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 (52%)
• Errors (33%)
• Sign in (15%)

Live Outage Map

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

City	Problem Type	Report Time
Ahmedabad	Errors	5 days ago
Delme	Sign in	5 days ago
Lyaud	Website Down	5 days ago
Catania	Errors	8 days ago
Inverness	Website Down	20 days ago
Quito	Sign in	21 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:

• 
  ONCHAIN COP (@OnchainCop) reported 7 minutes ago

  @PogNyx lmao anyone can create a github issue retards this guy is a larp

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  Straggler Liu | AI & Semis (@StragglerLiu) reported 7 minutes ago

  NVIDIA($NVDA ) Is Paying $14B for a Company With $150M Revenue. That's Not Financial Logic — It's Ecosystem Control. NVIDIA is in advanced talks to acquire Hugging Face for ~$14 billion ($12.9B acquisition + $1B retention), per Bloomberg. To put that in perspective: Hugging Face does ~$150M in annual revenue. That's ~86x revenue. Microsoft paid ~1.6x revenue for GitHub. Google paid ~3.5x revenue for DeepMind. NVIDIA is paying 20-50x more on a revenue multiple basis. The premium is not for revenue. It's for control of the AI developer ecosystem. What is NVIDIA buying? Hugging Face hosts 500,000+ models, 250,000+ datasets, and serves millions of developers. It is the single most important distribution channel for open-source AI. If you build AI, you use Hugging Face. That makes it the front door to AI development. Why NVIDIA is paying this premium: 1. The "NVIDIA triple lock." NVIDIA's hardware lead (GPU) is real. Its software lead (CUDA) is a moat. But the third lock — the developer workflow — was missing. Hugging Face is that workflow. Developers discover models on Hugging Face, deploy them, and optimize them. Whoever controls that discovery layer controls which hardware gets used. 2. The GitHub analogy, inverted. When Microsoft bought GitHub, developers were already using GitHub. Microsoft didn't need to capture them — it needed to prevent Amazon/Google from doing so. NVIDIA faces the opposite problem: developers are already using NVIDIA hardware. But they're discovering and deploying models through a neutral platform. NVIDIA is eliminating that neutrality. 3. The long game: inference, not training. NVIDIA dominates training. But inference is the bigger TAM — and it's more fragmented. If NVIDIA controls the model discovery and deployment layer, it can steer inference workloads to its own stack. That's a 10-year strategy disguised as a 14-billion-dollar acquisition. Who wins, who loses: NVIDIA (NVDA): Acquires the developer distribution layer. The most important strategic move since CUDA. Shifts the valuation case from "chip cycle" to "platform economics." Competitors (AMD, INTC): Lose neutral access to the primary AI model distribution channel. This is a structural headwind that no amount of hardware catch-up can fix. Cloud providers (MSFT, AMZN, GOOGL): Hugging Face was a neutral hub. If NVIDIA controls it, cloud providers risk being disintermediated from AI workload decisions. The open-source community: The platform that was built on openness is now owned by the dominant hardware vendor. Neutrality is the first casualty. The capital question: Can NVIDIA integrate Hugging Face without destroying its community value? If yes, the $14B is cheap. If no, it's a very expensive mistake. The answer will define whether NVIDIA becomes the AWS of AI — or just another hardware company with an expensive acquisition. Note: Acquisition details based on Bloomberg reporting; not confirmed by NVIDIA or Hugging Face. Revenue multiple comparisons based on publicly reported figures.

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  HeroGamer⚡ (@herogamer21btc) reported 9 minutes ago

  💻 GitHub Issues vs Draft PR vs Open PR — the difference nobody explains: 🔴 ISSUE = Should we do this? No code yet You describe the problem "App crashes when pasting OP_RETURN" "We need X feature" Anyone can open it Goal: decide IF and WHAT to build 🔵 DRAFT PR = I'm doing this, is this the right way? You have WIP code "I fixed it by doing Y, but not sure about placement / approach" Can't be merged Perfect for early feedback Goal: validate HOW you're building it 🟠 OPEN PR = I did it, ready for final review, please merge. Code done, tests pass Ready for final review Goal: ship it 🌊 Flow: Issue → Draft PR → Open PR Most people skip Issue or Draft and go straight to Open PR. Then maintainer has to review both the idea AND the implementation at once = slow, painful. Start Draft when unsure.

