I built a local-first macOS transcriber

Hacker News

Hi HN, I'm Alex. I built inkoscribe because I needed a transcription tool I could actually trust with sensitive meetings, interviews, and 1:1s — without uploading audio to anyone's cloud. It's a macOS CLI that does real-time speech-to-text fully on-device: brew tap uohzxela/inkoscribe brew install inkoscribe inkoscribe -s sys # captures system audio (meetings, calls, videos) inkoscribe -s mic # captures your microphone Requires macOS 13+ on Apple Silicon (M1/M2/M3+). A few things I cared about while building it: - Zero network calls. Your audio never leaves the machine. - Real-time, word-by-word output — not batch. - No accounts, no config, no Electron. Just a binary you brew install. It's my daily driver for work calls. Happy to answer questions about the model choice, latency tradeoffs, capturing system audio on macOS (ScreenCaptureKit was… an adventure), or anything else. Feedback and bug reports very welcome.

Visit product View on Hacker NewsMay 22, 2026

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

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 · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

46%46% 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: calls · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

38%38% 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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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