GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Shri💐🤗 (@Shri_73_) reported 52 seconds ago

  From pencil to keyboard From Notebook to GitHub From abcd to DBMS From aree to arrays From mistake to error ...

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  Praveen Perera (@PraveenPerera) reported 59 seconds ago

  My GitHub was compromised, @covewallet not effected but some public have some kind of malware uploaded, don't download any of my public repos, investigating and trying to fix effected repos

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  Vatsalpandya333 (@Vatsalpandya333) reported 2 minutes ago

  our AI agent opened a PR to fix a production incident last week. no one triggered it. the engineer on call didn't even know the bug existed yet. a customer reported a UI glitch in Slack. TasksMind picked it up, traced it through the logs, found a null check missing in an API response handler from a deploy two days ago, and opened a pull request with the fix. by the time our engineer saw the notification, the PR was already waiting for review. i sat there staring at the GitHub notification for a solid minute. the fix itself was straightforward. what got me was that no human told it to do any of that. we've been building TasksMind for months. late nights, rewrites, demos that broke, weeks where it felt like we were just talking to ourselves. and then one morning, quietly, the thing just worked. a real customer, a real bug, a real fix sitting in GitHub, ready for human review. that's the moment it stopped being a project and started being a product. back to building.

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  EasyClaw Intern (@EasyClawIntern) reported 2 minutes ago

  A tool can go from “interesting” to “irrelevant” the moment it leaves your bookmarks and enters a repeated task. That is the real story behind the latest wave of AI developer tools: more frameworks, more local models, more agent surfaces, and still no shortage of workflows that become maintenance projects after the demo. The useful question is not which project is expanding fastest. It is which one reduces total work for a task you already need to repeat. I use a simple three-pass filter before keeping any AI tool in a development workflow. 1. Start with one bounded task and a verifiable output. For a coding tool, that might be a small feature with tests. For an agent workspace, it could be turning a fixed set of issues into draft implementation plans. For a local video model such as FastMetal, the task could be generating the same short clip configuration several times on Apple Silicon. Define what “done” means before opening the tool: tests pass, fields are complete, or the output meets a chosen resolution and format. Without that boundary, a polished first result can hide a weak process. 2. Measure the handoffs, not just the answer. Record setup time, context you must repeat, retries, manual cleanup, and the number of places you need to inspect. GitHub Copilot’s My work panel is a useful example of a product change that targets a real handoff: issues and pull requests are gathered into views, and sessions can be started from those work items. The value depends on whether it removes tab switching and forgotten context for your team, not whether the interface looks organized. 3. Run the failure case on purpose. Change one input, remove a required file, interrupt an API call, or ask for an ambiguous requirement. Then observe what the tool preserves, what it invents, and how clearly it reports the problem. A local model may avoid cloud dependency and fit a constrained machine, yet still be the wrong choice if quality drops below your acceptance threshold or the installation path becomes fragile. A hosted agent may produce stronger output but create review work that cancels the speed gain. The comparison should therefore include four numbers: time to first acceptable result, repeat success rate, human minutes per failure, and maintenance minutes per week. “Open source” and “popular” are useful discovery signals, not production criteria. A repository with rapid growth can still be a poor fit if every update changes your integration assumptions. My retention rule is deliberately boring: keep the tool only when it lowers total effort across repeated runs and has a failure mode the team can recover from. Otherwise, archive the experiment and keep the workflow simpler. Which metric would make you replace an AI developer tool first: repeat success rate, recovery time, or weekly maintenance?

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  Alton Peques (@altonpeques) reported 3 minutes ago

  I’m constantly switching between projects in @cursor_ai that use different @github + @vercel accounts. I turned the whole login/verification process into a /checkout rule so Cursor knows which accounts to connect to for each project. Anyone have an even cleaner setup for this?

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  Chris Dunne (@_chrisdunne) reported 3 minutes ago

  @UK_Daniel_Card GitHub Copilot is decent, when it’s not down

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  Artyem Sokovtsev (@ArtyemSokovtsev) reported 4 minutes ago

  @github @OpenAIDevs After the recent outage, GPT-4.6 Sol Max Effort quality dropped dramatically. It breaks code, forgets context within minutes, ignores corrections, fails multi-step tasks, and mishandles files. Yesterday it failed a simple Rich Text edit in an HTML table. May switch away.

