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improve the accessibility of open source centered on three goals empower people with disabilities to contribute to open source increase the availability and adoption of open source assistive technologies improve the accessibility of mainstream open source projects this pledge came with a clear responsibility turn intention into practical community led action over the past year we ve had the chance to work with maintainers contributors educators accessibility advocates and people with disabilities who are doing the hard real work of building a culture of inclusion within open source communities this post shares our progress over the past year and two ways you can help right now mark your calendar if you want to help shape what happens next start here register for open source accessibility community day july 9 register for the open source accessibility summit october 19 progress since the pledge our approach has focused on programs partnerships and open tooling that support the pledge goals in real projects and communities empowering contributors with disabilities in open source in may 2026 we hosted the open source assistive technology hackathon at github headquarters in san francisco over two days contributors maintainers educators advocates and people with disabilities collaborated on open source assistive technology projects addressing real access barriers by the numbers 124 attendees on site across two days 16 featured projects at kickoff 25 projects worked on during the event 190 registrations with 120 people on the waitlist six event partners and supporting organizations to support meaningful participation we also hosted a github learning room with hands on guidance for github workflows including nvda screen reader and keyboard only navigation nv access office hours with the team behind nvda screen reader to support project teams and troubleshooting accessibility documentation the github accessibility documentation helps empower contributors with disabilities by making it easier to use github products with screen readers and other assistive technologies while also giving developers guidance for using copilot and agents with accessibility in mind because people with lived experience contribute to and review these guides the documentation reflects real world workflows reduces barriers to participation and helps more contributors engage confidently in open source projects supporting open source assistive technology hackathon contributors worked across a wide range of projects including camera based assistive technology for blind and low vision users pdf to accessible format workflows open wheelchair software and hardware efforts accessibility aware route planning pronunciation improvements for screen readers accessible gaming tactile experiences for a multiline braille display adaptable physical tools and accessories you can follow ongoing updates in the open source assistive technology hackathon repository improving accessibility in mainstream open source workflows in october 2025 we hosted the first open source accessibility summit 2025 in raleigh nc in partnership with all things open during this one day in person event we brought together members of the disability accessibility and open source communities to define a roadmap for improving the accessibility of open source software by the numbers 100 attendees 500 people on the waitlist 6 themes for accessibility improvement in open source identified 4 lighting talks improving workflows we ve also continued to invest in practical resources and tools that help teams build accessibility into day to day development practices open source accessibility organization for shared roadmap and collaboration open source accessibility best practices guide including guidance like creating an accessibility md and integrating accessibility in ci github accessibility documentation including guidance for using github products with assistive technologies github accessibility scanner an ai powered open source scanner that uses github actions and github copilot to find file and fix accessibility barriers accessibility annotation toolkit an open source figma toolkit to support accessibility focused design and handoff these resources are meant to reduce friction by helping teams write better issues collaborate and integrate accessibility throughout the development process two ways you can help right now 1 register for the open source accessibility community day july 9 2026 the open source accessibility community day is a virtual event for contributors maintainers accessibility practitioners and community members who want to make open source more inclusive and accessible the event features demos and updates from projects that participated in the open source assistive technology hackathon including what teams built what they learned and where contributors are needed next 2 register for the open source accessibility summit october 19 2026 we re hosting the open source accessibility summit in raleigh north carolina with all things open this year s summit is designed to teach people how to contribute to open source while following accessibility best practices no matter their level of experience grounded in the lived experiences of people with disabilities the event will create opportunities to learn directly from those perspectives while building practical skills the goal is simple help more people leave ready to make meaningful accessibility focused contributions across the open source ecosystem join us stay connected by following the open source accessibility organization and joining the open source accessibility slack we re proud of the progress so far and we re even more excited about what we can build together next the post from pledge to practice building a more inclusive open source ecosystem appeared first on the github blog 96917 how we built an internal data analytics agent https github blog ai and ml github copilot how we built an internal data analytics agent fri 19 jun 2026 16 00 00 0000 https github blog p 96857 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 the post how we built an internal data analytics agent appeared first on the github blog large data and analytics organizations often struggle to make access to data and insights truly self serve the industry tried to solve this problem quite unsuccessfully for decades but now ai is giving us a credible way to do just that at github scale providing dedicated analytics support to dozens of product teams is challenging and therefore many teams are left to solve this problem on their own though there is a lot of valuable product telemetry that product and engineering teams can use to make decisions figuring out which data model which grain which filter and then write the query and validate the result has always been difficult without the support of a data analyst enter qubot our internal github copilot powered analytics agent qubot allows any hubber that s what we call github employees to ask questions about any data model in github s data warehouse in plain language and get an answer within seconds qubot is not a reporting tool or a dashboard replacement instead it s intended for exploratory questions like which cohort of users has the highest retention on this feature or what product contributed to move this metric the most last week qubot has zero cost maintenance and helps teams ramp up quickly on datasets