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christiansafka

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christian@startpinch.com

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I would also describe haventerminal.com as making tmux pretty and usable! but without any setup steps, first class support for agents, and management of multiple machines. Disclaimer: It's a macOS app we just launched and it ships its own open source session persistence.

[dead] 5 months ago

We've published our learnings and results as part of our entry to the Interspeech 2026 Audio Encoder challenge. Code and model will be released on the same url

Over the weekend I built a website for teachers and parents to ask questions from Maria Montessori but couldn't fit all the relevant docs into context windows. The other RAG libraries were either doing too much (IMO) or involved connecting to an external Postgres which felt extra for what I was building. So I created this minimal RAG plugin, built for side projects. It is actually unrelated to nextjs, should run in any nodeJS env.

If anyone finds this useful I'll probably add some parsing for non-text formats like PDF, images, videos.

Repo: https://github.com/christiansafka/nextjs-rag

Yes, right now we're ranging from 0.75-3 seconds for the translation to start, and we're hoping to move the average time lower with our next updates. There will always be some limitation to how fast we can translate (different languages have different sentence structures and phrasing), but for livestreaming usually you'd have even a bit more wiggle room for the latency.

Also in case you're interested in the logistics of using us for livestreaming: If our current platform won't work for your use-case and you need to use OBS + a virtual camera, it's on our roadmap.

Fantastic question. Our opinion on this is that the higher-bandwidth we can make the communication, the more useful it will be. The reason we've moved from IRC->VoIP->Video is because of the efficiency of information transfer and additionally the empathic element of face-to-face conversation.

From the technical side, speech to speech models have more potential for accuracy (no explicit ASR, no audio->text information loss). We have a few options on mimic'ing nonverbal elements - we could decide when to naturally mix in the original audio, or train our end to end model to handle those nonverbal audio chunks. We'll be trying both but likely the first option on the sooner side!

Appreciate the feedback. On the video side, we currently synchronize it to play out with the translated audio (as often as possible), matching when you started speaking to the moment the translated audio starts. Mentioned in another comment but we're still working on audio mimicking (voice clone then inflection transfer). Our model does a lot that Google Translate doesn't, even just around translation, such as taking into account who you're talking to in the meeting and the conversation context. + we have to do it much faster, so smaller audio chunks at a time!

Thanks! We have a virtual camera on our roadmap as well, but by building the conferencing platform end to end we can optimize both latency and conversation UX to a much higher degree. We're also lucky to be building this now and not five years ago - there are some solid webrtc infra companies and open source projects to build on.