Fono – dictation/assistant for Linux in one Rust binary

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Hello everybody, I built this tool initially to dictate to my computer because the alternatives weren't very attractive to me. There was an Electron app that was eating a lot of my RAM and had too much GUI. There was another one with lots of separate dependencies and it looks like it's going to take a while to figure it out, and a project that was a bunch of Python scripts. Anyways, without giving it too much consideration, I decided to build my own... how hard can it be? :) So, the initial idea was that it's supposed to be light, self-contained and easy to use. And that escalated quickly because it turned out to be a very fun and rewarding project. And I am happy to present to you Fono. * One Rust binary, ~22 MB for CPU and ~60 MB for the GPU build, with basic glibc deps. Inside its belly, it has * Transcription: It does dictation through STT, optionally a cleanup with a small LLM and then it injects the text in the clipboard, at the cursor or both * Assistant: STT -> LLM -> TTS. * You can pick various backends or models for STT, cleanup, assistant, and TTS. I am planning to add more. * It can run completely locally or with different cloud providers. * Run it headless on your big PC, and all local clients find it through mDSN ... and I guess another nice thing that maybe some of you might want to check out. I am sure that there are bugs, but I would be happy to squash some if anybody would post issues on GitHub. And obviously, I am putting it here because I would like some honest feedback. Install: `curl -fsSL https://fono.page/install | sh`

Visit product View on Hacker NewsMay 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, model, computer · Missing: mac, agents, macos

83%83% 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: para · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

57%57% 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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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