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RecordMark – Annotate audio (pins, highlights, search)

Hacker News

RecordMark – Annotate audio (pins, highlights, search)

Hi HN! We built RecordMark to make voice notes easy to capture and easy to find later. What’s different • Drop pins while recording: text, tags, photos, highlights (time-stamped) • Live transcription + a concise summary from the transcript on Home • Zoomable waveform with auto-scrolling playhead and precise seek • Search across audio, transcript, and annotations • Folders to organize projects and move recordings • Clean audio: clip guard, input gain, optional auto-split • Flexible formats: WAV, M4A (AAC), MP3, CAF, AIFF Why we made it Most recorders help you capture, but not recall. RecordMark is built around recall: pin the moment, see words live, get a quick summary, and jump to the exact second. Privacy On-device by default. iCloud/Files export is optional. We don’t record phone calls. Pricing Free download. Pro unlock removes the time limit for long sessions. Looking for feedback — Anything rough or confusing? — Which export formats/workflows do you use most? — What should we optimize for meetings/interviews/lectures? App Store: https://apps.apple.com/ca/app/recordmark-annotate-audio/id67... Support / Privacy / EULA links are in the app and on our support page. Thanks for taking a look!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, apps, recordings · Missing: mac, agents, macos
70%70% 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
67%67% 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: io · Missing: https docs, excited, just released
37%37% 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
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
30%30% 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
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: 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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