caddy-mcp is a Caddy plugin for exposing MCP servers that live on private networks. The private box dials out to Caddy over QUIC, Caddy serves it as a normal HTTPS endpoint. No inbound ports, no third party in the request path. Public Internet | v +--------------------+ | Caddy :443 | TLS, routing, middleware | reverse_proxy |
If you spent any time on React Twitter or LinkedIn lately, you saw three names everywhere: shadcn/ui, Radix, and Base UI. People talk about them like they compete with each other, but they don't really. Let me explain what each one actually is, and when you should reach for which. Before we compare anything, you need this idea. A normal UI library like Bootstrap or Material UI gives you components
All tests run on an 8-year-old MacBook Air. Most AI integration tutorials assume you're paying for API access. HiyokoLogcat is built entirely on Gemini's free tier — and designed so users bring their own free API key. Here's what's possible, what the limits are, and how to design around them. Gemini 2.5 Flash Preview: 15 requests per minute (RPM) 1,000,000 tokens per day 250 requests per day For a
There's about $400 of meat, milk, and miscellaneous condiments in my kitchen fridge at any given time. It runs 24/7, makes a quiet humming noise, and gives no indication when something's wrong until you open the door three days later and recoil. The freezer compartment is worse: a slow failure can defrost everything before you notice the puddle. I already had a TP-Link P110 smart plug on the fridg
All Four Sentinel-1 Satellites Are Now Live
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AI coding agents are getting better at writing code, but they still struggle with one thing that every real-world repository has: context. Not syntax. Not boilerplate. Context. That means understanding how a repo is structured, which commands are the right ones, how tests are run, what naming conventions are actually used, what tooling is configured, and what rules are implicit but never properly
Most multi-agent demos look impressive on stage. Then they hit production and fall apart. Here's the pattern: agents that "worked" in a Jupyter notebook start conflicting, retrying infinitely, or silently failing when other agents are involved. The root cause isn't the LLM. It's the orchestration layer. No structured handoffs — Agents pass messages as raw strings. Context gets lost. Intent gets mi