Mirror AI – LLM agent that takes action, not just chat

Hacker News

Hi HN, TL;DR Mirror AI is a cross-platform desktop action oriented LLM . Instead of just replying with text, it can run terminal commands, move files, hit APIs, send email/messages, schedule calendar events, query databases etc. - and chain those steps in one request. Why I built it Chatbots are great for answers but stop at the “OK, now go do it” moment. I was copy-pasting between ChatGPT and my shell/IDE/Gmail far too often, so on red-eye flights I hacked a bridge that lets an LLM execute tasks under a permission layer. Friends asked for it, feature-creep happened, and here we are. What it can do today - Files & Shell – read/write/rename/compress/execute files, run shell commands - Productivity – draft + send Gmail, create Google Calendar events, read/write Notion, post to Slack/Discord/WhatsApp (via Beeper) - Dev/Ops – query Postgres/Mongo, interact with GitHub, call AWS, curl any REST endpoint - Search/GenAI – web search (Perplexity), summarise PDFs, generate images/video, keep long-term memory - Extensible via MCP (Modular Command Protocol) to add your own skills - And a lot more Example prompt after you have added a few integrations (copy-paste): Search Reddit for discussion on LangChain streaming, summarise it, email me the summary, and block 30 min in Calendar tomorrow. OR Investigate my AWS account and tell me how to easily save money. Also check my MongoDB and make some proposition to optimize it. How it works - Desktop app (macOS, Windows, Linux) written in TS - No SaaS backend: everything runs locally; outbound calls only hit the model provider you configure (OpenAI, Claude, local Ollama, etc.) - Safety: every risky actions require approval from the user Status & roadmap - Free alpha download: https://themirrorai.com (bring your own API keys) - Planning to open-source core in the coming months after tidying up if there is enough interest - Up next: improved working memory, event-driven triggers for autonomous responses, and expanded integrations. Ask Kick the tires, try weird edge-cases, look for security holes, and tell me what hurts or what you wish it did. I will be in the thread checking the comments. Thanks! Sat

Visit product View on Hacker NewsApr 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, agent · Missing: agents, cursor, apple

99%99% 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

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

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: video, month, google · 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 · Strong signals: platform, calls · Missing: plus, intuitive, reviews

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

20%20% 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.

Incorrect prediction on native model

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