Statistically tied across 676 clips. RambleFix's warm engine decode was 2.6× faster across 226 paired recordings.
Fast · private · free · built to run locally
Ramble. Release.
Text lands.
Hold the key, say what you need, release. RambleFix types it almost instantly, keeps the meaning, and stays on your Mac. Use voice to give AI fuller instructions. Choose Hindi + English when your thoughts mix languages.
Modern Mac, macOS 13+. English is smaller (~2 GB); English + Hindi is ~3.7 GB. View source. Fork it, build on top, or pick a language issue to help with the next route.
Install details
Current V0 builds support Apple silicon Macs: M1 or newer. Intel Macs are not supported yet.
2.6× fasterlocal engine in our tests
3-4× speedfor AI prompts by voice
Top-tier meaningEnglish meaning kept intact
$0 localno cloud, account, or subscription
Free and open source. DMG install flow. English and Hindi + English are the supported modes.
You say
okay so first check the launch list, then the security review, and keep it short
Listening
ENहिं
hold · speak · release
RambleFix pastes · meaning kept
First, check the launch list, then the security review. Keep it short.
Audio stays local
No screen recording
Pastes or keeps a copy
MIT licensed
Free.No payment, account, or subscription.
Private.Speech is processed locally, with no account.
Bilingual.Choose English or Hindi+English.
Compared with leading local tools
Fast without trading away meaning.
Across 40 saved English clips, RambleFix decoded in 153ms vs 1,105ms for whisper.cpp small.en, while keeping slightly more meaning.
RambleFix's Hindi+English mode kept 88.5% of meaning versus 66–70% for tested general local engines on the same 13 clips.
Dictation without ceremony
Use your voice wherever you would normally type.
For AI prompts, Slack, docs, email, and every awkward little text box in between.
Hold the hotkey
A tiny floating orb confirms RambleFix is listening. Nothing else gets in your way.
ENहिं
Speak normally
Ramble, use product names, or mix English and Hindi in the same thought.
Release to paste
Clean text lands at your cursor. No focused field? A compact copy option keeps the text safe.
Private by default
Your voice stays on your Mac.
Your voice and transcribed text never leave your Mac. Optional anonymous usage stats (counts & timings only) are on to help improve the app — turn them off anytime.
Read the security notes →Voice + text stay localSpeech is processed on your Mac, not sent to a transcription cloud.
Normal permissionsMicrophone to listen, Accessibility/Input Monitoring to paste.
No accountNo signup, subscription, workspace setup, or cloud transcript history.
Anonymous statsCounts and timings help improve the app, and can be turned off anytime.
Tested, not just claimed
Fast English. Strong meaning. Bilingual when you need it.
We run the same audio through RambleFix and popular local tools, then compare speed and meaning. Details and limits are public.
Statistically tied with Handy on English meaning across 676 clips, with 2.6× faster median engine decode across 226 paired recordings.
Versus 66–70% for tested local general engines on the same 13 mixed-language clips.
RambleFix transcribed and pasted with Wi-Fi off and no external network connection. No account or subscription required.
The simple version: RambleFix delivers top-tier local English meaning with a faster engine, and its Hindi+English mode keeps substantially more meaning than the general local engines we tested.
See sample sizes, exact scores, and claim limits
Same-WAV local benchmark. Every tested local engine hears the same audio. References use Gemini-cross-checked gold.
English meaning (n=676, updated 2026-08-18). RambleFix's shipped Apple Neural Engine path and Handy are statistically tied on the same saved WAVs and gold transcripts.
Engine speed (n=226). Warm median decode was 153ms for RambleFix vs 411ms for Handy: 2.68× measured, reported conservatively as 2.6×. Both models were warm and model loading was excluded.
Whisper comparison (n=40). Warm median decode was 153ms for RambleFix vs 1,105ms for whisper.cpp small.en: 7.2× faster. RambleFix meaning was 0.927 vs 0.908. whisper.cpp is an engine proxy for Whisper-based apps, not an app-level OpenWhispr result.
Voice input context. Public dictation tools commonly frame voice as a 3-4× input-speed advantage over typing: Wispr Flow cites 45 wpm keyboard vs 220 wpm Flow, and Willow cites 150 wpm speech vs 40 wpm typing. We treat this as market context for AI prompts, not a RambleFix-specific productivity guarantee.
What “faster” means. These are transcription-engine decode tests, not symmetric release-to-paste app tests. Handy is the paired source of the 2.6× figure. End-to-end time also includes capture, routing, pasting, and optional structure.
Claim boundary. The evidence supports English meaning parity plus faster local engine decode. It does not support “faster than every app end-to-end” or a superiority claim over paid cloud tools.
Hindi+English n=13. RambleFix 88.5% vs 66.1-70.2% meaning kept for tested local general engines; terms 70.1%.
Light cleanup targets fillers, punctuation, and casing without intentionally rewriting the thought.
Hindi+English keeps more meaning than the tested local alternatives and is available alongside English.
Wispr Flow comparisons remain directional: we have not run cloud tools on the same audio, so this is not a head-to-head superiority claim.
Read the public benchmark method →Built in public
Built with other builders.
The hard problem was not Hindi alone. It was keeping English fast and accurate while adding strong Hindi + English when your thoughts switch languages, all without cloud. We crowdsourced routes through Builderr.ai, tested them against the same audio, and shipped only the parts that improved verified outcomes.
View the Builderr challenge and final results →Sponsored the local Hindi + English challenge that helped crowdsource tested routes for RambleFix.
Helped improve product and technical terms in Hindi + English mode.
His Round 1 approach shaped how RambleFix remembers important words.
Their routing and faster follow-up ideas helped shape the roadmap.
Also studied
Rishchith, Sham, Harsimran, and Meet for fast-partial, comparison, and future route ideas.
Profiles and repositories link to GitHub, LinkedIn, and submitted work where shared. Builders can fork RambleFix, pick a language issue, and contribute corpus/model/eval evidence before opening a route PR.
English · Hindi+English · more languages next
What should we make bilingual next?
English and Hindi + English are ready. Vote for the language mix RambleFix should learn next. Builders who want to help can pick an issue, add corpus/model/eval evidence, and PR a route only when it improves the benchmark.
Tap once to vote anonymously. Want to build it? pick a language issue on GitHub.