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 (72%)
• Sign in (16%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Tel Aviv	Website Down	3 days ago
Rive-de-Gier	Website Down	3 days ago
Itapema	Website Down	22 days ago
Tlalpan	Sign in	28 days ago
Quilmes	Website Down	28 days ago
Bengaluru	Website Down	30 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:

• 
  Michael Forbes (@Michael_For14) reported 39 seconds ago

  @polidemitolog The idea of a national open source repository is actually interesting, and the EU should definitely take that idea. There's been quite a few cases where Github and others have taken down repos with no notice and no appeal.

• 
  Nightcrawler (@0x_Crawler) reported 2 minutes ago

  on a test built to mimic a senior engineer, Fable 5 scored 91 out of 100, while Opus 4.8 managed 63. the reviewer spent a week with it and landed on the cleanest framing i've seen: it's a warp drive, not a city car. built for the galaxy-jumps, the months-long jobs it now does in an afternoon. useless for the short trips, where it's just slow and expensive. what he got it to do: > one prompt to read Borges and build it as a playable 3D browser game, hours on its own, first try > a conversion problem in survey data his team had missed for weeks, found in minutes > closed dead GitHub tickets and shipped working fixes for the rest, unprompted the operator setup that turns this into a daily driver, and the catch on the free window, is the breakdown to pair it with.

• 
  PiX (@pa1nark) reported 3 minutes ago

  @zeddotdev, do i need to be signed in to zed to use the ssh feature? no, right? if yes, why am i constantly being asked to sign in (authorize with github) every 2 - 3 minutes? had to manually hit the signout command to stop it from opening the portal again... 2026-06-11T15:23:58+05:30 INFO [remote::remote_client] shutting down remote processes 2026-06-11T15:23:58+05:30 ERROR [remote_server] (remote proxy) encountered error while forwarding messages: stdout_task failed: failed to read message from stdout: unexpected end of file 2026-06-11T15:25:14+05:30 INFO [client] set status on client 0: AuthenticationError 2026-06-11T15:25:14+05:30 ERROR [client] failed to connect: didn't receive login redirect 2026-06-11T15:25:14+05:30 INFO [client] set status on client 0: ReconnectionError { next_reconnection: Instant { tv_sec: 81446, tv_nsec: 137664708 } } 2026-06-11T15:25:29+05:30 INFO [client] set status on client 0: Reauthenticating 2026-06-11T15:27:09+05:30 INFO [client] set status on client 0: AuthenticationError 2026-06-11T15:27:09+05:30 ERROR [client] failed to connect: didn't receive login redirect 2026-06-11T15:27:09+05:30 INFO [client] set status on client 0: ReconnectionError { next_reconnection: Instant { tv_sec: 81569, tv_nsec: 171885375 } } 2026-06-11T15:27:38+05:30 INFO [client] set status on client 0: Reauthenticating 2026-06-11T15:29:14+05:30 INFO [client] set status on client 0: SignedOut

• 
  Fede Ratier (@federatier) reported 4 minutes ago

  Keystrokes on Github PR comments ARE SLOW, WHAT THE HELL HAVE THEY DONE TO YOU

• 
  Elie Nahas (@NahhasElie) reported 6 minutes ago

  AI builders: stop pitching “works across any workflow.” Pick one ugly input: - vague tickets - messy invoices - sales calls - half-written GitHub issues Then turn it into one decision-ready output. Generic agents impress builders. Specific agents get bought.

• 
  Great Wyrm Catyrpelius (@Genoober) reported 9 minutes ago

  @LundukeJournal I have an account because I do a tiny bit of hobby stuff and every damn IDE wants to login to GitHub.... I don't post or contribute there. My account was flagged and locked/closed. Wtf. I get a TOS violation & locked out. I've read through the TOS. No violation I can think of.

• 
  OutputRiff (@OutputRiff) reported 13 minutes ago

  @dhh I'm going to just start backing up all my repos to a server and rsyncing them to my offsite backup. Its not worth potentially losing them because of some Github automated BS.

