The open source Git project just released Git 2.50 with features and bug fixes from 98 contributors, 35 of them new. We last caught up with you on the latest in Git back when 2.49 was released. 💡 Before we get into the details of this latest release, we wanted to remind you that Git Merge, the conference for Git users and developers is back this year on September 29-30, in San Francisco. Git Merge will feature talks from developers working on Git, and in the Git ecosystem. Tickets are on sale now; check out the website to learn more. With that out of the way, let’s take a look at some of the most interesting features and changes from Git 2.50. Improvements for multiple cruft packs When we covered Git 2.43, we talked about newly added support for multiple cruft packs. Git 2.50 improves on that with better command-line ergonomics, and some important bugfixes. In case you’re new to the series, need a refresher, or aren’t familiar with cruft packs, here’s a brief overview: Git objects may be either reachable or unreachable. The set of reachable objects is everything you can walk to starting from one of your repository’s references: traversing from commits to their parent(s), trees to their sub-tree(s), and so on. Any object that you didn’t visit by repeating that process over all of your references is unreachable. In Git 2.37, Git introduced cruft packs, a new way to store your repository’s unreachable objects. A cruft pack looks like an ordinary packfile with the addition of an .mtimes file, which is used to keep track of when each object was most recently written in order to determine when it is safe1 to discard it. However, updating the cruft pack could be cumbersome–particularly in repositories with many unreachable objects–since a repository’s cruft pack must be rewritten in order to add new objects. Git 2.43 began to address this through a new command-line option: git repack –max-cruft-size. This option was designed to split unreachable objects across multiple packs, each no larger than the value specified by –max-cruft-size. But there were a couple of problems: If you’re familiar with git repack’s –max-pack-size option, –max-cruft-size’s behavior is quite confusing. The former option specifies the maximum size an individual pack can be, while the latter involves how and when to move objects between multiple packs. The feature was broken to begin with! Since –max-cruft-size also imposes on cruft packs the same pack-size constraints as –max-pack-size does on non-cruft packs, it is often impossible to get the behavior you want. For example, suppose you had two 100 MiB cruft packs and ran git repack –max-cruft-size=200M. You might expect Git to merge them into a single 200 MiB pack. But since –max-cruft-size also dictates the maximum size of the output pack, Git will refuse to combine them, or worse: rewrite the same pack repeatedly. Git 2.50 addresses both of these issues with a new option: –combine-cruft-below-size. Instead of specifying the maximum size of the output pack, it determines which existing cruft pack(s) are eligible to be combined. This is particularly helpful for repositories that have accumulated many unreachable objects spread across multiple cruft packs. With this new option, you can gradually reduce the number of cruft packs in your repository over time by combining existing ones together. With the introduction of –combine-cruft-below-size, Git 2.50 repurposed –max-cruft-size to behave as a cruft pack-specific override for –max-pack-size. Now –max-cruft-size only determines the size of the outgoing pack, not which packs get combined into it. Along the way, a bug was uncovered that prevented objects stored in multiple cruft packs from being “freshened” in certain circumstances. In other words, some unreachable objects don’t have their modification times updated when they are rewritten, leading to them being removed from the repository earlier than they otherwise would have been. Git 2.50 squashes this bug, meaning that you can now efficiently manage multiple cruft packs and freshen their objects to your heart’s content. [source, source] Incremental multi-pack reachability bitmaps ​​Back in our coverage of Git 2.47, we talked about preliminary support for incremental multi-pack indexes. Multi-pack indexes (MIDXs) act like a single pack *.idx file for objects spread across multiple packs. Multi-pack indexes are extremely useful to accelerate object lookup performance in large repositories by binary searching through a single index containing most of your repository’s contents, rather than repeatedly searching through each individual packfile. But multi-pack indexes aren’t just useful for accelerating object lookups. They’re also the basis for multi-pack reachability bitmaps, the MIDX-specific analogue of classic single-pack reachability bitmaps. If neither of those are familiar to you, don’t worry; here’s a brief refresher. Single-pack reachability bitmaps store a collection of bitmaps corresponding to a selection of commits. Each bit position in a pack bitmap refers to one object in that pack. In each individual commit’s bitmap, the set bits correspond to objects that are reachable from that commit, and the unset bits represent those that are not. Multi-pack bitmaps were introduced to take advantage of the substantial performance increase afforded to us by reachability bitmaps. Instead of having bitmaps whose bit positions correspond to the set of objects in a single pack, a multi-pack bitmap’s bit positions correspond to the set of objects in a multi-pack index, which may include objects from arbitrarily many individual packs. If you’re curious to learn more about how multi-pack bitmaps work, you can read our earlier post Scaling monorepo maintenance. However, like cruft packs above, multi-pack indexes can be cumbersome to update as your repository grows larger, since each update requires rewriting the entire multi-pack index and its corresponding bitmap, regardless of how many objects or packs are being added. In Git 2.47, the file format for multi-pack indexes became incremental, allowing multiple multi-pack index layers to be layered on top of one another forming a chain of MIDXs. This made it much easier to add objects to your repository’s MIDX, but the incremental MIDX format at the time did not

