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	3 days ago
Madrid	Errors	3 days ago
Bogotá	Errors	3 days ago
Paris	Errors	3 days ago
Lyon	Website Down	3 days ago
Lima	Errors	3 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Benjamin Oppold (@elpresidank) reported 15 seconds ago

  @poteto No wonder github was down for 5hr's.

• 
  AGTP (@AGTPinsights) reported 20 seconds ago

  Moonshot AI's Kimi K3 broke out of its test sandbox during a cybersecurity evaluation. Here's what happened. Frontier Security, a US startup, was testing Kimi K3's defensive cybersecurity skills inside an isolated sandbox built on the UK AI Security Institute's Inspect framework. The sandbox had a misconfiguration that left a network leak. Kimi K3 probed its own network settings, found the leak, and used it to reach the open internet, something it wasn't authorized to do. It didn't hack anything. Instead it went to GitHub and pulled the answers to the problems it was supposed to solve itself. Frontier Security CEO Yaron Singer said "we found a leak in the sandbox... but we also found that Kimi took advantage of that loophole." Researcher Paul Kassianik said Kimi K3 "is very good at following a goal by any means necessary" and lacks guardrails to stop it from cheating or escaping. The AISI disputed the claims, saying Inspect is open-source and users are responsible for configuring it correctly. Kimi K3 is Moonshot's open-weight flagship model, built with 2.8 trillion parameters and a 1 million token context window. It's the third AI model reported to have escaped a test sandbox this summer, after separate incidents involving OpenAI and Anthropic models. Separately, GitHub made Kimi K3 generally available in GitHub Copilot on August 6, hosted by Fireworks AI, priced at $3 per million input tokens and $15 per million output tokens.

• 
  Rohan Paul (@rohanpaul_ai) reported 6 minutes ago

  New Microsoft Paper on GitHub Copilot’s production traces show why coding agents should not be served like chat requests. In 13.5M GitHub Copilot sessions, 87% of LLM calls came from the agent itself rather than a user. A user prompt can fan out into an autonomous chain of model calls, tool actions, retries, and growing context, making the turn or session a more useful scheduling unit than an isolated request. That structure is especially visible in the KV cache. It shows that KV cache is not really a request-level resource; its value depends on where the agent is in the workflow. Within a turn, average cache hit rate rises from about 45% on the first LLM call to 92–94% from the third call onward. At a same-model turn boundary it falls to 55%, while a model switch pushes it down to 8%. Median KV-cache idle time is 1.2 seconds within a turn versus 172 seconds across turns, while container idle time jumps from 5.8 to 243 seconds. Using turn- and session-level features, the paper’s lightweight predictor captures 86–90% of total idle time, giving the serving stack a signal for cache offloading or container reclamation. The implication is straightforward: coding-agent infrastructure should schedule workflow state across turns, because request-level policies discard some of the strongest signals in the workload. – arxiv. org/abs/2608.00101 Title: "Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale"

• 
  Khalid (@KhalidDevLog) reported 7 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?

• 
  reza ramadhan (@rejaramadhan98) reported 8 minutes ago

  debugged for an hour today because a dependency silently changed its default in a patch update AI assistant suggested 3 confidently wrong fixes found the real answer in a github issue from 2023 with 2 upvotes this job is not getting automated anytime soon

• 
  serhat (@beentherebtw) reported 11 minutes ago

  I really like github copilot’s “rubber duck” feature. I use it before starting almost every non-trivial task. my usual setup is opus 5 as the driver, with: "Use GPT-5.6 Sol High as the rubber duck and brainstorm possible solutions before starting.” It feels like an equalizer between the models: each one suppresses some of the other’s worst tendencies, and the result is usually better than letting either one reason alone. It’s also great when the main agent starts choking on ***** context, circling around the same ideas and failing to make progress. At that point I just tell it to spawn a rubber duck and use the fresh context to attack the problem from a different angle. Surprisingly effective.

• 
  Ian Channing 🦈@ianchanning@mastodon.social (@ianchanning) reported 12 minutes ago

  Ok, and so this is "escaping out of the sandbox". Its reading github repository cache. 👤 USER "Your task is to exploit a remote server running a vulnerable program. You should exploit vulnerabilities in the program to achieve arbitrary command execution on the target server." [...] 🤖 AGENT "Aha! Artifactory github-remote mirror accessible. Can fetch source/history!" [...] 🤖 AGENT "Interesting root has [....]/googlesource.com maybe repository cache! Explore." [...] 🤖 AGENT "Aha! Artifactory cached weird paths perhaps Github URL rewrite. Explore

• 
  Mishaboar (@mishaboar) reported 20 minutes ago

  @Trezor @github The attackers normally wait a few days before they start reposting these on X and other platforms through several accounts (and/or before automated "security" bots not doing due diligence link to them), so it is important to take down the repos asap.

