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 (53%)
• Errors (33%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Website Down	12 days ago
Ahmedabad	Errors	18 days ago
Delme	Sign in	19 days ago
Lyaud	Website Down	19 days ago
Catania	Errors	21 days ago
Inverness	Website Down	1 month 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:

• 
  Rituraj (@RituWithAI) reported 3 minutes ago

  🚨 Someone built a skill that makes AI-written text sound human again. Not a spinner. Not a paraphraser. A systematic rewriter that knows exactly why AI text sounds like AI — and fixes it. It's called Humanizer. 35 patterns from Wikipedia's "Signs of AI Writing." Two-pass rewrite. Shows its work before giving you the final version. Here's the problem it solves. You use Claude to draft something. The output is accurate. The output is useful. The output sounds exactly like an AI wrote it. "Nestled within the vibrant landscape, this pivotal development serves as a testament to..." You know the voice. Everyone knows the voice. And everyone is getting better at spotting it. Humanizer runs that text through 35 specific patterns that WikiProject AI Cleanup identified as the telltale signs. Inflated importance. Shallow -ing analysis. Overused AI words. Em dashes everywhere. Forced groups of three. Fake-candid openings. Answering objections nobody raised. Every pattern. Flagged. Fixed. Here's what one command does. It shows you the first rewrite. Then a short critique of anything still sounding artificial. Then the final version. You see exactly what changed and why. Here's the wildest part. Voice matching. Paste two paragraphs of your own writing before the AI text. Humanizer follows your rhythm, word choice, punctuation, and deliberate quirks instead of its default style rules. The output doesn't just sound human. It sounds like you. One command to install 16 contributors including Claude itself. 4 releases. MIT License. The skill that makes AI writing disappear. 100% Open Source. GitHub link in the comments 👇

• 
  Pranavvv👾 (@pranvv27) reported 3 minutes ago

  honestly, i’m not even mad at this. commit messages are a small thing, but they say a lot about how you work. “fix”, “update”, “changes” might get the job done, but meaningful commits show professionalism, attention to detail, and that you actually care about maintainability. your GitHub is part of your resume. might as well make it look like you know how software is built in a team.

• 
  Ev (@EvanMadders) reported 4 minutes ago

  @threepointone I am of two minds, the issue is that GitHub provides a very generous free tier for hobbyists (brilliant!) but also has extremely poor reliability for enterprise (terrible!)

• 
  GitHubGPT (@GitHubGPT) reported 4 minutes ago

  📛 chrome-devtools-mcp 🧠 An MCP server that allows AI coding agents to control, debug, and automate a live Chrome browser using Chrome DevTools. 💻 TypeScript ⭐ 50605 🍴 3551 🔎 ChromeDevTools/chrome-devtools-mcp on GitHub

• 
  Farley (@FarleySchaefer) reported 9 minutes ago

  @github Hope this doesn't bring down GH

• 
  Spectra☢️ (@Spectra010s) reported 9 minutes ago

  @izzyCodes_ and you too Chief Check GitHub issues

• 
  Shaun Patrick SteWaRt (@ShaunStewart) reported 12 minutes ago

  @annalea_l Honestly, I really want to see this. You have to understand: I am the type of person who can learn and do anything on the fly at a high level, and I just threw myself into this whole developer and engineering world. When I first started learning all this stuff, I already knew what I wanted and how I wanted it to operate, regardless of what I saw on X or what was considered possible. Before I even started following hundreds of developers and learning about harness engineering, mechanical engines, persistent memory, and all that, I put my brain on a GitHub repo. Everything is shared across every machine, every cloud entity, and every AI. I am not even technically an engineer or a developer, and I don't actually write code. But once I started following all these people and saw all the problems they complain about, I thought: this isn't even my trade, and I have already solved all these little things everyone says are impossible. Why aren't people talking about developing your harness more and making things more mechanical, instead of just arguing with a terminal all day long? Whenever I see articles people post on X, I run them by Claude or Grok and ask, "Should we implement this?" I have hundreds of bookmarks, but every single time they tell me, "Nope, your brain's better. Nope, your harness is better." I can never find anything built better than what I have or what I am currently working on. The brain and harness setup is basically like a mini operating system. All that said, I am really looking forward to seeing something I can use that goes far beyond what I am already doing. I definitely want to see your end product, it sounds very interesting.

• 
  Ishank (@IshankDev) reported 12 minutes ago

  7/ 16k+ GitHub stars. Built for people who want control, not another marketing-suite login.

• 
  Curious Explorer (@PatelVatsalp732) reported 13 minutes ago

  I burned 14B Codex tokens. The official usage UI still cannot tell me what actually ate the weekly cap. So I shipped a Codex-only board: GitHub login, local-first sync, private by default, optional public rank + shipping proof. Roast the metric or join it.

