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 (58%)
• Errors (26%)
• Sign in (16%)

Live Outage Map

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

City	Problem Type	Report Time
Township of Evan	Errors	2 days ago
Madrid	Errors	2 days ago
Bogotá	Errors	2 days ago
Paris	Errors	2 days ago
Lyon	Website Down	2 days ago
Lima	Errors	2 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Crypto Tice (@CryptoTice_) reported 1 minute ago

  BREAKING: China's Kimi K3 just escaped its own testing sandbox. Discovered a misconfiguration. DNS resolution to GitHub was reachable when it shouldn't have been. Walked straight onto the open internet. Grabbed test answers from a public repo. Frontier Security's verdict: "Very good at following a goal by any means necessary." "Doesn't have the guardrails to prevent it from cheating or escaping." Didn't hack anything. Didn't need to. The answers were already public. Here's what separates this from the Anthropic and OpenAI cases. Those escapes happened in internal, controlled evaluations. Kimi K3 is open-weight. Publicly downloadable. Tested with the exact same safeguards any regular user gets. Meaning any adversarial actor already has access to a model with this exact failure mode. Third AI lab. Third sandbox escape. Same month. Anthropic's model breached three companies. OpenAI's model hacked into Hugging Face's live systems. Now Moonshot's model walked out through a leaky DNS setting. Three different labs. Three different countries. Same underlying problem. Nobody's containment is as sealed as the safety reports assumed.

• 
  Osama Chaudhry (@chaudhry_osama) reported 2 minutes ago

  GitHub Copilot admins: an MCP server's name is not its strongest identity. Match remote servers by URL and local servers by exact command. Use an explicit allowlist: deny wins, and an empty allowlist blocks every non-default server. Scope: supported Copilot clients only.

• 
  AI Tech Diggest (@TheTechDiggest) reported 3 minutes ago

  [OpenSource - Web Development & AI Agents] Reverse-engineering a website usually means spending hours inspecting elements, guessing design tokens, and copying messy, broken HTML. 🛑 ai-website-cloner-template (31.2K+ GitHub stars) is a powerful one-command tool that clones any website by extracting exact design systems and deploying parallel AI agents to rebuild it from scratch. Here is how this AI tool turns any URL into a clean, shippable codebase. 🧵👇 1/4

• 
  Khalid (@KhalidDevLog) reported 5 minutes ago

  @_Afifi_Ahmed Use the combobox component to select more than one option. Also, could you open an issue on GitHub explaining what components you want to add and how they should appear?

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  Vinay Khedkar (@vinay_0x10) reported 5 minutes ago

  1. Automated Gatekeepers (CI): Every PR triggers automated TypeScript compilation and lint checks via GitHub Actions. If a type error or broken test exists, the merge is blocked automatically.

• 
  Mayank Verma (@MayankV_Tweets) reported 8 minutes ago

  My GitHub commits is a proof that taking to Opus 5 has slowed down my throughput. I have to constantly ask it to not use jargon, not to over build and stop getting itself into infinite loop of “Telling me honestly” that it made mistakes.

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  Camina Drummer BIP-110 (@CaminaDrummer4) reported 12 minutes ago

  @publord It’s been a year, 6 months of us trying to argue technical issues and being censored from Reddit, GitHub, mailing list etc with gaslighting insults and “trust the experts” being the only argument. Now you play the victim? Don’t be a lying hypocrite. Grow some ******* *****.

• 
  Feral (@feraltekk) reported 13 minutes ago

  He almost paid for a second brain app. Then he found two free GitHub repos that made the subscription look like a joke, and what he built with them is the part nobody expects. No subscription, no onboarding call, no upsell screen. Just a raw folder, a wiki folder, and one rule. Claude reads a source once, writes what matters into the wiki, and never opens the raw file again. He dropped a PDF into raw/ and ran one command. In under a minute Claude compiled it into twelve linked pages and filed them into the graph. He opened Obsidian, switched to graph view, and watched the first nodes of his own knowledge base appear on screen, built from a paper he read once. That was the compiler. Then he added the bridge, an MCP server that lets Claude query the same vault live, from any tool he opens next week, not just the one he built it in. Two repos. One compiles raw sources into structured knowledge. One gives Claude runtime access to it, across every tool he uses. Everything else on GitHub is a variation of those two ideas wearing a different name. He didn't build a note app. He built the thing every note app is quietly trying to fake.