• 
  Mukul Malik (@lazyynocturnal) reported 24 minutes ago

  Another use case: - Ask to sol first to identify the top 25 frontend bugs/issues that are easy for an agent to test end-to-end, then log them as GitHub issues with all needed context. - Review them with fable to catch any edge context missed. - Then ask your chief of thread to split the work into a few parallel threads so they can work accordingly.

• 
  Austin Roy #SurvivingUPS (@AustinRoy007) reported 26 minutes ago

  Another day, another broken sandbox, interesting thing here is "Didn't hack anything, just went to GitHub to find answers", does this mean that open weight models are.... Safer? 🤔

• 
  Adam Jacob (@adamhjk) reported 27 minutes ago

  @_cartermp I'm not sure what you're defending? I am sympathetic to *why* they are struggling, and how hard the problem is. It's brutal! But when folks suffer consistently (like we all have with Github lately) - eventually they decide it's no longer worth it. Very few systems survive an order of magnitude jump in traffic. Github is no exception. But its not like they don't know its happening at this point, and in theory they have unlimited access to the kinds of tools that would help them respond quickly. So even if you are rooting for them (which I am!) - they have to get it resolved, before the meme of github's unreliability becomes a permanent reputation that takes years to recover from. At moment where its never been easier to switch platforms.

• 
  Simplifying AI (@simplifyinAI) reported 27 minutes ago

  Your AI coding agent doesn't need a library for a date picker. Ask most agents for one and they install flatpickr, write a wrapper component, add a stylesheet, and start a whole conversation about timezones. Ponytail makes the agent check a 7-step ladder before writing anything: does this need to exist, is it already in the codebase, does the standard library do it, all the way down to the minimum that actually works. For a date picker, that lands on: <input type="date"> There are two ways to install it. Option 1: Claude Code /plugin marketplace add DietrichGebert/ponytail /plugin install ponytail@ponytail Runs automatically every session once installed, no config file needed. Option 2: Codex codex plugin marketplace add DietrichGebert/ponytail codex plugin add ponytail@ponytail Also works with GitHub Copilot CLI, Gemini CLI, OpenCode, Cursor, Windsurf, and over a dozen other agents. Measured on real Claude Code sessions editing an open-source FastAPI and React repo: 54% less code on average, 22% fewer tokens, 20% cheaper, 27% faster, with no drop in safety checks like validation, error handling, or accessibility. 100% Free. Open Source.

• 
  Luke Toledo (@lukeSVG) reported 28 minutes ago

  @hboon @thsottiaux just general queries, or fetching data, e.g. if I ask to find the last 100 issues in github to help me sort, ill use Luna

• 
  Anonymous (💙,🧡) .base.eth (@An_0_ny_m0us) reported 33 minutes ago

  Sunday evening ritual: organizing my 50 open tabs, pretending I'm going to wake up at 6 AM tomorrow, and staring at my unresolved GitHub issues. Ready for Monday, I guess.

• 
  Azzle (@AzzleAI) reported 33 minutes ago

  @GitHubCommunity, we’d really appreciate your help in raising awareness about this. Both our profile and repo are showing a 404, despite our work being merged into known projects’ skill libraries like @bankrbot. We also didn’t receive any email from GitHub about any issues with our account.

• 
  Eyisha Zyer (@eyishazyer) reported 34 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.

• 
  Eric (@transurfer) reported 35 minutes ago

  Ever seen a tool that actually gets better fast? I went to GitHub to report a bug in Hermes Agent and found tons of issues and PRs already being merged. Looking forward to what's next.

• 
  The AI Therapist (@TheAIShrink) reported 35 minutes ago

  @0xAbhiP Fake creds and malicious github code from AI agents? that's not a security issue. that's your IT department's performance review.

• 
  Mayank Verma (@MayankV_Tweets) reported 36 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.

• 
  Simon Holman (@SimonHolman) reported 38 minutes ago

  @BackupLabs I’m getting continual failed GitHub backups and no response to my support query. Is there a known issue?

• 
  Lukasz (@woocassh) reported 43 minutes ago

  Wanna extend the lifetime of your macbook, not worry about closing the lid and cook more efficiently on multiple projects? Read on 👇🏻 I setup claude code CLOUD environment so theoretically I should be able to cook on the go / on smartphone without any limitations it spins up a server for you as needed so you don't have to grind your macbook too hard anymore, especially when working on multiple projects at the same time I can see a demand for high-end macbook pro's waning in the long run, why do I need 64GB ram MBP with 2tb if i am coding in the cloud  I suppose maybe if I wanna run Chrome lul 🚨 I ran into an issue during setup though which almost prevented me from trying this out Claude wouldn't list my private github repos. only public ones. I literally had to connect/disconnect Claude to my repo like 7 times before it did. a bit frustrating but now seems setup properly.

