GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Inverness	Website Down	10 days ago
Quito	Sign in	11 days ago
Junín	Errors	11 days ago
Guadalajara	Errors	11 days ago
Paris	Website Down	11 days ago
Quito	Errors	11 days ago

Full Outage Map

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:

• 
  Alejandro Milián (@alejmilian) reported 37 seconds ago

  Your GitHub activity is a terrible way to judge how good a developer is. A lot of great developers spend all day writing private code, solving boring production problems, reviewing PRs and helping teams ship. A green contribution graph doesn’t tell you much.

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  Jake Liddell (@Jakeliddell) reported 40 seconds ago

  This morning I typed a 2-sentence feature request into our tracker for The One List, the task app we're building. Filed it at 8:55, went into my 9am meeting. At 9:32 there was a built, tested, reviewed pull request waiting for my click. Not for the feature I filed - for the better-specified version of it I'd filed in June and forgotten. My AI team closed this morning's as a duplicate, worked out why the June one had been invisible to its own sweeps for 10 weeks, repaired that gap, and built the June one instead. Six agent runs, five hand-offs: triage, a design pass, a re-triage, a build, and an independent review that re-verified the other agents' claims against the code rather than taking their word. Deployed to a PR environment, 2,500 tests run. My total involvement: one click. That's the shiny bit. Here's the messy bit. Getting here broke in every way you'd predict, plus a few you wouldn't. The worst day: one dispatch fanned out into nine concurrent builder runs on the same issue. Our agents were fired by GitHub label triggers, and the trigger matched the label SET, not the event - GitHub emits one webhook per label in a batch write, so one write woke every matching agent. Nine builders, one issue, each convinced it held the job. We paused the whole team for three days and rebuilt delivery: one dispatcher workflow that reads which label actually fired, dedupes on a concurrency group, and fires exactly one agent by API with an explicit dispatch message. The agent has to echo that message back in its claim comment, so every claim is traceable to the dispatch that caused it. Since the rebuild: every dispatch, exactly one run. Concurrency, generally. A label is our claim mutex - claim, then re-check, because two runs can both claim inside the same second and one has to stand down. And we learned that a full-set label update is a silent race: whoever writes second erases whoever claimed first, no error, no trace. Every label write is an add-one or remove-one API call now. That rule exists because we watched the other kind nearly cause a double implementation. Stale instructions. The agents read their own operating rules from the repo at run time. A failed *** fetch fails silently - the local ref stays pinned to last week and everything still "succeeds". One agent filed a confident bug report against a state of the codebase that no longer existed. Every run now fetches immediately before reading, and cross-checks the SHA against the live API before any decision rests on the content. Death. A run that dies between claiming work and finishing it leaves the mutex locked, and a locked mutex looks identical to a busy one. So a nightly reaper clears claims with no artefact behind them. Then GitHub's cron scheduler started dropping the reaper's own slot - two nights running - so now a watchdog on a completely different scheduler checks that the safety net ran, and fires it if it didn't. "Who watches the watchmen" turns out to be a real ticket in the backlog. And my favourite: the reviewer that never woke up. It claimed a PR while CI was still running, subscribed to a completion event that never fires for its trigger type, and slept forever. The PR merged with no review and no record. Reviews now finish in the pass that started them, polling CI with a hard cap. Three rules made it stable. GitHub is the only ledger. No agent keeps private state, ever. Every action leaves a comment, a label or a commit, and the corollary does the real work: an action that left no trace didn't happen. A triage with no comment is not a triage. Review trusts nothing. The reviewing agent re-verifies the builder's and designer's claims against the diff, not the comments. It has caught real contradictions between what a PR said and what its code did. And autonomy is earned on measured results, not on a good week. Every merge is still mine, for now. Each class of change - docs, dependencies, code - earns wider autonomy on a tracked streak of clean reviews, and one caught defect resets that class's counter. None of this needed exotic tooling. It's a repo, labels, one workflow file and a handful of scheduled agents with a page of instructions each. The hard part wasn't intelligence. It was what it always is in distributed systems: delivery, mutexes, dead processes and stale reads. The agents are smart. The plumbing still had to be built as if they're not.

• 
  Enjoyer (@Enjoyer100x) reported 1 minute ago

  @StandartXBT Brother, That github Ozzy posted, was made 4 days before Pons V2, they switched to it because they wanted to keep what was built for V2 private. It’s that simple. Either way, PePons is a strong meme and the team running it are doing a great job, perhaps they thought the info was true rather than larp. They are also in talks with Ozzy and he has been helping them transistion to V2. Why would he do that for larpers. People like you think you are doing good for the space, but actually you are part of the problem. If you can do true research the don’t speak.

