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TodayNovember 13Friday1 items
  1. GitHub Blog · AI & ML58

    GitHub guide explains how to build agent skills that run across Claude Code, Cursor and Copilot

    GitHub published a practical guide to agent skills, versioned directories centered on a SKILL.md file that describe when and how to perform a repeatable task. The guide uses two examples, Proof Agent for adversarial verification of AI-generated code and Data Forge for a multi-skill data pipeline, to show how skills share a project manifest, reuse one execution path, and place human approval at each phase. It also describes a packaging script that generates client-specific files for Claude Code, Cursor and GitHub Copilot from a single skills source.

November 7Saturday
  1. GitHub Blog · AI & ML38

    Beyond code generation: A developer's guide to agentic engineering

    GitHub published a developer's guide to agentic engineering, walking through how Copilot supports the Plan, Build, Verify, and Observe stages of software work. The guide cites GitHub's 1.2 billion commits in one week this October, over 400% more than a year earlier, and a Faros AI study finding pull requests spent 441% longer in review as teams moved from low to high AI adoption. It details features including Copilot in Slack or Teams, Plan Mode, GitHub Agentic Workflows, cloud sandboxes, session continuity, shared memory, Copilot Dreaming, and Copilot Fusion.

October 27Tuesday
  1. Replit Blog75

    Replit Cloud Agents now let non-engineers contribute to company codebases

    Replit launched Cloud Agents for existing repositories, letting anyone on a team describe a change in plain language and have the agent open a tested pull request in GitHub or GitLab, available to all Pro and Enterprise customers. The agent runs each change in isolated cloud sandboxes, clicks through the running app, and attaches screenshots and video to the PR, while shared environments give every contributor a preconfigured setup that updates from the main branch. Replit also introduced early-access self-improving workspace memory, which turns review comments into team skills that engineers approve, and a Slack integration that lets users mention @Replit in a thread to start a change.

    Why it matters: Replit 把 Cloud Agents 接入企业既有代码库，让非工程人员以带测试视频的 PR 形式提交改动，展示了 AI 编码工具向团队协作流程延伸的一种路径。

October 21Wednesday
  1. GitHub Blog · AI & ML22

    How 3 Java Champions Use GitHub Copilot for Productivity

    Three Java Champions with over a decade of Java experience each shared how they use GitHub Copilot in production and open source work. Vlad Mihalcea built a Hypersistence Optimizer web app in two days instead of weeks, and created custom skills trained on 65 of his articles to match his writing voice. Patrick Baumgartner migrated roughly 900 teaching slides across 15 presentations from Reveal.js to Marp with Copilot, verifying results through screenshot comparisons.

October 20Tuesday
  1. GitHub Blog · AI & ML26

    How to Continue a GitHub Copilot App Session in VS Code

    GitHub Copilot app sessions can now be opened directly in VS Code, carrying over the conversation, code changes, and worktree so developers can review diffs, edit by hand, and test locally without restarting. The tutorial walks through opening a session, reviewing the agent's changes line by line, continuing the same conversation in the Copilot sidebar, and pushing updates to the session's pull request.

October 14Wednesday
  1. GitHub Blog · AI & ML38

    GitHub's secret detection model: finding more secrets with less noise

    GitHub trained a fine-tuned ModernBERT classifier to replace its previous LLM-based pipeline for generic secret detection, improving owner-weighted alert precision by 11.5% and cutting median post-push scan execution time by 86%. The two-stage pipeline uses regex candidate generation followed by context-aware classification, which also lowered serving cost by more than 98% versus remote LLM calls. The efficiency gain made it practical to extend generic secret detection to push protection, returning results before changes are pushed.

October 13Tuesday
  1. GitHub Blog · AI & ML36

    GitHub Copilot for Beginners: Four Ways to Teach Copilot About Your Codebase

    GitHub's Copilot for Beginners series explains four ways to give Copilot project context: custom instructions in `.github/copilot-instructions.md` plus path-specific files in `.github/instructions`, agent skills in `.github/skills`, custom agents in `.github/agents`, and MCP servers that connect Copilot to external tools such as Playwright. The post walks through each mechanism and how they combine, and points readers to the community Awesome Copilot collection for ready-made examples.

October 8Thursday
October 5Monday
  1. GitHub Blog · AI & ML58

    GitHub releases ReviewBench, an open benchmark for AI code review

    GitHub released ReviewBench, an open benchmark for AI code review agents built on 219 public pull requests across 19 languages, with language and repository-size distributions modeled on 103.9 million GitHub pull requests. The golden set draws findings from human reviewers, author follow-up commits, static analysis and multiple frontier LLMs, graded by Claude Sonnet 5 under a published rubric, and senior engineers independently re-labeled every finding with 96.6% agreement. GitHub says its offline evaluation of Copilot code review anticipated the direction of a production A/B test, where addressed rate rose 8.0%, recall rose 13.6% and cost per review fell 8.0%.

October 2Friday
  1. GitHub Blog · AI & ML25

    GitHub Blog: Three Skills Developers Need as AI Reshapes Their Work

    GitHub Blog outlines three skills developers should strengthen as AI changes their work: directing AI agents rather than just using them, critically reviewing AI output instead of trusting the first answer, and using time saved by AI to tackle broader problems. It cites GitHub Copilot's built-in Rubber Duck agent, which uses a second model to critique plans, code, and tests before a developer proceeds.