interested in it then this might provide some help :)
Open-source Workspace (mail,docs,spreadsheet,drive) web/iOS
Show HN: Open-source Workspace (mail,docs,spreadsheet,drive) web/iOS
MCPs aren't enough, give Codex/Claude accurate memory of everything
Show HN: MCPs aren't enough, give Codex/Claude accurate memory of everything
Trychert
Hey HN! We’re Gary and Ian, and we’re building Chert (https://www.trychert.com/), an API for businesses to send, receive, and automate iMessage conversations at scale. Check out our demo: https://www.youtube.com/watch?v=SRdwvVxMMoI. We originally started by building products on top of iMessage because the blue bubble interface, typing indicators, and reactions made agentic conversations feel more human than ones on SMS/RCS. These included a one-shot iMessage agent builder that reached 2,000 users in one week and an automated iMessage outbound sequencer that sent thousands of outbound messages per day. The hard part is that iMessage does not have a native API like SMS/RCS. Sending and receiving iMessages requires a separate infrastructure that is difficult to set up and maintain, especially at scale. As we talked to more companies, we realized that the highest-volume use cases for iMessage were not B2C agents or even sales. They were things like customer service, missed-call text-back, cart abandonment, and inbound lead capture in verticals like home services, DTC brands, and property management that drive the highest volume. Furthermore, these companies often need additional support, such as custom infrastructure setup (e.g. contact card, area code, or local worker sessions), integration support with their existing SMS/RCS or voice agent systems, and a reliable way to scale their volume over time. We built Chert to be an infrastructure layer for businesses to handle iMessage conversations at scale. Businesses can use our API to send and receive iMessages programmatically, route replies to humans or agents, and integrate conversations into the systems they already use. To maintain stability across both outbound and inbound use cases, we built phone line health checks and SMS/RCS fallback systems. We also integrate with existing SMS/RCS systems, voice agents, CRMs such as Salesforce, HubSpot, and Attio, and tools like Slack. Finally, we let businesses reliably scale from a few test lines to hundreds of lines with automated line provisioning and a usage-based pricing structure. We’re working with companies doing conversational messaging in DTC, sports programs, property management, and home services at the scale of hundreds of lines. We’d love to hear your thoughts on this and other similar verticals where iMessage could be useful. All comments welcome!
Geomatic
All commands have the format `output = \func inputs` or just `\function inputs`. Points and scalars are built on the fly. Eg `\line a b` to an empty canvas creates points `a` and `b`, and joins them with a line. One can use broadcasting semantics similar to NumPy and PyTorch in a visual setting (imagine creating a list of circles where one dim corresponds to radius and another to the center). One can also use backpropagation, run gradient descent or visualize vector fields. Almost everything is reactive so changing a variable updates all of the downstream geometry. It also allows anyone to write and load their own visualization, which can be broadcasted and differentiated through.
Voxxy
Show HN: Voxxy – a minimal, fast voice-to-text app for macOS
A platform to find people to jam on side projects with
I have always found it funny how challenging it can be to find people to jam on side projects with. There are literally entire sub-Reddits where people post looking for someone to work on a project with. That is super inefficient. There are also newsletters for this (also pretty inefficient). Let's Jam is my attempt to solve this. This is NOT a cofounder matching platform. The idea is to connect people with ideas and skills so they can jam on them together. If they end up becoming cofounders, cool, but that is up to them. This is also NOT a place for freelancers to hunt job opportunities. Again, the platform is for people who have an idea or a skill and want to work on something together. How it works: > You either a) find a project and request to jam on it with that person, b) post a project and wait for someone to request to jam on it with you, c) claim an idea and wait for someone to request to jam on it with you. > Once someone requests to jam with you, you'll get an email, and you can vet them via LinkedIn or their past work. If you think they'll be a good fit then accept their request and they'll reach out to you. > That's it. Simple. Any feedback is greatly appreciated!
A satirical idle game about running an AI startup
I made an idle/clicker about running an AI startup. You start with a cat-vs-dog classifier and try to make it to AGI, but the NYT sues you for training data, Yann tweets that scaling is dead, and your fired ML engineer leaks the Slack.