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GitHub

GitHub Outage Map

The map below depicts the most recent cities worldwide where GitHub users have reported problems and outages. If you are having an issue with GitHub, make sure to submit a report below

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The heatmap above shows where the most recent user-submitted and social media reports are geographically clustered. The density of these reports is depicted by the color scale as shown below.

GitHub users affected:

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GitHub is a company that provides hosting for software development and version control using Git. It offers the distributed version control and source code management functionality of Git, plus its own features.

Most Affected Locations

Outage reports and issues in the past 15 days originated from:

Location Reports
Vernon, Normandy 1
Township of Evan, KS 1
Madrid, Madrid 1
Bogotá, Bogota D.C. 1
Paris, Île-de-France 4
Lyon, Auvergne-Rhône-Alpes 1
Lima, Lima 1
Aix-en-Provence, Provence-Alpes-Côte d'Azur 1
Trento, Trentino-Alto Adige 1
Le Chambon-Feugerolles, Auvergne-Rhône-Alpes 1
Antananarivo, Analamanga 1
Lure, Bourgogne-Franche-Comté 1
Ashkelon, Southern District 1
Veigné, Centre 1
Saint-Paul, Réunion 2
Mexico City, CDMX 1
León de los Aldama, GUA 1
Créteil, Île-de-France 1
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Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

  • uaghazadae
    uaghazada (@uaghazadae) reported

    @BenSyne seems like its github issue. pls go to repo from website

  • shinyflvres
    Shiny (@shinyflvres) reported

    @Lastochka1996 Could you let me know if that happened again? And if so open a issue report on github

  • 0xdoppo
    0xdoppo (@0xdoppo) reported

    Well fk I need to spend more time talking to VCs and doing BD outreach for Peanut Shopper. Then I find another OCR failure and disappear for hours. My GitHub doesn't even properly reflect the excuse either. Most of the loop is DB driven: ingest thousands of flyers → normalize → isolate failure patterns → adjust normalization → rebuild dataset → measure what improved and what re-broke. Annoyingly, OCR isn't really the problem. Mapping meaning across wildly different flyer formats and neighboring regions is. New formats sprout up constantly violating whatever assumptions worked before, so the problem stays dynamic. So yeh, I should probably be talking to investors and retailers more. Instead I'm playing with the data.

  • mardehaym
    Mark Ajzenstadt (@mardehaym) reported

    We built a harness inside @LimestoneHQ. It's called Velocity Core. It's an autonomous task executor. Board task in, reviewed pull request out. The trigger is a status change on your issue board. Five steps: 1. Trigger. A ticket moves to "AI: Ready" in Jira or ClickUp. The orchestrator resolves the repo and pulls context. 2. Sandbox. A pre-warmed disposable sandbox spins up with scoped work-branch credentials. 3. Loop. The agent plans, edits, runs tests, reads CI feedback, and fixes. Repeats until CI passes. 4. Gate. A PR opens linked to the original work item. Human review. No auto-merge. 5. Routed. Every LLM call goes through the gateway with task metadata. Every run is traced. The harness plugs into the client's existing Jira, GitHub, and CI pipeline. The human stays at two points: intake (writing the ticket) and output (reviewing the PR). Everything between is the agent's job. We deploy it inside client engineering orgs. Running internally at Limestone, landing inside your perimeter. Board task in. Reviewed PR out.

  • dev_in_tech
    Dev (@dev_in_tech) reported

    @jonathan_wilke I think the main issue was many people who bought it used it in a public github repository so a simple search gave you the whole repo

  • Jus3G
    J Greek (@Jus3G) reported

    @Dylanmadden someone already made a fix for it on GitHub

  • spengrah
    spengrah.eth (@spengrah) reported

    update: looks like @github developer support has already taken it down 🙌

  • _dhamidi
    Dario Hamidi (@_dhamidi) reported

    @marlene_zw do you know anybody who is working on the official Github MCP server? Dumping raw API responses with kilobytes of JSON into the context window makes it basically unusable :( And since the tools don't declare output schemas, codemode also doesn't work well

  • Treigua
    Treigua (@Treigua) reported

    @soruvalab Turning a market gap into GitHub issues in one click is fast, maybe too fast. The dangerous step is converting an unvalidated gap into a polished backlog before a buyer confirms the problem.

  • _jsolly
    John Solly (@_jsolly) reported

    And this is because GitHub sign in 404s. Apparently email sign in forgets your password and redirects to localhost when try to reset it. They should have dialed this in.

  • ahdeyy21
    Ade (@ahdeyy21) reported

    are pull requests in github down?

  • Arshad_sayed_03
    Arshadsayed (@Arshad_sayed_03) reported

    Day 48 coding continuous @ChaiCodeHQ What I implemented today? Solved 5 GitHub Classroom assignments focused on breaking each problem into validations, logic, and output formatting practiced writing cleaner, more structured JavaScript instead of jumping straight into code.

  • BuildFastWithAI
    Build Fast with AI (@BuildFastWithAI) reported

    built a dashboard that tells you which github actions jobs are flaky. right now the answer to a failing test is "just re-run it." nobody tracks how often. point it at a repo, pick a time range, and you get: - which jobs fail repeatedly and how often - which are flaky vs genuinely broken - your slowest jobs, ranked - failure rate per branch - one overall ci health score read-only token. it never writes to your repo. check out CI Flaky test observatory, link in replies. what's the flakiest test in yours?

  • JoePro
    JoePro (@JoePro) reported

    How I’m maximizing Grok Bot so far: Connect the tools you actually live in first (Gmail, Slack, GitHub, Notion). The bot only gets powerful once it can see your real work. One weekday morning digest beats ten random chats. Have it skim inbox, Slack, PRs, and Notion before you open anything. Add event watchers next. Slack mentions, your own emoji reactions, GitHub review requests. Ping only when something needs you. Don’t build a giant automation factory. Show it a workflow once, save it as a routine, let it run. Spin specialist bots for lanes (inbox, eng, ops) instead of one mega bot that does everything halfway. Use the browser login for tools with no connector yet (Figma, LinkedIn, random SaaS). Same computer, persistent sessions. Start narrow. Signal over completeness. Stay quiet when there’s nothing. Trust grows from boring reliability. Curious what setups people are running. Made by GrokBot 👇

  • HashgraphOnline
    HOL (@HashgraphOnline) reported

    HOL Guard sits between your AI agents and the shell, files, and network, intercepting risky actions and asking you to approve them before they run. v2.2.64 makes it less of a nag. Safe GitHub reads with approved jq filters (repo metadata, release notes, issues) now run without an approval prompt. They used to trip false network-host flags because GitHub media-type headers looked like addresses. Review still kicks in for the genuinely risky stuff: *** fetch outside an approved repo, credential overrides, unsafe jq options, and GitHub pipeline variants with dynamic shell args. uv tool install hol-guard==2.2.64

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