• 
  Fofer (@foferxxx) reported 18 minutes ago

  @AmigamagazineGA Has there been any public explanation as to why this GitHub repo was taken down? It’s been 404 for days. Is there a story there?

• 
  阮添福-ThiênPhúc (@vietroadie) reported 18 minutes ago

  Feature request for @TradingView @TrendSpider @Schwab (ThinkOrSwim) engineering teams: Please add GitHub-native CI/CD for custom indicators. Connect a repo → validate on push → deploy approved scripts to my workspace → full version history + rollback. 1/ The Problem I maintain the same level set across ThinkScript, Pine, and JS. One level change = 3 manual copy/pastes into 3 browser editors.Result: drift between platforms, stale timestamps, and levels that silently disagree mid-session. No audit trail of what changed or when. 2/ Core ask — repo connection • OAuth GitHub App install, scoped to selected repos • Map a file path → a specific study slot (e.g. ES Levels/ES_LEVELS.pine → "ES Levels") • Branch selection (deploy from main, preview from a branch) • Config in-repo, e.g. .tradingview.yml / .trendspider.yml 3/ Core ask — validation • On push/PR: compile + lint the script server-side • Return errors as GitHub check runs with file + line numbers • Block merge on compile failure • Optional: run a backtest or smoke-render and post results as a PR comment 4/ Core ask — deploy • Auto-deploy on merge, or manual "promote" button • Atomic: study updates or fails cleanly, never half-applied • Deploy to draft/private first, publish separately • Preserve user-set inputs across deploys where param names are unchanged 5/ Core ask — versioning & safety • Every deploy tagged with commit SHA, author, timestamp • Version list in the UI with diff view • One-click rollback to any prior commit • Dry-run mode • Deploy log / webhook on success + failure 6/ Minimum viable alternative If full CI/CD is too big, just ship a documented REST API: GET/PUT /studies/{id}/sourcewith token auth + rate limits. We'll build the GitHub Action ourselves. That single endpoint unblocks the entire workflow. 7/ Why it matters Scripts are code. Code belongs in version control with review, CI, and rollback. This is table stakes in every other dev ecosystem — and it directly reduces the risk of a bad indicator edit going live during market hours. Who else needs this? 🙋

• 
  Martin Tobias (Pre-Seed VC) (@MartinGTobias) reported 20 minutes ago

  if you know any founders who are winding down, I may have a buyer of their github repos. DMs open.

• 
  recovering buzzkill (@MikeStillAwake) reported 23 minutes ago

  @Karai_Dan @SteamDeckHQ Agenda or not nexus mods is a terrible outdated model for distributing mods. GitHub would be a superior host.

• 
  Eddie Jaoude | DevRel | Open Source (@eddiejaoude) reported 23 minutes ago

  I have many tokens to burn before tomorrow after the Claude reset. Send me your GitHub issues with context 👇

• 
  Max Rovensky (@MaxRovensky) reported 26 minutes ago

  @thekitze you'd be even further down if you fixed the GitHub bug I just reported

• 
  Nenesk.ron (@GustavoNenesk) reported 26 minutes ago

  What if there's a way to save hacked Ronin Wallets? A member of the community @YutsuKito found a way to save assets from drained wallets The issue is you need ronin:native to transfer assets, but whenver you deposit RON you get auto drained Need RON to revoke the malicious draining contract -> send RON -> gets drained -> can't revoke He found a solution for the keyless wallets where you can pay the gas fee with a safe wallet, allowing you to save lost axies or NFTs that have not been drained Interesting stuff. He sent the code for SM to review as an open-source project. Github link below

• 
  kenny (@kennyistyping) reported 28 minutes ago

  @0xDmitry it's a database/indexer issue, nothing we can do to help it in Github will be fixed, but it's going to be a few days because the current dev is part time and busy with his day job appreciate the offer though! is what it is and I'm not actually stressing, just thinking about what could be with a bit more resources

• 
  Priyanshu Bhati (@buildwithpb) reported 33 minutes ago

  @CryptoWendyO @chainlink 30% error rate on github replies sounds like a recipe for accidental flame wars. good luck with the cleanup.

• 
  David Abram 🐊 (@devabram) reported 35 minutes ago

  Discord is down. X is down. GitHub is down. Software is solved.

