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Nanosamur.ai – Ollama for Voice Models (STT)

Hacker News

Nanosamur.ai – Ollama for Voice Models (STT)

nanosamur.ai is a speech-to-tech platform that supports different models for realtime, semi-realtime, and batch transcription and provides a unified stack for robust speech processing, agentic workflows, and webhooks. You can run it locally on your computer via electron app + docker compose, but you can take the same stack and run it in cloud/k8s and/or on prem as it is built with scale in mind (it also has an observability stack built in). So in that sense it is more like Ollama + Ollama Cloud :) Currently it supports whisper, qwen asr, nemotron asr and parakeet tdt and I am adding other models. I am also adding Kserve+Triton integration for the batch transcriptions so as the MLOps can be abstracted away. Last couple of years I have been consulting for some organizations that work with sensitive data and have to keep them in air gapped environments and based on those experiences I built and open-sourced nanosamur.ai The easiest place to start is the main repo: https://github.com/nanosamurai/nanosamurai It is a docker compose starter setup, there are couple of services it uses, all are linked in the documentation; I used python for the voice ai services (see the xamurai monorepo) and java / clojure / clojurescript for the UI/BFF and some other services (yes, it is pg's fault i love lisps). Appreciate any feedback! Esp. from people working with speech infrastructure, self hosted AI etc. Also it currently sits at 6 stars, so feel free to star the repos if you like them :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para, organizations · Missing: reddit linkedin, podcasting, created
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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