Unified memory across all LLMs

Hacker News

Problem: Switching between Claude, ChatGPT, and Gemini meant losing all context. Every time I changed models, I had to start from scratch. Solution: I built Hiperyon, a Chrome extension that creates unified cross-LLM memory. You chat normally, and your context transfers instantly when you switch models. Impact: Save time when juggling multiple LLMs No more repeating the same prompts Faster, smarter interactions across tools About me: I’m 19 and a solo founder. Built this in 2 months because context-switching between LLMs was killing my productivity. Curious how you manage switching between LLMs, and what features you’d want next!

Visit product View on Hacker NewsNov 27, 2025

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

93%93% 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: gemini · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, 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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% 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

19%19% 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, smart · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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