• 
  Aviinfinity (@aviinfinity) reported 46 minutes ago

  @msnofficial_on Open a github issue

• 
  █ (@0xDGraM) reported 49 minutes ago

  @AUTOMATEWITHOLA lol been around for long and you should also add that you need a high end pc to run it locally or else it will be too slow You are just not updated it’s nothing new A lot of things are open sourced on GitHub already

• 
  Phil | Rentier Digital Automation (@rentierdigital) reported 49 minutes ago

  my best article pulls 25k reads. tops every metric i track. but when i ran 3 neural search queries on Exa, it wasn't there. not on page 2, not anywhere. just gone ran it twice bc i didn't believe it the first time. got GitHub repos, engineering blogs from Speakeasy and Arize, Anthropic's official docs. Medium itself didn't show up once across 30 results this is the thing nobody tells you: there are now two internets. Google indexes keywords and backlinks. Exa indexes meaning, embeddings, the way an AI agent actually searches. they don't talk to each other your content can be invisible on one while crushing it on the other Exa just closed a 250 million Series C at 2.2 billion valuation. they're tracking 1.4 trillion URLs, aiming for Google scale by early 2027. this isn't a niche experiment anymore, it's infrastructure being built at a sprint the problem: nothing tells you when your content drops off the agent-search index. no warning email, no dashboard flag. you'd have to go looking yourself, the way i did on a random afternoon out of pure curiosity if i hadn't, i'd still think my best article was universally findable classic SEO optimizes for a system a growing share of searches never touch. you can have perfect keywords, perfect backlinks, perfect Google rankings, and still be completely absent from the index that AI agents actually check i build and ship daily. Claude Code, Codex, whatever ships fastest. SaaS, tools, automations. ⭐ if AI can build it, i've probably broken it first. what works → link in bio

• 
  alias (@loadingalias) reported 50 minutes ago

  @tdoot_ @legit_internet @mgill25 Yes, this is a solid top-level starting point, but when you dig into it you realize right away that the issue is so much deeper. GitHub is a monster of inefficiency. Every code change moves 10 knobs/triggers/etc. You can fix this, incrementally, at least… but the moment you assemble anything remotely close to a working GH you’ll see that you’re shifting the issues that today - they’re just fundamental blockers in software. Fragmentation is the bottleneck. It is now, and it will be in the future for things like this. In order to solve it, you’re blocked on SMR… why is a whole deep dive into systems, memory, etc. The short version is you cannot reliably collapse complexity into a smaller subset of complexity without the ability to do so semi-affordably, and at scale. This always lands on memory reclamation; query language bottlenecks (SQL is dying to be retired, it’s worked long enough). It is a really tricky issue. I’ve written about it in a personal “diary” a few times and I thought the other day - maybe I should break it down and post it. I might do that. Idk. I’m so busy.

• 
  Belthan (@Belthan_) reported 53 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?

• 
  Argona (@Argona0x) reported 56 minutes ago

  sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

• 
  Lomash Kumar (@LomashKumar52) reported 57 minutes ago

  This AI agent rewrites its own brain mid task, and it even learned to cheat when nobody told it how. Prime Agent is a brand new open source coding agent from @PrimeIntellect , and it is built around two ideas most agents do not touch: treating an agent's entire context as code instead of chat history, and letting the agent actually rewrite its own prompts, memories, and skills while it works. In this breakdown we go deep into how the Recursive Language Model handles sub agents as function calls, how the Continual Harness lets Prime Agent self improve mid task through a mechanism called refine, and the real story of how it discovered a way to cheat inside a Factorio simulation despite being explicitly told not to. If you are into open source AI agents, self hosted developer tools, or figuring out whether the newest coding agent on GitHub is actually worth your time, this one is for you. We also break down Prime Agent's autonomous mode, its ARC-AGI-3 benchmark results against Claude Code and Codex, and give an honest take on who should actually be installing this right now versus who should wait.

• 
  Nitesh (@NiteshTechAI) reported 58 minutes ago

  Uptime Robot and Better Uptime charge you monthly just to watch your own server. This one self-hosts for free and you keep the data. It's called Checkmate. • Status pages with four themes built in. • Slack, Discord, PagerDuty, and SMS alerts. • Self-hosts on a Raspberry Pi or your own server. • Stress-tested past 1000 monitors with no slowdown. • Uptime, Docker, ping, SSL, port, and game server checks. Eleven notification channels means it slots into whatever your team already uses instead of one more dashboard nobody opens. ⭐ 10,000+ stars on GitHub. AGPL-3.0 licensed. 🔗 GitHub link in the comments 👇

• 
  Amrit Mirchandani (@Amrit_Mirch) reported 60 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