iCreatAI News
Back to main site
Skip to content
#Tutorials
Oct 21Wed
  1. GitHub Blog · AI & ML22

    How 3 Java Champions are using GitHub Copilot for increased productivity

    GitHub interviews three Java Champions—Vlad Mihalcea, Patrick Baumgartner, and Frank Delporte—about using GitHub Copilot in production and open source work, including a Hypersistence Optimizer app built in two days, a 900-slide Reveal.js to Marp migration, and diagnosing a Pi4J bug where virtual threads pinned in native poll() calls left a fifth button unresponsive. They emphasize reviewing generated code, defining verification gates, and keeping humans accountable for what ships.

Oct 20Tue
  1. GitHub Blog · AI & ML34

    How to continue a GitHub Copilot app session in VS Code

    This GitHub tutorial explains how to open a GitHub Copilot app session in VS Code so its conversation, diff, and worktree carry over, letting you review changes line by line, edit by hand, run tests locally, and push updates to the session's pull request. It lists prerequisites and walks through each step, including prompting Copilot further from the sidebar and committing with normal Git commands.

Oct 14Wed
  1. GitHub Blog · AI & ML34

    GitHub’s model for secret detection: Finding more secrets with less noise

    GitHub describes a two-stage secret scanning pipeline that uses regex candidate generation followed by a fine-tuned ModernBERT classifier that reads each candidate alongside its file path, variable names, and surrounding code. Compared with its previous LLM-based pipeline, GitHub reports 11.5% higher repository owner-weighted acceptance, 86% lower median post-push scan execution time, and more than 98% lower serving cost, with offline evaluation showing 13% higher precision and 19% higher recall. The efficiency gains made it practical to extend generic secret detection to push protection.

Oct 13Tue
  1. GitHub Blog · AI & ML38

    GitHub Copilot app for Beginners: How to teach Copilot about your codebase

    GitHub's beginner guide explains four ways to give Copilot project context: repository custom instructions in .github/copilot-instructions.md plus path-specific files in .github/instructions, agent skills in .github/skills for repeatable procedures, custom agents in .github/agents for broader specialized tasks, and MCP servers that connect Copilot to external tools and data sources such as Playwright. It suggests starting small by adding one convention you keep explaining to .github/copilot-instructions.md, and points to Awesome Copilot for community-shared examples.

Oct 2Fri
  1. GitHub Blog · AI & ML22

    AI is changing developer work. Here are three skills to strengthen.

    GitHub outlines three skills developers should strengthen as AI reshapes their work: learning to direct AI agents rather than just use them, not trusting AI's first answer and instead having a second model critique it before review, and using time saved on implementation to tackle broader problems like customer needs and architectural tradeoffs. The post illustrates these with examples such as coordinating multiple agents on an authentication flow and a second model critiquing a SQL query. It notes GitHub Copilot's built-in Rubber Duck agent uses a second model to critique plans, code, and tests.

Sep 26Sat
  1. GitHub Blog · AI & ML55

    GitHub Copilot app for Beginners: How to build custom workflows with canvases

    GitHub explains canvases in the GitHub Copilot app: customizable, bidirectional interfaces such as kanban boards, checklists, or dashboards that you create by entering the /create-canvas skill in an agent session and describing the workflow in plain English. The agent builds the interface in the right-side panel, and both you and the agent can update its shared state immediately without a separate sync step. Canvases are saved as extensions for reuse or team sharing, and ready-made ones are available through Awesome Copilot.

Sep 9Wed
  1. Mistral AI58

    Modernizing complex legacy code with AI agents.

    Mistral helped a European energy operator migrate 40,000 lines of Fortran 77 to C++ for a physics-intensive reservoir simulator that had no test suite or centralized documentation. The team built a numerical parity harness before migrating, used agents to document the codebase via a caller-callee tree, and settled on a human-supervised workflow of coder, tester and reviewer agents migrating module by module. The first sprint covered 40,000 of 300,000 lines, and the authors note that migrations depending on external systems or undocumented physics would face additional challenges.