they may be unfamiliar with in this blog post we ll go over how we built qubot how it s changed and what we learned how qubot works the architecture has three main components user interface context layer and query engine user interface qubot is accessible through slack vs code and the copilot cli the slack interface doesn t require any configuration and it is the preferred collaboration tool of hubbers when someone posts a question in the qubot slack channel a qubot instance is spawned as a copilot cloud agent running on github com the answer is provided directly in slack allowing the user to share the result with others but also iterate in the thread to evolve or refine the question all the results are also stored as a markdown report in a pull request that the user can reference to fine tune the query or use it in a dashboard qubot is also available in vs code and the copilot cli for users that want an experience more integrated with their workflows qubot can be installed with one command as a plugin and it becomes available in any agent session in vs code or copilot cli alongside any other custom agents skills and tools configured by the user context layer our data warehouse contains data at different stages of curation raw events bronze conformed facts and dimensions silver and curated datasets designed for specific business use cases gold the context layer is built in a federated way with knowledge that is tailored to the type of data for bronze data we have telemetry context contributed by product teams with schema information and metadata for silver data we have examples of queries usage guidance mandatory filters etc maintained by the data and analytics team for gold data we have business rules and metric definitions contributed by teams owning those datasets we also leverage our etl pipelines to systematically enrich the context layer with additional signals and derived metadata the context is loaded at runtime via the github mcp server fetching it from the context layer context agent the context layer is constantly enriched with new knowledge persisted across multiple repositories at github we primarily use markdown for documentation so we don t need to interface with multiple different tools we ve streamlined federated context contribution through a context agent teams can contribute via a standardized template or by referencing a repository containing relevant context the agent then ingests organizes and normalizes this information into a structured format that has proven effective for qubot based on our evaluations evaluation framework every change to the context layer or agent configuration gets evaluated before it ships when someone wants to enrich the context layer with new knowledge they can open a pull request the new context goes through an offline eval framework that measures accuracy of the response latency in finding the right answer and catches regressions before they reach users the benchmarking framework for evaluating qubot across structured test cases has three components test cases a curated dataset of prompts with known correct answers ground truth sql and metadata domain difficulty automated run orchestration a script that automates launching each test case as an agent task with the github cli gh agent task create runs multiple parallel trials polls for completion and saves detailed json results stats aggregation a reporting script that reads the saved results and computes per test case metrics completion rate accuracy and duration avg min max the end to end flow is define test cases run qubot n times per case collect results aggregate stats compare configurations query engine qubot connects to both kusto and trino the two query engines that power most of github s analytics workloads via a mcp server we developed a custom implementation of the trino mcp server while for kusto we deployed a local version of the fabric rti mcp server kusto is fast and well suited to exploratory questions over recent event data trino handles complex joins and deeper historical analysis rather than forcing users to know which to use qubot defaults to kusto and switches to trino automatically when the question requires it what changed and what we learned qubot has been widely adopted at github with hundreds of enthusiastic users running thousands of queries the number of questions that hubbers ask in the data and analytics slack channels has reduced dramatically because now they can explore the data with greater autonomy and reach out only for complicated questions it also allows hubbers that never dared to dip into the data warehouse to access the data they need to drive their decision making that is one of the reasons for offering multiple interfaces like slack copilot cli and vs code hubbers are very technical but we wanted to offer an option with no barrier to entry and zero configuration we quickly discovered that the context layer is key to enriching the reasoning capabilities of copilot and to create an expert analytics agent in our experiments we found that structured and well curated context not only makes qubot more accurate but also three times faster at returning the right answer this has profound implications on the analytics engineering discipline because it makes this type of artifact a first class citizen in how data is modeled rather than an afterthought qubot has been a rare example of successful hub and spoke execution it removes strain from the data and analytics team as product teams own the telemetry for their surfaces and business teams own the definition of their gold data qubot acted as a gravitational force to centralize all this distributed knowledge into a single tool that can benefit all github providing incentives to partner teams to contribute to qubot instead of creating multiple tools limited to their own domains acknowledgements qubot engineering team weijie tan tobias tschuemperlin vamsi anamaneni special thanks yaswanth anantharaju the post how we built an internal data analytics agent appeared first on the github blog 96857 how pull request limits are cutting down the noise https github blog open source maintainers how pull request limits are cutting down the noise thu 18 jun 2026 16 00 00 0000 https github blog p 96841 learn how pull request limits can help manage contribution volume in your repositories and see what s next on the roadmap the post how pull request limits are cutting down the noise appeared first on the github blog more people are contributing to open source than ever most of them trying to help the challenge is keeping up with the volume creating a pull request has never been easier reviewing one still takes a human about as long as it ever did when great contributions and low quality noise land in the same queue the ones that deserve attention are harder to find that s why we ve introduced pull request limits it takes on the problem we hear most too many incoming pull requests too much low quality noise and too few ways to manage the flow how it works a pull request limit sets the maximum number of pull requests a user without write access can have open at once in your repository hit the limit and you must close or merge one before opening another pull requests opened by copilot or another ai agent will counts toward your limit trusted contributors can be placed on a bypass list where they are exempted from limits but don t gain full ...
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