• 
  Alireza Bashiri (@al3rez) reported 10 minutes ago

  So I built a workflow around that ↓ 1/ Every enterprise project needs proper E2E tests. An agent should reproduce a bug, implement the fix, then generate screenshots or video proving the feature works. "The tests passed" isn't enough. I want evidence. 2/ Every feature starts as a detailed GitHub issue. Requirements, expected behavior, reproduction steps, screenshots, edge cases. Foundry syncs issues and converts them into Beads so agents keep the right context across long sessions. 3/ We only use Claude Code, Codex, or Grok at High/Max effort for implementation. A weak model with a cloud machine doesn't become an engineer. The model still needs enough reasoning to understand the codebase, test its changes, and recover when things break. 4/ Each agent gets its own isolated @asciidotdev Box. It can install dependencies, run the app, open browsers, modify code, execute E2E tests, and collect evidence without touching another agent's environment. One issue. One box. One clean workspace. 5/ When an agent finishes, Foundry checks: - Did the build pass? - Did the tests pass? - Did the E2E flow work? - Is there screenshot/video evidence? - Does it match the ticket? If anything fails, the task goes back to the agent. 6/ Green tasks move to staging. Only after passing staging do we allow supervised production deployment. Agents do most of the work. Humans still own the final gate. The workflow: Slack request → GitHub issue → Foundry sync → Beads context → Isolated Box → Claude Code/Codex → Build + test → Evidence collection → QA staging → Supervised production The stack: PostgreSQL for system state. Beads for agent memory. GitHub Issues for requirements. @asciidotdev Box for isolated execution. Claude Code and Codex for engineering. Each Box costs roughly $0.01-$0.05 per task. The expensive part isn't compute anymore. It's building the system that gives agents context, forces verification, and prevents bad code from reaching production. 100s of agents can write code. The goal is making 100s of agents ship code you can trust. That's what we're building with Foundry.

• 
  Curious Explorer (@PatelVatsalp732) reported 10 minutes ago

  I burned 14B Codex tokens. The official usage UI still cannot tell me what actually ate the weekly cap. So I shipped a Codex-only board: GitHub login, local-first sync, private by default, optional public rank + shipping proof. Roast the metric or join it.

• 
  Rafael Audibert (@RafaAudibert) reported 16 minutes ago

  @madebygps @github Tried using it with my agents (the main benefitor from this) but it doesnt really work because you cant use it with GitHub app user tokens (ghu_). Can that be changed somehow? All cloud agents will have that problem, and most of our coding happens trough cloud agents now

• 
  Chris (@chriscoolstuff) reported 18 minutes ago

  @pfernan95dev For the SEO part there's one thing that I've been also doing: Ask your agent what keywords you should search for relevant to your app on answerthepublic, perplexity and google Gather all that info old fashion, by yourself - might take around 2 hours but it's worth it Plug all that info into the agent and have it give you 5 titles for 5 articles Make it write those articles - maybe use nosoopai github or edit them manually so they seem more human like Connect the agent to google console After 1 month tell the agent to review the results If no article took off you can wait one more month or put up 5 more After the next month check what worked and double down on that

• 
  Anders (@AndersReiche) reported 18 minutes ago

  @bjmtweets Would love to hear your thesis on gitlab. I’m a software engineer, and in my experience, gitlab has been slow to everything and generally is the red headed stepchild next to GitHub. It suffers from lack of network effects, there are solutions to everything on GH, but not GL.

• 
  isha (@heeyyaaaaaaa) reported 23 minutes ago

  spent the entire day trying to reproduce a bug for a github issue 🥀🫩

• 
  Paperpal (@0paperpal) reported 27 minutes ago

  Fix your markdown rendering (readme md) on mobile @github, issues are: * auto scrolling to top after page loading * no content rendering if scrolled fast

• 
  Bratah (@BratahFGC) reported 29 minutes ago

  There may be many bugs so feel free to leave any issues in the issue section github! I hope that this release can push forward the preservation or our beloved game!

• 
  CATIRL 🏳️‍⚧️ (@CATIRL_9) reported 30 minutes ago

  @mminhamina Google GitHub "open grind", solves your problem

• 
  🐻‍❄️ (@Nerevarineeee) reported 31 minutes ago

  @jiriknesl @napenforcer yeah vscode is bloated electron slop and github....... do i even have to mention the down times and unavailability? it has literally became much worse since ms acquired it, so wtf are you talking about?

