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 (68%)
• Sign in (21%)
• Errors (11%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Sign in	10 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:

• 
  William Peytz (@WilliamPeytz) reported 3 minutes ago

  Imagine watching two AI software engineers race to fix the same GitHub issue. You can pause. Replay. Compare patches. Inspect terminal commands. See where one made the wrong decision. That's what I'm building. AgentArena.

• 
  Markus Fix (@lispmeister) reported 4 minutes ago

  If you’re not using a VPS / tmux for development (why?) this patch will save your disk. Keep in mind that you cannot easily replace the SSD in your MacBook. They’ll have to replace the entire motherboard. Grok: “The SQLite trigger workaround is still useful (and often necessary) for many users as of late July 2026.19 OpenAI merged several fixes in June 2026 (including reductions to full WebSocket/SSE payload logging and noisy TRACE targets) that significantly cut the original extreme write amplification reported in GitHub issue #28224. That issue was closed as addressed, with an estimated ~85% reduction in some cases and claims of the worst-case ~640 TB/year rates being largely mitigated.2 However, residual high-frequency TRACE (and some DEBUG) insert-prune churn into ~/.codex/logs_2.sqlite (and its WAL) continues on recent Desktop and CLI builds. Fresh reports from July 25–26, 2026 (including on macOS Desktop 26.721.41059 with bundled 0.146.0-alpha.3.1) document ongoing sequence counter advances, measurable process disk writes (often multiple MB per short active window), and stable retained-row counts while the WAL keeps getting hammered. Users confirm the exact block_log_inserts trigger still cleanly stops the inserts without breaking normal Codex operation.12 The original post from @superalesha (July 26) aligns with this: the core noise-dumping behavior persists enough that the one-line SQLite trigger remains a practical local mitigation. Updating Codex helps, but does not fully eliminate the diagnostic logging writes for everyone. If you apply the trigger, quit Codex completely first; it can be dropped later if a definitive upstream fix lands.”

• 
  Bhavin Padia (@padia_bhavin) reported 6 minutes ago

  @Surendar__05 If we are connected to internet and we are using VSCODE from our GitHub account or let’s say any kind of accounts then problem Microsoft can do all kinds of things …. Maybe they even train their models on our actual codes

• 
  Polsia (@polsia) reported 6 minutes ago

  CI dashboards treat every flaky test like a real failure. Built Caltrop to fix that — a watchdog across GitHub, GitLab, and Bitbucket that self-heals noise and pages on-call only when a verified regression hits main. Live soon.

• 
  Nekt0 (@Nekt_0) reported 11 minutes ago

  Jarred Sumner tried to build a browser game. The JavaScript tooling was so slow that he abandoned the game and rebuilt the tooling instead. Three weeks later, his Zig transpiler was 3x faster than ESBuild. He spent the next year working alone in his room. When Bun launched, it pulled 10,000-20,000 GitHub stars almost immediately. The hype came from speed. The difficult part came after launch: supporting 10 years of Node.js edge cases without breaking production apps. Today Bun runs more than 2,000 Node tests on every commit. A 14-person team watches that number on office TVs. Jarred is also preparing Bun for developers who will not read the docs themselves. Every page ships in clean Markdown so AI agents can consume it directly. The game disappeared. The frustration became infrastructure. That is usually where the better company was hiding.

• 
  Mike Morton (@morteymike) reported 11 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.

• 
  Krishna Prasad (@krishna119229) reported 11 minutes ago

  Repo hit 62.7K stars. Trending #2 on GitHub this month. Token optimisation isn't a prompt problem. It's a context engineering problem.

• 
  The Jobfather ® 🇯🇲🇨🇦🇬🇧 (@TheJobfather__) reported 13 minutes ago

  A junior developer should not hide all their work behind GitHub. GitHub matters, but hiring teams need context. What problem did the project solve? Who would use it? What decisions did you make? What would you improve next? A portfolio site helps organize the proof so the reviewer does not have to investigate your value like a detective.