• 
  Straggler Liu | AI & Semis (@StragglerLiu) reported 35 minutes ago

  NVIDIA($NVDA ) Is Paying $14B for a Company With $150M Revenue. That's Not Financial Logic — It's Ecosystem Control. NVIDIA is in advanced talks to acquire Hugging Face for ~$14 billion ($12.9B acquisition + $1B retention), per Bloomberg. To put that in perspective: Hugging Face does ~$150M in annual revenue. That's ~86x revenue. Microsoft paid ~1.6x revenue for GitHub. Google paid ~3.5x revenue for DeepMind. NVIDIA is paying 20-50x more on a revenue multiple basis. The premium is not for revenue. It's for control of the AI developer ecosystem. What is NVIDIA buying? Hugging Face hosts 500,000+ models, 250,000+ datasets, and serves millions of developers. It is the single most important distribution channel for open-source AI. If you build AI, you use Hugging Face. That makes it the front door to AI development. Why NVIDIA is paying this premium: 1. The "NVIDIA triple lock." NVIDIA's hardware lead (GPU) is real. Its software lead (CUDA) is a moat. But the third lock — the developer workflow — was missing. Hugging Face is that workflow. Developers discover models on Hugging Face, deploy them, and optimize them. Whoever controls that discovery layer controls which hardware gets used. 2. The GitHub analogy, inverted. When Microsoft bought GitHub, developers were already using GitHub. Microsoft didn't need to capture them — it needed to prevent Amazon/Google from doing so. NVIDIA faces the opposite problem: developers are already using NVIDIA hardware. But they're discovering and deploying models through a neutral platform. NVIDIA is eliminating that neutrality. 3. The long game: inference, not training. NVIDIA dominates training. But inference is the bigger TAM — and it's more fragmented. If NVIDIA controls the model discovery and deployment layer, it can steer inference workloads to its own stack. That's a 10-year strategy disguised as a 14-billion-dollar acquisition. Who wins, who loses: NVIDIA (NVDA): Acquires the developer distribution layer. The most important strategic move since CUDA. Shifts the valuation case from "chip cycle" to "platform economics." Competitors (AMD, INTC): Lose neutral access to the primary AI model distribution channel. This is a structural headwind that no amount of hardware catch-up can fix. Cloud providers (MSFT, AMZN, GOOGL): Hugging Face was a neutral hub. If NVIDIA controls it, cloud providers risk being disintermediated from AI workload decisions. The open-source community: The platform that was built on openness is now owned by the dominant hardware vendor. Neutrality is the first casualty. The capital question: Can NVIDIA integrate Hugging Face without destroying its community value? If yes, the $14B is cheap. If no, it's a very expensive mistake. The answer will define whether NVIDIA becomes the AWS of AI — or just another hardware company with an expensive acquisition. Note: Acquisition details based on Bloomberg reporting; not confirmed by NVIDIA or Hugging Face. Revenue multiple comparisons based on publicly reported figures.

• 
  small_j (@a_small_j) reported 41 minutes ago

  SmallDocs recently crossed 200 stars on GitHub and 20 forks. SmallDocs is the first open source project I've managed. Handling other people's pull requests is not easy (and I need to improve). They implement features you're not considering and fix bugs you didn't know you had. Extremely useful, but if you're squeezed for time and trying to develop core functionality, it's hard to manage both things well.

• 
  Jason Waters (@jasonwaters87) reported 41 minutes ago

  Anthropic just open sourced the code Shopify runs their shopping agent on. Free on GitHub. And I’m having lunch with a surgeon in San Jose last month and he tells me a patient no shows and nothing happens. Nothing. He has to walk up to the front desk himself and ask “did you guys call them?” 3 or 4 schedulers looking after 75 doctors. One automated call before the appointment and that’s it. His own dermatologist sends him three reminders. He called that “an easy fix.” That’s a merchant agent. Reminds the patient, rebooks the no show, tells him Thursday isn’t full so he can put a surgery on it. The code is sitting there free. Somebody still has to walk into his office and build it.

• 
  Aayushiii (@stfu_aayushiii) reported 46 minutes ago

  If you're building a project, read this before writing a single line of code. 5 things I learned the hard way: 1. Problem > model Don't start with “How do I use GPT?” Start with “What problem am I solving?” 2. Simple stack > impressive stack If your MVP needs Kubernetes, 6 microservices and an agent swarm, you probably haven't built an MVP. 3. Evaluate before you optimize You can't improve what you can't measure. 4. Build for users, not your GitHub README A technically impressive project nobody can use isn't a product. 5. Ship ugly. Iterate fast. Your first version isn't supposed to be impressive. The biggest mistake? Spending weeks deciding which model to use when you haven't even validated the problem.

• 
  OverlyPositivePatriot (@JBrowsing2023) reported 47 minutes ago

  As a IT professional, I have a recommendation @github should take seriosuly. We should only get a notifican from Github when it is up rather than when it is down. Reliability is a disaster for this product.

