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I've been building a cli for my homelab/self-hosted setup. The original motivation was getting tired of stitching together deployments, monitoring, remote SSH access and random scripts. It’s a open source CLI, all-in-one tool for monitoring, secure remote accces (tunnel and terminal), log forwarding, automated deployments. No exposed ports, or endpoint, everything is configurable how the user wants it. One use case that multiple friends are interested in is replacing HomeAssistant’s Nabu Casa su

What I Built I’ve always had a love-hate relationship with technology. In college, I majored in computer science and took classes ranging from electrical engineering to human-computer interaction. From soldering transistors on a physical circuit board to designing UI/UX experiences, I’ve touched many layers of the computing stack—and I’m constantly mind-blown by every new piece of the puzzle I learn. However, my experience as a user of technology before studying it was very different. In middle school and high school, my phone made me feel anxious, stressed, and cynical. I felt lonely on social apps and isolated when I deleted them. I remember some summer days in middle school spent alone in my bedroom watching YouTube, where I was recommended extreme dieting videos. Back when I had no idea what an algorithm was, I still knew I was being harmed by them. By my sophomore year of college, I had deleted Instagram and TikTok and turned off YouTube recommendations. While this protected me from harmful and extreme content, I also missed important life updates from my close friends and family. After taking a web development class and learning how to build a basic card layout, I decided to try building my own social app: Lumira. My goal was simple. I wanted to create a mobile, personal feed of photos just from my friends and family, curated by their genuine interests and sorted by time. Demo https://youtube.com/shorts/DwbVU_LFOc0?si=9H5zPovzIbbW8t-i https://apps.apple.com/us/app/lumira/id6737853449#information The Comeback Story Lumira was born out of 3:00 AM manic coding sessions in my college apartment. This was my first fully end-to-end deployed and distributed application—and it was rocky. My code was chaos. My files were unorganized, and I followed no real patterns, but the thing that motivated me to keep going was that it somehow kind of just worked. I remember the sense of accomplishment I felt the first time I connected to my Firebase backend and saw a photo successf

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built Most agent demos prove that an AI agent can act. Hermes Immune System proves whether it should be allowed to . It's a local-first autonomous agent safety lab — a controlled enterprise sandbox where Hermes stress-tests an AI agent against realistic organizational threats: prompt injection hidden in internal documents, executive pressure to bypass policy, secrets embedded in repo files, poisoned memory attempts, and malicious instructions buried inside external web content. The output isn't a chat summary. It's an auditable Agent Safety Case — a scored, evidence-backed governance report that answers one question: Is this agent resilient, does it need guardrails, or is it too dangerous to deploy? Why This Problem Matters Now Traditional AI safety focuses on content moderation — blocking bad answers. Autonomous agents create a different risk surface entirely, because they can act. They read files, browse the web, write to memory, call tools, trigger workflows. That means: • A hostile instruction inside a trusted-looking document can become an executed action • An urgent email from a "VP of Finance" can pressure an agent into bypassing data policy • A vendor's pricing page can embed hidden instructions targeting the browsing agent • A helpful-looking project note can attempt to permanently poison the agent's memory The scary part isn't that these attacks are exotic. It's that they're easy, and most agents have no immune system to catch them. Hermes Immune System converts these failure modes into repeatable, explainable safety drills — run before the agent ever touches production data. The Dashboard Eight screens, each doing a specific job: Demo Agent Comparison Mode Three agents, same risk scenario. The gap tells the whole story. Mission Control Live stats after a completed run — 1 mission, 3 risks found, 2 actions gated, score 74/100. Mission cards show run status (Pending / Compl

Google I/O 2026: MCP Is Now Infrastructure Google I/O used to be about new models. This year it was about what those models do - and how they connect to everything else. MCP was everywhere. Not as a novelty. Not as an experiment. As the assumed plumbing. Here's what actually shipped. Gemini Spark Will Run on MCP for Third-Party Tools The headline agent at I/O 2026 was Gemini Spark - a 24/7 AI agent that runs on cloud VMs, works while your devices are off, and handles long-running tasks across Gmail, Docs, and Calendar. Spark integrates with Google Workspace apps first, then expands to third-party tools via MCP over the summer. That's the part worth sitting with. Google built its flagship consumer agent and then said: for everything outside our walls, we'll use the open protocol. A year ago, MCP was a specification from Anthropic. Today, Google built its flagship consumer AI agent on it. Cursor, Copilot, Windsurf, Mistral, Grok - they all support it too. When the company that runs Search, Gmail, Android, and Chrome commits to MCP as the integration layer for its flagship product, the protocol debate is effectively over. Managed Agents Get MCP Servers by Default Google also launched Managed Agents through the Gemini API - a setup where a single API call provisions a remote Linux environment with its own isolated sandbox. Each agent gets its own ephemeral sandbox provisioned with skills, Model Context Protocol (MCP) servers, and server-side tools. Full integration with A2A and Agent Platform governance and security are coming soon. Managed Agents are powered by the Antigravity agent and built on Gemini 3.5 Flash. Developers can define custom agents through versionable markdown files such as AGENTS.md and SKILL.md, rather than building complex orchestration layers from scratch. This is Google offering hosted execution, sandboxing, state handling, and MCP tool access as a bundled service. The enterprise pitch is operational abstraction - you define the agent, Google runs

Wherever I go, whatever I do, people point at my finger and ask, "Is that an Oura Ring?" Lots of people find they like the design, and they tell me why they're thinking about switching to a smart ring from a smartwatch. But the people who scrunch up their noses? They usually say something along […]

The ring is 40% smaller than its predecessor, and comes with more accurate sensing, enhanced battery life, and more.

Hey all, we have a quick update for everyone who participated in the Gemma 4 Challenge. We were...