Lore

Lore

Product Hunt

Cursor for your memory. 100% private, open-source & free.

Lore is a lightweight "second brain" that lives in your system tray. Summon it with a keystroke to capture ideas, notes, or tasks instantly. Why Lore? 🛡️ 100% Private: Your data never leaves your machine. No API keys, no tracking. 🧠 Local AI: Powered by Ollama + LanceDB for secure, offline-first RAG. ⚡ Instant Recall: Ask questions in plain language and get answers from your own history. Own your memory. 100% local. Zero cloud.

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Actual performance

121upvotes
9comments
Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, cursor, tasks · Missing: agents, macos, agent
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
25%25% 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
18%18% 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
17%17% predicted probability of success on BetaList, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
14%14% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Correct prediction on native model

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