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…
Git AI - Track AI Code all the way to production
Cross-agent observability from prompt to production. Track AI-generated code from Cursor, Claude Code, GitHub Copilot, Gemini, and more through the entire SDLC.
Building an Open-Source Alternative to Cursor with Code Context - Milvus Blog
Code Context—an open-source, MCP-compatible plugin that brings powerful semantic code search to any AI coding agent, Claude Code and Gemini CLI, IDEs like VSCode, and even environments like Chrome.
In software, the code documents the app. In AI, the traces do.
Discover how traces for documentation replace code as the source of truth for AI agents. Learn to debug, test, and optimize agent behavior effectively.
GitHub - jarrodwatts/claude-hud: A Claude Code plugin that shows what's happening - context usage, active tools, running agents, and todo progress
A Claude Code plugin that shows what's happening - context usage, active tools, running agents, and todo progress - jarrodwatts/claude-hud
Sourcegraph — Code Understanding, Oversight and Evolution
Give humans and agents complete context to understand, oversee, and evolve the world's largest, most complex codebases.
Pi: The Minimal Agent Within OpenClaw
A gentle introduction to the Pi coding agent and why I think it’s a glimpse into the future of software.
Agent READMEs: An Empirical Study of Context Files for Agentic Coding
Agentic coding tools receive goals written in natural language, break them down into specific tasks, and write or execute code with minimal human intervention. Central to this process are agent context files (e.g., AGENTS.md and CLAUDE.md) that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of 2,303 agent context files from 1,925 repositories to characterize their structure, maintenance, and content. We find that these files are not static documentation but complex, difficultto-read artifacts that evolve like configuration code through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as test procedures (75.9%), implementation details (70.8%), and architecture (68.1%). We also identify a significant gap: non-functional requirements such as security (14.8%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tools and practices.
Introducing Cursor 2.0 and Composer · Cursor
A new interface and our first coding model, both purpose-built for working with agents.
Semble: Fast and Accurate Code Search for Agents
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
perplexityai/SPACE
Sandboxed Platform for Agentic Code Execution: pluggable-runtime sandboxes for running agent and user code in isolation.
perplexityai/SPACE
Sandboxed Platform for Agentic Code Execution: pluggable-runtime sandboxes for running agent and user code in isolation.
Introducing eve
Introducing eve, the open-source agent framework from Vercel for building, running, and scaling agents in production, with durable execution, sandboxed compute, approvals, channels, tracing, and evals built in.
