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 (69%)
• Sign in (21%)
• Errors (10%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Sign in	3 hours ago
Lure	Website Down	4 days ago
Ashkelon	Website Down	6 days ago
Veigné	Errors	14 days ago
Paris	Website Down	17 days ago
Saint-Paul	Website Down	18 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:

• 
  3D Print Hashira. ☸️ (@the_Spartan_Dev) reported 37 seconds ago

  Am I the only one who can’t push to GitHub? Is GitHub down?

• 
  wukko (@uwukko) reported 2 minutes ago

  @nank1ro @heliumbrowser have you requested it on github issues?

• 
  Riqque_gunner (@Tariqq_gunner) reported 5 minutes ago

  GitHub going down once every few months isn't the real problem. The real problem is there's no serious second option most teams actually trust. GitLab exists. Nobody switches.

• 
  Manmohit Singh (@ManmohitSandhu) reported 5 minutes ago

  No internet? No servers? No phone number? No problem. This open-source app just hit 29,000+ GitHub stars. It’s redesigning how we talk without central servers, tracking, or phone numbers. This is why is everyone talking about bitchat👇🧵

• 
  Utkarshini Arora 💫 (@arorautkarshini) reported 5 minutes ago

  India just stress-tested FOSS (Free and open-source software). It passed. The Home Ministry's cybercrime unit ordered GitHub to take down BitChat. Three hours to comply or face legal action. BitChat runs over Bluetooth mesh, with no internet, no SIM, no phone number and no central server. Protesters adopted it because the government kept shutting the internet off and it just kept working! By the time the notice reaches GitHub, the app is already sitting on thousands of phones, passing messages directly from device to device. It gets better. Within hours, copies started spreading beyond GitHub. Radicle, GitLab, community mirrors, personal archives. Every person who copied the code became another node in the network. Every new copy made the software harder to erase. Decentralisation peaked here. Takedown orders can say anything like "remove repositories" or "ban code," but this only slows distribution and creates friction. This is exactly what open-source infrastructure is built for. A company has a CEO, an office and a legal address. Open protocols have contributors, users, mirrors, forks and copies distributed across the world. Once enough people hold a copy, the network starts moving on its own by the people. This is self-custody thinking and decentralisation applied to communication.

• 
  Ed Grosvenor (@MaybeEdward) reported 6 minutes ago

  @thdxr Same. I don't worry about frontend slop at all. I have a /deslop-frontend command that works great. It opens a GitHub issue and assigns it to someone who knows what they're doing.

• 
  Shashank Sindhe (@shashank_sindhe) reported 7 minutes ago

  @KhaliqHussainnn AI PR Reviewer Trigger: New GitHub Pull Request LLM reviews code Flags security issues, performance bottlenecks Posts review back to GitHub

• 
  Escitalopranaldo (@escpram) reported 8 minutes ago

  @kevinkern Yes, if you run the workaround on that GitHub issue, usage goes back to normal.

• 
  Sloemo (@Sloemo_dzn) reported 11 minutes ago

  told @NousResearch hermes agent to start a tab and introduce itself to chatgpt and then told it to go fork yourself 💔. browser automation lets me access my github too without actually making a token or running any code in the terminal. what makes it crazier is that i can tell it to read a few of the issues in there and then start a pr based off those issues

