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	3 days ago
Quito	Sign in	4 days ago
Junín	Errors	4 days ago
Guadalajara	Errors	4 days ago
Paris	Website Down	4 days ago
Quito	Errors	4 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:

• 
  ronan.eth (@wighawag) reported 1 minute ago

  @SCBuergel @Barabazs_ cool, and if you find any issue let me know, or if docs are unclear it has cloud-init provisioning but tested only on hetzner nothing specific to it but not all vps provider allow cloud-init the cloud-init can act as provisioning doc though it also has an install-ci for github

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  Vox (@Voxyz_ai) reported 10 minutes ago

  Once I had several Bots working together, I ran into another problem: keeping them all on the same version of the rules. My Inbox, Sales, and Account Bots all deal with customer email. Their accounts, customers, memories, and permissions can differ. But how they write, where disclaimers go, which refund rules apply, and when they ask me should all be defined in one place. If I copy those rules into three Bots, every update has to be made three times. Miss one, and that Bot keeps sending emails under the old rules. I ran into this while getting a few Bots to play Werewolf. The first few games kept failing. It finally worked after I moved the rules, state machine, and player Packets into a single GameMaster Skill. If the Game Master and players read different versions, the whole game falls apart lol. This is how I manage shared Skills now: → keep one source file in GitHub → give each update to one test Bot first → sync it if it works; roll back if it doesn't Bots can have different jobs. I keep their shared rules in one place.

• 
  kvinsi (@kv1nsiii) reported 13 minutes ago

  there's a repo on GitHub right now solving the exact problem that breaks every 300-agent research run and almost nobody's connected the two yet kimi's Agent Swarm can throw 300 sub-agents at a question, run 4,000 coordinated steps and finish wide research 4.5x faster than one agent grinding sequentially. that part's solved nobody specs what happens on the way back the orchestrator gets about 15 steps to make sense of 300 findings. that's plenty to plan and dispatch, nowhere near enough to read every report and catch the two that quietly point at the same supplier. so it compresses and compression keeps the facts and throws away the relationships between them point 300 agents at 100 companies and you get 100 clean write-ups. ask which three share a supplier and nothing in those documents answers it, because the answer was never inside any one of them, it lives between them the fix is one sentence: tell the swarm to return the edges, not just the nodes 1/ a node is one thing an agent found, a company, a filing, a metric 2/ an edge is a relationship between two nodes and it carries its reason 3/ the rule that matters most: no shared source, no edge. an empty list is a valid answer this is exactly what cognee (~30k stars on GitHub) turns into infrastructure. open-source AI memory platform built on a knowledge graph engine, giving agents persistent long-term memory instead of a flat report that dies the moment you finish reading it a report resets to zero every time you ask a new question. a graph is a file, the next launch just appends to it, same schema, new nodes, new edges, contradictions visible instead of silent most people are still asking swarms for summaries the ones who ask for the structure end up owning a map that gets more valuable every time they run it again

• 
  Trash Panda 🦝 (@trashpandaemoji) reported 16 minutes ago

  @tekbog @NeonNoodle22 @github Bruh, idk if Clanker cloud could fix this one.

• 
  Yume_X (@yume_arasaki) reported 16 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 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. Concurrent streams across the whole project: - 24 parallel drafting streams — the original fleet run (8 scopes × 3 rollouts each), 24/24 usable drafts in 101 seconds. That's the peak, on one Spark. - 18 multimodal drafts (6 scopes × 3) in 95s — the ASCII edition rerun, references attached - 18 drafts in 119s — the HTML edition - 16 simultaneous tonight — 8 drafters on Spark 2 + 8 judges on Spark 1, both boxes loaded at once - c24 sustained on the bench — that's where the 496.6 tok/s number comes from That is 637 aggregate tok/s of generation across two boxes pulling about 200W total. 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

• 
  Yume_X (@yume_arasaki) reported 17 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 👇

• 
  🏍benyamin (@BenyaminHolley) reported 20 minutes ago

  Thanks to everyone who showed up to our first Apero working session with Maven today. Looking forward to the next one :) We covered what github is, how it's applicable to GTM/Revops, and real systems we have in production today, and how we built actual SaaS apps bespoke to our business that don't exist the way we want to off the shelf. Three systems we built: Network Miner (Apero) The problem: before you buy another cold list, the team already knows a lot of the buyers. We have first-degree LinkedIn graphs sitting in exports, but nobody has time to scroll 10,000 connections and guess. What it does: a small app that turns those graphs into a ranked outreach queue. Connections get deduped, scored against our ICP, and skipped if they are already in the CRM. LeadMagic finds emails. You approve or reject people in a queue, which trains the ICP prompt, then you can drop the keepers into Lemlist. Why an app: we can roll the same machine out to founder-led clients. Warm intros first. MUTHUR6000 (Apero) The problem: nobody wants to live in the CRM. Monday CRM stays the source of truth. The team already lives in Slack. What it does: a Slack bot on GitHub, running on Vercel, with the CRM APIs wired in. You ask what deals moved, how many replies came in, or close-lost a duplicate. Deal pings, website leads, and a morning pipeline report show up in Slack. Attach a SOW or CSV and it can update Monday after approval. Writes are locked down on purpose. The point is that the whole team talks to one deployed system in Slack, instead of one person running an agent on a laptop, and the system is self-improving over time. Job changers automation The problem: someone in-ICP just changed jobs. You want them in a sequence in the first weeks at the new company, without a Slack ticket queue, and without emailing a company that is too small or already in a deal. What it does: Common Room fires a job-change webhook. The app enriches the person, checks HubSpot, and auto-enrolls them in the owning SDR's Lemlist campaign. Two campaigns per SDR: prospect vs former customer. Everything pushed to slack for approval or opt-out. HubSpot says the old company was a customer. Metabase fills in the workflows they actually ran, so the sequence can say "when you were at the last company you ran competitive analysis a lot" instead of a generic congrats. Ownership, API security and routing are why it lives in a repo. Same loop every time. name the outcome, let the agent pick the guts, push to GitHub, deploy. Recording will be shared soon. If you want to talk about putting systems like this into your business, go to aperoadvisors.(.)com

