5MB Local macOS Transcriber App

Hacker News

Most transcriber apps were cloud-based, didn't store original audio, had confusing UI/UX, required logins, and costed a lot per month. Therefore this app is: - Low priced - No login - Native SwiftUI and MacOS APIs - Uses your own API keys for AI features I was in a bit of a catch-22 after college because I switched into Computer Science fairly late, and I had health and family issues. It was either apply to tons of jobs (and have the possibility of no job even after applying to hundreds of places) or build my skills and an income-generating app instead. Luckily LLMs got more advanced and I was able to build this without any prior experience in Swift in a few months. In theory the lowest price and highest quality app should win over time due to just outlasting everyone else. I'm kind of confused how a lot of recent SAAS are charging $20 per person, but maybe consumers have more money than I'd expect for some things. This app is just one step in a long-term vision I see society heading toward. Anyways, hope HN demands more from my apps! That gives me the drive to keep building.

Visit product View on Hacker NewsMay 31, 2025

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2points

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

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

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% 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

24%24% 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.

Incorrect prediction on native model

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