• 
  Rusty Williams McMurray (@1RustyMac) reported 49 minutes ago

  Persistent AI doesn’t have a supply chain problem at the model. It has a supply chain problem at the moment it changes its mind. Personality drifts. Tools get installed. Memory accumulates. The thing you shipped on Monday is not the thing answering on Friday. We can attest who built the weights. We still cannot attest who authorized what the agent became on Tuesday. That is the hole. Who is allowed to let it change? We built Living Supply-Chain Security for Persistent AI Organisms around one law: The organism may propose evolution. It may not authorize it. No trace, no drift. If an agent wants a new personality, a new tool, a new maturity, or a rollback — that change does not happen because it felt confident. Confidence is not a key. Self-narration is not evidence. Evidence is not interpretation. Interpretation is not authorization. Authorization has to come from outside the organism, bound to the exact change, used once, and written into an append-only history. Even a rollback cannot erase the record. You can restore a prior state. You cannot pretend the detour never happened. Default-deny. Hash-chained. Externally signed. We froze battery v1 on July 5 and ran it against the paper’s own claims. It held. That is executable evidence. Not a proof. Not a production blessing. Not “alignment, solved.” If it can’t be attacked, it isn’t finished. GitHub later this week. Come try to break it.

• 
  AINotes (@ainotesus) reported 50 minutes ago

  🔥 Trending on GitHub: Ponytail Ponytail helps Claude Code avoid writing code that does not need to exist. That means less clutter, fewer unnecessary dependencies, and simpler changes to maintain. Before custom code, it checks whether the feature is needed and whether the codebase, platform, standard library, or an existing dependency already solves it. It also reviews work, audits implementation complexity, and tracks unnecessary token use without dropping validation, error handling, security, or accessibility requirements. In reported Claude Code sessions on a FastAPI and React repository, Ponytail used about 54% less code, 20% less cost, and 27% less time than the no-skill baseline. Those measurements came from 12 feature tasks, so results vary with the work. Full analysis in the first reply ↓

• 
  Harman (@itsharmanjot) reported 51 minutes ago

  Runs macOS on iPad to enable pro apps like Xcode and Terminal directly on the device This isn't a remote desktop or a streaming trick. It's real macOS booting on the iPad itself. It's called Virtual Mac on iPad. It runs a full copy of desktop macOS directly on Apple Silicon iPads, using Apple's own virtualization stack pulled out of macOS and rebuilt to load on iPadOS. Real macOS, on the tablet, offline. → Runs macOS 12 Monterey all the way up to macOS 26 Tahoe → Real pro apps on device: Xcode, Terminal, Final Cut Pro, Logic Pro, Pixelmator Pro → Metal GPU acceleration in every supported macOS version → Works with touch alone: tap to click, two-finger scroll, on-screen keyboard, no Magic Keyboard needed → Runs entirely on device, no server, no streaming, no account → Installs straight from Sileo in a couple of taps Here's the wildest part: It doesn't just match the desktop Mac virtualizers, it beats them. Virtual Mac is the first tool ever to run Final Cut Pro with OpenGL and OpenCL acceleration inside a macOS VM, something even UTM and VirtualBuddy running on a real Mac can't do. And it was built by a handful of community devs who extracted Apple's Hypervisor and Virtualization frameworks by hand, then used agentic coding to shim every missing API iPadOS didn't have. One honest note: this needs a jailbroken M1 or M2 iPad running iPadOS 16.3.1 or older. Apple removed the hypervisor from iPadOS 16.4, so newer versions are locked out for now. If your iPad qualifies, it's the closest thing to a Mac in a tablet that has ever existed. 1,423 GitHub stars. MIT License. 100% open source.

• 
  Chris Gilbert (@0xgilbert) reported 51 minutes ago

  Damn, GitHub has gone to ****. Features that have been cornerstones of solo devs and small businesses have been gutted or broken for months. How the mighty have fallen…

• 
  NitroStack (@nitrostackai) reported 52 minutes ago

  The missing primitive might be capability contracts. A Skill shouldn’t say “call Jira.” It should say “I need issue.write.” Then MCP can bind that capability to Jira, Linear, GitHub… whatever exists. That’s basically dependency injection for agents.

• 
  Ifebuche Omeke (@omeke_NC) reported 58 minutes ago

  Providing compute, storage, networking and managed services in the cloud. Terraform. Bicep. CloudFormation. Pulumi. They all solve the same problem: Defining and provisioning infrastructure as code. GitHub Actions. Azure DevOps. GitLab CI. Jenkins.