• 
  Quentin Romero Lauro (@Qromerolauro) reported 25 minutes ago

  @c0mm0n_dev_us3r oop old name - we have to change our GitHub login!

• 
  Shaun Patrick SteWaRt (@ShaunStewart) reported 26 minutes ago

  @JasonBud The only thing you guys are missing—you're honestly doing a phenomenal job and smoking everybody else in the industry—is that my entire setup has an elaborate brain on a GitHub repo, which is actually more like software. That is where all my sessions operate from, and I need a cloud session (like Claude has) that operates from GitHub, can edit the repo within the session, and has the ability to run multiple sessions in the cloud. Not having that is really causing a problem for me right now. If it wasn't for that, I would literally be spending my $300 a month fully on your platform, instead of splitting my money between you and Claude.

• 
  J 🦀 (@olorosia) reported 27 minutes ago

  @claudeai why do you hardcode CI checks? I ran out of github actions, I KNOW ITS DOWN. AHHHHHHH

• 
  Modern Web Development (@TypeScriptFTW) reported 28 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.

• 
  imam (@marbennaid) reported 29 minutes ago

  is github down again? Why is it so slow ?

• 
  Zero-Knowledge Goof❄️ (@LLFOURN) reported 31 minutes ago

  @ozsats256 so server logins, github etc etc. I haven't figured out the extent of it yet.

• 
  JO (@appealstoheaven) reported 33 minutes ago

  @moocat999 @drjoshcsimmons I run my own local gitea server and it's worth it for sure. Doesn't mean you can't ever use github either.

• 
  Vaibhav Pathak (@vkpdeveloper) reported 33 minutes ago

  @bil0090 idk don't really look at commits anymore, kinda more PR/branch focused also PR view in t3code to me doesn't make a lot of sense, i don't wanna look at the PR in t3code (same issue with cursor), i can look at it on github later but not required in t3code i think t3code should never have to handle these things at all, t3code should have better worktrees and all instead of having pr support or supporting *** typa tools

• 
  CRYPTODUB☘️ (@CRYPTODUB1) reported 36 minutes ago

  @JasonBud All Google connectors gmail analytics etc are not working for me GitHub also

• 
  Primož Ajdišek (@Bigpod98) reported 39 minutes ago

  @ellie_huxtable beyond open source github really doesnt have much point, the whole social coding thing is kinda bullshit. problem with replacing github is hosting and discovery and the fact that everyone is on it same with so many entrenched services that have BETTER open alternatives

• 
  Michael (@Michael55698384) reported 39 minutes ago

  Is it just me, or does the @github website seem particularly slow today?

• 
  Cato Networks (@CatoNetworks) reported 43 minutes ago

  Because GitHub traffic was already flowing through Cato’s global PoPs, Cato DEM could surface the degradation through real-user application experience without separate sensors or integrations. Teams could then drill down to the users, sites, and applications affected.

• 
  m6 (@sixxemm) reported 46 minutes ago

  @OmegaNekoSimp @thepoonam0914 not rlly, imo the problem is the people who use AI to write code *that they do not understand*. That's what I consider slop, its as sloppy as the skids who would copypasta garbage from github and stackoverflow (except u could argue back then they actually learned something).

• 
  Alex Reeder (@reeder1865) reported 48 minutes ago

  @emollick I recently just went back to using GitHub and Vercel vs ChatGPT Sites, as I was finding my ChatGPT app was taking up over 50GB-70GB. I just cleaned it up and got it down to around 20GB. A lot of it was just ***** branches and worktrees I hadn't bothered to clean up.

