Voice to Text, but User Friendly

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We started having issues getting our notes done after having conversations with many other builders in 2023 Therefore, my friend and I started building Sona Insight, an AI-powered transcription app (iOS). I know every "indie-hacker" is doing something similar now (which I want to speak about later in this post), but our app has some features (e.g. creation of summary templates, own AI backend built on top of OpenAI's whisper, and a unique architecture) that make it unique. Everything is auto-saved to your account and synced to the cloud (so when we release our web app you can see and edit everything on your desktop). As we built this for ourselves the goal never was to make a gigantic thing out of it as we worked on it in the evenings. But the app gets used successfully and we have a lot of subscribers compared to the marketing effort we invested till now. What I want to leave for discussion here, with the context given, is why on earth are that many people building something so similar? When we started posting about it on X, we only saw apps from 2018 that had another approach, however, in October and November of this year it felt like every second post was about an app built similarly. Looking into it they always seemed to only do one API call to OpenAI or Deepgram directly, but some of them didn't even work right.

Visit product View on Hacker NewsNov 29, 2024

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

97%97% 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: started, ios · Missing: supports, reddit linkedin, podcasting

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

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs

50%50% 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

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: friendly, user friendly, builder · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscribers · 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 · 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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