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MiniClaw

The AI Agent That Lives On Your Desk. Not In The Cloud.

MiniClaw is a plug-and-play, hardware-accelerated local AI appliance that doesn't just answer prompts-it executes real-world workflows. Build websites, deploy apps, and automate pipelines locally with absolute privacy.

Top comment

Hey Product Hunt! 👋 I’m Martin Landa, creator of MiniClaw. As a software engineer for over 24 years, I’ve watched local AI agents evolve rapidly but running them privately always meant wrestling with terminal commands, complex Docker stacks and clunky setups. I built MiniClaw to change that: a dedicated hardware appliance that makes running private, local AI agents truly plug-and-play. 💡 What inspired me to build this? It actually started at home! I use local AI agents daily but when my 9-year-old son, Dave, got interested in coding, I wanted to give him a safe, dedicated environment to experiment with real code (beyond copy-pasting from web LLMs). Because MiniClaw was designed from day one with multi-user access and a local network shared drive I was able to spin up an isolated agent just for him. Seeing him build real projects in VS Code alongside his "AI tutor" showed me just how accessible local AI should be. 🚨 What problem are we solving? Setting up local AI agent environments usually comes with a huge setup barrier and unpredictable cloud costs. MiniClaw solves both: - Zero-stress setup: It comes with a complete pre-configured AI Agent - including a ChatGPT-style chat, file browser, SSO security, and Portainer for Docker management. - Cost savings: Instead of paying heavy, ongoing SaaS/API subscription fees, MiniClaw runs locally for a fraction of the cost. Workflow integrations: Out-of-the-box support for messaging apps like WhatsApp, Telegram and Slack, plus automated ticketing drafts for support teams (in business module). 🔄 How did the approach evolve? Originally, MiniClaw started purely as a developer tool for my own terminal-heavy workflow. But as friends, team members, and even my family started using it, I realized that simplicity is the ultimate feature. We shifted our focus from just building power features to making the initial setup practically effortless. We spent months refining the dashboard, local testing and user isolation so that anyone - from experienced devs to a 9-year-old learning to code, could unbox it and start delegating tasks in under 5 minutes. Dave and I are super excited to share MiniClaw with the PH community today! I’d love to hear how you currently manage your AI agent stacks or answer any questions you have. What’s your current go-to local setup? Let me know below! 👇

About MiniClaw on Product Hunt

The AI Agent That Lives On Your Desk. Not In The Cloud.

MiniClaw was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #124 on the daily leaderboard. MiniClaw is a plug-and-play, hardware-accelerated local AI appliance that doesn't just answer prompts-it executes real-world workflows. Build websites, deploy apps, and automate pipelines locally with absolute privacy.

On the analytics side, MiniClaw competes within Hardware and Artificial Intelligence — topics that collectively have 487.3k followers on Product Hunt. The dashboard above tracks how MiniClaw performed against the three products that launched closest to it on the same day.

Who hunted MiniClaw?

MiniClaw was hunted by Martin Landa. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.

For a complete overview of MiniClaw including community comment highlights and product details, visit the product overview.