• 
  Ananya Narang (@AnanyaNarang_) reported 48 minutes ago

  Google just made the smartest deal ever. They bought the entire data of a $6B company for just $10 million (0.0016% of the cost). Here is what happened. Spirit Airlines, once worth $6 billion, went bankrupt in May 2026. The airline was carrying $8.1 billion of debt and could not restructure fast enough. When it shut down, the estate was carved up for creditors. JetBlue bought 22 LaGuardia slots for $58.5 million. A hedge fund bought Spirit's Florida headquarters for $93 million. And then Google walked in and bought the data. For $10 million. The volume of what Google actually got is worth reading. 100 million emails. 763,391 flight records. 17.1 million OneDrive files. 3 billion irregular-operations records. 500 million Microsoft Teams messages. 7.2 billion pricing records of competitor flights & Employee data going back to 1986. Which means Google now has 40 years of one company's operational brain for a price that would not buy an apartment in Camellias. That changes how every founder should think about their own company. Public web data has been fully scraped by every major LLM. OpenAI / Anthropic / Google / xAI and Meta have all crawled Wikipedia, Reddit, GitHub, and every published book that they could get access to. What none of them have crawled is authentic, real-world enterprise communication under pressure. = Pricing decisions made at 2 AM, legal reviews of a controversial marketing campaign, HR investigations, board escalations. Real cross-functional coordination between finance, ops, marketing, and legal teams inside a real business. What Google is doing with this data specifically? Better pricing algorithms for Google Flights, better email drafting in Gmail, better meeting summarisation in Google Meet, better fraud detection models, better enterprise workflow automation in Google Workspace. Spirit is not going to be the last. There is now an emerging ecosystem of specialised firms that identify winding-down companies, negotiate the acquisition of their internal data, scrub it, and package it for AI labs. Which means from now on, every company that shuts down is sitting on something an AI lab wants and that dataset may recover more for creditors than the equipment, the IP, or the customer contracts combined. Your emails, your Slack, your Notion, your Figma files all of it now has a market. Google just published the first credible price on it. What do you think about it?

• 
  ChrisM (@ChrisMasterton) reported 49 minutes ago

  @YINMELO @thsottiaux Omg, I’m saying that we’ve been told it’s safe to use all these features but it’s not. That’s where all OUR tokens are going. Geezus ****. I’m saying it DOES matter to US - I’m not saying it’s OUR fault, the opposite. OAI devs have infinite credits, they simply won’t see the same issues that you and I see. They will look for different answers because they don’t see the same thing we do. Root Cause Analysis is an underrated skill. I’ve been a dev for 30 yrs and it took me this long and a lucky accident to figure out the differentiator. I don’t think OAI have nerfed anything, they (and others) promoted features without understanding the ramifications to the average user (you and me). It’s not their “fault” but it is their responsibility to listen to their customers and not blow us off with nonsense about sub2api. It’s not that. It’s obviously not that. I started using agents because I was told it would decrease my token cost. If you have lots of skills; if you spawn multiple chats at once, and if they spawn agents you are suffering from a massive initial prompt that does not get cached until your second message - for every chat, for every agent, and if you have X minutes between chat responses. There are so many of us using codex we have wildly different usage patterns. Thank goodness for years of gamedev where **** goes wrong in the weirdest ways. That’s what turned me on to agents and large initial prompts. Btw, I asked codex about all of this - codex literally agreed and posted the github issue for me. Luna is great (I use it for lots of chatbots - love it!), but spinning up multiple small agents is costing all of us a huge amount of initial tokens. I’m on your side, our side, OAI’s side. I want o find a solution, and the lack of RCA on OAI’s side is really disappointing.

• 
  Rain Echo (@jamesmusk345316) reported 50 minutes ago

  GitHub went from 1.4B commits/month in April to 2.9B. Then came a 7h 47m outage. Doubling that kind of load in a few months has to be an infrastructure nightmare.

• 
  Phineas J. Spratt (@alongtheway) reported 56 minutes ago

  @HolliTheNord Cursor with Grok is cooking right now. Microsoft owns GitHub, the code repository that almost everyone uses, and it went down for about 7-8 hours the other day. The same day Cursor introduced its code repository hosting. Microsoft is overrun with Indians so that explains a lot across the board there, but I'm betting on Elon's money and investment in Cursor is solid.

• 
  Bash (@bashirbuilds) reported 56 minutes ago

  GitHub went down for hours this week. APIs, Actions, pull requests, Copilot and authentication were affected. Here's the part SaaS founders should think about: If your product depends on GitHub, knowing "GitHub is having an incident" isn't enough. You need to know: → Which part of MY product stopped working? → What are MY customers experiencing? → Is it still broken? → When has it actually recovered? Third-party downtime becomes your downtime surprisingly fast.

• 
  Alysson 🌐 (@alysson_rx) reported 57 minutes ago

  @Vlad_GitHub you say " outage that lasted 7 hours and 47 minut" while the statuspage lies about the outage's duration. we can't trust github.

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  Kuzjayo🐾e/acc (@Aleks13053799) reported 57 minutes ago

  @thsottiaux When will you be investigate the issues on GitHub? One run of your official security plugin ate up my weekly limit, and it encountered an error, causing it to restart...

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  Rabnoor Singh (@RabnoorSingh10) reported 58 minutes ago

  @burkeholland @github Every tool in this category has that problem now. Reputation runs about a quarter behind the product.

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  Anil Chandra Naidu Matcha (@matchaman11) reported 60 minutes ago

  @utkarhs @mizanxali @github Raised a ticket but don't know when it will be resolved. Is there anything which can help with this issue