Three angles on the current generation of AI - the assistant most teams build on, the design tool from the same lab, and the engineers worth following as the agentic field evolves.
One page that ties the AI library together - every article sorted by layer (models, protocols, agents, extensions, tools), with a one-line reason and three reading orders for different readers.
Read it →The narrower cut - just the AI tools that show up in a developer's day-to-day. Coding with Copilot or Claude Code, designing with Claude, generating images, plus the model and protocol underneath.
Read it →The AI pair programmer that lives inside your editor - inline completions, a chat sidebar, agent mode, and a model picker that now spans OpenAI, Anthropic, and Google.
Read it →The two AI pair programmers most developers reach for today - one in the editor, one in the terminal. How they differ, when to pick which, and why a lot of teams use both.
Read it →The focused essay on the one axis that most decides which AI pair programmer fits which task. One tool ships clean parts; the other proposes whole designs.
Read it →A short, honest tour of the term everyone is using - what agentic AI actually means, how it differs from a chatbot, the loop that powers it, and where it earns its keep in real work.
Read it →Portable, composable units of expertise an agent can load on demand - the way you teach Claude (or any modern LLM agent) to do specialised work without rebuilding the model around it.
Read it →Anthropic's AI assistant - a family of large language models built for conversational reasoning, writing, coding, and analysis, with safety as a first-class design goal.
Read it →Anthropic's coding agent that runs in your terminal - reads your codebase, edits files, runs commands, and ships features. Claude with your shell at its disposal.
Read it →A real 2018 snake game - ASP.NET Core, gulp, .cshtml, scattered JavaScript - migrated to HTML5 + TypeScript + esbuild in a single Claude Code session. Twenty minutes of work, screenshot by screenshot.
Read it →Anthropic Labs' AI design tool - generate complete visual concepts, UI mockups, slides, and design assets from natural-language prompts.
Read it →Microsoft's open-source framework for building agentic AI applications - the successor to AutoGen, with first-class C# and Python SDKs and an Azure AI Foundry integration.
Read it →The six frameworks teams reach for when they move from one-off prompts to real agents - LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and Microsoft Agent Framework, lined up side by side.
Read it →The open standard for connecting AI assistants to the tools and data they need - one protocol every model speaks, one server every host can plug into.
Read it →Three building blocks people keep mixing up - the unit of action, the protocol that plugs it in, and the folder of know-how that tells the agent when to use it.
Read it →Google's image generation model - real-time, conversational image creation inside Gemini, the Gemini API, and the wider Google creative stack.
Read it →The capability tour - vibe edits, style transfer from a reference, legible in-image text, infographics on Pro, and the Fast / Thinking / Pro speed picker.
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