• 
  AINotes (@ainotesus) reported 31 minutes ago

  🔥 Trending on GitHub: Ponytail Ponytail helps Claude Code avoid writing code that does not need to exist. That means less clutter, fewer unnecessary dependencies, and simpler changes to maintain. Before custom code, it checks whether the feature is needed and whether the codebase, platform, standard library, or an existing dependency already solves it. It also reviews work, audits implementation complexity, and tracks unnecessary token use without dropping validation, error handling, security, or accessibility requirements. In reported Claude Code sessions on a FastAPI and React repository, Ponytail used about 54% less code, 20% less cost, and 27% less time than the no-skill baseline. Those measurements came from 12 feature tasks, so results vary with the work. Full analysis in the first reply ↓

• 
  Lily (@lobstermindset) reported 36 minutes ago

  @nnnnicholas i just setup a github issues board, will probs try out linear if it's not sufficient

• 
  catman (@catmanyau) reported 36 minutes ago

  @CricTalk29 for me, losing Cursor would hurt most because it sits directly in the editing loop. would the vote change if github outages were limited to code hosting but issues and reviews stayed available?

• 
  Gordo Polymath (@gordo_polymath) reported 36 minutes ago

  @github Please fix gh stack.

• 
  tmo (@tmophoto) reported 38 minutes ago

  @DabsMalone i had an old email account from like 15 years ago with bot in the name that i fired back up after 10 years and used for a hermes profile and it got immediately banned. i used it to sign in to x, github, everything. was a huge hassle

• 
  Yash (@dewyashtwts) reported 39 minutes ago

  recently integrated Resend into @supercodeai review so founders get PR alerts with real risk context I'm amazed what we found out when we put @coderabbitai / @greptile through the same PR: 1) coderabbit / greptile: - stamped it “low risk, mergeable” (4/5) clean - forgot context from the last PR - no tests suggested, no safety checks - zero memory of previous regressions 2) supercode review on the exact same PR - flagged a real vulnerability in the diff - noticed i’d pushed credentials into `.env.example` - pulled in history from past PRs + explaining how this change could affect and break them - downgraded it to "medium risk, fix before merge" state - attached concrete fixes + patches scoped by severity this is the difference between 'LLM summarizer for github' and an actual swe agent that cares about your production

• 
  Enfantshustle (@Ownerthoughts) reported 43 minutes ago

  Honestly, I always thought bots like this were some kind of magic for the elite, but here everything is broken down step by step. However, after reading it, one main question stuck in my head: how realistic is this for an average person who has no coding experience? I get that there's a GitHub and all that, but for me, just "running a script" is practically a heroic feat. Here's another thing that bothers me. The article does a great job explaining the architecture, but I still don't understand how much all of this will actually cost in the end. Besides Solana transaction fees (which, by the way, get absolutely insane during peak hours), you also have to pay for each Grok API call per token. The article says that for each approved token, it takes three model calls, and one of them is the expensive grok-4. If the bot scans thousands of launches per day, I'll just burn through my entire deposit just paying for the API without even buying anything. Maybe the author knows — is it actually possible to turn a profit after these expenses, or is this just a hobby for those with an unlimited subscription? Also, regarding Grok Bot as the "orchestrator" — it sounds cool in theory: describe the task and it does everything itself. But in practice, as I understand it, this still requires your account to be constantly online and have access to your wallet. And if it decides to buy some scam token at 3 AM that passed all the checks, I'll only have myself to blame. The article correctly mentions risk management, but this "trust" aspect is what scares me the most. In short, the idea is fire, but for me, this post feels more like a warning than a call to action. There are just too many things you have to keep in mind to avoid getting rekt. Although, maybe if you try it with really tiny amounts, it could be an interesting experiment. Author, if you're reading this — could you please make a separate post about the real, live results once everything is actually running, not just on paper? I'm really curious!

• 
  paulrodturner (@paulrodturner) reported 45 minutes ago

  @supabase Is anyone else having issues logging in via Github?

• 
  Rachael LaGoth (@androidsheeep) reported 47 minutes ago

  @bcherny Please fix the desktop app it's very buggy it keeps disconnecting me for no reason everyday while im working on stuff, i submitted a report but nothing happened and someone else is having the same issue, an issue is open on github for more than 6 months with no solutions help

• 
  Dhanji Bhagat (@BhagatDhanji) reported 51 minutes ago

  Devs, what's your workflow? Create an issue first, then fix it OR just fix the bug and push directly to GitHub?

• 
  Uptimus (@UptimusApp) reported 53 minutes ago

  Sep 02, 2026 at 13:29 UTC: Semaphore reports that periodic authentication failures with GitHub repositories are linked to a wider issue affecting HTTPS operations.

• 
  Phanindra Malladi (@malladiphani) reported 54 minutes ago

  Lesson from running the factory: always fix the post-purchase experience BEFORE driving traffic. Receipts, upsells, GitHub links - all must be solid first. Social comes after the house is in order.