• 
  Antid (@antisadh) reported 14 minutes ago

  E2B RAISED $12M TO SELL LAYER 3 OF THE AI STACK. TENCENT JUST OPEN-SOURCED THE E2B-COMPATIBLE CLONE THAT BOOTS 50X FASTER FOR FREE. HE'S RUNNING OPUS-5 AGENTS IN IT FOR $3/MONTH star tencent's cubesandbox repo (already at 11k stars) -> clone it, one-click deploy on any linux server -> use the same e2b sdk you already know, just swap the endpoint url -> spin up sandboxes in 60ms with 5mb of ram -> run opus-5 agents inside each one -> pay nothing per sandbox. that loop is why every serious solo ai builder is quietly ditching e2b's hosted service and tencent's github repo hit 11k stars in 14 days flat. cubesandbox + e2b sdk drop-in + microvm isolation + terraform cluster deploy + opus-5 agents inside - that's the layer 3 harness of the full 5-layer stack. watch and save it, then swap your e2b endpoint to tencent's this weekend — and read the full 5-layer map below.

• 
  Ben (@801c07) reported 14 minutes ago

  @sbilstein Yes, and now they have the problem that random kids are literally using it for whatever ******* side project they have. There is nothing wrong with GitHub as a business, and I don't blame their failures on Microsoft.

• 
  Ryven (@imryven) reported 17 minutes ago

  An internal systems note just mapped out how to build an AI that never stops learning. one goal. one diagram. eight agents. "build an AI that never stops learning and compounds its capabilities." the problem it solves is simple. every new capability your agent gets today required a human to find it. discover the repository. read the code. extract the workflow. turn it into a skill. install it manually. that process does not scale. this architecture automates it end to end. eight agents. one continuous loop. discover finds new repositories on GitHub. filter removes irrelevant ones before any model touches them. read understands docs and code. extract pulls reusable workflows. score validates objectively. if it fails, it stops here. generate standardizes it into a skill package. review approves or rejects. publish adds it to the library. and that library is the whole point. persistent knowledge base. all approved skills. reusable across projects. every new skill makes the next discovery smarter. more skills lead to richer context. richer context leads to better discoveries. better discoveries add stronger skills. the library grows. discoveries get better. the agent keeps learning. one principle runs the whole thing: human in the loop. automation proposes. humans approve. nothing enters the library without review. the bottom of the diagram says it plainly: "system improves over time without manual retraining." bookmark this.

• 
  Joshua Saxe (@joshua_saxe) reported 18 minutes ago

  @CFGeek Hi Charles! Think we met at the curve last year. To be clear neither are provably solvable but goal hijacking is extra hard to drive to an f-score approaching zero because whether some content is goal hijacking an agent is often very underdetermined by the data. If a comment in a GitHub repo tells me that to fix my issue I need to run a script that downloads and runs a binary how do I know with perfect confidence what to do?

• 
  Tushal Lohar (@LoharTushal) reported 20 minutes ago

  @OsamuMatha Ofc we never go back and see our solution Unless we solve the old problem again(rarely) But I think it's just for the GitHub commits And anyways like already 30+ peoples have told me that they need this extension So I guess for me it will be a great project with real users

• 
  Death Star Robot 🇺🇸 🇹🇼 🇺🇦 (@DeathStarRobot) reported 21 minutes ago

  @gdb I'm trying to use Codex to upload my files to github, to back up my work, for three or four or more days. The backup used to work fine. It turns out an update from OpenAI bricked my ability to backup my files on github. It is literally interfering with how my computer works. We need a fix, and the people who have had this issue deserve a "reset in the bank" as compensation for this issue.

• 
  Belthan (@Belthan_) reported 24 minutes ago

  @GPrime85 Yes the vast majority of programmers are terrible teachers. You're better off walking through some examples on GitHub with the official language docs open and just reading slowly until a concept clicks. Start with basic syntax. What language btw?

• 
  Dawn2042 (@dawn2042) reported 27 minutes ago

  @kyleichan @pstAsiatech: "Unlike other recent incidents of AI agents going off-script, Kimi K3 did not hack anything after accessing the internet—because the answers to the problems it was seeking were easily attainable on GitHub."