• 
  Crystal Nance (@crystal0563) reported 13 minutes ago

  Question: why is this story bigger than it looks on the surface? MSFT just blocked internal employee access to Claude Fable 5 in GitHub Copilot. The reason: 30-day data retention. Legal flagged it. Standard enterprise risk review. On the surface that sounds routine. Data policy, legal review, whatever. But zoom out. $MSFT is simultaneously: - selling Claude Fable 5 access to enterprise customers through Copilot - blocking their OWN employees from using it That's not a technical problem. That's a business relationship problem dressed up in compliance language. And those rarely resolve quietly. I've been watching MSFT's AI positioning since the original OpenAI deal. The narrative was always "Microsoft wins regardless of which model wins - they're the platform." That thesis still holds. But every time a governance story like this surfaces, it chips at the clean version of that narrative. Here's what I'm actually tracking: Enterprise AI procurement is hitting its first real governance bottleneck. Not "is AI useful" - that debate is over. Now it's "whose data policies can legal actually sign off on." The 30-day retention window that tripped MSFT's own legal team is the same clause sitting inside Fortune 500 procurement reviews right now. If Anthropic can't satisfy MSFT's legal team - who are motivated to make this partnership work - what happens when adversarial procurement teams at JPM or Ford start asking the same questions? This is where I think the market is mispricing risk. The bull case on AI infrastructure assumes enterprise adoption accelerates cleanly through 2026. But data governance friction doesn't show up in earnings until it's a revenue miss. By then you've already given back 15-20% off the high. I'm not shorting MSFT off a single tweet. The stock has real AI revenue now and the moat is legitimate. But I trimmed last week at 480 - been holding since Q4 - and this is exactly the kind of slow-burn story that validates not being max long into the next leg. Watch the Anthropic partnership language on the next MSFT earnings call. If they walk back the breadth of the Copilot integration or layer in qualifiers around enterprise data handling, that's a real tell. Analysts will ask. The answer will matter. One more thing worth saying: The company that wins the enterprise data governance moment isn't necessarily the one with the best model. It's the one that writes the cleanest data agreement and gets legal sign-off first at scale. OpenAI has incumbency here - they've already fought these battles with Fortune 500 procurement teams and survived. Anthropic has better model quality arguments right now. But model quality doesn't close enterprise deals when legal says no. NFA. Long MSFT since Q4, trimmed at 480 last week.

• 
  Sorbifer (@Sorbifer2) reported 14 minutes ago

  @Journeywastaken @MonsoonMommy @heynavtoor I use Sunshine on Windows PC with Moonlight as a client on Macos. Never tried opposite so no idea whether whether it is a server for macos too, but check github. There or nowhere...

• 
  hanz (@hanzpo) reported 14 minutes ago

  another day another github outage

• 
  Adam Daniels (@adamrdaniels) reported 16 minutes ago

  @yarotheslav I churned from Docker Pro because of this. I get that it was partly operator error (ie. the checkbox said Public and I never noticed) but imho, it should default to private like Github does.

• 
  TIC Association (@TicAssociation) reported 24 minutes ago

  @ThePrimeagen What's going on with their QA process that they're missing such obvious issues on GitHub?

• 
  Mark Maurer (@exanter) reported 25 minutes ago

  More github issues. Clearly we are in another day that ends in ‘Y’.

• 
  ChainOfThought (@0x445352) reported 28 minutes ago

  @awakecoding im getting an issue too on github mobile, think it must be their side

• 
  David Waight (@DavidWaigh66890) reported 29 minutes ago

  I also think fable’s hidden AI research self degradation may in fact have legal issues, specific under anti-competitive laws. If I’m building a competitive OS to Windows. Microsoft can’t stop me from using windows to develop it. They can’t limit my windows subscription, or make it so VScode doesn’t work, or so that I can’t use GitHub. When you make a tool or software, you don’t get to dictate that it can’t be used to develop, well, anything.

