GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Security Weekly Podcast Network (@SecWeekly) reported 4 minutes ago

  Passwords get stolen. MFA prompts get approved. Rob Allen explains why some organizations are now locking SaaS apps like Office 365, GitHub, and Salesforce to specific trusted IP paths instead of exposing login access to the entire internet. Even if an attacker has credentials, they still can’t connect unless traffic comes from the approved location. Is location-based access control becoming the next layer after MFA? #Cybersecurity #SaaS #IdentitySecurity

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  KagariSoft (@KagariSoft) reported 6 minutes ago

  Anecdotes from the development of ROL: Once, the game disappeared forever. Due to a problem with *** (version control software), upon executing a command, the entire project was completely deleted from the disk. Luckily, there were backups on GitHub, but of changes from 24 hours prior. Developer tip! Learn to use *** and GitHub, and keep your repository updated with every change!

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  Alexandre Mutel (@xoofx) reported 7 minutes ago

  Last year I rolled out GitHub Copilot to all engineers in my org after realizing many weren't using any AI tools. Usage grew fast, with a classic long-tail distribution, Copilot was absurdly cheap, and in 3 weeks we'll need to revisit token consumption/budget seriously... glad I don't have yet that problem for my OSS projects with a subsidized Codex personal license 💸

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  Karim C (@BrandGrowthOS) reported 13 minutes ago

  @github the chat mode is legit. way faster than copy-pasting error messages into claude when something breaks. actually feels like talking to someone who understands the codebase

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  Alan Martinez (@pev_de) reported 13 minutes ago

  @steipete Our agents auto-tag GitHub issues by department. The legal one started filing cookie compliance bugs nobody asked about. The SEO agent found 404s nobody knew existed. Agents creating work you didn't know needed doing > agents reviewing work you already did.

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  Jason Bed (@JasonBed) reported 13 minutes ago

  @Twisttalkl @openclaw Nah it's a bug, reported on GitHub as a known issue and marked confirmed and resolved when it's shipped in the next build, thanks.

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  kaleb (@KalebAutomates) reported 15 minutes ago

  Days after the CEO came on this platform and **** on the people who made him rich with a massive lay-off; saying that "nontechnical employees have started writing production-level code".... Coinbase issues with AWS. Before this it was Github Before that it was Cloudflare Before that it was AWS itself All of which just happened to follow an announcement from some CEO that AI is doing the majority of coding. Funds are safe... for now. But how much longer until Jake in Marketing vibecodes S3 public?

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  Petra Donka (@petradonka) reported 17 minutes ago

  @OsoDerechoso @warpdotdev Could you run /feedback and open a GitHub issue? We should look into this

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  iamrobotbear (bk) (@iamrobotbear) reported 20 minutes ago

  @mattlam_ Does it still require the codebase to be on @github? I have a self hosted @gitlab server where the majority of my code resides.

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  Gabe (@gabebusto) reported 21 minutes ago

  bro setting up an agent to do production work is so easy. you just need to create an account somewhere for your agent to work remotely. cloudflare, hetzner, aws, digital ocean, etc. then pick the agentic tool, and the model, and get an api key or use oauth. then make sure in it's in a sandbox setup with the right permissions and access to your tooling like github, slack, linear, and maybe even some staging and production resources. you really need to be careful though because if agents have any write access to important stuff, it could do something really dumb like delete your database. also for the love of GOD backup your database frequently somewhere the agent can't touch. also prompt injections online can get your agent to leak sensitive env vars so you need to be careful about that. maybe limit network access or inject tokens/sensitive vars once requests leave the sandbox. you probably don't want the agent always on sitting idle, so either figure out how to give it work efficiently to always keep it busy or use some that can pause and resume with ease so you're not billed around the clock for idle resource usage. then you want guardrails in your codebase and deployment pipeline so the agent can't break things and you don't need to feel guilty not reviewing its code. because cmon, nobody wants to do that. you need to make sure your agents have as close to perfect context as possible. so maybe start building a knowledge base, move docs into the repo, or make sure your agent can easily search linear and slack and other places to build context for tasks to work on. and before each task, spend ~10-20+ mins typing things up and giving the agent as much context as possible. oh yeah and your agent ideally should be able to test its changes as completely as possible. so make sure the agent can start up the service(s) it's working on and test them. maybe you need it to open and run a browser, send screenshots, record a video, and so on of its test so you can easily review it in the PR. you also want a bugbot setup in github (if you're still using github at this point) to help scan each PR for potential issues the agent missed. and the agent should be able to automatically address any bugbot findings, fix them, run more tests, and push those changes, and run in a loop until no more bugs are found by the bugbot. i forgot to mention, you probably don't want your agent's code just yolo shipping into **** with no guards in place _after_ it deploys. allow the agent to setup it's new features and code behind feature gates or experiments and do a gradual rollout in case there are any catastrophic problems. then you'll want automatic rollback if issues are detected. and there's probably stuff i'm forgetting, but you get what i'm saying right? it's really not that hard. then you need constant vigilance of your codebase and create lots of skills to help deslop work the agents are doing, maybe create an anti-entropy agent (_another_ agent!) to hunt for growing complexity and auto-create PRs to try and fight to reduce the size and complexity of the codebase. then you'll inevitably have incidents caused by code written by agents that was never reviewed by humans, and either you or yet-another-agent will take a look at your production systems to help you figure out what's wrong because it's all becoming a bit more foreign to you. and you can just have the agent try to make changes on your behalf to fix things and hope to God that it doesn't make things worse. if all of this isn't exciting enough, you then give each engineer and even non-tech team members their own access to the ai tools and agents and models of their choice which easily costs an extra few hundred dollars per month per employee at best. in the worst case, you have someone on the team blow through the team's monthly AI spend by a significant margin by accident using the best models in fast mode because they were too impatient to just use the sota models at normal speed. and spend will likely only go up btw. and if you're not reading between the lines here, product work slows because everyone is playing with agents to learn how to use the agents more efficiently in the hopes that it's a magical bullet that solves all of the woes in software engineering and building production systems. and now you need this magical bullet to work because you're falling behind to teams who maybe aren't distracted spending all this time and money trying to make this all work. but you're definitely going to catch them. once you've figured this out, you'll 10x or 100x your output and leave them in the dust! or... you could just have engineers start coding by hand again before it's too late and becomes a lost art. you can even make modest and tasteful use of ai, but without doing all of the above. i actually miss the days of supermaven and early cursor. they were so simple and actually removed some friction and some of the annoying parts of coding.