• 
  Ascendant Odyssey (@AscendOdyssey) reported 2 minutes ago

  @vheeorji22 The simplest way I can break it down is that GitHub is just a place to save versions of your code. If you make a mistake in your code and you want to go back to when the code was working properly, you can bring back the version of your code that was working properly before the changes you made broke it.

• 
  Dosuka (@DosukaSOL) reported 3 minutes ago

  @CryptoExpert101 yupp and took down one of the websites. So either she rugged. Or someone hacked her x, website and github

• 
  DFIR Lab (@DFIR_Lab) reported 3 minutes ago

  A vulnerability was identified in chenhg5 cc-connect up to 1.4.1. This affects the function shellExecCommand of the file core/engine.go of the component Management API. Such manipulation of the argument exec leads to os command injection. It is possible to launch the attack remotely. The exploit is publicly available and might be used. The reported GitHub issue was closed automatically due to inactivity.

• 
  Duy /zuey/ (@goon_nguyen) reported 4 minutes ago

  i moved my development environment to the cloud, and i do not think i am going back linux is much closer to production than my laptop, so fewer bugs hide behind local differences and debugging gets less annoying but the bigger change is that development no longer depends on my laptop being open i can send instructions from my phone while i am outside. the agents keep working on cloud machines, run tests, open worktrees, and report back without turning my laptop into a portable space heater GitHub is now part of the runtime when an issue gets the ai-handle label, a webhook wakes the cloud agents. they inspect the issue, create an isolated workspace, implement the fix, run the checks, and report the result i do not need to open Terminal or launch Codex just to start the work my Discord support flow goes even further: - a customer reports a bug - an AI support agent gathers the details and creates an issue - the label triggers a coding agent on the cloud - the agent fixes it, validates it, and sends the result back into the support flow that is the first time "autonomous agent" has felt operational to me instead of being a demo with a chat box cloud machines also remove several stupid local bottlenecks the internet connection is fast. parallel agents and test processes stop fighting with the apps on my laptop. multiple worktrees stop eating my mac M1 512GB SSD that was never designed to host an AI engineering team there are tradeoffs, of course. cloud agents need strict permissions, isolated environments, budget limits, logs, and a clean path for human review. running 24/7 without guardrails is just a faster way to create incidents but the direction feels obvious AI coding agents should live where software runs, events happen, and automation can continue without waiting for a developer to open a laptop for me, the laptop is becoming a control surface the development environment is becoming infrastructure

• 
  Cyber_Racheal (@CyberRacheal) reported 7 minutes ago

  In early 2024, Microsoft engineer Andres Freundaccidentally thwarted one of the most sophisticated cyberattacks in history. While testing an unstable version of Debian, he noticed a tiny 500ms delay in his SSH logins, a blip most people would ignore. Curiosity led him to find a massive backdoor hidden in XZ Utils, a standard data compression tool used by almost every Linux server on the planet. The deal here was : The backdoor targeted the SSH protocol, which is the primary way admins securely log into remote servers. If Freund hadn't spotted it, attackers would have gained a "master key" to bypass authentication and execute code with root privileges on billions of devices. The culprit (under the name "Jia Tan") spent two years building trust in the open-source community, slowly gaining enough "cred" to become a project maintainer and plant the malicious code. How It Was Resolved Once Freund confirmed the malicious code, he emailed the Debian security team and went public on the Openwall mailing liston March 29, 2024. Major Linux vendors like Red Hat, Fedora, and Debian immediately reverted to older, safe versions of the software. GitHub quickly suspended the compromised accounts and disabled the affected repository to stop the spread. A clean version (XZ Utils 5.6.2) was released in May 2024, effectively closing the vulnerability tracked as CVE-2024-3094. Essentially, the internet was saved from a "digital apocalypse" because one guy was annoyed that his computer felt half a second too slow.