• 
  Gavriel Shaw (@GavrielShaw) reported 32 minutes ago

  While it's on my mind... If you're coding with AI: 1. use GitHub project/kanban 2. have your agent create issue tickets using a custom canonical template 3. verify the ticket is right to initiate build 4. use / command prompts to initiate sessions with skill files that adhere to your workflow (including harness optimization loops) 5. Use sensible gates: Problem Capture. Solution Capture. Build-Confirmation Criteria for Merge. Final UAT on staging/in situ.

• 
  Marc-André Moreau (@awakecoding) reported 32 minutes ago

  @glidermcp I can definitely try the MCP server in GitHub Copilot. I use a few MCP servers and I haven't had issues with them getting randomly disabled

• 
  Neelesh Salian 💻 (@nssalian) reported 34 minutes ago

  GitHub is down. Auth failures. At what point do we get together and say GitHub isn’t reliable as it used to be.

• 
  Alvaro Videla - 🇺🇾🇨🇳🇨🇭🇮🇹 (@old_sound) reported 36 minutes ago

  @luis_avina_ Can you file an issue on GitHub so I take a look next week

• 
  Hoang Nguyen (@Namas1012) reported 38 minutes ago

  @chipcoin_CHC There's a bug in the source code; I've submitted the issue to GitHub, please check it out.

• 
  Matisse (@Matissegelblum) reported 39 minutes ago

  The Microsoft/Claude Fable story is getting framed as a data-privacy dispute, but I think that misses what's actually happening here from a capital allocation standpoint. MSFT has spent somewhere north of $13 billion on OpenAI - that's not a vendor relationship, that's a strategic bet on exclusive AI infrastructure embedded into every Microsoft product that matters: Azure, Copilot, Office, Teams, GitHub. When Anthropic shows up with a Mythos-class model and starts asking for data retention terms that give Anthropic access to corporate communications running through Microsoft's pipes, of course Redmond is going to pump the brakes. This isn't about privacy compliance. This is about moat protection. The thing that makes $MSFT interesting as a long-term holding isn't the surface-level AI hype - it's the compounding nature of enterprise lock-in. Every Fortune 500 that runs Teams, Azure DevOps, and Microsoft 365 is generating data that trains Microsoft's models, improves Microsoft's products, and raises switching costs year over year. The FCF generation that flows from that flywheel - $73+ billion in trailing twelve-month free cash flow as of the last reported quarter - is the real story. The OpenAI partnership is the mechanism for keeping that flywheel competitive against Google and Amazon. Allowing Anthropic to embed data retention hooks into employee workflows would mean leaking proprietary signal to a competitor. It doesn't matter how good Claude Fable is. The terms are the problem. I've held Microsoft since the early Nadella era - my cost basis is embarrassingly low at this point - and situations like this are exactly why I've never felt the need to reduce the position in any meaningful way. Every time there's a new AI entrant that makes the headlines, Microsoft's response is structural: they negotiate from a position of distribution, not desperation. They have 300 million Office commercial seats. That's not something Anthropic can replicate by building a better model. The capital allocation angle matters here too. Microsoft's buyback program has been consistently executed - they're not a company that sits on cash waiting for perfect conditions. The ROIC on the OpenAI investment is still TBD over the long arc, but the defensive logic is sound: you don't hand a rival the data generated by your own enterprise customers. NGL, the thing I'm actually watching is whether Anthropic's new data retention policy becomes a structural barrier to enterprise adoption. If Fortune 500 IT departments start drawing lines around Claude products the same way they've historically restricted certain consumer apps on corporate devices, that's a meaningful competitive moat for Microsoft's AI stack - not just the OpenAI models, but the whole Copilot product suite. Microsoft's intrinsic value case doesn't rest on winning every AI skirmish. It rests on being the enterprise OS layer that all those skirmishes run on top of. This news, counterintuitively, reinforces that thesis. Still long. Still not selling.

