Motörhead, LLM Memory Server Built in Rust

Hacker News

Motörhead is an open-source project aimed at helping developers build and manage LLM-based chat applications. We built Motörhead in Rust and it currently handles user sessions and context windows with the following features: -Short-term memory management: Motörhead maintains chat session histories for n users, keeping track of messages sent and received. -Incremental summarization: When the message buffer reaches a maximum window size, Motörhead incrementally summarizes the content into a context key. This process involves taking an existing summary and the messages removed from the list, then generating a new summary. -Semantic search with Redis: All messages are indexed in Redis alongside their respective embeddings, enabling efficient retrieval through semantic search when required. If you’re a developer working with LLM-powered applications please feel free to submit a GH issue for Motörhead and thanks for giving this a read!

Visit product View on Hacker NewsApr 20, 2023

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

36%36% 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

13%13% 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 · Missing: web3, crypto, cryptocurrency

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

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