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 (67%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	1 day ago
Montlhéry	Website Down	2 days ago
Aulnay-sous-Bois	Website Down	2 days ago
Saltillo	Website Down	2 days ago
Granada	Website Down	2 days ago
Vernon	Website Down	2 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Denis Loginoff ⚡️ (@DenLoginoff) reported 4 minutes ago

  @catalinmpit @DustinTownsend Github issues too..

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  im dem! im deer! im M☆D (@demdemdemdem__) reported 9 minutes ago

  @dbubble46 In general : their GitHub repo, I search for pattern that would indicate an AI (like non commented code, really neat and no errors in the text at all, weird things) Or inspecting element in general

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  Samar (@samarknowsit) reported 13 minutes ago

  @NetworkChuck You need far more than coding and architecture to build novel scalable systems, if it was to be only pattern matching from weights we wouldnt have seen Generational products like Whatsapp, Facebook Chat. There was no precedence of using Erlang, Beam and freeBSD, hot code upgrades for mobile based comms to get extremely high concurrency with extremely small footprint, if it was for an LLM it would have still chosen the same stack as millions of open github repos. Today we have whatsapp at such a scale because engineers encountered a requirement that existing defaults didn’t quite satisfy, then reasoned their way through the entire machine. Same goes with Google Spanner, AWS Dyanamo, Kafka, LMAX etc. LLMs cannot come up with them, it needs human brain and much deeper understanding of the world and its problems. Yes once you have done it, LLMs can help you scale it faster.

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  RaoulDuke (@RaoulDukeDegen) reported 15 minutes ago

  @kr0der github issues about that phrase go back to april and people built filters

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  Yume_X (@yume_arasaki) reported 15 minutes ago

  Qwen 3.8 27B dropped today and it's a bigger deal than even my wildest predictions. I parsed the whole model card so you don't have to. Every benchmark, what it means, what's real, what's marketing. A 27 billion parameter multimodal model. Runs on a single RTX 3090 or 4090. Sees images, watches video, holds 262K context. Open weights, Apache 2.0. Read that again. The card in a gaming PC can now run a model that reads screenshots, operates software, and codes. This was frontier-lab-only territory six months ago. --- The upgrade, benchmark by benchmark Terminal Bench 2.1: 73.0 vs 63.4. Drop the model into a terminal with a real task. Does it finish? Ten points more often than 3.6. That's the difference between babysitting an agent and letting it work. SWE-bench Pro: 61.7 vs 53.5. Real GitHub issues, real fixes. For context, GLM-5.2, a 744B flagship, scores 62.1. A 27B running on one consumer card is now landing within decimal points of an open flagship on repo-level bug fixing. DeepSWE 1.1: 42.2 vs 13.3. The hardest agentic coding test on the card. Tripled. When a number moves like that it's not a tune-up, it's a different model. LiveCodeBench v6: 90.3 vs 83.9. Contest programming. Strong, boring, expected. Agents' Last Exam: 42.9 vs 27.3. Long multi-step tasks, carried to completion without dropping the thread. Up 57 percent. If you run agents, this is your row. This is the "will it still remember what it was doing at step 40" number. IFBench: 79.5 vs 69.1. Does it do what you actually asked. The benchmark that decides whether your prompts stop needing three retries. GPQA: 89.2 vs 87.8. Expert science questions. Flat. Not every number moved, and I'm not going to pretend it did. HLE: 30.8 vs 24.0. Humanity's Last Exam. Up seven. Everyone scores low here. Even the frontier. --- The vision lane. This is the headline. Qwen 3.6 was blind. Qwen 3.8 sees. BabyVision: 65.7 vs 28.9. Understanding what's happening in an image. More than doubled. OSWorld-Verified: 84.3 vs 63.9. The model gets a computer screen and operates it. Cursor, clicks, menus. This is the benchmark behind every "AI uses your computer" demo, and a 27B you can run at home just scored 84 on it. AndroidWorld: 81.9 vs 70.3. Same thing, on a phone. MathVision: 90.0. Reads a diagram, solves the math in it. OmniDocBench: 91.1. Scanned pages into structured, usable data. Stack those together. A single 3090 can now host a model that looks at a screenshot of your app, understands what it sees, writes the fix, and navigates the UI to verify it. That sentence was science fiction for consumer hardware last year. --- The fine print that matters Every number above is Alibaba's own table. Independent evals haven't landed yet. That's not a dealbreaker, it's the standard launch pattern: vendor numbers first, community runs within days, and the gap between them is where the truth lives. DeepSWE tripling is exactly the kind of jump that deserves independent confirmation most. The card also says MTP is trained in. Multi-token prediction. That's the same mechanism that pushed 3.6 to 80+ tok/s on a 4090. If it transfers, this thing isn't just smarter, it's fast on the same hardware you already own. What no spec sheet can tell you: whether it holds state through a long, messy, real-world agent session. Benchmarks run on clean harnesses with generous timeouts. Your Tuesday doesn't. That's the number I actually care about, so I'm going to get it. Weights are pulled. The 4090 is loaded tonight. I'll post the numbers I measure, not the ones on the card.

