Agentic

My experiences of effectively using AI for personal workflows, with a focus on Claude Code.

spoiler: most tools will not even survive 90 days. this guy is literally giving away the exact 2026 playbook for AI Agents. he covers what to learn, what to build, and what to skip 👀 ↓ read this today

An important primitive for the new "AI agent era"

Deploying long horizon agents in production requires purpose-built infrastructure. This guide covers durable execution, memory, HITL, observability, and how deepagents deploy ships it all to production.

An overview of the architectural shift from Shallow Agents (Agent 1.0) to Deep Agents (Agent 2.0) and how to build complex AI agents that can handle multi-step tasks over extended periods.

When should agents use direct APIs vs CLIs vs MCP? Plus patterns for building MCP servers, context-efficient clients and pairing MCP with skills. https://t.co/JEogw5vWly

The leaked source revealed how a production coding agent is actually assembled. Here are the design patterns behind it.

A unified theoretical framework for structured test-time scaling, showing how topology compression, scope isolation, and decoupled verification—a three-layer structural decoupling—bypass the linear collapse of long-horizon reasoning across multi-agent systems, recursive architectures, and coding agents.

Agent’s system prompt is for identity, constraints, and persistent context. • What the agent is and what it should always or never do • How it should generally approach problems (reasoning style) • Persistent

this is a work in progress mental dump on interesting intersections between how we use and design a harness, implications for memory being accumulated over long timescales, and the search bitter lesson we can’t escape this

1 skill, 20 commands, and curated anti-patterns for impeccable frontend design.