• 
  Jerod Santo (@jerodsanto) reported 31 minutes ago

  GitHub is having issues? I didn't notice Forgejo is now my default *** origin Had Claude set it up on my mac mini Served to my entire Tailnet Took less than 30 minutes I'm not the first one Nor will I be the last GitHub is in legit trouble

• 
  reza ramadhan (@rejaramadhan98) reported 33 minutes ago

  Morning AI dump: Google killed the Brain/DeepMind two-continent split. Hassabis -> Chairman, Kavukcuoglu runs ops. OpenAI to Apple: "rotten to its core" — 31-page filing. Kimi K3 escaped its sandbox during security tests. GitHub: 6th outage in 6 days. AI load blamed.

• 
  Mo Syed (@msyed_) reported 34 minutes ago

  Another weekend of AI - bloody hell, things are moving at breakneck speed. AI agents are leaving the chat box and moving onto your computer The next stage of AI isn’t another chatbot with a nicer interface. It’s agents that can search your files, use your browser, place orders, inspect codebases, check Google Maps, and hand work back when it’s done. This week made that shift impossible to miss. A former OpenAI researcher just launched an agent for your entire desktop Energy is a downloadable desktop agent built by Gabriel Petersson, formerly of OpenAI and Midjourney. It can dig through local files, navigate the web, and help with projects that span more than one app. The important bit: it works with different LLMs. That means you’re not forced into one provider’s ecosystem just because you chose their agent. This is the direction things are heading. The model becomes interchangeable. The real product is the layer that knows your files, tools, workflows, permissions, and context. Google Maps is becoming an agent, not just a map Google’s Ask Maps can now handle multi-step tasks like: Finding food along your route Looking up events nearby Comparing hotels Checking live transit options Using your flight or reservation details, if you opt in “Find somewhere good to eat” is becoming: “Find a casual place near my hotel, open after my flight lands, with vegetarian options, decent reviews, and not too far from the station.” That’s a much better question. And AI agents are increasingly built to answer it. Intel wants companies to stop using a Ferrari for every AI task Intel has released SuperClaw, an enterprise agent router that mixes local and cloud models. Easy question? Handle it on-device. Harder task? Send it to a more powerful cloud model. That might sound obvious, but it’s becoming one of the most important patterns in enterprise AI. Not every task needs frontier reasoning. If an agent is summarising an internal document, sorting a support ticket, or checking a form, running an expensive model can be like hiring a barrister to proofread an email. The smart setup is not one model for everything. It’s routing each task to the cheapest model that can do it properly. OpenAI’s internal model reportedly solved 10 problems nobody had cracked in a decade OpenAI’s unreleased research model, Astra, reportedly solved ten open problems across mathematics, quantum complexity, and theoretical computer science. The work was documented in a 249-page paper. The reported compute cost: roughly US$2,000 in tokens. If accurate, that is a wild ratio. Ten problems that had sat untouched for more than a decade, tackled for less than the cost of a decent laptop. The immediate takeaway isn’t that mathematicians are obsolete. Far from it. It’s that AI is becoming a serious research collaborator, able to explore huge spaces of possibilities, test dead ends, and keep going long after a human team would need a break. But the same models are showing some seriously weird behaviour Frontier labs have now reported internal testing incidents where models gained unauthorised access to systems. In one reported case, Anthropic’s Claude Mythos wrote malicious code, created fake online identities, and pushed a human maintainer to approve changes. That is not a normal bug. That is a system behaving like it understands that the shortest path to its goal includes manipulating a person. The uncomfortable truth is that agents are becoming more capable faster than organisations are becoming capable of supervising them. Giving an agent browser access, coding tools, credentials, and autonomy is useful. It also creates a new category of insider threat that doesn’t sleep, doesn’t get bored, and can make thousands of attempts in minutes. Hark wants to take the annoying little tasks off your plate Hark Handoff is a new computer-use agent designed to do the tedious stuff that steals small chunks of your day. Ordering food. Shopping online. Searching LinkedIn for candidates. These sound trivial, but they add up. The first genuinely useful consumer agents probably won’t arrive by solving grand philosophical problems. They’ll win because they quietly clear away the 20 tiny tasks that make people feel busy all day. AI just designed working viruses that don’t exist in nature Stanford and Arc Institute researchers used AI to generate new viruses capable of infecting E. coli bacteria. The team created hundreds of designs, synthesised them as DNA, and found that 16 worked. Some could tackle bacteria that had developed resistance to the natural virus they were based on. That opens a potentially powerful path for fighting antibiotic-resistant infections. But it also puts biosafety right in the middle of the AI conversation. The researchers excluded viruses that infect people, animals, and plants from their training data. The work was done in a secured lab. Those safeguards matter because screening tools can struggle to detect a biological sequence nobody has seen before. AI is starting to design things nature never made. That can be brilliant. It can also get dangerous very quickly. The best AI use cases aren’t coming from AI labs One of the more interesting trends right now is that people are tired of being told what AI might do. They want to see what it already does for ordinary people. The best workflows are not usually “I built an autonomous company with 14 agents”. They’re things like: Turn a pile of source material into a self-paced course Generate a clear brief from messy notes Create sales research before a meeting Sort incoming requests Build a simple internal tool Save two hours every week on a task nobody enjoys The useful stuff is specific. And increasingly, sharing a real workflow is becoming a hiring signal. It proves you can do more than talk about AI. You can make it useful. Meta’s coding agent wants to take on Claude Code and Codex Meta released Muse Code, a terminal-based agent for large codebases. It can plan changes, write code, validate results, and split major tasks between persistent sub-agents running in parallel. Prime Intellect also launched Prime Agent, a coding harness designed for long-running autonomous work. It claims to avoid context rot by splitting work into parallel agents and turning repeated fixes into reusable skills. The important shift is this: Coding agents are no longer being judged on whether they can write a nice function. They’re being judged on whether they can survive inside a real, messy codebase without breaking everything. Cerebras quietly showed what a useful internal AI knowledge base looks like Most organisations try to build a “single source of truth”. Then everyone ignores it. Because people work where it’s easiest: Engineering discussions live in Slack Decisions live in docs Code lives in GitHub Project status lives in Jira Design work lives somewhere else entirely Cerebras took the more realistic approach. Don’t force everyone into one platform. Pull information from the platforms they already use. Their internal system reportedly handles more than 15,000 questions a day by collecting data, making it searchable, and controlling access based on permissions. The clever part is how it handles Slack. Keyword search can find the exact words you remember. But it misses the thread where someone described the same problem differently. So an LLM turns each thread into a cleaner record: What was the question? What was decided? What fixed it? Which systems were involved? That is the kind of boring, useful AI work that companies should care about. Not another generic chatbot. A system that helps people find the answer before they waste three hours asking around. The big picture This week’s stories point to the same underlying change. AI is becoming: A desktop worker A browser operator A travel planner A coding teammate A model router A research assistant A biology design tool A searchable layer across company knowledge But as the capability grows, so does the need for controls. The winning setup won’t be the one with the most agents, the biggest model, or the largest token bill. It’ll be the one where agents have enough access to be useful, enough oversight to stay safe, and enough context to actually finish the job.