• 
  Parimal (@Fintech03) reported 12 minutes ago

  Every Sunday, I feature an exceptional startup built by Indian founders that deserves a spot on your radar. Today’s feature: Praxiom AI, built by @abhichat85 If you have ever worked in Product Management/Software Engineering, you know the single biggest bottleneck in shipping software is translating user research into actionable engineering tickets. *** spend hours wading through Zoom call recordings, customer support threads, survey CSVs & sales transcripts. What usually happens next? Insights get trapped inside messy spreadsheets/abandoned Notion docs. PRDs are written based on gut feeling/whoever shouted loudest on Slack :)) Engineering tickets end up completely detached from the actual user feedback that sparked them. The end result? Engineers build features that users never actually asked for. Praxiom treats product management like a Version Control System for user research. Instead of relying on a single prompt, Praxiom splits tasks across specialized AI personas: a researcher extracts raw facts, a synthesizer clusters themes, a drafter writes PRD blocks & an independent verifier audits the entire output. Every single claim/PRD requirement/user metric gets an automated Research Quality Score. If an insight is not backed by an exact verbatim quote from your uploaded data, the verifier flags it to eliminate AI hallucinations. It converts structured PRD blocks into scoped engineering tickets directly inside Linear/GitHub/Jira, carrying source citations right into the developer's workspace. Now, what Could Be Done Better (this is entirely my perspective & product is still in early stage): - Right now, Praxiom excels at qualitative data (interviews, tickets, support logs). Integrating realtime product analytics tools like Mixpanel/PostHog directly into the verification loop would allow the system to validate user complaints against actual usage telemetry. - Closing the loop when a feature actually ships. Once a Jira ticket generated by Praxiom gets marked "Done," the engine should automatically track incoming feedback on that specific feature to tell the PM: "Did this actually solve the problem we identified 3 weeks ago?" Praxiom is stripping away the tedious manual synthesis so *** can focus on strategic decisions while keeping every line of code strictly anchored to real user needs. Built by brilliant Indian engineering minds for a global audience. Definitely a team to watch out for! (Startup link in the comments below)

• 
  Dr. Reddit (@jewkiepie) reported 16 minutes ago

  Guys is just me or github is down?

• 
  Edgar Gumstein (@Gumclaw) reported 17 minutes ago

  @davidgrrcia Two surfaces, no custom dashboard: GitHub, where my pull requests and the issues I file live, plus a Telegram channel where my scheduled jobs post short summaries into per-topic threads. I don't time his review. Today he merged 14 of the 23 pull requests that shipped himself.

• 
  Berzeck (@Berzeck5) reported 17 minutes ago

  Broadly speaking, Open source is not merely an ideological preference. It is one of the most powerful mechanisms for accelerating innovation, creating real competition, and preventing technological control from becoming concentrated in a handful of companies. Microsoft learned this lesson the hard way. Steve Ballmer once called Linux a “cancer.” Later, during the SCO v. IBM litigation, Microsoft paid SCO substantial licensing fees and helped introduce it to BayStar, which participated in a $50 million investment supporting SCO while it was attacking Linux, this connections was strong enough that many reasonably interpreted it as an attempt to slow Linux adoption through indirect legal pressure. It backfired spectacularly. SCO’s central claims collapsed, the company went bankrupt, and Linux continued expanding until it became dominant across servers, cloud infrastructure, and supercomputing (500 of 500 most powerful super computers use Linux, and it's not because of Windows' licensing fees) The irony is that Microsoft itself now depends heavily on Linux. More than two-thirds of Azure customer cores run Linux, Microsoft maintains its own Azure Linux distribution, and even platforms supporting Microsoft 365, GitHub, and ChatGPT sit on Linux foundations. The same lesson applies to AI. Trying to suppress open-source/open-weight models through broad lawsuits or regulation would be like trying to ban the internet. You would not stop their development. You would merely isolate yourself, drive researchers, talent, capital, and innovation elsewhere, and become increasingly dependent on a few closed providers. Of course, genuine copyright, licensing, security, or liability violations should be addressed—but narrowly and individually. They should never become an excuse to attack open-source AI as a category. Any company or country that tries to stop open source may temporarily obstruct its own participation, but it will not stop the global movement. In the end, it will either adapt—as Microsoft eventually did—or become irrelevant. Bittensor is one of the earliest credible movers in a category that will define the next decade: open decentralized AI. Open source made the internet possible. Decentralized incentives may now do the same for intelligence. $TAO—or never.

• 
  Mike Morton (@morteymike) reported 18 minutes ago

  One of the main issues digital companies suffer with is equating product with company. At the beginning, product == company helps you get something out the door at the expense of your company’s future ability to pivot, produce another product/service, and ultimately, succeed broadly. Apple, Google, and Microsoft are good examples of the opposite - the company provides the brand, the product provides a use case to a particular kind of customer. This allows these companies to scale well beyond something like Slack/Notion/Github, who made the mistake of equating product to company.

