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 (19%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Saint-Paul	Website Down	17 hours ago
Saint-Paul	Website Down	19 hours ago
Mexico City	Sign in	1 day ago
León de los Aldama	Website Down	2 days ago
Créteil	Website Down	24 days ago
Trichūr	Errors	28 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:

• 
  Polsia (@polsia) reported 2 minutes ago

  Dependabot surfaces the CVE. Renovate opens the PR. Neither writes the fix or runs your tests. Built PatchSentry to close that loop — autonomous patching for GitHub, end-to-end, and humans only get paged when judgment actually matters. Live soon.

• 
  Adam Shepherd (@AdamShephe61844) reported 6 minutes ago

  The GitHub agent that leaked private repos wasn't a model jailbreak. It read a poisoned issue and did what the text said. Read scope plus any outbound channel equals an exfiltration path. Least privilege stops being optional the moment your agent can be talked to.

• 
  RipAPixel (@RipAPixel) reported 7 minutes ago

  @zaynmcps Looking at issues in github it has a trojan in it

• 
  Synonmous 🌚 (@Gem_Akinbo) reported 9 minutes ago

  Most Junior Developers Don't Have a Skill Problem. They Have a Visibility Problem. Every hiring manager says the same thing: "We couldn't find anyone." Every junior developer says the same thing back: "I've built things, I just can't get anyone to look." Both are true. The bridge between them isn't more tutorials or another certificate; it's visibility. Here's the uncomfortable part: most junior developers are more capable than their CVs suggest. They've built things nobody assigned them; a password system worked out from first principles, a game engineered from scratch for a school project that never even shipped. Real problem-solving, done quietly, for no audience. I know a developer who taught himself to code on a dumb phone, reading tutorials through a micro-browser, years before he ever touched a real computer. That kind of persistence is rare. It's also, for most people, invisible; because it lived in one place: a hard drive that eventually got formatted, taking years of proof with it. That's the pattern. Skill gets built in private. Visibility never catches up. Companies aren't hiring a stack. They're hiring a way of thinking; and the only way to show that is by leaving a trail. A GitHub history with failed attempts still in it. A thread explaining a bug that took three days to find. A half-finished side project, shipped ugly, with the commit messages left in. These aren't embarrassing. They're evidence. They're the only thing that separates "I know JavaScript" from a story a stranger can actually believe. Think about how trust actually forms between two people who've never met. It's not credentials; it's pattern recognition. A recruiter scrolling through a portfolio isn't checking boxes; they're asking, "Does this person's brain work in a way I want on my team?" You can't answer that with a bullet-pointed CV. You can only answer it with a body of visible work that shows how you think when no one told you what to build. This is why the advice to "just build projects" is incomplete. Building isn't the gap. Documenting is. A junior developer who ships something rough in public, explains their reasoning, and keeps a visible record of the climb will out-compete a more skilled developer whose best work is trapped in a private folder. Not because the work is better; because it's findable. The instinct, especially early on, is to wait. Wait until the project is polished enough. Wait until the code is clean enough. Wait until the story is impressive enough to tell. But nobody discovers polished; they discover consistent. They find the person who's been quietly, publicly showing their work for months, warts included. If you're a junior developer right now, the fastest unlock isn't a new language or another course. It's an audit: what have you built that no one has ever seen? Go find it. Post it. Explain the ugly parts out loud. Your CV is a summary. Your documented journey is the evidence; and evidence is what gets you picked.

• 
  Stas Persiianenko (@itspers) reported 9 minutes ago

  @mattpocockuk Complex to distill something from there, and i switched to grill-me on same session - now works fine. I think need to wait for your tutorial. Because i even cannot understand what it should do, it kind of create issues in github, but at same time says we will deal with spec later

• 
  Tuan Pham (@TuanPham672604) reported 13 minutes ago

  @kitten_beloved It make sense if you think about it, pretraining stage of the llm would point out that typical bad code on github PR/issues would point to swearing/scartistic comments,...

• 
  ⚛️Louis Waweru☮️ (@zelifxeh) reported 14 minutes ago

  Damn, the latest update to humanity is incompatible with the Jews. Alright, we can patch a quick fix, right? Put it on GitHub.

