Kortyx – Personal memory layer for every AI agent

Hacker News

Hey HN I’m Dima. I built Kortyx after a failed attempt to find product-market fit with an AI product-analytics startup. When it was time to pivot i was looking for other ideas and figured that i constantly struggled with getting users to provide "ideal" context to my agents. it resulted in weak prompts, degraded agent performance, etc etc. So i thought what is instead of asking every user to provide background, Kortyx captured the context directly and then served it at the right time. Kortyx is a desktop app (Windows + macOS) that quietly sees what you see and builds a private memory layer from your digital life. You can: Ask Memory – recall anything you’ve read, watched, or discussed with an exact snippet and timestamp. Memory Boost – superpower your prompts to other AI agents with detailed context about you. I’m launching today and would love feedback, especially from Mac users and anyone exploring agent ecosystems.

Visit product View on Hacker NewsSep 18, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, claude, model

98%98% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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 · Missing: arr, mrr, revenue

21%21% 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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