Control Your Telegram with AI

Hacker News

I put together a small side project that lets me use AI to work with my own Telegram account — searching chats, summarizing long threads, and sending messages without opening the app. It’s not a product or a framework. I mostly built it because I’m in a lot of noisy Telegram groups and wanted a better way to answer questions like “what did we decide last week?” or “did anyone mention X recently?” The setup uses a Model Context Protocol (MCP) server that connects Telegram to AI clients like Claude Desktop or a CLI. Once it’s running, you can just ask things in plain English and get results from your chats. I figured this might be useful to others dealing with similar Telegram overload, so I wrote up a step-by-step guide (linked above). Code is here if you want to look around: https://github.com/OrrBin/telegram-mcp Happy to answer questions or explain how it works.

Visit product View on Hacker NewsDec 28, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, mcp · Missing: mac, agents, macos

88%88% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

54%54% 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

48%48% 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

36%36% 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

19%19% 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

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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