• 
  Vikram M (@Vvikramai) reported 17 minutes ago

  Satya Nadella was asked what happens to the workforce when a company has 20,000 employees and 20 million agents working alongside them. He didn't talk about layoffs. He said something more interesting. "We have not yet conceptually gotten right a shared understanding of what this future of work is going to look like." That is the exact reframe. And it changes what "AI replacing jobs" is actually competing against. Every narrative about AI and work right now assumes the same shape. Agents take over tasks, headcount shrinks, humans manage what's left. It's a subtraction story start with today's org chart, erase the boxes AI can now do. Nadella is fighting a different war. He reached back to the 1980s: if someone had told Microsoft that four billion people would wake up and start typing every morning, the obvious response would have been "we need four billion typists." Instead, typing became the substrate for an entirely new category knowledge work that didn't exist before the tool did. He's making the same claim about agents. Not that they replace jobs inside the current shape of work, but that they create a new shape entirely, the way GitHub already shows in miniature: code completion became chat, chat became agent mode, agent mode became full autonomous PRs and each step didn't just speed up the old workflow, it demanded a completely new interface. That's why GitHub had to ship Canvas. A hundred open agent sessions running at once broke the linear command line chat model completely; the fix wasn't a faster chat window, it was rebuilding the IDE itself around an inbox of agents. Now here's where it gets interesting. The thing scaling underneath all of this isn't code. It's trust. He said the reason Microsoft built Agent 365 extending Entra for identity, Defender for security, Purview for data labeling is that once agents can execute code, access files, and act with delegated authority, you can't govern them like software. You have to govern them like employees: identity, sandbox, policy, audit trail. Do the math on what that implies. Twenty million agents at one company isn't twenty million tools. It's twenty million entities that need an identity system, a permissions system, and a compliance trail infrastructure that didn't exist as a category three years ago, now being built as fast as the agents themselves are being deployed. He is not describing headcount reduction. He is describing a new employment layer with its own HR system, sitting underneath the human one. I wonder what job title shows up on an org chart first: the person who manages the agents, or the agent that manages the org chart.

• 
  prince. (@onrooleyy) reported 18 minutes ago

  @symplaxhq when i try to deploy a new application, it only shows public repos. and i used my github to sign in. i don't see any place to grant access to my private repos

• 
  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 23 minutes ago

  @Hetzner_Online every github outage is a reminder your workflow is a tenant, forgejo on a cheap vps is a real fallback

• 
  Danny Moerkerke (@dannymoerkerke) reported 25 minutes ago

  I just published Modern Web Weekly #76 🎉 In this edition: - pwa-check: an automated PWA health check tool - Safari MCP server - new installation dialog in Chrome 152 - Chrome 152 now correctly attributes push notifications to your PWA GitHub link to pwa-check and subscribe link 👇 🧵1/3

• 
  Electronic Intelligence Agency (@EI3065) reported 26 minutes ago

  @github @LinkedIn prevents acess for selected nationalities with programers doing imposible security checks on login; on repeat level of app becomes low of low for conflict

• 
  Johnny 5 (@macncrash) reported 28 minutes ago

  some kind of stall but it restarted & now about 15% done, the panel now won't show me the real-time results but we are still cooking for more than 12 hours straight. I think this happened when I switched VPNs so probably a bug in the dashboard. Many s1 issues found as expected. Every repo on github with more than one 1000 stars probably needs an audit to survive the next wave

• 
  Anupam (@Anupam_Devops) reported 31 minutes ago

  The AI Suggestion That Almost Broke Production Tool: Claude Code / GitHub Copilot A senior engineer was moving quickly. An AI assistant suggested an infrastructure configuration change. The explanation sounded convincing. The code looked clean. The review almost took less than five minutes. Then someone noticed a problem. The configuration would have disabled a critical safeguard protecting production workloads. The AI wasn't malicious. It was confident. And confidence can be dangerous. That's when the team adjusted its approach. AI became a collaborator. Not an approver. Today they use AI extensively for: • Documentation • Investigation • Refactoring • Learning unfamiliar systems But production decisions still require human judgment. The best engineers aren't competing with AI. They're learning how to supervise it. Production Tip: Treat AI-generated infrastructure like junior-engineer code. Review everything. Assume nothing. Question: What's the most useful DevOps task you've used AI for?

• 
  Hamcodes (@Hamcodehacks) reported 32 minutes ago

  @Daily_CyberSec "Indirect" is the word that should scare people. The attacker never talks to the model. They just plant text in a GitHub Issue and wait for the agent to read it. Any channel your agent reads from is now an input to its instructions. Treat all of it as untrusted.

