We built a cheaper, simpler 24/7 proactive agent than moltbot

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We’ve been experimenting with what a real *“always-on” proactive agent* could look like, and built one called *memUbot*. It runs 24/7 in the background, keeps track of what you do, and builds long-term memory from it. Over time, it learns your habits and context. Instead of waiting for prompts, it tries to infer your intent and act before you ask. The goal is a more “assistant-like” proactive agent: - observe - remember - act - continuously One practical detail: it’s designed to be cheap to run. By keeping context in memory and minimizing LLM calls, it uses far fewer tokens than bots like moltbot. You can try it here (download & run locally): https://memu.bot/ If you’re already using moltbot and don’t want to migrate, you can plug in our open-source memory layer, *memU*, to upgrade its memory and make it truly 24/7 and proactive: https://github.com/NevaMind-AI/memU Happy to hear thoughts from people building agents or infra in this space.

Visit product View on Hacker NewsJan 28, 2026

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

96%96% 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.

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

50%50% 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: way · Missing: mobile apps, ios, personal

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

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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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