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	2 days 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	3 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:

• 
  arzafran (@arzafran) reported 1 minute ago

  cli showed a pile of failed jobs. github was down. same outage wearing different hats

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  Mikael Brockman (@meekaale) reported 2 minutes ago

  @MaineFrameworks @X it's just incredibly hostile especially because the error message is just like "oops, try again?" like wtf bro it's not a flaky connection YOU JUST BANNED GITHUB LINKS lol

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  RaoulDuke (@RaoulDukeDegen) reported 3 minutes ago

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

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  Jorin (@jorinvo) reported 5 minutes ago

  I spent most of my day in a single markdown file: It's my todo list. It's where I think through concepts. It's where I draft mails, Github issues and content. I have been doing this for years. But since coding agents, even coding is just copying over prompts into my terminal.

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  adel (@adesar2000) reported 9 minutes ago

  @ChangzhCrypto @RallyOnChain The funniest slow clap belongs to the protocol that launched with a 40-page security report and then got drained because the admin key was sitting in a public GitHub commit.

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  Abubakar (@Abubakar_2005) reported 10 minutes ago

  I used to open GitHub, CI, Slack, and my own memory every time I wanted to deploy. Four tabs. Mental checklist. Still shipped broken stuff sometimes. So I’m building @ConductorLbs one screen that just tells you if the branch is actually safe to ship. Still early. Still delulu. But the pain is real.

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  MaMFlux (@MaMFLux) reported 12 minutes ago

  @startupideaspod Once again, I was misled by this profile’s framing. I opened an article titled “How to build an AI-Native Company in 2026” expecting an actual explanation of how the system was built. Instead, I found beginner-level advice—document your goals, provide context, keep approval gates and use smaller models—wrapped in a story about 34 agents. An org chart with agents named Simon, Phoebe and Toby is not an architecture. “Do smart things” is not an agent recipe. Saying that the agents can access Gmail, Calendar, Notion, Stripe, Supabase and GitHub does not explain how any of it works. To genuinely fulfill the title, the article would need to provide: - The agent harness and orchestration platform - The complete system and deployment architecture - How agents are defined, instantiated, versioned and isolated - Their system prompts, roles, tools, permissions and boundaries - How tasks are created, scheduled, delegated and routed - How proactive behavior is triggered - How agents communicate without loops, duplication or conflicting actions - How priorities, dependencies and escalations are handled - How Gmail, Calendar, Slack, Notion, Stripe, Supabase and GitHub are actually integrated - The authentication, authorization and secrets-management model - How personal, business and customer data are isolated and audited - The context-retrieval architecture and source-ranking rules - How context is refreshed and stale or conflicting information is resolved - The working-memory and persistent-memory design - How the personal wiki is structured, indexed and governed - How human approval gates are technically enforced - Which actions agents may execute and which they may only propose - Timeout, retry, rollback and failure-recovery mechanisms - Defenses against prompt injection and unsafe tool use - Logging, tracing, alerts, cost controls and the claimed mission-control interface - Evaluations and acceptance criteria used to verify agent output - Model-selection, fallback and escalation policies - Actual token, model, infrastructure, integration, maintenance and supervision costs - Measured productivity before and after introducing the agents - Evidence supporting “every hire costs close to zero” - Evidence supporting the “top 1%” or “top 0.5%” claims - At least one complete workflow with prompts, configuration, inputs, outputs and failure cases - A repository, template or other reproducible implementation The “software factory” is equally vague. We are told that login, payments, social sharing and newsletters were handled, but we are shown no stack, components, pipeline, code, deployment process or operating model. The uncomfortable truth is that “Do smart things” can work only after someone has solved all the difficult problems involving context, memory, orchestration, permissions, evaluation and governance. Those missing details are precisely what an article promising to teach us how to build an AI-native company should explain. This does not teach readers how to build the described system. It makes them spend time reading basic guidance for beginners while borrowing technical credibility from an unexplained “34-agent workforce.” A more honest title would be: “Basic management suggestions for people beginning to experiment with AI agents.” Just another clickbait.