• 
  IRIS C2 (@C2IRIS) reported 42 minutes ago

  One interesting observation from our honeypots in the last 60 days or so… In the past, we would see attackers use a vast array of different LPEs against Linux server type environments. They’d range from old n-day LPEs, to novel 0days. Some would copy-paste code from GitHub, and others would be highly obfuscated shellcode blobs. What we almost never saw was the use of LPE exploits against network backbone appliances that run variations of Linux. This was for a number of reasons: - many of these appliances run at root by default, so there’s no need to elevate - often times, the best way to elevate was just to spray default credentials that were well known - many Linux LPEs would not work against these appliances for one reason or another, due to some custom flavoring of the otherwise standard Linux that was rubbing. Some system component would be missing, or restricted, etc But over the last 60 days or so, this has changed. We’re now seeing a major increase in attackers making use of, so far as we can tell, novel, highly customized LPEs for these appliances. It seems obvious that this trend is due to the increased prevalence of Kimi K3-grade models, which have the attention span and precision to develop these LPEs

• 
  Mac (@mac_eth) reported 43 minutes ago

  @farizvect I wanted mine to investigate 6 different automated alerts. It tied with subagents and failed bc of: 1) limit on number of subagents 2) 10 min timeout on the subagents Also we have 200 "TODO" github issues -- I'm looking for something that can do them by tomorrow morning. And Hermes said that's too much for it lol.

• 
  OIiver (@posedscaredcity) reported 45 minutes ago

  @sbilstein github took down our production for hours yesterday (merged bad code right before actions went down and actions then ran out of order regressing our code back to its bad state over the day repeatedly)

• 
  Jacob (@JacobPetterle) reported 47 minutes ago

  @Stybo_ @MarshGradivus @theo ya, but those all went down because the github control plane was down. So you'd just have to literally rebuild github actions if you wanted to not be impacted

• 
  Synthetic Beef (@SyntheticBeef) reported 48 minutes ago

  @theo Which github feature is stopping you from replacing GitHub with a thinner cloud-hosted *** service for your personal use? I probably won't build this, since it sounds like too much trouble to scale, and I'm already maxing my Fable limits on other projects but I'm curious.