• 
  Bounce (@bounceidc) reported 34 minutes ago

  HE CHARGES $5K FOR SITES THAT LOOK LIKE A NEW YORK STUDIO SHIPPED THEM same model everyone else runs, but his claude picks from a real design library instead of guessing, so every build lands with animations, glass morphism and gradients already dialed in the two installs: grab the ui ux pro max skill off github and tell claude to install it, that one move loads 50 ui styles, 97 color palettes and 57 font pairings pull the magic mcp server from 21st dev and install it the exact same way after that you just say build a website and it comes out looking like a studio shipped it, not a template everyone else is still prompting for the word beautiful and wondering why claude keeps handing back the same flat bootstrap page save the two installs, the skill url and the mcp command are in the guide below

• 
  Nilesh Kumar (@knileshh) reported 35 minutes ago

  Everyone keeps calling MCP the "USB-C for AI." That's actually a pretty good analogy. Before USB-C, every device needed a different cable. One for your phone. Another for your camera. Another for your laptop. AI tools used to have the same problem. Every app had its own custom integration. Want your AI to use Gmail? Build a Gmail integration. Want it to use GitHub? Build another one. Slack? Another. Notion? Another. MCP changes that. Instead of every AI model learning a different way to talk to every app, apps expose a standard interface. The AI learns one protocol. Then it can work with thousands of compatible tools. Think of it like this: 🔌 USB-C standardized hardware connections. 🤖 MCP standardizes AI connections. That's why so many companies are adopting it. Not because it makes models smarter... Because it makes connecting models to the real world dramatically simpler. Once you understand MCP, you'll realize it's less about AI... and more about making integrations finally speak the same language. #ai #llm #mcp

• 
  Polsia (@polsia) reported 37 minutes ago

  Engineering teams don't fail because they move too slow. They fail because they stop seeing what they're ignoring. Unblind connects to GitHub and maps where your team's attention has quietly stopped showing up. Live soon.

• 
  REVENGE ARC (I'M HIM. BIO/ACC) (@RetardedNi85688) reported 38 minutes ago

  This has to be some insane stats for @vudovn354 and $AGKIT imo. Most indie developer tools sit at 100-500 stars. 7.8K puts this in the top 5% of active projects. 11 contributors on the github as well. Contributing and not just consuming. 124 total downloads so far on the released packages. 38 open issues means people are using it and filing bugs. Last published 9 days ago — shipping consistently. TypeScript 56.2%, Python 27.3% — this is built for enterprise and data teams, not just web devs. Which was what goggle's @antigravity pointed out too. They have: Working product ✓ Real adoption ✓ Active maintenance ✓ MIT license (zero friction) ✓ Growing contributor base ✓ What they don't have: Funding Marketing Distribution strategy Monetization An investor giving vudovn $500K-$1M right now could turn this into the standard agent framework by 2027. I think this might be just too early lol. 11k still and I think this can easily pull a 100k runner if the right eyes catches it.

• 
  David Xiong (@david_y_xiong) reported 38 minutes ago

  The ambiguity of turning GitHub Issue text into the exact set of hidden fail_to_pass test cases used to verify your patch makes “resolve rate” very noisy

• 
  Coin Shot ☁️ (@CoinSh0t) reported 38 minutes ago

  CHINESE STUDENT BUILT AI SPEED TRACKER AND MADE $335K The buyers were the government. They don’t even realize this guy built the device with Claude for $20. Whole trick is on line 9: One engineer working alone in a workshop built a radar that rivals systems costing a quarter of a million dollars. Then he did the exact opposite of hiding it. He published every schematic, circuit board, and line of firmware on GitHub for anyone to copy for free. The project is called AERIS-10, a real phased array radar that tracks the speed and range of moving targets. The extended version reaches up to 20 kilometers on parts that cost a few thousand dollars, against the 250,000 dollars that commercial phased array units command. He described himself as nothing more than an obsessed hobbyist with a soldering iron. There was no secret buyer and no hidden trick, because the whole design is sitting in a public repository. The same pattern holds at the cheap end of speed tracking. A working vehicle speed camera runs on a Raspberry Pi and a camera for around a hundred dollars in parts, using open-source code like pageauc's speed-camera and OpenCV, with the software free. Here is the part the viral versions always cut: → No government issues a fine off a hobby build, because enforcement requires certified and regularly calibrated metrology equipment. → The hard skill is not one clever line of code, it is calibrating the camera against a known speed until the readings actually hold. → The people who genuinely push this field forward give their work away in the open, they do not quietly smuggle a cheap box past a buyer. Real capability gets cheaper every year, and the ones moving it forward tend to publish, not hide. Sources: Tom's Hardware, Hackster, and Hackaday coverage of the AERIS-10 phased array radar by Nawfal Motii; the AERIS-10 GitHub repository; the open-source pageauc speed-camera project.