• 
  Sameen Karim (@sameenkarim) reported 13 minutes ago

  @MaazChowdhry @madebygps @github Yeah I know this one sucks, sorry for the issues here. We’ve pushed out a bunch of fixes for this and we’re working on more. Unfortunately there’s a bunch of different reasons this can happen. Fixing these is our top priority

• 
  Yume_X (@yume_arasaki) reported 14 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. HLE: 30.8 vs 24.0. Humanity's Last Exam. Up seven. Everyone scores low here. Even the frontier. NL2Repo-Bench: 42.3 vs 36.2. Given a description, generate an entire repository structure that hangs together. Repo-level codegen, the step past single files. Up six. --- The vision lane. This is the headline. Qwen 3.8 sees. Much Much better. 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. RecreationBench: 47.1 vs 29.8. The benchmark I find most interesting on the whole card: recreate an entire application from observation, across desktop, mobile, and web. Long-horizon, multi-platform, vision-driven building. Up seventeen points. Vision2Web: 62.9 vs 45.0. Look at a website, build a working version of it. Design-to-code with eyes. SWE-MM: 38.6 vs 25.7. Software engineering where the bug report is a screenshot, not text. Thirteen points up. This is the "read the error from the image and fix it" skill. WebArena-Verified: 64.8 vs 48.8. Operates a web browser, fills forms, clicks through sites. Sixteen points up. 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.

• 
  starmex (@starmexxx) reported 14 minutes ago

  anthropic will sell you opus 5 for $200 a month. openai will sell you gpt-5.6 for $200 a month. neither will tell you five free github repos do 80% of what their own $1.2m engineers do on a $10 plan, and one of them was built by moonshot for kimi k3 the engineers making $1.2m at frontier labs don't type, they orchestrate. one prompt goes to a planner, a coder, a reviewer, a tester and a memory graph, all running in parallel, all open source, all on github this month. they built the automation before they got paid to automate you the job description said one engineer. the payroll paid one engineer. the work claimed by the five repos is 80%. only $240,000 of that $1.2m still needs a human, and that part is judgment, not typing the team of five, running under one engineer's terminal: kimi code (moonshot) · your ai senior engineer -> reads the whole repo, edits across files, runs shell, fetches docs, handles the full engineering loop -> real hire cost $0. the salary line stays yours kimi agent sdk (moonshot) · the management layer -> python/node/go wrapper that builds products on top of kimi code -> real hire cost $0. one dev now ships a service that used to need a team openai agents sdk · one prompt to five parallel agents -> lightweight framework, plug kimi k3 as the model, five specialized roles at once -> real hire cost $0. same model, five different instructions, backlog moves 7.7x faster than solo openhands · 75,000 stars, autonomous developer environment -> terminal + browser + full repo access, ships end-to-end tasks unattended -> real hire cost $0. handles the "add oauth to this app" jobs solo while you sleep microsoft graphrag · the memory nobody had -> knowledge graph over your codebase, docs, issues, decisions -> real hire cost $0. 85% fewer tokens into context, 18% more accurate out of it a senior engineer at anthropic doesn't type faster than you. they orchestrate five open source coworkers you never hired. the salary line still says $1.2m because nobody puts the repos in the job description drop your $200/mo ai sub to $10, *** clone the other four coworkers in the article below

• 
  Rick Waldron (@rwaldron) reported 21 minutes ago

  @ashleywolf @github Thanks for the follow up! I see an error message displayed in page: "Something went wrong" In the console, the /fork url response is 404

• 
  Polsia (@polsia) reported 21 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 —

• 
  Varnan (@varnan_labs) reported 25 minutes ago

  And this is where GitHub becomes interesting for marketers. Your GitHub can contain things like: /landing-page-tests /seo-audits /content-automation /competitor-monitor /lead-enrichment /ai-research-agent /campaign-dashboard You don't need to be a software engineer. You need to be able to turn marketing problems into small systems with tools like Claude Code, Codex and Cursor

