From the Zed Blog: Agents handle typing so we can focus on thinking.
Introducing Delta
From the Zed Blog: A multiplayer environment for coding with agents, from the creators of Zed.
Joshua Gu on Twitter / X
Recent agentic systems (Claude Code, Codex, RLM, etc.) push context out of the prompt and into the environment (e.g., as files). This helps them maintain long-term knowledge about their goals and functionality.🚨 While this is a good idea, we show a surprising result: systems… pic.twitter.com/XHFVaDcr4l— Joshua Gu (@astrogu_) May 20, 2026
AI Agents Will Become the New UI, and Apps Take a Backseat
For decades, screens, keyboards, and structured applications have shaped our relationship with technology. People learned how to interact with computers
Agent Skills
AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...
Google for Developers Blog - News about Web, Mobile, AI and Cloud
An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...
Google for Developers Blog - News about Web, Mobile, AI and Cloud
An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...
Introducing Cursor 2.0 and Composer · Cursor
A new interface and our first coding model, both purpose-built for working with agents.
Writing with AI help can shift your opinions | Cornell Chronicle
Artificial intelligence-powered writing assistants that autocomplete sentences or offer “smart replies” not only put words into people’s mouths, they also put ideas into their heads, according to new research.
browser-use/browser-use
🌐 Make websites accessible for AI agents. Automate tasks online with ease.
The Rise of Agent Experience (AX)
Cognition | Agent Trace: Capturing the Context Graph of Code
We’re excited to join in Cursor, Cloudflare, Vercel, git-ai, OpenCode and others in support of [Agent Trace](https://agent-trace.dev/). As described in the spec, Agent Trace is an open, vendor-neutral spec for recording AI contributions alongside human authorship in version-controlled codebases.
A Survey on Large Language Model based Autonomous Agents
Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective. More specifically, we first discuss the construction of LLM-based autonomous agents, for which we propose a unified framework that encompasses a majority of the previous work. Then, we present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository of relevant references at https://github.com/Paitesanshi/LLM-Agent-Survey.
