Riverse – Local AI agent with memory that grows over time

Hacker News

Hey HN, I built a personal AI agent that runs locally and remembers you across conversations. The problem: ChatGPT/Claude memory is basically a flat list — a few facts, no timeline, no confidence levels, everything in the cloud. Switch platforms and you start over. Riverse uses what I call the River Algorithm — conversations flow through like water, important stuff settles like sediment into your profile, contradictions get washed away over time. There's an offline "sleep" process that consolidates memories, kind of like how human sleep works. v1.0 supports text/voice/image input, Telegram & Discord bots, pluggable tools, custom YAML skills, and MCP protocol. Everything stays on your machine. I also built a companion tool (RiverHistory) that imports your existing ChatGPT/Claude/Gemini chat history and extracts a starter profile — so your AI knows you from day one. Would love feedback. GitHub: https://github.com/wangjiake/JKRiver

Visit product View on Hacker NewsFeb 25, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

92%92% 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: supports, gemini · Missing: reddit linkedin, podcasting, created

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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

28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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