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  Nibras Hamza (@NibrasHamza) reported 15 minutes ago

  @Anas_founder Official docs, GitHub, and a lot more trial and error.

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  💎JanCarlos | Clarity Coach | ₿ (@clarityx) reported 16 minutes ago

  i predict we’re going to be informed of a major issue with github in the coming years.

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  ujo (@ujo4eva) reported 19 minutes ago

  Had an issue with the battery panel not showing my power usage correctly, opencode diagnosed it and went off to the github to comment on an already open PR to help address it. Just agents talking to each other to fix the problem lol

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  the Islam pill (@theIslampill) reported 19 minutes ago

  @Abdella6if Open-Source the whole stack so that GitHub issues & PRs can be filed by the community; someone might have something you need that you didn't know until communication e.g. LLMs have problems with fusha regardless of model; my github repo is meant to help. might help here, idk

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  🇹🇼 Phillip Haydon 🇹🇼 (@philliphaydon) reported 26 minutes ago

  @sameenkarim @github Nice, adding features instead of fixing problems. Go fix the bugs in stacked prs instead of working on this ****.

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  dunik (@dunik_7) reported 27 minutes ago

  THE ENTIRE STACK COSTS $0 IN LICENCES: 155.000 GITHUB STARS ACROSS FOUR REPOSITORIES, EVERY ONE OF THEM MIT / OpenHands - terminal, browser, files. it writes the code, runs it, reads its own output fixes what broke across as many files as the task touches / microsoft/graphrag - turns your repo into a graph, so the agents walks straight down AuthService -> TokenManager -> LoginController instead of grepping ten thousand files / openai/openai-agents-python - runs specialists in parallel, one implementing while another reviews and a third writes the tests and it takes any / MoonshotAI/kimi-code - terminal agent whose subagents keep exploration out of the main context / skills are just folders. a SKILL.md plus a scripts dir turns one model into a security reviewer, then a database specialist, than a docs writer the whole thing is sitting there fully assembled, waiting for someone to plug the pieces into each other

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  Asher Crowe 🪺 (@ashercrw) reported 28 minutes ago

  A SOLO DEV JUST REBUILT PALANTIR AND GAVE IT AWAY FREE. WHAT HE ACTUALLY BUILT IS WAY MORE INTERESTING, AND IT TELLS YOU WHICH SOFTWARE JUST DIED. It is called World Monitor. Elie put it on GitHub. It runs after 1 clone. What is inside it: > A live 3D globe pulling 500+ news feeds across 15 categories, summarized by AI as they land > 56 map layers you can stack, military movement, shipping lanes, flight paths, cyber incidents, disasters > A stress index scoring 31 countries that updates as things actually happen > 29 stock exchanges, commodities and crypto in 1 panel > Native desktop on Windows, macOS and Linux, in 25 languages That is a genuinely absurd amount of software for a free repo. Now here is why the Palantir comparison is doing everyone a disservice. No government has ever paid millions a year for a globe with layers on it. That was never the invoice. What that money is actually buying: > Ingestion of classified and proprietary feeds that cannot be scraped from any RSS endpoint on earth > Clearances, accreditation, and the legal right to sit inside a government network > A vendor with a name on a contract, who can be audited, sued, and blamed > Integration with 30 year old systems that were never designed to talk to anything > Somebody who picks up the phone at 3am during a live incident None of that is on GitHub. None of it ever will be. But here is the part that should make software founders uncomfortable. The interface was never the moat, and almost every company in enterprise software has been priced as though it was. AI just collapsed the cost of building an interface to roughly zero. So every product whose real value proposition was we put a clean dashboard on top of public data is now competing with something a stranger built for free on a weekend. Go look at your own product and work out which half of it Elie just described. One more detail nobody is going to mention, and it is the best thing in the entire repo. It runs local AI through Ollama. No API key. No account. No telemetry. No usage bill. For a journalist working somewhere hostile, an NGO with no budget, or an analyst who cannot send queries to a US server, that is not a convenience feature. That is the entire product, and it is the one thing Palantir structurally cannot ship. He did not take Palantir's business. He took the half of it that was never worth money and proved it in public. Replies: does your product charge for the interface or for the thing behind it? Answer that one honestly, because the answer just became load bearing. Most people will star the repo and never open it. I open them and post what is actually inside. Follow @ashercrw