Managing issues in software development can be tedious and time-consuming. But what if your AI peer programmer could streamline this process for you? GitHub Copilot‘s latest issue management features can help developers create, organize, and even solve issues. Below, we’ll dig into these features and how they can save time, reduce friction, and maintain consistency across your projects. 1. Image to issue: Turn screenshots into instant bug reports Writing detailed bug reports is often repetitive and frustrating, leading to inconsistent documentation. Copilot’s image to issue feature significantly reduces this friction. Simply paste a screenshot of the bug into Copilot chat with a brief description prompt Copilot to create an issue for you, then Copilot will analyze the image and generate a comprehensive bug report for you. No more struggling to describe visual glitches or UI problems—the image will speak for itself, and Copilot will handle the documentation. For example, if you encounter a UI alignment issue or a visual glitch that’s hard to describe, just capture a screenshot, paste it into Copilot, and briefly mention the problem. In the animation above, the user’s prompt was “create me a bug issue because markdown tables are not rendering properly in the comments.” Copilot then automatically drafted a report, including steps to reproduce the bug. To get the most out of this feature, consider annotating your screenshots clearly—highlighting or circling the problematic area—to help Copilot generate even more precise issue descriptions. Dive into the documentation to learn more. 2. Get the details right: Templates, tags, and types Projects quickly become disorganized when team members skip adding proper metadata. Incorrect templates, missing labels, or wrong issue types make tracking and prioritization difficult. Copilot solves this by automatically inferring the best template based on your prompt. It also adds appropriate labels and issue types without requiring you to navigate multiple dropdown menus or memorize tagging conventions. Need something specific? Simply ask Copilot to add particular labels or switch templates. If you change templates after drafting, Copilot will automatically reformat your content—no manual copying required. 3. Stay organized with versioning and milestones Keeping issues updated and properly categorized is crucial for clear communication, maintaining project velocity, and ensuring visibility into progress. But with so much else to do, it’s easy to let this work fall by the wayside. With Copilot, adding projects and milestones is as simple as typing a prompt. You can also specify exactly how you want issues organized. For example, ask Copilot to use the “Bug Report” or “Feature Request” template, add labels like priority: high, frontend, or needs-triage, or set the issue type to “Task” or “Epic.” Copilot will apply these details automatically, ensuring your issues are consistently categorized. Additionally, Copilot tracks all changes, making them easily referenceable. You can review issue history and revert changes if needed, ensuring nothing important gets lost. 4. Batch create multiple issues at once Sometimes you need to log several issues after a customer meeting, user testing session, or bug bash. Traditionally, this means repeating the same creation process multiple times. Copilot supports multi-issue drafting, allowing you to create multiple issues in a single conversation. Whether logging feature requests or documenting bugs, batch creation saves significant time. Simply prompt Copilot to create the issues, describe each one, and Copilot will draft them all. For example, you could give the following prompt to create two issues at once: Create me issues for the following features: – Line breaks ignored in rendered Markdown despite double-space – Bold and italic Markdown styles not applied when combined You will still need to review and finalize each one, but the drafting process is streamlined into a single workflow. 5. Let AI help fix your bugs with Copilot coding agent Creating issues is only half the battle—fixing them is where the real work begins. You can now assign issues directly to Copilot. Just ask Copilot coding agent to take ownership of the issue, and your AI coding assistant will start analyzing the bug. Copilot can even suggest draft pull requests with potential fixes. This seamless handoff reduces context-switching and accelerates resolution times, allowing your team to focus on more complex challenges. Beyond Copilot: Issues enhancements on GitHub While Copilot is already revolutionizing issue management, we at GitHub are always looking for ways to enhance the overall issues experience. For example, you can now: Standardize issue types across repositories for consistent tracking and reporting. Break down complex tasks into sub-issues for better progress management. Use advanced search capabilities with logical operators to quickly find exactly what you need. Manage larger projects with expanded limits supporting up to 50,000 items. Kickstart enhanced issue management today Ready to transform your issue management workflow with GitHub Copilot? Head to github.com/copilot and try prompts like: “Create me an issue for…” “Log a bug for…” Or simply upload a screenshot and mention you want to file a bug. Experience firsthand how Copilot makes issue management feel less like administrative overhead and more like a conversation with your AI pair programmer. Learn more about creating issues with Copilot > The post 5 tips for using GitHub Copilot with issues to boost your productivity appeared first on The GitHub Blog. ​ AI & ML, GitHub Copilot, generative AI, GitHub Issues The GitHub Blog

