SuperVoiceMode universal voice layer for AI-assisted development

Hacker News

I wanted to see if I could one-shot build a dictation tool for my own use. I built it. Surprisingly easy, except for all the nuanced edge cases I ended up having to throw in since then. Then it kept growing. Wouldn't it be cool if i could talk to a coding agent directly, like the Star Trek computer? Oh, maybe like Iron Man (says everyone..). Why am i spinning up another terminal to run research on this? Hey Iris, can you run the research and throw it in the Vault? Let me know when you're done. Send a Slack message, check my emails, red team that PR request. Super Voice Mode is now the thing I use for work — coding, research, thinking out loud, orchestrating agents. It sits above every tool on your Mac and gives you one consistent voice layer across all of them. Claude Code, Codex, Gemini, local models, whatever's running. Transcription is local, no audio saved no internet needed. The whole thing was built spec-driven with AI coding agents — Claude Code, Codex, the lot. Engineering background, but the agents did the building. Which feels appropriate given what the product is. The whole thing was built spec-driven (a little vibing) with AI coding agents — Claude Code, Codex, the lot. Engineering background, but the agents did the building. Website design everything Experiment continues. This is early. There's more coming. Free tier, no email required. Download and it works. Happy to get into how the local transcription works, the model integrations, or the agent-driven build process.

Visit product View on Hacker NewsApr 28, 2026

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

99%99% 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: gemini · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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