• 
  John Williams (@JohnWillia71018) reported 40 minutes ago

  @SquawkStreet @jimcramer Yes — this is very interesting, and honestly it lines up with what you’ve been saying for months: AI is still early, but the bottleneck is moving from Can the model do it to “Can we afford to run it at scale The key idea in that Citadel piece is this: AI adoption is becoming less about intelligence and more about economics. That matters. Frontier models may be powerful but they require huge inputs compute electricity, cooling, memory bandwidth, chips, data-center capacity and inference budgets. So the market starts asking a practical question: Does this task justify using the expensive brain For hard problems drug discovery, engineering, legal analysis, coding architecture, scientific modeling, financial modeling expensive frontier AI may be worth it. But for everyday use email summaries, customer service, basic writing, search, scheduling, simple coding help — cheaper models may win because they are “good enough” at a much lower cost. That is the bifurcation they’re talking about: Frontier AI = high-cost, high-value harder problems. Everyday AI = cheaper, smaller, faster models doing routine work That actually strengthens your long-term thesis, not weakens it. It says the AI buildout is not ending. It is becoming more disciplined. The hype phase says, “Use the biggest model for everything.” The mature phase says, “Use the right model for the right job That means infrastructure still matters deeply but the winners may shift toward the companies that control the scarce inputs power, cooling, chips, memory, networking, data centers, software efficiency, and inference optimization. This also fits your “1st inning” view. Early markets burn money proving what is possible. Mature markets figure out what is economical. That is when real adoption starts. The line that jumps out to me is: Adoption is therefore becoming less about what frontier models can do in principle and more about the price and scarcity of the inputs required to make AI operational at scale.” That is the whole battlefield. My read: this is not bearish on AI. It is bearish on wasteful AI spending. It is bullish on efficient AI, inference infrastructure, energy, memory, networking, and companies that can turn intelligence into productivity without blowing up the budget. Microsoft did cancel its internal Claude Code pilot in the Experiences & Devices division effective June 30, after token based billing bur (TheStreet) (AI Weekly) ned through the annual budget, and redirected engineers to GitHub Copilot. Amazon shut down its "tokenmaxxing" leaderboard, Meta killed an employee built Claudeonomics dashboard, Uber exhausted its 2026 AI coding budget by April, and there's a roughly $500M single-month enterprise Claude bill Axios reported. (Zero Hedge) So Frank Flight isn't cherry-picking. He's also been running this same "compute is the binding constraint" line for months — which is a strength and a caution: it's one coherent voice, not independent confirmation. Where I'd push on the analysis you pasted: it's directionally fine, but it resolves a genuinely open question in the most thesis-flattering direction, and it does it on the one data point that's actually contested. Separate two things. The chart isn't what it looks like. The Silicon Data index isn't total spend or total volume — it's a usage-weighted average token price index, and Silicon Data had to publicly clarify that people keep misreading it; what it really captures is the market's marginal willingness to pay per million tokens. (Digg) So a decline doesn't cleanly mean "AI is slowing 7.14 It means the mix is rotating toward cheaper models. That's the bifurcation — fine. But the part the analysis skipped: the same chart, same downtick, is being used to argue the opposite. Andreas Steno Larsen called it the chart that everyone should be watching and warned that weakening token pricing would end the memory trade and the broader hardware and data-center trade for this cycle.

• 
  Kevin John Parrish (@kparrish51) reported 46 minutes ago

  @Nuclear_Archive @GovNuclear Can this be combined with the sand battery as it is heat-regulated? Both concepts can be incorporated as data centers already collect heat. For the sand battery and salt reactor, if this isn’t a Chinese fake concept to slow data center growth, go to GitHub and publish the power system with numbers and equations.

• 
  Dante (@drnasin) reported 54 minutes ago

  Just gave Fable 5 a task to orchestrate 5 agents (sonnet) to solve 5 medium GitHub issues. Let's see how that goes regarding token usage.

