Mcp Plumbing
Webhooks are the event-driven plumbing underneath most of the internet's automation layer. Service A sends an HTTP POST to a URL you control whenever something happens — a payment completes, a commit lands, a form gets submitted. It's conceptually simple. In practice, the gap between "conceptually
Mcp Plumbing
The hardest part of connecting AI to anything is not the AI. It's not the model, the prompt engineering, the MCP server, the connector ecosystem, or whatever the latest abstraction layer calls itself. It's authentication. The boring, unglamorous, deeply annoying process of proving to one computer
Mcp Plumbing
Three ways to connect AI tools to external services: no-code platforms like Zapier and Make, direct API integration, and MCP servers. Each has a sales pitch. Zapier says you don't need developers. API integration says you don't need abstractions. MCP says you don't need
Mcp Plumbing
You've looked at the MCP server ecosystem. Nothing covers your internal API, or the existing server for your service is broken, or your use case is specific enough that no generic server fits. So you're thinking about building your own. Good — custom MCP servers are sometimes
Mcp Plumbing
There are hundreds of MCP servers available right now. The GitHub topic page for "mcp-server" returns a wall of repositories. Registries like Smithery list hundreds more. The ecosystem looks enormous — until you start actually using them. Then the number that work reliably, handle errors gracefully, and survive a
Mcp Plumbing
Nobody budgets for maintenance. The pitch deck shows the architecture diagram with clean arrows between services. The sprint plan covers building the integrations. The roadmap shows the features those integrations enable. Nowhere in any of these documents is a line item that says "keep this working after we build
Mcp Plumbing
Anthropic's Model Context Protocol — MCP — is an open standard for connecting LLMs to external tools and data sources. It launched in late 2024 and has since become the closest thing the AI tool ecosystem has to a shared plumbing specification. The pitch is "USB-C for AI"
Llm Platforms
Qwen is the best model family most Western developers have never used. Alibaba's open-weight LLM lineup has quietly become competitive with GPT-4-class models on coding and multilingual tasks, and the 2.5 series — released in stages through late 2024 and into 2025 — closed gaps that previously made it
Llm Platforms
Command R+ is Cohere's flagship model, and it is not trying to be ChatGPT. It's not trying to write your novel, debug your side project, or generate memes. It's trying to be the model that enterprises deploy for retrieval-augmented generation at scale — the one
Llm Platforms
DeepSeek is the Chinese AI lab that made the entire industry recalculate its assumptions about what good models should cost. When DeepSeek V3 and R1 shipped at prices that undercut OpenAI by 10-50x on comparable tasks, the reaction split cleanly: half the industry said "this changes everything" and
Llm Platforms
Grok is xAI's large language model, and the honest summary is this: it's a capable LLM with one genuinely unique feature — real-time access to X/Twitter data — wrapped in marketing that makes it hard to evaluate on its merits. Strip away the culture war positioning and
Llm Platforms
Mistral AI is the French lab that ships models faster than most companies ship blog posts, then barely mentions it. In a landscape dominated by OpenAI discourse and Anthropic positioning, Mistral has quietly built a model lineup that's genuinely competitive on price, speed, and multilingual capability — and almost