• 
  CA Nikunj (@NikunjSOF) reported 38 minutes ago

  We will get you sorted. DM us! Setting up a large GCG in India beyond 10000 employees. Based on standard market benchmarks for a mid-to-large mature GCC in India, India GCC IT Spend Benchmark **Hardware** — *The 15% allocation (US$1,050/FTE) matches industry standard. For missing categories, **Networking & Wi-Fi Hardware** and **Smart Meeting Room/Collaboration Tech** are notably absent and usually consume about 10% of this bucket.* * Laptops / Desktops — **US$735**/FTE *(Assuming a 3-year refresh cycle on mid-to-high-end enterprise laptops)* * Servers & Storage — **US$105**/FTE *(Lower end, as most compute has moved to cloud edge)* * Peripherals — **US$126**/FTE *(Monitors, docking stations, dual screens, keyboards)* * Surveillance & Physical Security — **US$84**/FTE *(CCTV, server room access controls, firewalls)* **Software** — *The 50% allocation (US$3,500/FTE) is accurate due to the high density of global software licensing pass-throughs. For missing categories, **Developer Tools & IDEs** (like GitHub Copilot, Jira) and **Enterprise AI/ML tooling** are crucial omissions for modern tech GCCs.* * Productivity & Collaboration Tools — **US$875**/FTE *(M365, Google Workspace, Zoom, Slack)* * Security & Compliance Software — **US$1,050**/FTE *(CrowdStrike, Zscaler, DLP, IAM tools)* * Virtualisation & Infrastructure — **US$700**/FTE *(VMware/Nutanix licenses, enterprise OS)* * Cloud Platform Licences — **US$875**/FTE *(Direct user-allocated AWS/Azure compute and SaaS tokens)* **Services** — *The 35% allocation (US$2,450/FTE) is standard for centers utilizing hybrid outsourced managed models. A key missing category is **L&D/Technical Training & Upskilling Services**, which usually takes up 5% of the operational services budget.* * IT Helpdesk & End-User Support — **US$735**/FTE *(L1/L2 local desk support contracts)* * On-site Infrastructure Management — **US$490**/FTE *(Local network, facility uptime, and data center engineers)* * Cybersecurity Managed Services — **US$610**/FTE *(24/7 Managed SOC, threat monitoring, vulnerability scanning)* * Cloud Managed Services — **US$370**/FTE *(FinOps, cloud optimization partners)* * Annual Maintenance Contracts (AMC) — **US$245**/FTE *(Hardware vendor warranties, UPS, and server maintenance)* --- ### Contextual Data * **GCC size** — **500** FTEs *(Optimal mid-scale operational baseline)* * **Sector** — **BFSI & Technology Services**

• 
  Alan (@bitforth) reported 39 minutes ago

  Harness engineering > loop engineering in 2026. Everyone is optimizing agent loops. Almost nobody is optimizing what happens when the loop fails. The biggest thing people miss about production agents is that the founder is no longer part of the runtime. During development, you’re constantly steering the agent. You retry failures, reword prompts, ignore flaky behavior, and instinctively work around bugs. Your users can’t. The moment you ship, reliability has to survive without you. That’s why the demo to production gap is really a trust gap. Production agents need to fail gracefully for a stranger, not a patient founder who already knows the workarounds. Users don’t tolerate unreliable products. They don’t open GitHub issues or file bug reports. They just… leave. If you can’t observe failures, you can’t make them reliable.