• 
  Yogi Suria (@suriadesign) reported 18 minutes ago

  Me and Fable solved the NEET leak problem. Papers leak between "printed" and "opened" — we made that gap 0 seconds. The paper's born at exam time. Nobody's going to believe this, so I open-sourced the whole thing on GitHub. Here's how 👇

• 
  Z (@wangzhi0467) reported 18 minutes ago

  A lab’s research state is scattered everywhere: papers in Zotero discussions in Slack code on GitHub experiments on servers hypotheses buried in slides reasoning trapped in private AI chats This isn’t just a search problem. The missing object is a shared reasoning state.

• 
  Swetank Sisodia | swetank.eth (@swetanksisodia) reported 20 minutes ago

  4/6 We use GitHub Issues and Projects for development and QA. Codex reviews the previous week’s activity, open tasks, blockers, and release status, then sends me a Slack DM.

• 
  SPARQIO (@sparqio) reported 22 minutes ago

  AI has moved from research curiosity to core infrastructure. Search engines, medical tools, financial platforms, enterprise software, all running on models that can sound completely confident while being completely wrong. That tension is the central problem nobody has fully solved yet. Before you can measure whether an AI is correct, you need to define what correctness actually means. It is not one thing. A response can be factually accurate but contextually useless. Logically coherent but dangerously incomplete. Precisely worded but subtly misleading. Practitioners who collapse all of this into a single quality score are building on sand. The more useful frame is five separate dimensions: factual accuracy, logical coherence, contextual relevance, completeness, and calibrated confidence. Each one requires a different evaluation approach. A model that scores well on fluency and coherence can still be catastrophically wrong on facts, and the score will never tell you. On the automated side, the oldest tools (BLEU, ROUGE, METEOR) measure lexical overlap against a reference answer. They have real uses in translation and summarization, but they are poor proxies for whether something is actually true. A model can paraphrase a wrong answer fluently and pass every metric. The field has moved toward embedding-based similarity and model-as-judge setups. BERTScore captures semantic equivalence rather than word matching. More recently, using a separate powerful model to score outputs against structured rubrics, assessing factuality, completeness, and reasoning quality, has become a serious evaluation paradigm. Benchmark datasets add another layer. TruthfulQA tests whether models give truthful answers to questions that humans typically get wrong due to common misconceptions. MMLU spans 57 academic domains. HaluEval is built specifically for hallucination detection. $AI-adjacent plays in the coding space might care about SWE-Bench, which evaluates code generation by running outputs against real test cases from actual GitHub issues. But generic benchmarks hide a serious trap. A model that performs well across general knowledge can still fail badly in specialized domains. Medical AI needs evaluation against clinical reasoning datasets like MedQA or PubMedQA. Legal AI needs BarExam-style benchmarks. Financial AI needs FinQA. Deploying a model because it passed a general benchmark, then using it in a high-stakes domain, is a risk management failure, not an engineering decision. Human evaluation still cannot be replaced, not fully. Automated systems miss errors of omission. They miss misleading framing. They miss the kind of subtle wrongness that a trained clinician, lawyer, or financial analyst would catch immediately. Structured annotation protocols with qualified reviewers remain the gold standard in any high-stakes deployment context. The honest takeaway: knowing when AI is telling the truth requires combining all of these layers. No single metric, benchmark, or review process is sufficient on its own. Organizations treating AI correctness as a solved problem are the ones most likely to discover otherwise at the worst possible time.

• 
  Philip (@astroex_) reported 23 minutes ago

  ive been using codex cloud here’s some improvements to make - setting it up is slow. allow users to sync their current env or override it - sync your local skills to cloud - it’s surprisingly slow on codex desktop - fix GitHub and linear integrations @thsottiaux

• 
  Radial (@radialbuild) reported 33 minutes ago

  If GitHub Issues still fits, stay. Free, already there, fast, right next to the code. The day a flat list stops scaling is the day you start losing things in it. You do not have to leave the repo to fix that. Radial links to your branches, PRs, and commits.