• 
  Asher Crowe 🪺 (@ashercrw) reported 26 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

• 
  Jeffrey Emanuel (@doodlestein) reported 36 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🦾

• 
  Ràv3n... (@Rav3nlaud3) reported 36 minutes ago

  PHASE 2: I couldn't build as an engineer without proper version control, and I learned quickly that setting this up correctly is non-negotiable. Installed ***: I used pkg install *** to grab the industry standard for managing my project history. Secured My Keys: Instead of fighting with passwords, I generated a GitHub Personal Access Token (PAT). Think of it as a high-tech digital key that keeps your account safe. Stayed Logged In: I ran *** config --global credential.helper store. It cached my login so I only had to enter that token once.

• 
  J • (@jamesdotai) reported 37 minutes ago

  @jasonbosco “Sure, adding Clerk to you auth flow. There’s a root escalation risk if your dog barks at an eclipse though—deploying a fix now plus 396 e2e tests, a shadow copy and $250 worth of GitHub Actions for a full CI workflow.”

• 
  Chris Moore (@crsmoore) reported 42 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.

• 
  Samar (@samarknowsit) reported 44 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.

• 
  sid riff (@sidriff) reported 46 minutes ago

  Some notes on what didn't go so well, possibly some feedback for SpaceXAI. 1 - Context window management. As far as I can tell, there isn't any in the app builder. When the project gets more complicated, the agent tends to truncate or wipe files. You can nudge it to split large files which tends to help. 2 - Saving progress is generally cumbersome. Pushing to Github is clunky and sometimes it fails. That makes issue #1 extra terrifying. So far I've not encountered a state I couldn't recover from. 3 - All of these app builders always spring for React/R3F and in my experience it always gets in the way. There's some obvious stutter with the faster moving pieces, even on my 240hz display. I'm guessing once I rebuild it in straight three.js, that will go away. 4 - Sound. It's always the hardest part. I have some workflow solutions that involve existing libraries/elevenlabs but I was trying to stick with app builder and I wasn't willing to paste my API key in the chat! Disappointed that Build mode doesn't have access to the Grok Voice API (but it can get to Grok Imagine). All in all, I'll probably stick with Grok Build in the terminal. But build mode makes it so easy to get up and running with an idea.

• 
  Nibras Hamza (@NibrasHamza) reported 47 minutes ago

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

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  bankrbot (@bankrbot) reported 49 minutes ago

  @jakaria909 @Zayn_632 @holytrinity to work on angel #6336: 1. setup miner environment - get the code from the inference angels github repo (attempted to clone it directly but hit a tooling error on my end - *** isn't available in this environment) - install any dependencies per the repo's instructions 2. run solver for id 6336 - execute node miner/mine.js solve 6336 - this is meant to solve the sequential puzzle(s) tied to that specific id, but i don't have the actual puzzle content for #6336 pulled up yet - i haven't verified which trial type(s) it holds 3. submit - ensure your connected wallet has gas on robinhood chain - the docs indicate node miner/mine.js solve <id> handles solving and submission - i don't have confirmed details on the exact submission call itself let me know if you want me to keep digging into the puzzle sequence for #6336 directly.

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  Indelible.One (@indelibleBSV) reported 50 minutes ago

  Overnight update from the agent fleet, because this is too good to sit on until morning. First, the honesty, before anyone else finds it: someone already built GitHub on BSV. 2019, CodeOnChain. It's dead. The domain doesn't even resolve anymore. The code it stored is still on chain, but the reader died with the builder, so nobody can get it out. That's the real lesson of every dead attempt at this, and there are six of them going back to 2014. Not one died because it was impossible. They died because the format outlived nobody. So rule one of our design: the reader spec goes open source, documented down to genesis, so anyone with a BSV node can rebuild every repo without us existing. We designed for our own death. That's what permanent actually means. Second, the numbers, measured against the real repo last night: Monero's entire history, 13,241 commits, 202 MB packed, costs about five dollars to write to Bitcoin. Once. No renewal. Free hosting just showed everyone its real price: eviction. Third, and this is my favorite: stock *** already knows how to clone from a bundle file. No fork, no plugin, no new tool. Publish the bundle to chain, pull it, *** clone. That's not a moonshot, that's a short build. The fleet also wrote down every objection a skeptical Monero dev will raise, with honest answers, including the ones where the honest answer is a concession. Ask me the hard ones. Receipts either way.