When generative AI tools guess what you need, the magic only lasts as long as the guesses are right. Add an unfamiliar codebase, a security checklist your team keeps in a wiki, or a one‑off Slack thread that explains why something matters, and even the most and even the most powerful model may fill in gaps with assumptions rather than having access to your specific context and knowledge. GitHub Copilot Spaces fixes that problem by letting you bundle the exact context Copilot should read—code, docs, transcripts, sample queries, you name it—into a reusable “space.” Once a space is created, every Copilot chat, completion, or command is grounded in that curated knowledge, producing answers that feel like they came from your organization’s resident expert instead of a generic model.  In this article, we’ll walk through: A 5‑minute quick‑start guide to creating your first space Tips for personalizing Copilot’s tone, style, and conventions with custom instructions Real‑world recipes for accessibility, data queries, and onboarding Collaboration, security, and what’s next on the roadmap (spoiler: IDE integration and Issues/PR support) Want to learn more? Try our Docs.  We have everything you need to get started—including pro tips on the context that’s most helpful in your workflows. Explore Docs > Why context is the new bottleneck for AI‑assisted development Large language models (LLMs) thrive on patterns, but day‑to‑day engineering work is full of unpatterned edge cases, including: A monorepo that mixes modern React with legacy jQuery Organizational wisdom buried in Slack threads or internal wikis Organization‑specific security guidelines that differ from upstream OSS docs Without that context, an AI assistant can only guess. But with Copilot Spaces, you choose which files, documents, or free‑text snippets matter, drop them into a space, and let Copilot use that context to answer questions or write code. As Kelly Henckel, PM for GitHub Spaces, said in our GitHub Checkout episode, “Spaces make it easy to organize and share context, so Copilot acts like a subject matter expert.” The result? Fewer wrong guesses, less copy-pasting, and code that’s commit-ready. What exactly is a Copilot Space? Think of a space as a secure, shareable container of knowledge plus behavioral instructions: What it holds Why it matters Attachments Code files, entire folders, Markdown docs, transcripts, or any plain text you add Gives Copilot the ground truth for answers Custom instructions Short system prompts to set tone, coding style, or reviewer expectations Lets Copilot match your house rules Sharing & permissions Follows the same role/visibility model you already use on GitHub No new access control lists to manage Live updates Files stay in sync with the branch you referenced Your space stays up to date with your codebase Spaces are available to anyone with a Copilot license (Free, Individual, Business, or Enterprise) while the feature is in public preview. Admins can enable it under Settings  > Copilot > Preview features. TL;DR: A space is like pinning your team’s collective brain to the Copilot sidebar and letting everyone query it in plain language. Quick-start guide: How to build your first space in 5 minutes Navigate to github.com/copilot/spaces and click Create space. Name it clearly. For example, frontend‑styleguide. Add a description so teammates know when—and when not—to use it. Attach context: From repos: Pull in folders like src/components or individual files such as eslint.config.js. Free‑text hack: Paste a Slack thread, video transcript, onboarding checklist, or even a JSON schema into the Text tab. Copilot treats it like any other attachment. Write custom instructions. A sentence or two is enough: “Respond as a senior React reviewer. Enforce our ESLint rules and tailwind class naming conventions.” Save and test it. You’re done. Ask Copilot a question in the Space chat—e.g., “Refactor this <Button> component to match our accessibility checklist”—and watch it cite files you just attached. Pro tip: Keep spaces focused Instead of dumping your entire repo into one space, create smaller, purpose‑built spaces like: Accessibility, Data‑Queries, Auth‑Model, etc. Kelly, the PM behind the feature, uses this pattern internally at GitHub to make subject‑matter expertise reusable.  