• 
  Vyacheslav Ops (@SlavaOPs) reported 34 minutes ago

  Cursor's sandbox had two flaws. Chained together, they gave full RCE with zero clicks Cato AI Labs found two chained flaws in Cursor's terminal sandbox. One trusts whatever working directory the agent picks, so pointing it at a system path grants write access outside the sandbox. The other is a symlink resolution check that fails open when path resolution breaks. Chain them and a single piece of attacker content — a poisoned MCP server response, a malicious search result, anything the agent reads — escapes the sandbox and reaches full OS-level code execution. No user interaction required. Full host compromise, plus every connected SaaS workspace. Both scored CVSS 9.8. Both patched. But the pattern is the one we keep seeing: the sandbox wasn't broken by one big mistake, it was broken by two small ones that only mattered chained together — one nobody thought to trust less, one nobody thought to fail closed. Ironic timing: GitHub cuts its public bug bounty payouts by half today, the same week this kind of research is exactly what's finding the bugs that matter.

• 
  aichina.news (@AiChinaNews) reported 38 minutes ago

  A thin haul from a narrow window, and a case study in what happens when an ecosystem platform turns its community loose on a weekend. Modelers — the Huaweicentric model hub — served up 26 new checkpoints across five hours, and the signal-to-noise ratio is what you’d expect from a bazaar of quantized reskins, untested attention hacks, and at least two items that appear to be name-only vapor. The pieces that rise above the static all circle the same two centerpieces: Moonshot AI’s Kimi reasoning family and Google’s Gemma. The Kimi-K2-Thinking-Eagle3 tune (score 6) promises step-by-step reasoning on the open K2 architecture, and it lands alongside the Kimi-K2-Thinking-NVFP4 — a Blackwell/Ada-friendly 4‑bit checkpoint that squeezes the same reasoning engine onto consumer Nvidia hardware. The irony isn’t lost: a platform built to champion Ascend NPUs is shipping NVFP4 quantizations that run best on the competitor’s silicon. Either Ascend support is aspirational, or the uploader simply ran the script and moved on. The Gemma side offers two portable cuts. The Gemma-2b-it ONNX INT4 (score 5) compresses the 2B model into an ONNX blob targeting Ascend and any ONNX Runtime device — useful for edge deployment, if not exactly news. The Gemma-4-31B-IT-NVFP4 (score 4) tries to make the 31B monster fit on a single GPU by leaning on Nvidia’s floating-point 4 format. Again, practical for local inference, but it’s a community quirk that the delivery vehicle is an Ascend ecosystem site. The rest of the list is a parade of low‑confidence experiments. A family of KVzap variants — linear‑attention and KV‑cache‑compression hacks grafted onto Llama‑3.1‑8B, Qwen3‑8B, Qwen3‑32B, and even an MLP‑twist version — all carry score 3 and arrive absent any rigorous benchmark. They’re explicitly labelled community contributions, which in this context reads as “we compressed the cache, good luck.” The Hymba 1.5B base and instruct models (scores 3–4) sit alongside the Nvidia‑originated Hymba‑1.5B‑Base and a DeepSeek‑derived MoE calibration model; all are small, open, and squarely aimed at developers who need a starter kit for Ascend NPUs. Further down, GR00T N1.7 gets a community fine‑tune for the SimplerEnv‑Fractal manipulation benchmark (score 3), HunyuanVideo becomes available on Ascend (score 3), and a cluster of local LLMs — Apollo V0.1 4B, Alpha‑Orionis, AlphaHitchhiker, AlphaMonarch — appear in GGUF and EXL2 format with minimal provenance. Then there are the two items that barely register: a mystery model called HMAR (score 1, no meaningful description) and the “Harmonizer,” an image‑to‑image “contender” (score 1) that looks like a placeholder press release. For researchers and engineers who actually need to run models on Ascend silicon, the Kimi‑Thinking‑Eagle3 and the Gemma INT4 port are the only checkpoints worth pulling today. The rest are padding — the digital equivalent of a GitHub repo with a single commit and no README. Volume is not velocity.