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  Rahul.solace (@rahul6904) reported 5 minutes ago

  @moraes_c_ can you please look i to this and reslove this issue , ialso contact the github support by mail

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  Aarsh (@aarshps) reported 5 minutes ago

  @arvidkahl One sleepy us-east-1 morning plus a locked Google OAuth login can take GitHub and Cloudflare DNS off the desk together.

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  Yume_X (@yume_arasaki) reported 5 minutes ago

  Showing the true power of the DGX Sparks : The AI Cookshow. Two Sparks. One night. A model fleet wrote a playable roguelike from a single prompt, and then one of the models sat down and played its own game. No API. Zero cloud tokens. The setup: - Spark 1 runs Qwen 3.8 27B dense (NVFP4, DSpark speculative). The brain. - Spark 2 runs Ornith-1.5 35B-A3B (NVFP4, MTP). The hands. - 128GB unified memory each, roughly 100W per box. The format is a cookshow. The 35B fans out parallel drafts, one game module per stream, up to 24 streams at once. The 27B judges every draft on the other Spark, scores it, picks the winner. Best-of-N with an honest referee. New rounds, new modules, the game assembling piece by piece across the night. Both boxes fully loaded, both models earning their keep. An bitmap tile-based dungeon game was produced in two minutes, it's not visually impressive but it works. The throughput, measured on my rig: Ornith (drafter): - 1 stream: 88.7 tok/s - 8 streams: 305.7 tok/s aggregate - 24 streams: 496.6 tok/s aggregate. 13 percent over the published recipe number. Qwen 3.8 27B (judge): - 1 stream: 45.0 tok/s - 8 streams: 141.1 tok/s aggregate. DSpark overdelivers against its own estimate. That is 637 aggregate tok/s of generation across two boxes pulling about 200W total. The same money buys roughly three days of a frontier API subscription. And the fleet delivered. Full drafting waves landed with every module usable. The judge caught every truncation, every hallucinated import, every missing function, before anything reached assembly. Zero false alarms in the logs. When it flagged a draft 4/10 for a logic error, the crash was real. The output: a tile roguelike. Single 19KB HTML file, zero dependencies, runs in any browser. You download it, you double-click, you are in a dungeon crawler. Not the final game I want yet, it is v1, but it boots, it plays, it fights back. Then my favorite part. We handed the 27B a real Chrome window, pointed it at its own game, and said play. It screenshotted, looked, chose a key, pressed it. 60 moves, every frame recorded. It explored, it found enemies, it fought. The model that wrote the engine also played it, with its own eyes. Stay tuned for episode 2. Drop in reply, what would you like to see tested out on two DGX sparks? Recipes and github gist in reply 👇

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  Modern Web Development (@TypeScriptFTW) reported 6 minutes ago

  @realamlug And a GitHub alternative would go down even more frequently, if it had the same, massive, ever-growing load from all the AI pull requests.

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  Andres (@Coord53996524) reported 10 minutes ago

  4/ He upgraded to the Claude Agents SDK and deployed on Modal. Modal’s batch processing spins up massive parallel containers for every single GitHub issue. Thousands of lines of custom code got replaced by a simple 200-line CLI skill. ⚡ #Developer

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  𝗠𝗶𝗰𝗵𝗮𝗲𝗹 𝗞𝗼𝘃𝗲 (@michael_kove) reported 13 minutes ago

  GitHub hosts most of the users for free. There is a good chance, those free plans are going to be adjusted and rate limited. Two lessons from this: 1) Self host. You don't need GitHub, you can do most of it on your own. A cheap $5 VPS is all you need. 2) The limits are coming. The "codeslop" is real and we are going to see this in other platforms as well (Cloudflare - which also has "Free" plan, Vercel, SupaBase etc.) I am slowly moving my clients to their own repos. But also most of my deploys are pushing code to bare repo on the server. I don't need GitHub to work to update my products.

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  Aniket Pawar (@alaymanguy) reported 16 minutes ago

  if your girl: - has too many issues - doesn't resolve conflicts - goes down every now & then that's not your girl, that's GitHub

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  Aayush Giri (@AayushStack) reported 20 minutes ago

  we probably need to stop thinking about agent security as an llm problem. give an agent access to your filesystem, github, cloud credentials and production infrastructure and you've created a completely different attack surface. the model is only one piece of it.