• 
  Mohammed Raza (@raza_yaps) reported 8 minutes ago

  finally got access to GPT 5.6 Sol Fast but in Github Copilot.... still doesn't feel like fast, don't know what's the issue

• 
  Lord Bean (@lifeisameeme) reported 8 minutes ago

  imagine handing someone your codebase and saying: break in, and don't tell me about anything you couldn't actually pull off. that's basically Shannon, an open-source AI pentester. it reads your source, finds an attack path, then drives a real browser to exploit it for real. no working proof-of-concept, no entry in the report. its sample run on OWASP Juice Shop: 𝟮𝟬+ 𝗿𝗲𝗮𝗹 𝘃𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 including auth bypass, SQLi, IDOR and SSRF, each with repro steps. on the crAPI test app: 15+ critical/high across JWT and broken API auth. why it lands now: teams ship every day with Claude Code and Cursor, but the pentest still happens once a year. that's ~364 days where nobody's actually checking. 𝟰𝟳𝗸 𝘀𝘁𝗮𝗿𝘀 since last fall, and it runs locally from your terminal. be honest: what would your side project survive? 🔗 GitHub in the reply 👇

• 
  tender (@tenderizzation) reported 13 minutes ago

  of course you think CUDA is better, the shine is starting to wear off of cutedsl and you finally profiled the kernel launch overhead. not to mention you just saw a github issue showing how many microseconds you're never going to get back from torch.library.wrap_triton. you're going to be convinced of that until next month when your workload hits a shape you didn't template specialize for. then it'll be back to DSLs until one innocent little API deprecation throws you under the bus and

• 
  Tobias Schmidt (@tpschmidt_) reported 20 minutes ago

  • cold starts are slower than GA for now, the optimization work happens during the preview Billed at normal Lambda rates. Upstream stable is expected in October, GA within two months after that. Feedback goes into a GitHub issue per runtime 👀

• 
  Sudo su (@sudoingX) reported 23 minutes ago

  watch anon! i had a 124B moe with 5.1B active parameters build me the octopus invaders game, driving hermes agent on a single dgx spark, 128gb unified. locally. the model is ling 3.0 flash from @AntLingAGI, a new emerging open lab from china, i am running the official int4, serving is vllm with ling's mtp drafter, decode opens around 40 tok/s. the prompt is mine, 157 lines, public on my github since may, and it has a body count, it broke a 36B in march, needed babysitting on a 9B in may, a 35B passed it in june, ling is the fourth model to face it and the biggest. and the run was a thriller. it read the spec and went building module by module, node checking every file behind itself, and the serve crashed on it, oomed the box mid write more than once, and every single time it came back up it read what was on disk, found the open function and kept building like nothing happened. no restart from zero, no broken files, the game grew through every crash until the suite ran 18 of 18 green. then it kept iterating while i played, denser waves, wider spawn field, tapered bullet bolts, nav lights on the ship, small verified diffs landing on a running game. my best run so far reached 524,915 points, level 13, max combo x138. i was enjoying. a 124B on a desk box wrote me an arcade game and survived every crash doing it. that is what local looks like now in 2026. and not a lot are aware of it. next i am giving the same prompt on same metal, same harness, against laguna, also one of my favorite models on a single dgx spark. the versus is coming.

• 
  Dharmendra Jagodana (@Dharmendra_Jago) reported 31 minutes ago

  Shipping notes from this week on BuildBase. Nothing on the headline features. A quiet infra week - the kind that doesn't sell a landing page but decides whether a platform feels tight or janky six months in. What went out: - New Relic wired in for logs. Structured search across the tenant server instead of grepping stdout when something goes sideways at 2am. - Status check endpoint reworked so uptime probes and internal dashboards reflect what's actually happening under the hood (Mongo, Redis, queues) instead of just returning 200. - Console login page redesigned. Docs, changelog, GitHub, support - all baked directly into the login screen instead of buried three clicks deep. - Handful of small fixes across the console. We're pre-first-paying-customer, so I feel every rough edge before customers do. That's the deal for now - fix it in the dark, then invite people in. If you're building something on React or Next.js that needs auth, workspaces, usage-based billing, notifications - happy to show you around. Links in the first comment.

• 
  Doc (@quantum_binary) reported 32 minutes ago

  @burkeholland Whos "we" I can certainly believe it for GitHub that is down every other day for 12 hours. But I think real engineering will win in the end and a better platform, built by people who care more than people at @github will replace them

• 
  notreroute (@notreroute) reported 33 minutes ago

  most founders spend $40,000 a month on payroll before discovering 80% of operational workflows can run on autonomous agent pipelines with zero human intervention. in 6 minutes ByteByteGo breaks down how deterministic execution order replaces entire management layers: state coordinator logging directly to GitHub worker agents executing API queues and pull requests independent validators running 12-second test loops the surface read is treating models like chat interfaces, the actual lever is orchestrating autonomous micro-services. i turned the whole architecture into a practical deployment guide you can run in production. worth more than an entire tier of middle management salaries. watch the clip first, then the full architecture breakdown is below. you'll find the full breakdown in the article below

• 
  Gabriel Rubens (@gabrielrubenss) reported 33 minutes ago

  VPS deploy via GitHub (6/8): then my own fix bit me. I tagged Pensio v0.73.1, every job went green, the release published itself, and production kept running the old version. In GitHub Actions a skipped job travels down the chain, so my new retry job took the deploy with it.