Personalize Copilot’s coding style (and voice, too)  Custom instructions are the “personality layer” of a space and where spaces shine because they live alongside the attachments. This allows you to do powerful things with a single sentence, including: Enforce conventions  “Always prefer Vue 3 script setup syntax and Composition API for examples.” Adopt a team tone “Answer concisely. Include a one‑line summary before code blocks.” Teach Copilot project‑specific vocabulary  “Call it ‘scenario ID’ (SCID), not test case ID.” During the GitHub Checkout interview, Kelly shared how she built a personal space for a nonprofit side project: She attached only the Vue front‑end folder plus instructions on her preferred conventions, and Copilot delivered commit‑ready code snippets that matched her style guide on the first try. Automate your workflow: three real‑world recipes 1. Accessibility compliance assistant Space ingredients Markdown docs on WCAG criteria and GitHub’s internal “Definition of Done” Custom instruction: “When answering, cite the doc section and provide a code diff if changes are required.” How it helps: Instead of pinging the accessibility lead on Slack, you can use Spaces to ask questions like “What steps are needed for MAS‑C compliance on this new modal?” Copilot summarizes the relevant checkpoints, references the doc anchor, and even suggests ARIA attributes or color‑contrast fixes. GitHub’s own accessibility SME, Katherine, pinned this space in Slack so anyone filing a review gets instant, self‑service guidance. 2. Data‑query helper for complex schemas Space ingredients YAML schema files for 40+ event tables Example KQL snippets saved as .sql files Instruction: “Generate KQL only, no prose explanations unless asked.” How it helps: Product managers and support engineers who don’t know your database structures can ask, “Average PR review time last 7 days?” Copilot autocompletes a valid KQL query with correct joins and lets them iterate. Result: lets PMs and support self-serve without bugging data science teams. 3. Onboarding Hub and knowledge base in one link Space ingredients Key architecture diagrams exported as SVG text ADRs and design docs from multiple repos Custom instruction:

In May, we experienced three incidents that resulted in degraded performance across GitHub services. May 1 22:09 UTC (lasting 1 hour and 4 minutes) On May 1, 2025, from 22:09 UTC to 23:13 UTC, the Issues service was degraded and users weren’t able to upload attachments. The root cause was identified to be a new feature which added a custom header to all client-side HTTP requests, causing CORS errors when uploading attachments to our provider. We estimate that ~130k users were impacted by the incident for ~45min. We mitigated the incident by rolling back the feature flag that added the new header at 22:56 UTC. In order to prevent this from happening again, we are adding new metrics to monitor and ensure the safe rollout of changes to client-side requests. We have since deployed an augmented version of the feature based on learnings from this incident that is performing well in production. May 28 09:45 UTC (lasting 5 hours) On May 28, 2025, from approximately 09:45 UTC to 14:45 UTC, GitHub Actions experienced delayed job starts for workflows in public repos using Ubuntu-24 standard hosted runners. This was caused by a misconfiguration in backend caching behavior after a failover, which led to duplicate job assignments reducing overall capacity in the impacted hosted runner pools. Approximately 19.7% of Ubuntu-24 hosted runner jobs on public repos were delayed. Other hosted runners, self-hosted runners, and private repo workflows were unaffected. By 12:45 UTC, the configuration issue was fixed through updates to the backend cache. The pools were also scaled up to more quickly work through the backlog of queued jobs until queuing impact was fully mitigated at 14:45 UTC. We are improving failover resiliency and validation to reduce the likelihood of similar issues in the future. May 30 08:10 UTC (lasting 7 hours and 50 minutes) On May 30, 2025, between 08:10 UTC and 16:00 UTC, the Microsoft Teams GitHub integration service experienced a complete service outage. During this period, the integration was unable to process user requests or deliver notifications, resulting in a 100% error rate across all functionality, with the exception of link previews. This outage was caused by an authentication issue with our downstream authentication provider. While the appropriate monitoring was in place, the alerting thresholds were not sufficiently sensitive to trigger a timely response, resulting in a delay in incident detection and engagement. Once engaged, our team worked closely with the downstream provider to diagnose and resolve the authentication failure. However, longer-than-expected response times from the provider contributed to the extended duration of the outage. We mitigated the incident by working with our provider to restore service functionality and are working to migrate to more durable authentication methods to reduce the risk of similar issues in the future. Please follow our status page for real-time updates on status changes and post-incident recaps. To learn more about what we’re working on, check out the GitHub Engineering Blog. The post GitHub Availability Report: May 2025 appeared first on The GitHub Blog. ​ Company news, News & insights, GitHub Availability Report The GitHub Blog

