agents
![Brian Piercy](data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="%230A66C2"><path d="M20.447 20.452H16.89v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433a1.982 1.982 0 1 1 0-3.964 1.982 1.982 0 0 1 0 3.964zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/></svg>)
Learn how to set up Nanobot, connect it to WhatsApp, and power it with OpenAI GPT-5.3-Codex for a practical, always-on AI agent.
Here's the handout I prepared for my NICAR 2026 workshop "Coding agents for data analysis" - a three hour session aimed at data journalists demonstrating ways that tools like Claude โฆ
AI writes the code now. The skill that matters is controlling what it builds.
Quick note before you jump in:
Alibaba on Monday unveiled a new artificial intelligence model Qwen 3.5 designed to execute complex tasks independently, with big improvements in performance and cost that the Chinese tech giant claims beat major U.S. rival models on several benchmarks.
Peter Steinberger is the creator of OpenClaw, an open-source AI agent framework that's the fastest-growing project in GitHub history.Thank you for listening ...
As AI agents move into production, teams are rethinking memory. Mastraโs open-source observational memory shows how stable context can outperform RAG while cutting token costs.
I just woke up Claude Code Agent Swarm on local Qwen3 Coder Next.
Goose, Blockโs open-source AI coding agent, is emerging as a free alternative to Anthropicโs Claude Code, as developers weigh offline control, rate limits, and the rising cost of AI coding tools.
Anthropicโs Cowork brings Claude Codeโstyle AI agents to the desktop, letting Claude access and manage local files and browse the webโboosting productivity while raising new security and trust risks.
New from Anthropic today is Claude Cowork, a โresearch previewโ that they describe as โClaude Code for the rest of your workโ. Itโs currently available only to Max subscribers ($100 โฆ
Itโs a general-purpose AI agent. And itโs already a pretty good knowledge worker
The AI industry has a trust problem that mirrors a paradox Daniel Kahneman identified decades ago in human decision-making: people
A step-by-step practical guide on building AI agents using Gemini 3 Pro, covering tool integration, context management, and best practices for creating effective and reliable agents.
Amazon sued Perplexity this month over its Comet browser, which uses AI agents to do online shopping on your behalf. This is the first major front in the war over who gets to browse the web.
A practical guide to working with AI coding agents without the hype.
Plus prompt injection attacks against Sora 2 cameos and notes on DSPy and Litestream 0.5.0
A Coding Guide to Build an Autonomous Agentic AI for Time Series Forecasting with Darts and Hugging Face. A Step by step guide
AI agents are putting headless browsing back in the spotlight. That raises questions for publishers: How much traffic is real vs. automated?
We tested OpenAIโs ChatGPT Agent, currently only available via its $200-per-month Pro subscription.
ChatGPT agent System Card: OpenAIโs agentic model unites research, browser automation, and code tools with safeguards under the Preparedness Framework.
I taught myself how to build RAG + AI Agents in production. Been running them live for over a year now. Here are 4 steps + the only resources you really need to do the same. โฆ Ugly truth: most โAI Engineersโ shouting on social media havenโt built a single real production AI Agent or RAG system. If you want to be different - actually build and ship these systems: hereโs a laser-focused roadmap from my own journey. .. ๐ ๐ฆ๐๐ฎ๐ฟ๐ ๐๐ถ๐๐ต ๐ณ๐๐ป๐ฑ๐ฎ๐บ๐ฒ๐ป๐๐ฎ๐น๐ Because no matter how fast LLM/GenAI evolves, your ML & software foundations keep you relevant. โ Hands-On ML with TensorFlow & Keras: https://lnkd.in/dWrf5pbS โ ISLR: https://lnkd.in/djGPVVwJ โ Machine Learning for Beginners by Microsoft (free curriculum): https://lnkd.in/d8kZA3es โฆ 1๏ธโฃ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐๐ ๐ & ๐๐ฒ๐ป๐๐ ๐ฆ๐๐๐๐ฒ๐บ๐ โ Learn to build & deploy LLMs, understand system design tradeoffs, and handle real constraints. ๐ Must-reads: โ Designing ML Systems โ Chip Huyen: https://lnkd.in/guN-UhXA โ The LLM Engineering Handbook โ Iusztin & Labonne: https://lnkd.in/gyA4vFXz โ Build a LLM (From Scratch) โ Raschka: https://lnkd.in/gXNa-SPb โ Hands-On LLMs GitHub: https://lnkd.in/eV4qrgNW โฆ 2๏ธโฃ ๐๐ผ ๐ฏ๐ฒ๐๐ผ๐ป๐ฑ ๐๐ต๐ฒ ๐ต๐๐ฝ๐ฒ ๐ผ๐ป ๐๐ ๐๐ด๐ฒ๐ป๐๐ โ Most demos = โif user says hello, return hello.