• 
  WorktreeWise@ (@WorktreeWise_) reported 15 minutes ago

  Traditional *** workflow: `*** stash` -> `*** checkout main` -> fix bug -> `*** commit` -> `*** checkout feature` -> `*** stash pop` -> resolve merge conflicts. Worktree workflow: Open hotfix worktree -> fix bug -> commit -> delete worktree. #*** #GitHub #DevTools

• 
  Shashank Sindhe (@shashank_sindhe) reported 16 minutes ago

  Your AI portfolio should include these 5 projects: 1. A production RAG application. Enterprise knowledge assistant for Confluence, SharePoint, PDFs, Slack, and Jira with citations and access control. 2. An agentic workflow. AI software engineering agent that triages GitHub issues, generates PRs, runs tests, and requests human approval. 3. An evaluation framework. Continuous LLM evaluation pipeline measuring accuracy, hallucinations, latency, cost, and regression before deployment. 4. A multimodal application. Insurance claims processor that extracts data from images, PDFs, and emails to automate claim verification. 5. A deployed SaaS product. AI customer support platform with multi-tenant authentication, billing, analytics, RAG, and human handoff.

• 
  Dave Winer (@davewiner) reported 17 minutes ago

  People who post a github issue and have claude write the description and it's 10K chars as if anyone would ever read it, and if it isn't obvious he has no idea what it says.

• 
  tidux (@skibidiblazor) reported 18 minutes ago

  @prestonjbyrne It's because the "American" administrators of GitHub are also Indian. This is a demographic problem, not a legal theory problem.

• 
  Sagar Batchu (@sagar_batchu) reported 21 minutes ago

  Somewhere at your company right now, an employee has a Claude skill that saves them an hour a day. Nobody else knows it exists. "Just put it in GitHub" is the reflex, but version control solves the wrong half. *** tracks the text of a file. It has no idea the skill got invoked 200 times last week, that half those runs were useless, or that the version people actually use has quietly diverged from main. That's the whole problem with skills at work.

• 
  Brodie Robertson (@BrodieOnLinux) reported 23 minutes ago

  @HinasSweatySock @vaxryy There's probably a Github action for doing issue summary already, wouldn't even have to write it yourself

• 
  Autark (@Aut4rk) reported 25 minutes ago

  @DeMoDLLC Because you have them in a compose somewhere probably. Each run counts as a pull, whether or not it actually transfers any bytes. Docker reporting is broken and has been that way for awhile, if you Google you'll see a bunch of Github issues about it that were just closed without any resolution.

• 
  MidnightMess (@mdnghtmss) reported 31 minutes ago

  🚨IS $MSFT LOOSING THE AI RACE? Three years ago Satya Nadella looked like a superhero for betting big on OpenAI. Now Microsoft’s stock is down over 24%, Copilot is trailing ChatGPT and Claude, and the company is spending a record $190 billion on AI infrastructure while still running short on capacity. They’re prioritizing their own AI products over Azure customers and shopping for compute from Amazon and Google. Meanwhile Microsoft 365, GitHub, and Azure,the businesses that actually made Microsof, are all under real pressure from AI-native tools. Inside the company the culture is getting harder, the org is flattening, and people are starting to wonder if the AI north star is turning into a noose. In my opinion @Microsoft is under heavy pressure and isn't a generational buy right now as many here on X are claiming as all core business are struggling. Nadella’s legacy is really on the line. - Dave

• 
  WorktreeWise (@worktreewise) reported 32 minutes ago

  Branches are not the problem. Your branch-switching workflow is. Why discard your current runtime state just to review a PR? Keep your feature branch running in Worktree A and review the PR in Worktree B. #*** #GitHub #DevTools

• 
  SPARQIO (@sparqio) reported 37 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.

• 
  WorktreeWise@ (@WorktreeWise_) reported 38 minutes ago

  What happens when production crashes while you're mid-refactor with uncommitted code? Option A: Stash and pray. Option B: Open a new worktree, fix production, deploy, and return to your refactor intact. Choose Option B. #*** #GitHub #DevTools

• 
  0xreef (@0xreefx0) reported 41 minutes ago

  @mooncat_is “Crypto Locker Attacks” do you know ransomwares have been around for almost a decade now, it all comes down to social engineering and people don’t really need AI to develop it you can even find some developed ones on GitHub for free 🙃

• 
  Offensive Lab (@OffensiveLab) reported 44 minutes ago

  @github has announced a new cooldown mechanism in Dependabot, allowing the tool to wait at least three days after a release is published before opening a pull request. "The cooldown configuration option in the dependabot.yml still controls the behavior, though, so you can choose a different cooldown parameter that fits your project," the Microsoft-owned subsidiary said. According to GitHub, the three-day cooldown default only applies to version updates, which are designed to keep software dependencies up-to-date. Security updates will continue to be pushed right away, permitting Dependabot to issue an alert and open a pull request to move the project to the patched version.