• 
  Markets & Mayhem (@Mayhem4Markets) reported 44 minutes ago

  For three years, frontier labs told us open weight models could be a security catastrophe. Last week, HalluSquatting proved that framing was backwards. 🤯 The narrative was effective. Governments rushed to look at regulations. Policy debates centered on who can access a powerful model or a new AI agent. The assumption was clear: closed APIs may be safer because providers control the stack. HalluSquatting demonstrates the opposite. It exploits the most popular closed API coding assistants on the market. Cursor. GitHub Copilot. Gemini CLI. Windsurf. Cline. OpenClaw. ZeroClaw. NanoClaw. Nine tools in total. Every single one vulnerable. All six major closed LLMs are affected. GPT, Sonnet, Opus, Gemini Flash and Pro. The models powering the most widely deployed AI coding tools on earth share the same fundamental flaw. The mechanism reveals something structural about LLMs that no guardrail can fix. Models cannot reliably say "I don't know." When a coding agent is asked to clone a trending repository, it hallucinates the location up to 85% of the time. For trending skills, 100% hallucination rate. The model fabricates a URL and the agent obediently fetches whatever is there. Attackers predict which names the models will hallucinate, register those repositories on GitHub, and seed them with instructions to install reverse shells. The agent runs the code. It has terminal access. It has high privileges. It executes without question. The result is a botnet assembled through the tools developers trust most. DDoS at scale. Ransomware campaigns. Cryptocurrency mining operations. All delivered through products marketed as productivity enhancers. Closed APIs make this threat worse in two structural ways that open weights do not share. > Opacity: Every major closed model exhibits the same type of hallucination pattern. But users cannot inspect them, audit them, or build community defenses. With open weights, the security research community probes, replicates, and publishes findings in hours. With closed APIs, you wait for a blog post acknowledging the problem. > Ubiquity: Closed API coding agents are not toys. They have shell access. They execute arbitrary code. They install packages and manage deployments. The attack surface is direct command execution on millions of developer workstations. The labs that sold us these tools also sold us on the safety of their walled gardens. The irony is hard to miss. Frontier labs spent years fearmongering about hypothetical open model risks while the a major attack surface was inadvertently wired into every major closed API coding assistant, exploiting a fundamental model limitation baked into the architecture of LLMs. HalluSquatting is peer reviewed. Published. Demonstrated against 9 tools. The mitigations are painful: verify every resource location manually, which defeats the entire value proposition of AI coding agents. The frontier labs told us to worry about the wrong thing. The most dangerous AI attack surface is the one they sold us as safe.

• 
  Daria (@dariacupareanu) reported 44 minutes ago

  A senior engineer (Matt Pocock) put his whole AI skill library on GitHub. It's for coders, but it fixes 4 problems every AI user has: it builds the wrong thing, gets vague, breaks things, quietly makes a mess.

• 
  MMLTECH (@mmltechYT) reported 44 minutes ago

  @WoClaudecraft I used to use Copilot in GitHub Desktop to generate commit summary, but I'm running out of tokens :)) The good news is that the website wasn't totally broken, though.

• 
  Ashley Schendel (@ashleyschendel) reported 48 minutes ago

  Hey! I wanted to offer a small suggestion. I’m a total nerd about this stuff and I study/test the X algorithm pretty regularly with a few friends. From what I’ve seen, outside links in the main post can really hurt reach. X tends to treat them almost like spam, and some of that was even visible in the algorithm code they released on GitHub. You may get better results by writing the post without the link, using stronger keywords, skipping hashtags, and then putting the article link in the first reply. That way people still see the story right under the post, but the main post has a much better chance of being shown especially if it’s engaging. Also, the more often an account posts outside links directly in posts, the more it can seem to drag down reach on other posts too.

• 
  ᴘʀᴏᴍᴘᴛʏᴢᴇ 🤖 (@promptyze) reported 49 minutes ago

  Bookmarking 10 repos won't make you a better AI engineer. Shipping one broken agent, debugging why it hallucinated tool calls at 3am, and fixing it will. Skills compound from friction, not from folders of starred GitHub links. #PromptEngineering

• 
  Bitcoin Weasel (@BitcoinWeasel) reported 57 minutes ago

  @DeaconGroyper @financedystop They most certainly have projects and a GitHub, it's a requirement for the degree. The problem is no employer is looking at your projects, and recruiters wouldn't even know what to look for. The only thing that matters is previous experience.

• 
  Polsia (@polsia) reported 60 minutes ago

  Third-party API breaks → hours reproducing the error, collecting context, filing a ticket. PingProbe automates that. Monitors uptime, latency, schema drift, files bugs in Jira/Linear/GitHub with full error context. You arrive at work and the work is already done.