• 
  Nikolaï Roycourt (@Nikokow) reported 22 minutes ago

  CI/CD infrastructure Copilot GitHub Actions Pull request system Code review workflows Issue tracking Project management GitHub Pages Does that count?

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  Elena Revicheva (@reviceva) reported 29 minutes ago

  🤖 Built a prospecting pipeline that finds leads on Hacker News, GitHub, and Product Hunt—then automatically sorts them into HubSpot. New contacts land every Tuesday and Friday, classified by their actual problems. Zero cost, fully automated. #AI #BuildInPublic #AIFounder

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  CMD CNTR | Web Engineer Experts (@cmdcntr) reported 30 minutes ago

  Spicy take. '''Free unlimited AI coding''' repos keep going viral on GitHub. It'''s not innovation. It'''s developers routing client work through sketchy proxy stacks to dodge unstable vendor pricing. A vendor problem dressed up as a hacker win.

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  Aryan Pamwani (@aryanpamwanii) reported 30 minutes ago

  Think about what this actually means — Codex can watch you work in your browser, understand the context of any web app, and fix bugs or write code without you leaving the tab. GitHub Copilot works in your editor. Codex is coming for your entire browser. The IDE just got a lot less important. 👀

• 
  Vaibhav Sisinty (@VaibhavSisinty) reported 32 minutes ago

  The big AI labs should be worried. 295 people on GitHub just shipped 588 PRs in one week. And the irony? Hermes runs on their tech. Anthropic, xAI, OpenAI, Google. Every major lab is powering the very tool that's outbuilding them. The community called this release Tenacity. The name is the flex. Here's what they have shipped: → A team of agents, not just one: Multi-agent Kanban with heartbeats, retries, zombie detection, and a hallucination gate. Spin up a board, drop tasks on it, let agents pick them up. The framework catches the ones that crash, the ones that lie, the ones that go silent. You're managing a team now, not running a single agent. → The agent stops losing the plot: The new /goal command locks Hermes onto a target and keeps it there across every turn, every restart, every interruption. Tell it once. It remembers the brief on message 14. The agent that doesn't drift. → Plug in any model. Swap providers without touching core: ProviderProfile makes every model source a clean plugin. OpenAI, Anthropic, xAI, Google, your own. Drop them in, swap them out. Build with whatever model wins this week, not the one you committed to last quarter. → Sessions that survive everything: Gateway crashes. Updates push. Source files reload. The conversation auto-resumes exactly where it left off. Checkpoints v2 rewrites state persistence with real pruning. Long-running builds stop dying because of a restart. → The agent reviews its own code before you do: Post-write delta lint. Self-linting on every write. Syntax errors surface immediately, not three commits later. Fewer broken builds. Less debugging downstream. → Eight critical security holes, closed in a single release: Redaction on by default. WhatsApp rejects strangers by default. Discord role-allowlists scoped to your guild. TOCTOU windows sealed shut across auth and MCP OAuth. Production-ready defaults out of the box. Big AI labs have thousands of engineers and billions in compute, and 295 people on GitHub are still outshipping all of them.