AI & ML

GitHub engineers and industry thought leaders offer tips, best practices, and practical explainers about various aspects of AI and ML, ranging from fundamental concepts to advanced techniques and real-world applications. For more detailed documentation and practical guides on GitHub’s own AI coding tool, GitHub Copilot, check out GitHub’s official documentation.

Featured

The harness is all you need (mostly)

A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software without chasing every new AI tool.

GitHub Copilot app for Beginners: Getting started

New to the GitHub Copilot app? Learn how to start projects, work with AI agents, explore canvases, and streamline your development workflow.

Copilot vs. raw API access: What are you actually paying for?

Copilot now bills usage at listed API rates. Compare direct model access with the coding workflow, policy, and harness work around it.

How to build interactive experiences with canvases

Canvases turn AI into interactive workspaces where you can visualize information, explore workflows, and take action across complex tasks.

Latest

Better tools made Copilot code review worse. Here’s how we actually improved it.

How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence.

Automating cross-repo documentation with GitHub Agentic Workflows

Explore how the Aspire team turns merged product changes into SME-reviewed docs pull requests, closing the gap between release and documentation.

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How GitHub Copilot enables zero DNS configuration for GitHub Pages

Go from an empty repository to a live custom domain with HTTPS in about 14 minutes, without manually editing a single DNS record.

Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks

Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency, while maintaining flexibility to choose among more than 20 models.

I automated my job (and it made me a better leader)

Explore how my day as a senior leader looks now that I use 40 automations to help, and learn more about some of my favorites.

How we built an internal data analytics agent

Qubot, our internal Copilot-powered analytics agent, allows any GitHub employee to ask questions about our data in plain language. Here’s what we learned as we built it.

Getting more from each token: How Copilot improves context handling and model routing

How GitHub Copilot is making more of each session go toward useful work, so your credits go further.

What are git worktrees, and why should I use them?

Git worktrees have been around since 2015, but it wasn’t until recently they became popular. Learn what they are, how to use them, and why you might.

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GitHub Copilot CLI for Beginners: Overview of common slash commands

GitHub Copilot CLI for Beginners: Learn how to use slash commands to control your terminal AI agent.

Accelerating researchers and developers building multilingual AI with a new open dataset

A new repository-level dataset, published on GitHub under CC0-1.0, helps researchers and developers discover multilingual developer content across READMEs, issues, and pull requests.

How we made GitHub Copilot CLI more selective about delegation

Better orchestration, fewer handoffs, faster progress, without a single new knob.

Making secret scanning more trustworthy: Reducing false positives at scale

Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning.

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Give GitHub Copilot CLI real code intelligence with language servers

Install and configure LSP servers for GitHub Copilot CLI, replacing brute-force grep/decompile with real code intelligence.

From one-off prompts to workflows: How to use custom agents in GitHub Copilot CLI

Custom agents let GitHub Copilot CLI understand your stack and team workflows, turning one-off terminal prompts into repeatable, reviewable processes.