• 
  Louround (@Louround_) reported 35 minutes ago

  Nvidia is paying $13b for HuggingFace (3x last valuation) because the real issue in robotics isn't compute or hardware but data. Every robot needs millions of human demonstrations to learn how to move, and that data is spread across labs with no standard. HuggingFace became a leader with Lerobot (the github of robotics data) and Pollen robotics, Nvidia already owned the sim, models and chips but buying HF gives them the full stack with data → training → deployment. solana:69LjZUUzxj3Cb3Fxeo1X4QpYEQTboApkhXTysPpbpump is building the same coordination layer for VLA agents, but on chain and permissionless, Simarena for testing, Foundry for training and Machinefun for incentives. Same thesis Nvidia just validated with $13b but solana:69LjZUUzxj3Cb3Fxeo1X4QpYEQTboApkhXTysPpbpump is at $4m fdv. ⏳

• 
  BURKOV (@burkov) reported 36 minutes ago

  I'm glad I no longer need to figure out what the hell that means to make my app work. In the past, overcoming a difficulty when some image or binary failed for some obscure reason and digging through Stack Overflow and GitHub issues for hours, days, or forever instead of bulding was killing me.

• 
  Bluewall (@Bluewall) reported 36 minutes ago

  Found a malicious GitHub repo posing as a curated list of cybersecurity Telegram channels, every link led to the same malware ZIP. Reported it. Today GitHub Trust & Safety confirmed they took it down. Do report the shady stuff, it pays off.

• 
  Sentient (@sentient_agency) reported 37 minutes ago

  I cancelled YouTube Premium last week. The thing that replaced it costs nothing, runs in any browser, and was built by 361 volunteers on GitHub. Invidious does the four things people actually pay Premium for. No ads. Background audio on mobile. Watch without an account. Privacy from Google's tracking. All of it, free. The part that surprised me most is how clean the experience is. The entire page renders without JavaScript. Pages load in milliseconds because there's nothing to load. No tracking pixels. No autoplay traps. No recommended-for-you algorithm trying to swallow your evening. You can: > Subscribe to channels without a Google account > Get notifications when they post > Import your full YouTube subscription list in one click > Switch between dozens of public instances if one goes down > Self-host it on a $5 VPS if you want full control > Use it with the Privacy Redirect extension to auto-redirect every YouTube link The repo has been actively maintained for years. Latest release was February 2026. (100% Opensource and free to use)

• 
  Anton Martyniuk (@AntonMartyniuk) reported 39 minutes ago

  @mjovanovictech 3000 integration tests in my product run in GitHub Actions in 11 minutes (using free runner). Someone will say it's too much, but honestly, it has so much value. Without integration tests (with only unit tests), I will probably waste 100x more time on fixing issues than on running tests.

• 
  Yash Agrawal (@YashAg946) reported 40 minutes ago

  @meshapi_ai @github @Azure There are not 1000+ models. I have checked after login also

• 
  Gabriel Rubens (@gabrielrubenss) reported 47 minutes ago

  VPS deploy via GitHub (5/8): for now a blocked deploy simply runs again on a fresh runner, and that rescued five of the next six. It is a workaround though, not a fix. The strange part: it started out of nowhere and I changed nothing in my infra, so I still want the real cause.

• 
  Second Mind Systems (@Secondmindsys) reported 47 minutes ago

  @chribjel That would be an awesome capability. But once an MCP server can wake an agent up on its own, you’ve changed the trust model. Before, the agent usually started because a person or workflow deliberately invoked it. With webhooks, anything connected to it can suddenly say, hey, something happened. That’s useful. GitHub can tell it a PR merged. Monitoring can tell it something broke. Stripe can tell it a payment failed. But hearing about an event and being allowed to act on it are two different things. The agent still needs to figure out whether the event is real, whether it’s still relevant, whether it already handled it, and what it’s actually allowed to do next. A PR merged might mean check the build. It doesn’t automatically mean deploy. So the more ways we give agents to wake themselves up and react to the world, the more important it becomes to keep the original job and permissions intact. Governance must always scale with new capability because new capability also emerges new consequential surface area/blast radius.