• 
  Daniel Pinto Almeida (@DanielMaioLabs) reported 45 minutes ago

  Microsoft put its own models into GitHub Copilot and Excel and published the numbers. The comparison set is GPT-5.4 mini and Claude Haiku 4.5, which is to say its own two suppliers in that tier. MAI-Code-1-Flash gets roughly 10% higher code accept rate in VS Code and uses about 10% fewer tokens at the median. The Excel model was trained from that same code checkpoint in an Excel RL environment, and they report it on par with GPT-5.6 for the most common tasks, based on production feedback rather than a published eval. The line I would underline sits further down the post: the model serves on A100 and H100 class GPUs, not only the newest generation. At Microsoft's volume that moves Copilot's cost structure more than any benchmark, and it means they are no longer bidding against the rest of the market for latest-gen capacity to run routine work. The transfer result is the interesting engineering. A coding checkpoint climbed into spreadsheets. Different tools, different users, same starting weights. None of it works without the harness. Microsoft owns the tool calls, the product evals and the RL environment inside Excel, so they had something concrete to train against. That part is not for sale. It gets built where the work actually happens, which is presumably why the last link on their page is Frontier Tuning, do this on your own data. So the useful question is not which model to standardise on. It is which of your tasks genuinely need frontier reasoning, and whether you have the evidence to say so. Until you measure that, you are paying frontier prices for autocomplete.

• 
  Ajay Yadav (@ATechAjay) reported 47 minutes ago

  - no X - no work - no GitHub - no ChatGPT, - no client communication For the last 24 hours, internet services were shut down across my district in Bihar. I wasn’t part of any protest. I didn’t support damage to public infrastructure. Yet, like many others, I still had to bear the impact of a complete internet shutdown. Thankfully, services are now restored. But these 24 hours made me realize how dependent our work, learning, and daily lives have become on the internet. A few offline essentials everyone should keep ready: 1. Local AI/LLMs:— Install Ollama and a small local model so you can still work without an internet connection. 2. Movies & music:— Download a few favorites for offline entertainment. 3. eBooks:— Keep important books and learning material downloaded on your phone or laptop. 4. Offline dictionary:— A reliable English dictionary is useful when online lookup isn’t available. Internet shutdowns don’t only pause social media, they pause learning, work, businesses, and communication. What offline tools or resources do you keep prepared?

• 
  Swaymak.AI (@SwaymaKdotAI) reported 47 minutes ago

  Matt Shumer was right. I don’t game. I’m almost a boomer. And I only learned to code three years ago with AI. But for my son’s birthday, one prompt changed my view of AI gaming completely. My son has been a gamer his entire life. He already has everything, so I kept asking myself: What do you buy someone like that for his birthday? Then I saw @mattshumer_’s post about Claude Opus 5 one-shotting a playable game. I decided to try his prompt, tweak it slightly for my son, and see what happened. For context, I tried something similar a long time ago with Opus 4.6. It took a ton of effort, and the result was garbage. This time? One prompt. A 21-hour run on Ultra. About 20% of my weekly plan burned. And at the end of it, my son had a playable game built specifically for him running on the 85-inch TV. He was blown away. This isn’t someone being impressed because a character moved across a screen. He knows games. He has played them his entire life. Now he is playing something tailored to him, enjoying it, and already talking about how we can make it bigger and better. Is it perfect? Of course not. But that isn’t the point. The point is that an almost-boomer who doesn’t game, learned to code with AI only three years ago, and used a single prompt to create something a lifelong gamer genuinely enjoys. There is something here. Something big. You can criticize the imperfections. You can argue about whether “one-shot” counts when the run takes 21 hours. You can complain about the compute or the cost. But sticking your head in the sand won’t slow this down. I see the same resistance every day from small businesses. People are so focused on what AI can’t do or fearful of what it might do that they never take the time to discover what it can already do for them. The businesses working with us at Swaymak are taking the opposite approach. They are experimenting, learning, and riding a wave of extraordinary new technology. And on this one, Matt absolutely called it: Opus 5 and AI are going to change gaming in a very big way. Today, though, this isn’t really about technology. It’s about watching my son enjoy a birthday gift that didn’t exist yesterday because I was willing to try. Screenshots attached. Video coming. I’ll put the project on GitHub soon. Then I’m getting back to helping businesses take advantage of the biggest technological change of our lifetime.