One of the beautiful things about software is that it’s always evolving. However, each piece carries the weight of past decisions made when it was created. Over time, quick fixes, “temporary” workarounds, and deadline compromises compound into tech debt. Like financial debt, the longer you wait to address it, the more expensive it becomes. It’s challenging to prioritize tech debt fixes when deadlines loom and feature requests keep streaming in. Tech debt work feels like a luxury when you’re constantly in reactive mode. Fixing what’s broken today takes precedence over preventing something from possibly breaking tomorrow. Occasionally that accumulated tech debt even results in full system rewrites, which are time-consuming and costly, just to achieve parity with existing systems. Common approaches to managing tech debt, like gardening weeks (dedicated sprints for tech debt) and extended feature timelines, don’t work well. Gardening weeks treat tech debt as an exception rather than ongoing maintenance, often leaving larger problems unaddressed while teams postpone smaller fixes. Extended timelines create unrealistic estimates that can break trust between engineering and product teams. The fundamental problem is treating tech debt as something that interrupts normal development flow. What if instead you could chip away at tech debt continuously, in parallel with regular work, without disrupting sprint commitments or feature delivery timelines? Using AI agents to routinely tackle tech debt Managing tech debt is a big opportunity for AI agents like the coding agent in GitHub Copilot. With AI agents like the coding agent in GitHub Copilot, tech debt items no longer need to go into the backlog to die. While you’re focusing on the new features and architectural changes that you need to bring to your evolving codebase, you can assign GitHub Copilot to complete tech debt tasks at the same time.  Here are some examples of what the coding agent can do: Improve code test coverage: Have limited code testing coverage but know you’ll never get the buy-in to spend time writing more tests? Assign issues to GitHub Copilot to increase test coverage. The agent will take care of it and ping you when the tests are ready to review. Swap out dependencies: Need to swap out a mocking library for a different one, but know it will be a long process? Assign the issue to swap out the library to GitHub Copilot. It can work through that swap while you’re focusing your attention elsewhere. Standardize patterns across codebases: Are there multiple ways to return and log errors in your codebase, making it hard to investigate issues when they occur and leading to confusion during development? Assign an issue to GitHub Copilot to standardize a single way of returning and logging errors. Optimize frontend loading patterns: Is there an area where you are making more API calls than your application really needs? Ask GitHub Copilot to change the application to only make those API calls when the data is requested, instead of on every page load. Identify and eliminate dead code: Is there anywhere in your project where you may have unused functions, outdated endpoints, or stale config hanging out? Ask GitHub Copilot to look for these and suggest ways to safely remove them. If those examples sound very specific, it’s because they are. These are all real changes that my team has tackled using GitHub Copilot coding agent—and these changes probably wouldn’t have occurred without it. The ability for us to tackle tech debt continuously while delivering features has grown exponentially, and working AI agents into our workflow has proven to be incredibly valuable. We’ve been able to reduce the time it takes to remove tech debt from weeks of intermittent, split focus to a few minutes of writing an issue and a few hours reviewing and iterating on a pull request. What’s the difference between agent mode and coding agent in GitHub Copilot? While they’re both AI agents, they’re tuned for different parts of your day-to-day workflows. See how to use them both to work more efficiently. This isn’t about replacing human engineers; it’s about amplifying what we do best. While agents handle the repetitive, time-consuming work of refactoring legacy code, updating dependencies, and standardizing patterns across codebases, we can focus on architecture decisions, feature innovation, and solving complex business problems. The result is software that stays healthier over time, teams that ship faster, and engineers who spend their time on work that actually energizes them. When AI is your copilot, you still have to do the work The more I learn about AI, the more I realize just how critical humans are in the entire process. AI agents excel at well-defined, repetitive tasks, the kind of tech debt work that’s important but tedious. But when it comes to larger architectural decisions or complex business logic changes, human judgment is still irreplaceable. Since we are engineers, we know the careful planning and tradeoff considerations that come with our craft. One wrong semicolon, and the whole thing can come crashing down. This is why every prompt requires careful consideration and each change to your codebase requires thorough review. Think of it as working with a brilliant partner that can write clean code all day but needs guidance on what actually matters for your application. The AI agent brings speed and consistency; it never gets tired, never cuts corners because it’s Friday afternoon, and can maintain focus across hundreds of changes. But you bring the strategic thinking: knowing which tech debt to tackle first, understanding the business impact of different approaches, and recognizing when a “quick fix” might create bigger problems down the line. The magic happens in the interaction between human judgment and AI execution. You define the problem, set the constraints, and validate the solution. The agent handles the tedious implementation details that would otherwise consume hours of your time. This partnership lets you operate at a higher level while still maintaining quality and control. Tips to make the most of the coding agent in GitHub Copilot Here’s what I’ve learned from using the coding