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Transcription and summarization. No app, no account. Just a phone call

Hacker News

Transcription and summarization. No app, no account. Just a phone call

Good morning. My name is Mark. I am the creator of Hello Notes. I get a lot of ideas pacing my house, taking walks, etc, but for a developer I have severe app and subscription fatigue. I created a service that turns the device I talk to the most into a transcriber. Here's how it works: * You call +1 (435) 562-9028 * You will hear a brief message telling you what to do and expect and how much time you have remaining (60 minutes to start with). * You hear a beep. Start talking. * Hang-up and you will receive a text message (the shorter the recording, the quicker the text message comes) with 3 keywords and a link to the transcription and summarization. Paying for the service is also simple; a Stripe Link page that requires a phone number. This adds time to the phone number provided. That time never expires. With LLM's, summarization and transcription are commoditized but the user experience is not. I wanted the facilitating technology out of the way so that I could quickly get access to the thing I wanted to do; transcribe what I say and provide me a summary including any action items. If you decided to give us a call. I thank you for your time. Have a great day. Service: https://hellonotes.net Try it out: tel://+14355629028 Example output: https://lnk.hn/g8tx95K

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Actual performance

1points
4comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, including · Missing: supports, reddit linkedin, podcasting
88%88% 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: user, stripe, notes · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
40%40% 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 · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · 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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