Resona – Finds connections across what you save

Hacker News

Built Resona ( https://useresona.com ) to solve a recurring frustration: you save something, think "this relates to something I read before" — and never find what. Those connections are where the aha moments live. Same underlying idea, different domain, months apart. What it does: - Save anything (Chrome extension or forward emails) — no folders, no organizing - Weekly digest surfaces connections across months of reading - Interactive graph ("Overtones") shows how ideas relate over time - Chat with your library — "What have I saved about scaling teams?" Tech: TypeScript monorepo, React SPA, Supabase (Postgres + Auth), OpenAI for embeddings and semantic search, Chrome extension. Would love feedback: Do the surfaced connections make you want to revisit what you saved?

Visit product View on Hacker NewsJan 28, 2026

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best fitHighest predicted score across all platforms for this description.

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% 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 · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: recurring, active · 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: chat · 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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