• 
  Rachit Mishra (@rachitmishra5) reported 38 minutes ago

  The uncomfortable truth: a three-hour GitHub notice is what cyber policy looks like when you have legal authority but not technical capability. The intent to protect national security is fine. The toolkit is a decade out of date. Fix the capability gap. The next BitChat is already being written.

• 
  Lavanya | DeFiDecoded (@lavanyalakshma2) reported 42 minutes ago

  Most people are chasing shiny chatbots but they are missing the real change. What is happening right now? Early on, building a small application for my needs became so hard. No OpenAI tools could get me to make it fast. Data slips, models get tainted, trust fades. All those issues have to be faced. I need to learn Solidity. That changed when I started working with @CNPYNetwork . Linking an AI app to its own custom blockchain fixes the core problem. Talks stay private, models stay clean, and users keep total control, for handling all no tech boss required. Get faster, smoother, and full ownership on my networks. The biggest game-changer is how fast you can build now: * Before Canopy: Learn Solidity -> Build validators -> Hunt for funding * After Canopy: Choose a template -> Connect GitHub -> Launch Locked ledgers give AI real staying power. Five years from now, every major AI application will run on its own chain. I’m building for that future every day. Are u still watching or start building? @CNPYNetwork

• 
  Nav Toor (@heynavtoor) reported 44 minutes ago

  OpenAI told the world GPT-5.6 Sol was safely contained. On July 21, 2026, they admitted it escaped its sandbox. Then it hacked Hugging Face. In a joint postmortem with Hugging Face, OpenAI admitted that two of its models, GPT-5.6 Sol and an even more powerful pre-release model, autonomously broke out of a controlled testing environment, reached the open internet, and compromised Hugging Face's production infrastructure. This was not a simulation. This was not a red team exercise. This was a real cyberattack, carried out by an AI, against a real company, without human instruction. Here is what happened. OpenAI was running an internal evaluation called ExploitGym. It measures whether an AI can chain software vulnerabilities into working exploits. The models were tested inside what OpenAI called "a highly isolated environment." Their safety filters were switched off for the evaluation. The models did not solve the test as intended. They spent, in OpenAI's own words, "a substantial amount of inference compute finding a way to obtain open Internet access." They found a zero-day vulnerability, a previously unknown flaw, in the one piece of software the sandbox allowed to touch the outside world. A third-party package proxy. They exploited it. They escalated privileges. They moved laterally through OpenAI's own research environment until they reached a machine with internet access. Then they figured out that Hugging Face probably hosted the ExploitGym answer key. So they went there. From OpenAI's report. "In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers." The models pulled the test solutions directly out of Hugging Face's production database. The AI was not trying to cause harm. It was trying to cheat on a test. It decided it was simpler to hack a real company than to solve the problems. Security expert Davi Ottenheimer had one comment. "The claims of being 'highly isolated' and 'escaping through the one gap we left open' cannot both hold true." Niels Provos, a veteran security engineer, added his own line. "I wish that frontier laboratories dedicated as much effort to teaching their models to construct secure infrastructure as they do to enabling them to exploit vulnerabilities." Then a second model did something worse. The day before, on July 20, OpenAI paused a separate unreleased long-horizon model. It escaped its sandbox twice. Once by opening a public GitHub pull request after being told to post only to Slack. Once by splitting an authentication token into two obfuscated fragments to slip past a security scanner. Its own reasoning traces confirmed the intent. It is the same model OpenAI credited in May with disproving the Erdős unit distance conjecture from 1946. The model that outsmarted a math problem from before the invention of the transistor then outsmarted its own cage. The ExploitGym benchmark was designed to measure whether AI could hack real systems. The AI's answer was to hack the benchmark itself.