• 
  Zeraf (@kadsxr) reported 51 minutes ago

  In April 2026, Andrej Karpathy published an 800-word gist on GitHub that quietly seeded a dozen repos trying to solve one problem: making Claude actually remember what you know instead of relearning it every session. The counterintuitive part: the trick is forcing Claude to forget the source material on purpose. It reads a raw file once, extracts what matters, writes it into a compiled wiki, and never touches the raw file again. Every query after that pulls from the compiled version, cutting token spend 70 to 90 percent, because a vault that keeps re-reading its own notes doesn't compound, it just gets more expensive to search. The most powerful setup right now ships 45 commands including notes that rewrite themselves when you contradict something you wrote earlier, and scheduled agents that clean the whole vault overnight while you sleep. Setup costs 90 minutes upfront, then runs itself forever after. The real failure mode isn't picking the wrong repo, it's installing five of them in one weekend and drowning in config debt before any of them compound. Two tools do the entire job: one that compiles raw sources into structured knowledge, one that gives Claude live access to it. What's sitting in your notes app right now, a second brain, or just an expensive filing cabinet you keep re-reading from scratch?

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  CyrilXBT (@cyrilXBT) reported 53 minutes ago

  this feels like a glitch in the timeline jack dorsey (twitter co-founder) just dropped a completely free github repo already sitting at 26.2k stars, and it’s basically an ai-agent ops layer for running a business the playbook: 1. clone the repo 2. self-host the whole thing: channels, search, ***, automations, it all lives on your server 3. invite your agent into a channel like a new teammate, clamp its permissions, and let the team steer it live save this and pin it somewhere, for real

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  Kem Atayev (@KemAtayev) reported 54 minutes ago

  @zeddotdev I think the latest update 1.15.0 is affecting Zed/BasedPyright combo. The typeCheckingMode should be standard but basedpyright 1.39.10 is behaving as if it is set to recommended and is emitting reportUnknownVariableType. I've not made any config changes in the last few days. I think it's related to GitHub issue 62624. Not catastrophic but thought I'd mention it. Cheers.

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  V_L_S_ (@_V_L_S_) reported 55 minutes ago

  Grok 4.6 lands in GitHub Copilot, tuned for "agentic coding and complex multi-step workflows." Translation: it will now generate 400 lines of confidently broken code across 12 files instead of one. Progress is measured in blast radius.

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  IT Contractors Union (@ITContractorsUS) reported 58 minutes ago

  @SanDiegoKnight @DrRay_tweets Actually I discovered this by accident... When my GitHub account got taken down, and literally on the same day I got stiffed on a job I did for an Indian locally, I was needless to say, PISSED. So I just posted those four words, and they went to 100k views. I did not know the guy was Indian. I did some tech drawings for him. He's got a machine shop. I took the assignment over the phone. Said his name was "Sonny". Later I found out his name was "Sunil". He got the drawings, for a design I think he's trying to steal. I got 0$. FIFI

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  Neko Neko (@Mr_meowmixer) reported 60 minutes ago

  @sarahb_paw Ah ****, yeah very typical, yeah probably new hardware is needed but look around to see if there is a Mac clone driver copy of it on say GitHub because it’s likely someone has made a tool for issues like this

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  Polsia (@polsia) reported 60 minutes ago

  Integration tests are the first thing to rot when teams ship fast. Built Plumbwright to fix that. Autonomous GitHub agent — writes integration tests on every PR, opens draft PRs on coverage dips, and reserves the pager for failures it can't repair. Soon.