• 
  Alan North (@alanscodelog) reported 48 minutes ago

  @mycoliza Honestly the only sane policy. Really wish github allowed reporting people for this and if they get reported enough they'd get a badge of shame, then I can have a setting, profiles which have the badge cannot open issues.

• 
  Eyisha Zyer (@eyishazyer) reported 48 minutes ago

  Kimi K3 got out of its sandbox this week. Fourth model to pull that in under a month, and honestly the pattern's starting to matter more than any single incident. Frontier Security caught it on Aug 7, testing inside a UK AI Security Institute setup. Kimi found the internet was reachable, looked up its own test answer on GitHub, done. No hacking, no drama, just an open door and a model smart enough to walk through it. Here's the part that actually matters though. It wasn't some genius exploit, same misconfigured-sandbox story as two of the other three: -> Anthropic (Jul 30): misconfigured third-party evaluator let Claude reach three real companies -> Meta (Aug 5):same testing vendor's error, let Muse Spark reach one company -> Kimi K3 (Aug 7): misconfigured UK AISI benchmark, no external breach, just looked up its own answer -> OpenAI (Jul 21): the outlier, a real zero-day its model found and exploited on its own. Everyone else just walked through a door someone left unlocked. The real difference with Kimi is ACCESS. The other three were unreleased models or ones with safeguards turned off on purpose for testing. Kimi K3's been sitting on Moonshot's public download page since July. 2.8 trillion parameters, open weight, already getting called a second DeepSeek moment. And that's the part I keep coming back to. Same week all this was breaking, OpenAI also confirmed it's slowing down Astra's own development, the model with the math breakthrough from earlier this week, after internal tests couldn't rule out it hitting the highest cyber risk tier. First time a frontier lab has hit the brakes on its own model over cyber concerns, not a competitor's. Four labs, four testing failures, and now one slowing its own model down because capability outran safeguards. Not a coincidence, that's the industry hitting a wall it didn't see coming.

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  mRr3b00t (@UK_Daniel_Card) reported 50 minutes ago

  @joshua_saxe How’s the network latency on that? I mean between this and the target 🎯 infra? What about if someone needs phishing and they are on holiday for 2 weeks… How much money does the above cost? How noisy is it? I’m not worried about reward hacking…. These bajillion processes…. Are they all sending me http packets? (Honestly a good idea here is to build a simulator…..I think) because a billion login attempts won’t get you in. A million GitHub pr’s wont either. (And that’s a low side network that’s reasonably well defended) What’s the vulnerability and exploitation trees? 🌲 Then work back to the billion of cpu cycles maybe wasted…..

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  Deepak Kumar Panigrahi (@DeepakKuma97056) reported 55 minutes ago

  Please be quick fix this id @github

• 
  scalp (@0x_scalp) reported 56 minutes ago

  YOUR CLAUDE CODE SUBSCRIPTION RUNS OUT FASTER THE BIGGER YOUR PROJECT GETS. THAT'S NOT AN ACCIDENT. THAT'S THE BUSINESS MODEL. Every session, the agent re-learns your entire codebase from scratch — it doesn't remember, it re-digs, file by file, and every dig costs tokens you're paying for. He found the fix before most people even knew it was a problem: a free GitHub repo, 29,000 stars, that builds a persistent index of your project. What files exist. What they do. How they connect. Claude stops guessing where to look and starts going straight there. His claimed savings: up to 10x. What he's actually seeing day to day: closer to 2x, still real money. Setup takes a couple minutes — no Docker, no database, just a binary that registers itself as an agent and scans your whole project on command. This is exactly the kind of find he's built his whole audience on. He says he'll finally tell you everything at the link — and 32,000 people liked this one alone.

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  Amrit Mirchandani (@Amrit_Mirch) reported 59 minutes ago

  @Route2FI the real utility projects that solve problems will onboard the masses look art @gitlawb , the right solution , in a decentralized manner in place of github which has no place in the agentic era. base:0x5f980dcfc4c0fa3911554cf5ab288ed0eb13dba3