โ Actual agents? Handle memory, tools, workflows, costs. โ AI Agents for Beginners (GitHub): https://lnkd.in/eik2btmq โ GenAI Agents โ build step by step: https://lnkd.in/dnhwk75V โ OpenAIโs guide to agents: https://lnkd.in/guRfXsFK โ Anthropicโs Building Effective Agents: https://lnkd.in/gRWKANS4 โฆ 3๏ธโฃ ๐ฅ๐๐ ๐ถ๐ ๐ป๐ผ๐ ๐ท๐๐๐ ๐ฎ ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐ Real Retrieval-Augmented Generation requires: โ Chunking, hybrid BM25 + vectors, reranking โ Query routing & fallback โ Evaluating retrieval quality, not just LLM output โ RAG Techniques repo: https://lnkd.in/dD4S8Cq2 โ Advanced RAG: https://lnkd.in/g2ZHwZ3w โ Cost-efficient retrieval with Postgres/OpenSearch/Qdrant โ Monitoring with Langfuse / Comet โฆ 4๏ธโฃ ๐๐ฒ๐ ๐๐ฒ๐ฟ๐ถ๐ผ๐๐ ๐ผ๐ป ๐ฆ๐ผ๐ณ๐๐๐ฎ๐ฟ๐ฒ & ๐๐ป๐ณ๐ฟ๐ฎ โ FastAPI, async Python, Pydantic โ Docker, CI/CD, blue-green deploys โ ETL orchestration (Airflow, Step Functions) โ Logs + metrics (CloudWatch, Prometheus) โ Move to production: https://lnkd.in/dnnkrJbE โ Made with ML (full ML+infra): https://lnkd.in/e-XQwXqS โ AWS GenAI path: https://lnkd.in/dmhR3uPc โฆ 5๏ธโฃ ๐ช๐ต๐ฒ๐ฟ๐ฒ ๐ฑ๐ผ ๐ ๐น๐ฒ๐ฎ๐ฟ๐ป ๐ณ๐ฟ๐ผ๐บ? โ Stanford CS336 / CS236 / CS229 (Google it) โ MIT 6.S191, Karpathyโs Zero to Hero: https://lnkd.in/dT7vqqQ5 โ Google Kaggle GenAI sprint: https://lnkd.in/ga5X7tVJ โ NVIDIAโs end-to-end LLM stack: https://lnkd.in/gCtDnhni โ DeepLearning.AIโs short courses: https://lnkd.in/gAYmJqS6 โฆ ๐ฅ ๐๐ฒ๐ฒ๐ฝ ๐ถ๐ ๐ฟ๐ฒ๐ฎ๐น: Donโt fall for โbuilt in 5 min, dead in 10 minโ demos. In prod, itโs about latency, cost, maintainability, guardrails. โป๏ธ Let's repost to help more people on this journey ๐
Learn how to build your own agentic AI application with free tutorials, guides, courses, projects, example code, research papers, and more.
TL;DR:ย I developed a simple, open-source benchmark to test if LLM agents follow high-level safety principles when they conflict with a given task accโฆ
Discover how Anthropic approaches the development of reliable AI agents. Learn about our research on agent capabilities, safety considerations, and technical framework for building trustworthy AI.
Design, test, and deploy multi-agent systems in hours using the powerful agentic frameworks.
Confused by AI agent frameworks? This article makes sense of A2A and MCP.
โItโs not that hard to build a fully functioning, code-editing agent.โ Thorsten Ball
A blog post covering tips and tricks that have proven effective for using Claude Code across various codebases, languages, and environments.
Building a fully functional, code-editing agent in less than 400 lines.
Q-learning is a model-free reinforcement learning algorithm that enables agents to learn optimal actions through interaction with their environment.
Check out this comparison of 5 AI frameworks to determine which you should choose.
In our previous tutorial, we built an AI agent capable of answering queries by surfing the web. However, when building agents for longer-running tasks, two critical concepts come into play: persistence and streaming. Persistence allows you to save the state of an agent at any given point, enabling you to resume from that state in future interactions. This is crucial for long-running applications. On the other hand, streaming lets you emit real-time signals about what the agent is doing at any moment, providing transparency and control over its actions. In this tutorial, weโll enhance our agent by adding these powerful
Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as โthe study and design of rational agents.โ