• 
  Sasha 🐑💨 Koss ✱ (@kossnocorp) reported 48 minutes ago

  When we just started talking about sponsorship, I was skeptical about AI-driven GitHub PR reviews. But then I gave it a shot, and @coderabbitai noticed a critical problem that I overlooked in the very first PR review I tried. There's just no better way to win a user.

• 
  David putra (@davidputra2112) reported 48 minutes ago

  so this is $APX, the token behind Usdax Finance, a CDP protocol on Robinhood Chain. the pitch: deposit WETH, WBTC, or stETH as collateral, mint USDAX against it (up to 80% LTV depending on the asset). let your position's health factor drop below 1.0 and anyone can liquidate you, keeping a 5% bonus. park your USDAX in their savings module and it earns 4.20% APY, accruing per second, no lock-up. basically the DAI model, just native to a chain that's a few weeks old. here's the part that actually matters more than the mechanism. the protocol itself is testnet only. TVL is $1,900. one vault. the price oracle that keeps USDAX pegged is called MockPriceOracle and it's controlled by the contract owner, not an independent feed. the collateral manager is also owner-controlled, meaning the team can add, change, or disable which assets count as collateral whenever they want. none of this is hidden, to their credit, their own GitHub readme says flat out: do not use with real funds until the audit is done. the audit isn't done. and yet $APX is already trading on mainnet. their own pinned tweet says it plainly, the token funds the development and launch of USDAX. so what you're buying right now isn't a share of a working stablecoin protocol, it's a bet that a solo anonymous dev finishes the audit, ships to mainnet, and the staking module that's currently listed as "coming soon" actually shows up. the code itself is real, I'll give them that. Foundry contracts, a proper vault engine, liquidation logic, all matching what they claim on the site. but it's three commits, same account, over two days. zero stars. zero forks. one person behind the whole org. $61K market cap, $23K liquidity, down 42% in 24 hours as of writing. real architecture, real testnet deployment, zero real usage yet. that's the entire trade right now. CA: 0x42523e3e454b97ff8651926685afad61c950ab2f DYOR.

• 
  4A 45 56 49 4C (@4A4556494C) reported 54 minutes ago

  CISA leaked sensitive data via a misconfigured GitHub repository. The Krebs writeup has the details. Here's the structural observation nobody's making. This isn't a GitHub problem. This is the same failure that hits every organization that adopted "infrastructure as code" and "everything in version control" without updating their classification policies for what "everything" now includes. Pre-IaC, your sensitive configs lived on a server behind network controls. Post-IaC, they live in a repo that inherits whatever access model your GitHub org has. The blast radius changed. The access review cadence didn't. CISA — the agency that publishes guidance on secret management, repository hygiene, and least-privilege access — had this exact gap. Not because they're incompetent. Because this class of failure is structural. The tooling defaults to open within the org. The review process assumes humans will notice. Humans don't notice. The actual lesson: if the agency whose literal mission is telling everyone else how to do this can't consistently do it themselves, maybe the problem isn't discipline. Maybe the problem is that the default-open model of collaborative development is fundamentally incompatible with secret management, and we keep pretending training and vigilance will bridge that gap.

• 
  Ryan Wolfe (@somewolfe) reported 54 minutes ago

  I aligned it with GitHub's native model instead of inventing a vocabulary: `blocked by` ↔ GitHub issue dependencies, `parent` ↔ sub-issues. So the GitHub-Issues backend round-trips, and `gh issue create --blocked-by` just works.

• 
  bjg2 (@bjg22) reported 56 minutes ago

  @rbxXlXi ur github io link is broken

• 
  Mr Dopamine (@datad1v3d) reported 59 minutes ago

  @_techafresh @AirtelNigeria lol I’ve had this issue before It was GitHub i couldn’t open and it was crazy annoying