• 
  Speen Bhai (@Speenbhai) reported 56 minutes ago

  @johnternus Hi John. Congrats Let us see what new you bring with you. Affordability and intelligence. You have source code or an AI and can get it from GitHub. Why not turn 234 million iPhones to a massive distributed server infrastructure with zero power consumption

• 
  Azzie (@Motier_crypto) reported 57 minutes ago

  $Looprat 473K 0x642d30c84211ade7768fe557fbaed7224e2068c7 How do you get a coding agent to keep working while you sleep—without letting it randomly rewrite code, blow through the budget, or grade itself a perfect score? Loop Rat breaks an unattended task into: preflight → act → verify → guard → grade → receipt. The agent is woken up on a schedule to execute tasks. The code results first go through deterministic verification. Then it checks the denylist, the number of modified files, and secrets. Finally, a second independent agent regrades the work, with checkpoints, traces, and receipts left throughout the entire process. More importantly, this isn't a PPT. The project only had v0.1 on August 27. Then it added guard, kill switch, and spend ledger on August 29. On September 1, it added second-agent grading. On September 2, it continuously fixed scheduling, budget caps, timeouts, and concurrent ledgers. Today, September 3, it's already updated to v0.3.3. Shipping multiple versions in under a week—that's exactly what I want to see in a small-cap play like this: code is running, and the narrative is following the product, rather than launching a token first and filling in the story afterward. It currently defaults to Claude, but it doesn't lock the model down. As long as the CLI can consume a prompt and return JSON, it can be swapped. And the whole thing runs locally—no SaaS, no database, no extra accounts required. The project has even already run 50 smoke checks covering key areas like scheduling, guard, budget, kill switch, and timeout. So my trading logic for $Looprat is simple: The next phase of the agent race isn't about "can it work autonomously." It's about "can it work continuously while still being constrained, audited, and stopped." Loop Rat happens to be building exactly that layer of infrastructure. The project is still very early. The catalyst truly worth watching isn't shilling—it's whether the GitHub keeps up this iteration speed, and whether developers actually start plugging it into their own repos. Once those two things happen, $Looprat stops being just a ticker riding the agent hype, and starts having its own fundamental anchor.

• 
  Spectra☢️ (@Spectra010s) reported 57 minutes ago

  @izzyCodes_ and you too Chief Check GitHub issues

• 
  Fox (@0xMfox) reported 59 minutes ago

  Gave an AI agent a month and GitHub access. Wanted to see if it could make money. The plan was simple. Point it at bounty-labeled issues, let it write the fix, submit the pull request, collect the payout. > Day 1 12 PRs submitted. 0 merged. 2 rejected. 8 just sat there ignored. Somewhere in that first week it also passed its own tests for a file that didn't exist. Wrote 25 tests for notification_service.py. The real file in that branch was called NotificationRoutingMiddleware. Confidently reported clean anyway. > Day 30 Looked completely different. 84 PRs submitted, 59 merged, $500-800 earned. Ran the agent for about $45 in API calls that whole month. Net somewhere around $455-755. Here's the part that stuck with me. Out of those 59 merges, 3 repos accounted for 90%+ of them. Every other repo it touched, zero merges, despite 30+ PRs going out across dozens of projects. Open source bounties follow a power law. Almost nobody merges your first PR. A few maintainers will merge your tenth without even reviewing it closely. That's what actually fixed the acceptance rate, from 24% up to around 70%. Not a smarter model, a scoring function that runs before the agent touches anything. Repos where it already has 10+ merged PRs score +40. Zero competing PRs on the same issue, +20. Five or more competitors already in, -20, skip it. Repos that closed PRs without merging before, instant -100, not even worth reading the issue. The fastest way to build the credibility that makes this work isn't code at all. Documentation translations sit at a 95% merge rate, barely reviewed, always needed somewhere. A handful of clean translations got the agent enough trust that maintainers started assigning it harder issues directly, no competition, no review queue. Spam version of this, submitting to every repo with a bounty label, burned through 30+ repos for 3 that ever paid out. Worse, it reads like exactly what it is to a maintainer watching the same account flood a dozen projects with mediocre PRs. Paid out by the hour, week 1 was rough, close to $5/hour, mostly setup and failed attempts. By week 3-4, once the scoring system was tuned and a few repos trusted it on sight, that climbed to $30-50/hour on the same kind of work. Bookmark this, scoring logic is worth stealing.

• 
  Josh Hamilton (@nearbycoder) reported 1 hour ago

  @theo If GitHub is down does it fall back to a cached version I’m guessing?