Li

Lilo – An open source personal AI assistant that lives in Telegram

Hacker News

Lilo – An open source personal AI assistant that lives in Telegram

Hi everyone, I wanted to share an open source Telegram-based personal AI assistant I built. It’s a model-agnostic agent with memory, skills, tools (like web search, browser user, etc.) operating in a persistent workspace. It also has support for scheduled tasks, and can build powerful HTML-based apps that live in the workspace. Here are some of my favorite use cases: * Send Lilo photos of food, and it tracks your calories. * Leave a voice note on your run to pause your supplements, and Lilo adds a TODO. * Have Lilo remind you when the Knicks game starts and even send you score updates every 5 minutes. * Have Lilo read an article out loud. Or give you a summary of the top stories on Hacker News. * Forward a Uber receipt, and pull it up later to file for a reimbursement at work. * Schedule a meeting with Jess next week, ask for suggestions on location, and next week, remind you to leave for the meeting on time. While Telegram is my most frequently used channel, Lilo can also be accessed by email, WhatsApp, a website and a mobile app. Email is particularly useful: I often forward receipts, invites, etc for Lilo to handle. How is this different from OpenClaw and Hermes Agent? Here are some reasons: - Runs on a remote machine/in the cloud rather than your local machine - your local data is safe, and the assistant is available 24/7. - More visual/ more GUI - Lilo comes with a default set of apps like a TODO list that you can interact with not just by text, but also with a GUI on the mobile and web app. - The Telegram integration is very comprehensive (handles replies, voice notes, reactions, etc.). I use Lilo a ton to manage my life. Would love to hear your feedback! Github: https://github.com/abi/lilo

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, model · Missing: agents, 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
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, hacker news, io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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.

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