Run AI coding tools in autonomous loops with Ralph Wiggum. 11 tips for AFK coding: scope, progress tracking, feedback loops, and shipping code while you sleep.
Update - Someone just shared an existing free project that does this called Vibe-Kanban.
I just tried it. Super easy to get rolling.
1. Install via npx
2. Reference existing GitHub repo
3. Creating a task on the board and Claude starts working and streams output!
So. slick.
putting the Claude Agent SDK to the test with an Ad Agent
- finds competitors, learns from their ads
- prompts and generates on brand creatives
- packages it in a shareable report
I can run this autonomously or with a prompt. no node based workflows needed
https://t.co/xHeuWPuIk7
For our first big drop of the year, excited to bring you @trq212's full 2 hour workshop covering all of @AnthropicAI's agentic SDK (formerly known as Claude Code SDK).
By far the most popular workshop of AIE CODE!
I built a custom Claude Code command, /interview, to create bulletproof specs.
• Create a plan using plan mode
• Run the /interview command
• Claude asks 20-50 clarifying questions
• Claude updates the plan file based on your answers
Great for removing any ambiguity!
Agent-Skills-for-Context-Engineering/examples/llm-as-judge-skills at main · muratcankoylan/Agent-Skills-for-Context-Engineering
A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require e...
tl;dr: Anthropic recently introduced the idea of agent skills. Skills are simply folders containing a SKILL.md file along with any associated files (e.g., documents or scripts) that an agent can discover and load dynamically to perform better at specific tasks. We've added skills support to deepagents-CLI.
The Rise of Generalist Agents
General purpose agents like Claude Code and Manus have gained widespread adoption. While we might expect generalist agents to use many tools, a surprising tren
If you’ve built an agent, you know that the delta between “it works on my machine” and “it works in production” can be huge. Traditional software assumes you mostly know the inputs and can define the outputs. Agents give you neither: users can say literally anything, and the space
Over the past month at LangChain, we shipped four applications on top of the Deep Agents harness:
* DeepAgents CLI: a coding agent
* LangSmith Assist: an in-app agent to help with various things in LangSmith
* Personal Email Assistant: an email assistant that learns from interactions with each user
* Agent Builder: a no-code agent building platform powered by meta deep agents
Building and shipping these agents meant adding evals for each of them, and we learned a lot along the way! In this
Learn about our hierarchical Bayesian model for A/B testing AI agents. It combines deterministic binary metrics and LLM-judge scores into a single framework that accounts for variation across different groups
The best agent products aren't the most flexible, they're the most opinionated. Learn why agents need fewer knobs, not more, and how to design around model intelligence spikes.