I missed a $2M deal due to bad notes, so I built this

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Last year I was in a crucial client meeting discussing a potential $2M partnership. I was so focused on appearing engaged that I missed the key technical requirements they mentioned. When I followed up with generic questions, they went with a competitor who "clearly understood their needs better." That weekend, I was furious. I decided to build something to never let this happen again. *Technical stack (built in 72 hours):* - Swift + AVAudioEngine for audio capture - OpenAI Whisper API for transcription (99%+ accuracy even in noisy rooms) - ChatGPT API for intelligent note structuring and action item extraction - Core Data for local storage (privacy-first approach) - Background processing to handle long meetings without draining battery The AI workflow: 1. Records audio locally in chunks 2. Streams audio to Whisper API for transcription 3. Raw transcripts fed to ChatGPT every 30 seconds to: - Identify key decisions and action items - Tag speakers and topics - Generate concise summaries - Flag follow-up questions Interesting technical challenges: - iOS kills background processes aggressively, solved with silent audio trick - Chunking audio without cutting words mid-sentence (used silence detection) - Handling poor audio quality in conference rooms (noise filtering + gain adjustment) - API rate limiting during long meetings (intelligent batching) Day 1: Core recording + Whisper integration Day 2: ChatGPT processing + note structuring Day 3: UI polish + App Store submission The irony? That same client reached out last month after seeing how prepared I am in recent meetings. We're now discussing an even bigger partnership. Would love your technical feedback!

Visit product View on Hacker NewsJul 25, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · 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: chatgpt, openai, notes · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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