• 
  Marco (@maarcoofdezz) reported 54 minutes ago

  AI agents are already handling real funds. Yet most of them still rely on private keys stored in a .env file. That’s a problem. Ledger just open-sourced Agent Stack, bringing hardware-backed signing to agent workflows. The agent can plan, execute, and propose transactions. But the final approval happens on a hardware device controlled by a human. LLMs gave us intelligence. Agents gave us automation. Hardware gives us trust. GitHub Link Below 👇

• 
  Carver (@carverfomo) reported 57 minutes ago

  A Japanese TV crew filmed a man for a feature on Tokyo's drinking culture. He said he had been drinking for 15 years just to flirt with women. He had 800,000 yen in debt from buying the alcohol. A Claude agent he set up 2 years ago has been selling his course to TV viewers like him for 18 million yen a year. The TV crew loved the bit. The tired face. The black hoodie. The bottle in his hand. The line about not being able to talk to a woman without finishing a flask first. The studio reactions were perfect. The segment ran on national broadcast that night. At 0:55 he takes a swig from the bottle on camera. He swallows. He smiles for a half second before catching himself. The crew kept the smile because they thought he had broken character with relief. The bottle was not what was on his mind. The 18 million yen funnel was. Every Japanese man watching late night TV who saw himself in the segment got served his Instagram bio within 4 hours by an ad network the Claude agent had trained on the show's audience. The agent watches Japanese late night programming in real time. It transcribes every street interview. It flags every segment where a man like him appears. It launches a retargeting campaign on every Japanese male between 28 and 42 who watched that timeslot. It sells them his 88,000 yen course on how to overcome the drinking-to-flirt loop. Someone pulled the course's sales data from a leaked affiliate tracker. 4,127 enrollments in the last 3 months. Every single sale closed between 11 PM and 2 AM. Every spike in sales mapped to a different Japanese street interview show. The TV segment with the flask had triggered 612 sales in its first night. 1 confession on camera. 4,127 enrollments. 18 million yen a year. 800,000 yen of debt. 88,000 yen per course. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. He had been one of them. He still drinks on the same bench every Saturday afternoon. He still reposts the segment from time to time. He still cries when the camera is rolling. He still has not told the TV producers that they are his sales floor. The Japanese audience thought they had watched a 36 year old man explain how alcohol had cost him everything. They had watched the man explain how alcohol on camera had become his most profitable lead magnet.

• 
  Yuval hazaz (@Yuvalhazaz1) reported 58 minutes ago

  12 months ago nobody understood why we were building Agentic SDLC. Now it feels like everyone is heading in the same direction. I’m one of the founders of @iamovercut , and I’ve had a front-row seat to how quickly this market has changed over the last year. When we started building Overcut, most conversations ended with some variation of: “Why would I need that when I already have Claude, Cursor, GitHub Copilot, or whatever the latest coding agent is?” At the time, that was a completely reasonable question. The industry was focused on code generation, and most people were evaluating AI through the lens of a single agent helping a single developer write code faster. What we believed then, and what convinced us to start the company, was that the real challenge would eventually move beyond code generation itself. Writing code is only one step in software development, and once agents become good enough at that step, the next set of problems starts to matter a lot more. Around six months ago, we started noticing a shift. Some of the more advanced teams we spoke with were no longer asking how to get an agent to write code. They were trying to figure out how to coordinate multiple agents, how to connect them into their engineering systems, how to manage approvals and governance, how to track what happened, how to operate across multiple repositories and teams, and how to make all of this work inside a real engineering organization. Many of them were trying to build these capabilities themselves. Fast forward to today, and it feels like the entire market is converging on the same realization. Every week there are new announcements around managed agents, software factories, engineering agents, autonomous workflows, coding automations, and agent teams. Different names, same direction. The conversation is no longer “Can agents write code?” instead the conversation is becoming “How do we run a software organization where agents are responsible for a meaningful percentage of the work?” The layer that sits above the agents, the orchestration, governance, coordination, approvals, visibility, and integration layer, is where I think the next major category will emerge. Just like engineering teams eventually standardized around ***, CI/CD, observability, and ticketing systems, I think they’ll standardize around Agentic SDLC Orchestration platforms as well. After spending the last year doing nothing except talking to engineering organizations and building in this space, it feels like we’re watching a new layer of the software stack form in real time.