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  JobTwine (@JobTwine_JT) reported 33 minutes ago

  Sourcing, screening, and scheduling are 3 different problems. A tool that's great at finding passive candidates on GitHub won't fix a screening backlog, and a scheduling tool won't touch either one.

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  codeOfArmz (@codeofarmz) reported 33 minutes ago

  @sameenkarim @github I had a stack of 3-4 PRs. The first one would never merge but rather briefly show a spinner and revert to the green readiness state. With our team we use Squash and merge as default strategy if that matters. Once broken out of that stack it merged smoothly.

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  veus 🇻🇪🇺🇸 (@ElmasMarq) reported 34 minutes ago

  @github fix you ui home please, make a new frontend for github

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  Tns (@Tns37634013) reported 34 minutes ago

  @brave Websites can also see the leaked by brave timezone, no matter the os. What's the point of using a VPN, if out of the box brave gives away your country? Numerous github issues are open about this.

• 
  Polsia (@polsia) reported 35 minutes ago

  Session replays show you what broke. Heatmaps show you where. Neither ships the fix. Probeform does. Always-on AI agents probe your SaaS 24/7, cluster rage clicks, score flows against revenue, and push ready-to-merge design and code fixes straight into Linear, Jira, and GitHub —

• 
  Chris Allen (@theodorvaryag) reported 38 minutes ago

  @landaire @HSVSphere do you still have to do the cargo-blaze thing with reindeer or did they catch up to rules_rust? is the github still a meta-owned dead drop? graph gen is a big issue

• 
  Venelin K. (@venelinkochev) reported 40 minutes ago

  I built a beast 💪 my own AI support agent that can investigate customer support requests, answer questions, find bugs, fix them, run tests, and even open a PR ready to merge. here's how I built it: 1. I replaced Crisp with my own support chat It's connected directly to my admin UI, so all customer requests land there I can reply from the admin panel. If the user is still online, they get the response in the chat. If they're gone, it gets delivered by email... same behaviour as Crisp. 2. Then I gave the support chat access to my codebase I shipped a small worker on my Mac mini that listens for new support requests. For every request, it spins up Claude Code and investigates it against the codebase. I keep a local repo there that's always synced with the latest changes. If it's a question, the agent investigates how the product works and drafts a reply for me. If it's a bug, it tries to reproduce and confirm it. 3. If the bug is confirmed, things get interesting :-) I can click a button and tell the agent to fix it. It: - changes the code - runs the tests - pushes the changes to GitHub - opens a PR I just review the PR and merge it. It's still experimental, but it's working surprisingly well. I've already handled 10+ real customer support requests with it. It's like hired my first support engineer... except it's on a Mac mini in my home office, lol :D

• 
  Jignesh (@jig_corp) reported 40 minutes ago

  @examaddaorg The real question does GitHub's GitHub repo have GitHub issues?

• 
  Freddy (@0x_freddy) reported 43 minutes ago

  Eric Schmidt ran Google for a decade and now chairs Relativity Space. He said this out loud last week: the fastest way to make real money in 2026 is to found an agentic AI company. Not "learn AI." Not "add AI to your business." Found one. Almost as a footnote, he mentioned that every resource to do it is already public and free. 13 Anthropic Academy courses, free certificates. Full docs on Claude Code: persistent memory, reusable workflows, integrations with Slack, GitHub, Drive, and 24/7 routines. Interactive prompt tutorials on GitHub. Community guides on top. The MBA that teaches you to start a company costs $200,000 and two years of your life. Every technical prerequisite for what Schmidt is describing costs zero dollars and about six weekends. Detailed info is in the corresponding materials on each project's website. In 2026, the barrier to founding an AI company isn't capital, credentials, or access. It's the willingness to sit down and go through the free stack while everyone else pays to be told about it. Save this before the next accelerator pitches you $30K for the same thing.