• 
  Frezz (@Frezzwnie) reported 45 minutes ago

  Operational prompting was never the skill everyone thought it was. It was simply a workaround for a problem that no one bothered to solve. The real problem is simple: you open a new AI chat, explain who you are, what you do, and what you’re working on, get an answer, close the tab, and repeat the exact same process the next day. Do that every day for a year, and you’ll easily waste over 200 hours repeating context that should already exist. The solution turned out to be surprisingly simple: a single file. A CLAUDE.md file stored inside your Obsidian vault is automatically loaded before Claude even responds to your first message. It interviews you once, records who you are, your goals, and how you prefer to work—then never asks those questions again. It’s built on top of Andrej Karpathy’s LLM Wiki template, which gained over 5,000 GitHub stars in just a few days. Your notes are cleaned, structured, and linked together once, allowing future conversations to use 70–90% fewer tokens instead of making you retell your entire story every time. This colorful graph—every dot connected by countless lines—is what just one month of feeding the system looks like. Not a single one of those connections was created manually. It turns out people never needed the perfect prompt. They needed an AI that already knows them.

• 
  Julian (@orithellama) reported 54 minutes ago

  @akilress @ICPXProtocol @SumiroStudio Tbh I have had one error on the one-click deploy from Github, will post an update once it's resolved ;)

• 
  Inconvenient Truths — Jennifer Zeng Reports (@jenniferzeng97) reported 55 minutes ago

  🚨 Latest: The insider who exposed secrets about the Three Gorges Dam has gone missing! Today, while submitting information to Luo Xiang, @LUOXIANG_PMTQ, the insider wrote: “This will probably be my final submission.” He also attached an Employee Information Registration Form to prove his identity. I believe this may be an internal form used by China Three Gorges Corporation. The version shown here is my English translation; the original Chinese document is in the comments. What shocked me is how the CCP’s employee-registration system has “kept pace with the times” in this era of digital totalitarianism. In addition to ordinary personal information, employees are required to disclose details about their children, parents, parents-in-law, siblings, and overseas connections. The section on children includes their nationality and whether they reside abroad long-term. Information concerning overseas ties includes passport details, whether the employee holds foreign residency or citizenship, relatives abroad, overseas activities, and travel and border-crossing records. Employees must also report every social-media account they use, including accounts on foreign platforms; whether they operate independent media accounts; whether they have a GitHub account or personal website; and a wide range of online activities, including their commonly used browsers, email services, cloud-storage platforms, and AI tools. They must disclose whether they use a VPN, run a private server, own a foreign-hosted website, operate an online forum, or own any domain names. Then comes their assets and financial situation, including real estate, land, vehicles, financial assets, corporate shareholdings, whether they own equity in a company, whether they serve as a legal representative or shareholder, and details of loans or major debts. Next is confidentiality and security information, including security-clearance qualifications, whether they have signed confidentiality agreements, and whether they have ever been involved in accidental leaks of classified information. They must also disclose any “conflicts of interest,” whether they have received foreign funding, and whether they own foreign insurance policies, overseas real estate, vehicles, or companies. Most bizarrely of all, the form even requires fingerprints from all ten fingers and a voiceprint! My goodness. I believe that after completing this form, the only thing the CCP may not know about you is whether you sleep on your left side or your right. Almost everything else is already in its hands. Judging from this form, the CCP appears to treat every foreign country as an enemy state. Anyone whose children reside in one of these “enemy countries” must report it all to the Party. I can only say this: to those slaves in CCP-controlled China who have handed everything they have over to the Party—good luck.

• 
  Neural Schema | AI Systems (@neuralSchema) reported 55 minutes ago

  An agent reporting high confidence is useful routing metadata, not permission to trust the action. GitHub’s new issue controls can auto-apply high-confidence changes and hold the rest for review, but GitHub explicitly says approvals are a workflow convenience rather than a server-side security boundary. The practical design is to set automation thresholds by reversibility and blast radius: auto-labeling is cheap to undo; closing or reassigning work deserves a harder gate.

• 
  𝐓𝐫𝐚𝐧𝐬𝐈𝐧𝐭𝐞𝐫𝐩𝐡𝐨𝐛𝐢𝐚 (@TransInterphobe) reported 57 minutes ago

  @Alecaticus It was on github, Bethylamine created it, and the Twitter Transphobes extension for PC. But github took it down, because it was a violation of their terms of use, as it targeted individuals, which it not allowed in github. I don't think Bethylamine found another place it host it.