• 
  Naymur Rahman (@naymur_dev) reported 50 minutes ago

  @MaedahBatool @utquoverload @CommandCodeAI Create a github issue, I'll take it from there

• 
  Daniel (@itsdanielpurgal) reported 52 minutes ago

  Nvidia in talks to acquire Hugging Face at $12.9B at ~80x annualized revenue of $150M. Hugging Face is essentially GitHub for AI. Is this an attempt to lock down the open-source developer ecosystem?

• 
  Subham (@iamsubham067) reported 52 minutes ago

  I shipped RecoverRev in roughly 5–6 hours. And the entire build ran on the $20 subscription I was already paying for. 0 extra credits. RecoverRev is an ecommerce revenue-recovery system designed around a fairly simple problem: "Marketplaces generate thousands of orders, settlements, returns, deductions and reimbursements. Somewhere inside those records, money gets missed." So before building, I documented the product. I defined the modules, workflows, data relationships, rules, expected outputs, evidence requirements and what each screen was actually supposed to help someone decide. The knowledge base covered things like: - Command Center — total supported exposure, primary cases, highest-value rules and marketplace concentration - Leakage Map — relationships between rules, cases, evidence and settlement impact Cases — reconstructing exactly what happened to an order - Evidence — preserving the records required to support a recovery claim - Recovery — prioritising actions based on recoverable value - Policy Watch — tracking marketplace rules that can change recovery logic - Audit Trail — showing how raw inputs became validated cases - Evidence Health — identifying whether a case actually has enough support - Review Workflow — moving cases through investigation and submission - Exposure by Rule — showing where the largest financial leakage exists - Action Optimizer — ranking which cases deserve attention first I also defined the reconciliation rules behind it. - Delivered but not settled. - Under-settlement. - Missing commission reversal. - Weight-slab overcharge. - Bank payout shortfall. - Then I gave the agent a reference image for the kind of interface I wanted. - That was basically the build brief. From there, the agent planned the application, created the modules, connected the data, built the UI, broke parts of it, inspected what had gone wrong, fixed them and kept iterating. At one point the interface was nowhere near the reference. I didn’t manually redesign it. I sent it back with the reference and the constraints. It analysed the difference and rebuilt it surprisingly close to what I had originally shown it. The original reference image is still inside the GitHub repository, so the comparison is verifiable. - No additional credits. - No additional inference bill. - No engineering team. - No extra cost to ship. This is the part of agentic development that I think gets underestimated. I also used Claude Opus 5 as a separate QA layer. I gave it browser access and had it actually move through the product like a user — opening screens, clicking through flows, checking states, looking for broken interactions, visual inconsistencies and things that simply didn’t make sense in use. So the loop wasn’t just agent builds → I review. It became one agent builds → another agent uses the product → issues are identified → the build agent fixes them → QA runs again. Entire video demo in the comment. #psychepreneur #subhamojha

• 
  Nandkishor (@devops_nk) reported 57 minutes ago

  I see the same problem in DevOps teams. - One AI agent for Kubernetes. - Another for CI/CD. - Another for observability. - Another for GitHub security. The hard part isn’t running multiple AI agents. It’s always making sure every agent has the right context without repeatedly explaining your entire infrastructure.

• 
  OG (@ogwithsauce) reported 58 minutes ago

  Why I think there needs to be some sort of control on social media. So... Stremio launched in 2015. Torrentio around 2020. Nothing "just died in Sofia" this week. And the two Bulgarians didn't build Torrentio. Different developer and completely unaffiliated. These two are two completely unrelated projects mixed into one origin story. "MIT License. 100% Opensource." Stremio's core is MIT. Its web client is GPL-2.0. Torrentio itself is CLOSED SOURCE. The part actually doing the scraping isn't on GitHub at all. Everyone can check by themselves. "No central server to seize." Torrentio IS a central server. A hosted service on somebody's VPS. Your client queries it and when it drops, it drops for everyone at once, and it has, repeatedly. "No account. No subscription." Reliable 4K means Real-Debrid or AllDebrid. That's an account and that's a monthly fee. Raw P2P is where the buffering lives. "Netflix cannot shut this down." They don't have to. Italian courts already forced Cloudflare to block torrent sites at the DNS layer. A Spanish court ordered NordVPN and Proton to block streams. RARBG, KAT are all seized, you simply attack the layer below, not the app - this is how these are handled. Now the biggest problem with this post is torrenting UPLOADS. To all you noobs cheaping out on Netflix sub and think this is actually a solution. Your IP is visible to every peer in the swarm. That's the enforcement surface and that's the actual risk that nobody mentions at all. The formula for these posts: Half of what is wrote here is checkable in five minutes. Goddamn people are stupid.