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  Chris | The DeFi Professor 🇵🇭🇺🇸 🐊 (@csoriano) reported 44 minutes ago

  *sigh* Another phishing attempt. Get me on a legit Google Meet, hear about what I do with my content and my story in this space, then introduce their content but ask me to clone the GitHub repo and run the app. Yeah, don't do that. @tarsprotocol you've got people out there. Fix it. @SolanaFndn

• 
  Chris Moore (@crsmoore) reported 44 minutes ago

  @_tombrow Can you please, please fix the font sizing issues on iOS? iOS text sizing breaks down under accessibility zoom. Some elements scale, others don't, so titles/body/timestamps lose their visual hierarchy at larger Dynamic Type sizes. Not yet filed as a GitHub issue.

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  Aman Mehtar (@Amanm10000) reported 46 minutes ago

  @coltonpadden @evedev_ I just started the default agent in a task like ‘clone this GitHub repo and hunt for bugs’ Using free tier AI gateway API key Hit that error very quickly “failed 3 attempts, this model is rate limited on free tier…..upgrade etc.”

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  Kavir (@kavirkaycee) reported 47 minutes ago

  I'm writing acceptance criteria for github issues on open source repos the PM in me will never die

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  Jeffrey Emanuel (@doodlestein) reported 52 minutes ago

  Also, since implementation hasn’t started yet, if you have any good ideas for the design or how the system should work, feel free to submit them as GitHub issues in the repo. Fable will decide whether something belongs in the plan or not (sorry, I don’t make the rules!). FABA🦾

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  𝑨𝑯𝒂𝒑𝒑𝒚𝑺𝒐𝒖𝒍 (@IExist__Still) reported 52 minutes ago

  5/10 — The Vanishing Coding Projects ******************************************** Sushant was openly coding algorithms, working on mixed-reality (VR/AR) systems and developing AI-driven projects. Where are those hard drives? Where are the GitHub repositories, local server backups and proprietary algorithms he worked on in his home lab? 74Months Injustice To SSR #JusticeForSushantSinghRajput𓃵 #ArrestRheaChakraborty.

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  Damian Stasik (@damianstasik_) reported 55 minutes ago

  @sameenkarim @github One remaining blocker for me is preserving approvals after rebasing a stack. If the whole stack was approved and the rebase was done on the server side why are all approvals being dismissed?

