llms-agents
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"How a personal AI agent built on markdown skills lets a frontier model teach smaller, local models to do real work, without retraining."
The AI coding agent field in 2026 is more capable, more fragmented, and harder to benchmark than it looks. Claude Code leads on code quality at 87.6% SWE-bench Verified. GPT-5.5 tops Terminal-Bench at 82.7%. But the benchmark OpenAI itself declared contaminated in February 2026 is still being used to rank these tools — including by the labs publishing their own scores.
Anthropic has launched “dreaming” for AI agents, giving Claude new self-improving tools designed to boost enterprise automation, reliability, and scale.
LlamaIndex CEO Jerry Liu argues the framework era is over: agent loops are now capable enough that context quality is the real competitive edge.
An important primitive for the new "AI agent era"
Learn faster, build smarter, and unlock the full power of Claude Code through real examples, reusable templates, prompts, workflows, subagents, and system design.
How coding agents use tools, memory, and repo context to make LLMs work better in practice
What actually happens when you type a message into Claude Code? The agent loop, 50+ tools, multi-agent orchestration, and unreleased features, mapped from source.
Skimming the leaked Claude Code TypeScript snapshots suggests that much of its coding performance comes from the surrounding software harness, including repo context, tooling, caching, memory, and subagents.
How to Build Advanced Cybersecurity AI Agents with CAI Using Tools, Guardrails, Handoffs, and Multi-Agent Workflows
Build an AI weather agent in 40 lines of Python using Hugging Face's smolagents library. Learn to create tools, connect LLMs, and run autonomous tasks.
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 …
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. Mastra’s 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, Block’s open-source AI coding agent, is emerging as a free alternative to Anthropic’s Claude Code, as developers weigh offline control, rate limits, and the rising cost of AI coding tools.
Anthropic’s Cowork brings Claude Code–style AI agents to the desktop, letting Claude access and manage local files and browse the web—boosting productivity while raising new security and trust risks.
New from Anthropic today is Claude Cowork, a “research preview” that they describe as “Claude Code for the rest of your work”. It’s currently available only to Max subscribers ($100 …
It’s a general-purpose AI agent. And it’s 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 OpenAI’s ChatGPT Agent, currently only available via its $200-per-month Pro subscription.
ChatGPT agent System Card: OpenAI’s 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. … Ugly truth: most “AI Engineers” shouting on social media haven’t built a single real production AI Agent or RAG system. If you want to be different - actually build and ship these systems: here’s a laser-focused roadmap from my own journey. .. 🚀 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 Because no matter how fast LLM/GenAI evolves, your ML & software foundations keep you relevant. ✅ Hands-On ML with TensorFlow & Keras: https://lnkd.in/dWrf5pbS ✅ ISLR: https://lnkd.in/djGPVVwJ ✅ Machine Learning for Beginners by Microsoft (free curriculum): https://lnkd.in/d8kZA3es … 1️⃣ 𝗠𝗮𝘀𝘁𝗲𝗿 𝗟𝗟𝗠𝘀 & 𝗚𝗲𝗻𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 → Learn to build & deploy LLMs, understand system design tradeoffs, and handle real constraints. 📚 Must-reads: ✅ Designing ML Systems – Chip Huyen: https://lnkd.in/guN-UhXA ✅ The LLM Engineering Handbook – Iusztin & Labonne: https://lnkd.in/gyA4vFXz ✅ Build a LLM (From Scratch) – Raschka: https://lnkd.in/gXNa-SPb ✅ Hands-On LLMs GitHub: https://lnkd.in/eV4qrgNW … 2️⃣ 𝗚𝗼 𝗯𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗵𝘆𝗽𝗲 𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 → Most demos = “if user says hello, return hello.” Actual agents? Handle memory, tools, workflows, costs. ✅ AI Agents for Beginners (GitHub): https://lnkd.in/eik2btmq ✅ GenAI Agents – build step by step: https://lnkd.in/dnhwk75V ✅ OpenAI’s guide to agents: https://lnkd.in/guRfXsFK ✅ Anthropic’s Building Effective Agents: https://lnkd.in/gRWKANS4 … 3️⃣ 𝗥𝗔𝗚 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 Real Retrieval-Augmented Generation requires: → Chunking, hybrid BM25 + vectors, reranking → Query routing & fallback → Evaluating retrieval quality, not just LLM output ✅ RAG Techniques repo: https://lnkd.in/dD4S8Cq2 ✅ Advanced RAG: https://lnkd.in/g2ZHwZ3w ✅ Cost-efficient retrieval with Postgres/OpenSearch/Qdrant ✅ Monitoring with Langfuse / Comet … 4️⃣ 𝗚𝗲𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝗼𝗻 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 & 𝗜𝗻𝗳𝗿𝗮 → FastAPI, async Python, Pydantic → Docker, CI/CD, blue-green deploys → ETL orchestration (Airflow, Step Functions) → Logs + metrics (CloudWatch, Prometheus) ✅ Move to production: https://lnkd.in/dnnkrJbE ✅ Made with ML (full ML+infra): https://lnkd.in/e-XQwXqS ✅ AWS GenAI path: https://lnkd.in/dmhR3uPc … 5️⃣ 𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝗜 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺? → Stanford CS336 / CS236 / CS229 (Google it) → MIT 6.S191, Karpathy’s Zero to Hero: https://lnkd.in/dT7vqqQ5 → Google Kaggle GenAI sprint: https://lnkd.in/ga5X7tVJ → NVIDIA’s end-to-end LLM stack: https://lnkd.in/gCtDnhni → DeepLearning.AI’s short courses: https://lnkd.in/gAYmJqS6 … 💥 𝗞𝗲𝗲𝗽 𝗶𝘁 𝗿𝗲𝗮𝗹: Don’t fall for “built in 5 min, dead in 10 min” demos. In prod, it’s about latency, cost, maintainability, guardrails. ♻️ Let's repost to help more people on this journey 💚
Learn how to build your own agentic AI application with free tutorials, guides, courses, projects, example code, research papers, and more.
TL;DR: I developed a simple, open-source benchmark to test if LLM agents follow high-level safety principles when they conflict with a given task acc…
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.
“It’s not that hard to build a fully functioning, code-editing agent.” 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, we’ll 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 “the study and design of rational agents.”