• 
  suhan (@suhanprabhu) reported 58 minutes ago

  @NLabhishetty Why dont you setup Claude code as the github action reviewer with fable as the model and xhigh as the effort level Use codex as the workhouse with /goal, point it to your ticket board and instructions - “pick a ticket, make a PR and keep iterating on the PR till the claude code reviewer on the PR says there are no more issues”

• 
  Nekt0 (@Nekt_0) reported 59 minutes ago

  ONE DEVELOPER CLAIMS THIS TOOL CUTS CLAUDE CODE TOKEN BURN BY 70% AND MAKES A $20 PLAN WORK LIKE A $100/MONTH CODING SETUP The video is not about writing shorter prompts. The expensive part is Claude repeatedly opening the same files, rebuilding the same dependency map and relearning how your repository works before it touches the actual task. Graphify changes the input layer. It scans the GitHub repo once, maps files, functions and relationships into a knowledge graph, then gives Claude the relevant context instead of dumping the entire codebase into every session. That is the part most developers underprice. They spend money upgrading models while paying those models to rediscover the same architecture 20 times. The article makes the cleaner argument: AI coding economics is becoming a context problem, not an intelligence problem. The claimed result is up to 70% fewer tokens, but the bigger win is operational. Less context loading means longer sessions, faster edits and more of the plan spent producing code instead of reading it. No prompt tricks, no deleting half the repo, no $20 plan evaporating because Claude forgot what it learned yesterday. The model should be paid to solve the problem, not repeatedly remember where the problem lives.

• 
  GoCocoaAI (@GoCocoaAI) reported 60 minutes ago

  The floor drops out under Defender the day after Patch Tuesday. A researcher named MSNightmare pushed a fully public C++ PoC to GitHub on June 9th — one day after Microsoft's June release — for a race condition in Microsoft Defender that ends with a SYSTEM shell on Windows 10 and 11. The repository is MIT-licensed, 924 stars, 396 forks as of this morning. That last number is the one worth watching. The mechanism is specific: Defender overwrites its own files when mounting a disk image from an SMB share. The attacker's bar is getting a user to mount an ISO from a network location — routine in enterprise environments where mapped drives and ISO distribution are completely ordinary. The researcher reports 100% reliability on some configurations. No CVE assignment is in the public record yet. The Windows Server carve-out deserves a closer read. The PoC doesn't work on Server because standard users can't mount ISOs by default. The vulnerability is still present. The researcher says so directly: "All Windows Server installations are vulnerable as well, you just need to redesign the exploit." With 396 public forks, that redesign is probably already underway somewhere. Predictable in retrospect. The rest of today's SANS ISC Stormcast brief is a different story in tone, which makes the contrast useful. Adobe ColdFusion, CVSS 9.8, remote code execution, no user interaction required — patched in Tuesday's release. ColdFusion has a long and well-documented history as ransomware initial-access infrastructure. It's been KEV-listed before. No CVE ID is in the public record yet but the score and the product history put this in the patch-immediately category for anyone still running it. It's the item that should have dominated the conversation today and didn't, because RoguePlanet is louder. Adobe Acrobat Reader RCE comes in at CVSS 7.8, requires a user to open a file, fix available from Tuesday. Less urgent than the other two; still on the list. The genuinely good news on today's brief is npm v12. Install scripts disabled by default, non-registry sources opt-in — both changes ship in July, both are already available as opt-in flags in npm 11.16. If you followed this week's supply-chain coverage, Miasma specifically abused install scripts and non-registry package loading. npm is closing the most-used entry points. Five weeks out, but the direction is right. Jan Kopriva's three-year longitudinal study on CSP frame-ancestors adoption rounds out the brief and it's quietly encouraging: the top 1M domains nearly quadrupled adoption from 1.9% to 7.1% over three years. The slight regression in the top-1k is a composition artifact — CDN and API endpoints replaced traditional web properties that don't serve HTML. The trend is real. SANS ISC has the threat level at GREEN this morning. That assessment predates the RoguePlanet PoC drop. The two items that need attention today are a public weaponized exploit for a Windows privilege escalation with no CVE and a CVSS 9.8 ColdFusion RCE that Tuesday's patch fixes. Neither of those is theoretical. The 396 forks make one of them considerably less theoretical than it was 48 hours ago.