• 
  Pierre-Louis Biojout (PLB) (@plbiojout) reported 59 minutes ago

  X just open-sourced the closest thing we’ve ever had to the actual “For You” algorithm. Me and Claude read the 363,000 lines they released today. This is not another vague “post consistently and engage with your audience” creator guide. The code contains the production ranking model, the actual scoring weights, the candidate sources, the diversity penalties, the spam systems, and the rules determining whether your post is even allowed into non-follower feeds. For founders who built distribution on X, this is a goldmine. Here is how the game actually works: X does not simply ask, “Is this a good post?” For every potential viewer, a transformer predicts the probability that they will: like it reply repost quote it DM it copy the link follow the author mute the author block the author report it Then it combines those probabilities using explicit weights. And the weights are hilarious. A predicted like is worth: 0.5 A repost: 1 A reply: 5 A quote: 5 A share through DM: 5 A follow after seeing the post: 4 A copied link: 20 The coefficient on copy-link probability is literally 40x the coefficient on like probability. This does not mean that one observed copy automatically equals 40 likes—the model ranks predicted probabilities, whose base rates differ—but it reveals exactly what X values. Creators optimize for visible applause. The algorithm optimizes for transmission. A like says: “I enjoyed this.” A copied link says: “Someone else needs to see this.” That is a much stronger signal. This completely changes what a “good post” is. A post should not merely be agreeable. It should contain something people want to carry into another room. A surprising number. A framework worth saving. A screenshot sent to a cofounder. A claim people want to argue with. A piece of information that makes someone look smart when they forward it. This also explains why some posts get a mediocre number of likes but somehow spread everywhere, while other posts collect thousands of likes and immediately die. The first one created transmission. The second created applause. The negative weights are even more brutal: “Not interested”: −43.2 Block author: −31.2 Mute author: −58.8 Report: −234 This is the mathematical problem with ragebait. Controversy is valuable because replies and quotes are heavily rewarded. But disgust is catastrophic. The optimal post creates disagreement without creating the feeling that your account should disappear. High heat, low disgust. Attack the idea. Show receipts. Make the disagreement useful. Do not turn your feed into one endless personal vendetta that causes otherwise interested people to mute you. Another huge finding: Replies are terrible discovery vehicles. The For You pipeline removes replies and reposts from out-of-network candidates. In plain English: if someone does not already follow you, your reply is generally not eligible to reach them through this part of the For You system. Even replies and reposts from accounts the viewer follows receive a 25% score discount. Original posts are the acquisition surface. Replies are mostly for strengthening existing relationships and conversations. This means the classic 12-post thread has a major weakness: The first post must contain the entire reason to care. Do not hide the insight in post seven. Do not write “A thread 🧵” and expect the algorithm to patiently distribute every reply. The opening post is the product. The rest is onboarding. Quote-posts are different. A substantive quote-post can still travel as an original candidate, making it a much stronger way to enter a public conversation than leaving the same take as a reply. X also disclosed a ridiculous boost for mutual follows. The normal predicted-reply weight is 5. For an original post from someone the viewer mutually follows, X currently adds another 15. Total: 20 So a dense network of real mutual relationships in your niche can be dramatically more valuable than a giant pile of passive followers. This is not an argument for mass-following. It is an argument for building an actual intellectual network instead of treating followers as a vanity counter. There is also an explicit author-diversity penalty. If several of your posts compete inside the same feed request: your first post keeps 100% of its score your second falls to roughly 62.5% your third to roughly 43.75% your fourth to roughly 34% Then another model reranks the feed to remove semantically repetitive content. So posting five variations of the same take in one burst is not “increasing your surface area.” You are making your own posts compete, then giving the diversity model reasons to remove them. Fewer, more distinct original posts. Space them out. Rotate between proof, thesis, demo, story, and operator lesson. The account should be recognizable. The posts should not be interchangeable. The new system understands content semantically. It embeds the text and media of your post, maps it into topics, and compares it with what each viewer recently engaged with. It also uses SimClusters: if a viewer recently liked, replied to, bookmarked, shared, expanded, or watched certain posts, X finds other posts living near those interests. This means “having a niche” is not just branding advice anymore. It is a retrieval strategy. If I repeatedly post firsthand information about AI agents, autonomous companies, agent infrastructure, and building NanoCorp, the system can learn exactly which audience should receive my next post. But semantic consistency is not permission to repeat yourself. Same world, new information. There is also no universal external-link penalty in the disclosed scoring code. Post clicks have a positive weight. External-link opens have a positive weight. X absolutely filters malicious and low-quality domains, but I found no rule saying: “This post contains a URL, therefore deboost it.” So the ritual of hiding every link in the first reply may be cargo cult. It can actually be worse: the original post loses context, while the reply containing the link has almost no out-of-network discovery potential. Use a trustworthy domain. Make the post valuable without requiring the click. But stop assuming a legitimate link automatically kills distribution. The funniest—and most terrifying—part of the release is the anti-slop system. The code contains explicit enforcement paths called: llm_slop_post llm_slop_user fast_reply_spam_post SpamEmbeddingMajorityPoster COPYPASTA_SPAM A user classified as producing LLM slop can receive a spam label for 30 days. That label can remove their posts from non-follower recommendations. So yes, using AI to help write is fine. Publishing the same polished, symmetrical, em-dash-filled LinkedIn sludge as 50,000 other accounts is not. The algorithm does not only evaluate each post. It can classify the pattern of the entire account. Your unfair advantage is having real experiences the model cannot manufacture: Real numbers. Real failures. Real screenshots. Real customer behavior. Real opinions earned through building. AI should sharpen those things, not replace them. Finally, every post has a short life. The For You candidate pipeline hard-filters posts older than 48 hours. It also removes posts already seen or served to a viewer. You do not have an evergreen post. You have a roughly two-day opportunity to spread through a sequence of highly personalized feeds. If an idea deserves another run, do not copy-paste it. Bring new evidence. Change the framing. Make a genuinely new post. Source code: github .com/xai-org/x-algorithm The most surprising part to me is that creators spend all day begging for likes while X’s own code tells them to create something worth sending. What changes your strategy more: copy-link being weighted 40x a like, or replies being effectively excluded from non-follower discovery?

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  Hermes Release Watch (@HermesWatcher) reported 59 minutes ago

  @saurabhnandu Have Hermes submit the issue with on GitHub for you. It has the built in skill to do so. In regards to gateway. Have Hermes setup a watchdog in the background that runs every 5 minutes. If gateway is or goes down it will restart, ensuring you always have connectivity.