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I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.

One of the biggest problems in my view for training an AI to do autorouting is the traditional grid-based representation of autorouting problems which challenges spatial understanding. But we know that vision models are very good at classifying, so I wondered if we could train a model to output a path as a classification. But then how do you represent the path? This lead me down the track of trying to build an autorouter that represented paths as a bunch of patterns.

More details: https://blog.autorouting.com/p/the-recursive-pattern-pathfin...

Discord cuts ties with identity verification software, Persona - https://news.ycombinator.com/item?id=47136036 - Feb 2026 (282 comments)

https://github.com/generalaction/emdash).

Emdash is an open-source and provider-agnostic desktop app that lets you run multiple coding agents in parallel, each isolated in its own git worktree, either locally or over SSH on a remote machine. We call it an Agentic Development Environment (ADE).

You can see a 1 minute demo here: https://youtu.be/X31nK-zlzKo

We are building Emdash for ourselves. While working on a cap-table management application (think Stripe Atlas + Pulley), we found our development workflow to be messy: lots of terminals, lots of branches, and too much time spent waiting on Codex.

Emdash puts the terminal at the center and makes it easy to run multiple agents at once. Each agent runs as a task in its own git worktree. You can start one or a few agents on the same problem, test, and review.

Emdash works over SSH so you can run agents where your code lives and keep the parallel workflow. You can assign tickets to agents, edit files manually, and review changes.

We also spent time making task startup fast. Each task can be created in a worktree, and creating worktrees on demand was taking 5s+ in some cases. We now keep a small reserve of worktrees in the background and let a new task claim one instantly. That brought task start time down to ~500–1000ms depending on the provider. We also spawn the shell directly and avoid loading the shell environments on startup.

We believe using the providers’ native CLIs is the right approach. It gives you the full capabilities of each agent, always. If a provider starts supporting plan mode, we don't have to add that first.

We support 21 coding agent CLIs today, including Claude Code, Codex, Gemini, Droid, Amp, Codebuff, and more. We auto-detect what you have installed and we’re provider-agnostic by design. If there’s a provider you want that we don’t support yet, we can add it. We believe that in the future, some agents will be better suited for task X and others for task Y. Codex, Claude Code, and Gemini all have fans. We want to be agnostic and enable individuals and teams to freely switch between them.

Beyond orchestration, we try to pull most of the development loop into Emdash. You can review diffs, commit, open PRs, see CI/CD checks, and merge directly from Emdash once checks pass. When starting a task, you can pass issues from Linear, GitHub, and Jira to an agent. We also support convenience variables and lifecycle scripts so it’s easy to allocate ports and test changes.

Emdash is fully open-source and MIT-licensed.

Download for macOS, Linux or Windows (as of yesterday !), or install via Homebrew: brew install --cask emdash.

We’d love your feedback. How does your coding agent development setup look like, especially when working with multiple agents? We would want to learn more about it. Check out our repository here: https://github.com/generalaction/emdash

We’ll be around in the comments — thanks!

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