Enterprise Java developers have a new superpower—drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. The post Using the GitHub Copilot SDK for Java appeared first on The GitHub Blog.
AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
Enterprise Java developers have a new superpower—drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. The post Using the GitHub Copilot SDK f…
Go beyond chat in the GitHub Copilot app with these slash commands. They'll help you plan, collaborate, automate, and customize your dev workflow. The post A guide to slash commands in the GitHub Copilot app appeared first on The GitHub Blog.
AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
Go beyond chat in the GitHub Copilot app with these slash commands. They'll help you plan, collaborate, automate, and customize your dev workflow. The post A guide to slash comman…
Learn how to build tools to simplify how you work—without writing a single line of code. The post How the GitHub legal team used Copilot CLI to streamline their workflows appeared first on The GitHub Blog.
AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
Learn how to build tools to simplify how you work—without writing a single line of code. The post How the GitHub legal team used Copilot CLI to streamline their workflows appeared…
The author describes how they modernized a decade-old frontend codebase using stacked sessions and pull requests in the GitHub Copilot app, breaking down tasks to avoid scope creep and achieve progressive updates.
The author used GitHub Copilot's stacked sessions to modernize a personal app with outdated dependencies (React 15, Less, react-bootstrap).
Initial one-shot plan failed due to overlooked dev branch, but Copilot allowed seamless switching.
If you're overwhelmed by new AI tools, this practical GitHub Copilot workflow focuses on prototyping, planning, implementing, and reviewing with the existing harness. Key steps: pick one tool, enable YOLO mode (sandboxed), prototype first, plan methodically with edge cases, implement with Autopilot, and review ruthlessly for quality.
Use a single tool like GitHub Copilot CLI or app; the workflow is consistent across platforms.
Enable YOLO mode (autonomy) but run agents in sandboxed environments for safety.
GitHub Copilot now bills usage at listed API rates. This article compares direct model access with the coding workflow, policy, and harness work around Copilot to help developers choose based on their needs.
Copilot consumes AI credits for chat and agentic work at model rates; code completions remain included in paid plans.
Raw API access suits building custom systems but requires handling prompts, retrieval, routing, logging, and security yourself.
GitHub Copilot's 'canvases' transform AI from a conversational tool into a visual, interactive workspace. Developers can create custom canvases via prompts for tasks like issue triage, code visualization, session management, prompt coaching, and knowledge finding. Canvases support real-time collaboration, allowing users and AI agents to iterate together.
Canvases are GitHub Copilot extensions providing visual interfaces for complex tasks.
Users can create different canvases via prompts, such as issue triage helper or codebase diagram.
How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence.
The post Better tools made Copilot code review worse. Here’s how we actually improved it. appeared first on The GitHub Blog.
Migrating to shared Unix tools initially increased review cost and reduced effectiveness.
The problem was not the tools but the instructions, which caused the agent to browse broadly instead of focusing on the diff.
The Aspire team uses GitHub Agentic Workflows to automatically generate documentation pull requests from merged product changes, reducing median time-to-docs from weeks to 44.8 hours with 100% merge rate.
Cross-repo documentation automation using GitHub Agentic Workflows, from product PR merge to docs PR in median 44.8 hours.
Security: agent emits intent only, executed by scoped GitHub App token limited to two repos and allowed branches.
Learn how to go from an empty repository to a live custom domain with HTTPS in about 14 minutes, without manually editing a single DNS record, using GitHub Copilot CLI and a Namecheap skill.
Publish a site with GitHub Pages
Register an inexpensive domain and enable registrar API
How the GitHub Copilot agentic harness achieves task completion on par with model-vendor harnesses while using fewer tokens, and supports over 20 models.
GitHub Copilot agentic harness achieves on-par task resolution with lower token consumption across benchmarks.
The harness supports over 20 models including GPT, Claude, Gemini, allowing flexible model selection.
A senior director at GitHub shares how she uses 40 automations to manage her workflow, freeing up mental space for what really matters. She explains how automations help with meeting prep, follow-ups, team alignment, and especially for her as someone with AuDHD, they serve as an essential accessibility tool.
Automations are not about replacing human connection but enabling leaders to show up fully for their teams
Start with the single biggest friction point (e.g., meeting prep) and build from there
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.
Qubot offers multiple interfaces (Slack, VS Code, Copilot CLI) for low-barrier access to data analytics.
A federated context layer with structured knowledge is key to improving accuracy and speed (3x faster).
GitHub Copilot is improving efficiency by reducing redundant context through prompt caching and deferred tool loading, and by introducing Auto model selection that routes tasks to the best-fit model based on intent and real-time health, saving credits without sacrificing quality.
