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Pallium, a local-first memory sidecar for agent conversations

Hacker News

Pallium, a local-first memory sidecar for agent conversations

Built this as a side project to explore a problem I keep running into with agents: they lose useful continuity across threads and resumed work. Pallium is a local-first memory sidecar. It stores selected evidence, derives compact memory like decisions/findings/checkpoints, and returns evidence-backed memory blocks for follow-up questions. It is intentionally not a general knowledge base or vector DB. The focus is scoped memory for agent-mediated conversations. Repo: https://github.com/rore/Pallium

Visit product View on Hacker NewsMar 24, 2026

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

68%68% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

37%37% 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

36%36% 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

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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

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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