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.
The agent slice walked in full - the idea, the window, the extensions, the architectures, production, and the frameworks - with three reading orders for different readers.
Read it →One page that ties the AI library together - every article sorted by layer (foundations, models, protocols, agents, production, 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 →Answer engines write you a paragraph instead of ten links - but crawl, index, and rank still run underneath. What retrieval-augmented generation changes, and what it leaves exactly where it was.
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 →The six things sitting in the window on any given turn, what each one costs, and what gets thrown away when it fills up.
Read it →The engine underneath every AI product you use - what a token is, what the model is actually doing, why it invents things, and why it cannot remember yesterday.
Read it →Three words used as if they were interchangeable, plus the two labels that started causing trouble in 2026 - and when to use which.
Read it →Why a run costs many times what its prompt suggests, where the money goes, and the gap between an agent that works and one you can afford to run every day.
Read it →Orchestrator-workers, pipelines, fan-out, critique, routing - the five ways production systems wire agents together, and how to pick the least dynamic one that fits.
Read it →The move from one agent that does everything to several that each do one thing well - why teams make it, and what the handoffs cost.
Read it →A chatbot answer is one artifact you can read. An agent run is twenty hidden steps and a confident summary - what to measure, and how to catch quality sliding rather than breaking.
Read it →The failure modes that only show up once an agent leaves the demo - runaway loops, exhausted context, compounding errors, and the one that costs most: a run that reports success on work it never did.
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 free course catalog and its four proctored exams - what exists, what each credential costs, which track fits you, and the order to work through it in.
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 →Anthropic's agent for knowledge work - hand it a goal and it plans and runs the multi-step task across your files and tools, returning a finished deck, doc, sheet, or brief. Same engine as Claude Code, no terminal.
Read it →Two agents, one engine, two jobs - knowledge work versus software engineering. Where each lives, what it acts on, what it hands back, and how to tell which a task belongs to.
Read it →Bundle skills, MCP connections, hooks, and agents into one installable unit - what a plugin is made of, how a marketplace serves it, and how install and updates actually work.
Read it →The building blocks you extend an agent with - instructions it reads, live links to your tools and data, deterministic guardrails, and the plugin that bundles them. One overview, with links to the deep dives.
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 →Describe what you want and an AI designer builds it as a working artifact - mockups, prototypes, and decks your whole team clicks, comments on, and changes in one place.
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.
Read it →OpenAI's image model family - gpt-image-2 and its siblings, the two APIs that reach them, and what each quality knob actually costs.
Read it →The plain markdown file Claude Code reads at the start of every session - what belongs in it, where the four scopes live, how they load, and how it differs from auto memory.
Read it →No cards match that combination.