Prompt caching and tool search reduce repeated context across turns.
Auto model selection uses task intent and model health to choose the right model.
Learn how to use slash commands in GitHub Copilot CLI to switch models, manage context, resume sessions, inspect changes, navigate directories, and reset permissions for efficient terminal AI control.
Slash commands provide control over model selection, context management, and session handling.
Use /model to choose the right model based on capabilities, availability, and cost.
GitHub releases the GitHub Multilingual Repositories Dataset (CC0-1.0), a metadata dataset covering over 80 million classification rows across more than 40 million repositories, helping researchers discover non-English developer content and build more inclusive AI tools.
Dataset provides language classifications for READMEs, issues, and pull requests from three classifiers (fastText, gcld3, lingua-py) with confidence scores.
Covers over 40 million repositories and 80 million classification rows. Korean is most common non-English in issues; Portuguese tops READMEs.
GitHub Copilot CLI now uses smarter subagent delegation to reduce unnecessary handoffs and wait times. Production A/B testing shows a 23% reduction in tool failures and a 5% improvement in user wait time. The article details how the team identified delegation bottlenecks, refined the orchestration policy, and validated improvements.
Copilot CLI now delegates more selectively, using subagents only when they create real leverage.
Production A/B test results: tool failures down 23%, P95 wait time reduced by 5%.
Install and configure LSP servers for GitHub Copilot CLI, replacing brute-force grep/decompile with real code intelligence. The LSP Setup skill automates the process, supporting 14 languages. This post explains how it works and how to get started.
GitHub Copilot CLI previously relied on text search and binary extraction to understand code, which was inefficient and inaccurate.
The LSP Setup skill automates installation and configuration of LSP servers for 14 languages.
Custom agents let GitHub Copilot CLI understand your stack and team workflows, turning one-off terminal prompts into repeatable, reviewable processes. This article covers the concept, creation, and usage of custom agents with three practical workflow examples: security audit, IaC compliance, and release documentation.
Custom agents are defined using Markdown files with YAML frontmatter, specifying role, tools, guardrails, and output format.
Agent profiles are stored in the .github/agents directory of a repository, enabling version control and team review.
GitHub Copilot now serves 140,000 organizations, with over 100% year-over-year growth. Gartner positions GitHub as a Leader for the third consecutive year, highest in ability to execute.
GitHub recognized as Leader in Gartner Magic Quadrant for third year
GitHub Copilot serves 140,000 organizations, triple last year
Remote control for GitHub Copilot CLI sessions is now generally available on github.com and GitHub Mobile. Developers can start a session in VS Code or the CLI, then monitor and adjust it from another device. Features include real-time monitoring, mid-flight instruction changes, permission approvals, and a seamless cross-device workflow, with privacy by default.
Remote control for GitHub Copilot CLI sessions is now GA on github.com and GitHub Mobile.
Support for remote control in VS Code and JetBrains IDE enables multi-surface workflows.
GitHub is piloting an experimental general-purpose accessibility agent to provide engineers with just-in-time accessibility answers and automatically catch and remediate simple issues before production. The agent has reviewed 3,535 pull requests with a 68% resolution rate, focusing on structure, control naming, announcements, text alternatives, and keyboard focus order. The article shares lessons on mindset, leveraging past issues, sub-agent architecture, linear instruction execution, templated content passing, and handling complexity and risk patterns.
GitHub pilots a general-purpose accessibility agent to assist engineers and auto-fix common accessibility issues.
Agent reviewed 3,535 PRs with 68% resolution; top issues include structure, naming, announcements, text alternatives, and keyboard navigation.
GitHub systematically optimized token usage in its agentic workflows by logging via API proxy, identifying inefficiencies like unused MCP tools, replacing MCP calls with CLI commands, and building automated auditor/optimizer workflows, achieving up to 62% cost savings.
GitHub used an API proxy to normalize token logging across agent frameworks and built daily auditor and optimizer workflows to detect inefficiencies.
Removing unused MCP tools reduced per-call context by 8–12 KB, saving thousands of tokens per run.
Learn how to validate autonomous AI agents using dominator analysis to focus on essential outcomes instead of rigid scripts, reducing false negatives in CI pipelines.
Current testing tools assume deterministic behavior, causing false negatives in agent-driven workflows.
The Trust Layer framework uses Prefix Tree Acceptors and dominator analysis to extract essential states.
Learn the difference between CLI interactive and non-interactive modes. Interactive mode offers a chat-like experience for deep collaboration, while non-interactive mode provides quick one-off answers.
Interactive mode is default; allows back-and-forth conversation and iteration.
Non-interactive mode uses -p flag for quick, single prompts without entering a session.