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MuseBot–An AI Chatbot for Telegram, Discord, Slack, Lark, QQ, and More

Hacker News

MuseBot–An AI Chatbot for Telegram, Discord, Slack, Lark, QQ, and More

I built MuseBot, a multi-platform chatbot that integrates with LLM APIs to provide AI-powered responses. It supports platforms like Telegram, Discord, Slack, Lark (Feishu), DingTalk, WeCom (企业微信), QQ, and WeChat, so you can talk to your favorite model anywhere. MuseBot connects seamlessly with OpenAI, DeepSeek, Gemini, and OpenRouter models to make conversations feel natural, dynamic, and responsive. Key Features AI Responses – Intelligent chatbot replies using LLM APIs. Streaming Output – Real-time responses that feel conversational. Easy Deployment – Run locally or on any cloud server in just a few steps. Image Understanding – Send an image, and the bot can interpret and respond. Voice Support – Communicate using voice messages. Function Calling – Supports MCP-style function calls for extending capabilities. RAG Support – Enhances context understanding with retrieval-augmented generation. Admin Platform – Web-based platform to manage bots and configurations. Service Registration – Automatically register bot instances to a service registry. Metrics and Monitoring – Built-in Prometheus metrics for observability. MuseBot is built entirely in Golang, designed for performance, modularity, and easy extensibility. I’d love to hear feedback from developers working with chatbots, LLM integrations, or Go-based infrastructure — especially ideas to improve scalability and real-time performance.

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, gemini · Missing: reddit linkedin, podcasting, created
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, mcp, slack · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
51%51% 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
29%29% 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 · 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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