GenAI

- 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!

It's a keep-it-simple-stupid approach to AI coding that lets you ship while you sleep. So here's a full explanation, example code, and demo.

Ideas and notes

Hi, welcome to my website! This is where I share my thoughts on things that interest me, mostly tech, philosophy, history, and math.

Find the best Claude Code sub-agent prompts for your framework and language

• 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!

Shifting bottlenecks after a year of dramatic improvement in the quality of AI-generated code.

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...

Devin is cooked, chat.

Enable your agents to share universal knowledge, principles, and best practices that compound across all interactions.

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

Some findings of using Skills and the loadout pattern with MCP.

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

Notes on applied AI engineering, machine learning, and data science.

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

Deep agents can't be evaluated like simple LLM tasks. After building and testing 4 production agents over the past few months, we learned that evaluating deep agents requires: 1. Bespoke test logic for each datapoint — each test

Why does EDA fit so well with DDD principles?

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

A walkthrough of using the bootstrap fewshot and GEPA optimizers in DSPy

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.

Most software doesn’t break because of syntax errors or flawed if-else logic.

Let's buy the fridge next month, honey.

Claude can now discover, learn, and execute tools dynamically to enable agents that take action in the real world. Here’s how.

We're excited to introduce langchain-azure-storage, the first official Azure Storage integration package built by Microsoft for LangChain 1.0. As part...

Anthropic economic research on productivity gains