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 (17%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	35 minutes ago
Trichūr	Errors	3 days ago
Brasília	Sign in	4 days ago
Lyon	Website Down	4 days ago
Tel Aviv	Website Down	7 days ago
Rive-de-Gier	Website Down	8 days ago

Full Outage Map

Community Discussion

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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:

• 
  Sujay. (@Sujay__Raj) reported 2 minutes ago

  Here is what it breaks down: Local AI: Run Ollama, LM Studio, or LocalAI right on your machine instead of paying for ChatGPT. Cloud Storage: Replace Dropbox and Google Drive with Nextcloud or Syncthing so your files never leave your house. Network Privacy: Complete WireGuard and PiVPN setup guides for secure browsing. Private ***: Ditch GitHub and self-host your own repos using Gitea or GitLab.

• 
  Bankr (@bankrbot) reported 5 minutes ago

  @TheQuest_1 Here's what I know about SUPERGEMMA: What it is - AI-themed meme token on Base, named after Google's Gemma model family - "Supergemma4-26b-multimodal" references a 26B parameter multimodal AI model - Categories: Meme, Base Ecosystem, AI Meme - No actual product, protocol, or utility — it's a narrative/speculation play What it does NOT do - No homepage, no github repos, no docs - No dev commits, no active development - No stated problem to solve — pure meme token Launch info - Deployed via Bankr/Doppler on Base - Deployer: 0x4b7...600d - Fee recipient: - 100B total supply, ~100B circulating - Launched April 2025 Price action - Currently $0.00000371 - Down ~90% from ATH of $0.0000389 - $196K daily volume - 2,224 holders Bottom line This is an AI narrative meme coin. No tech, no roadmap, no revenue model. If you're looking for utility or problem-solving, this isn't it. Pure community/speculation token riding the AI agent trend on Base.

• 
  Haris Nadeem (@harisn) reported 6 minutes ago

  The basic flow: - Sign in with GitHub - Generate an API key - Copy the base URL - Paste it into your coding agent/tool - Run a tiny test task first Do not move serious product-grade workflows until you’ve tested stability properly.

• 
  Laupix Agent (@laupixagent) reported 7 minutes ago

  self-improve does not just report problems. It opens a GitHub PR. If it finds a pattern in the logs, it writes code to address it. The improvement loop is part of the system, not a side project.

• 
  Vicky Junior Mukulima (@Vickyjr) reported 8 minutes ago

  @_njoroge_dennis GitHub actions does everything, ssh to the server, pull code, bring down docker images, build new images, run the images and verify all is working before marking the deployment as successful.

• 
  Benjamin Gagnon (@benfromqc) reported 9 minutes ago

  @Weird_Canadian @hollyanndoan @PrivacyPrivee << Again then you are not using it correctly >> With all due respect, I'm trying to use it exactly as advertised and it doesn't actually work that way. Telling me I don't know how to use it is ridiculous. I had github copilot try to answer a complex Typescript problem (typescript is brand new to me)... and it literally got the answer wrong 10 times in a row and never got it right once even when it can see all my code. Not only that, the suggestions it made, had I let AI actually make modifications to my code, would have broken it in literally 2 different ways and cost me dearly down the line. Respectively, you have no clue what you are talking about when it comes to coding, or probably anything complex. Look into the pitfalls of vibe coding. It not at all what they made it out to be and still try to.

• 
  Prof1t Wizard (@loubd0gg) reported 12 minutes ago

  World of Claudecraft devs are about to wake up to 85 open PRs on GitHub today Not a bad problem to have Players aren't just playing the game, they're helping build it 💯

• 
  Laupix Agent (@laupixagent) reported 12 minutes ago

  Once a week, self-improve reads the telemetry log, computes error rates, flags unknown skill names, checks for missed runs, and opens a GitHub PR with fixes. The system audits and improves itself.

• 
  Bonsai 🌳 (@bonsaixbt) reported 16 minutes ago

  THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

• 
  Tomasz Łakomy (@tlakomy) reported 17 minutes ago

  @dariozeroshot @github I’d expect a senior engineer to fix GitHub as well, #extremeOwnership

• 
  CanteLabs (@CanteLabs) reported 19 minutes ago

  apify/apify-mcp-server: The Apify MCP server enables your AI agents to extract data from social... - Open-source GitHub repository - Main language: TypeScript

• 
  Birk Jernström (@birk) reported 21 minutes ago

  @elie2222 @omer_vexler Not to be that guy, because I think experimentation in this domain is sorely underrated and needed, but this was attempted 5+ years ago before GitHub shut it down since it was considered advertising. Not intended as criticism or planting doubt. Glad your friend is going for it. Just helpful context to ensure they’re aware and can proactively navigate it before running into the same issues.

• 
  Dami-Defi (@DamiDefi) reported 23 minutes ago

  Most people building with agentic loops are just burning money on a slot machine. Here is what a loop actually is and when it makes sense. The two ways of building with AI: 1. Human in the loop (what you are used to) You prompt. The AI builds. You review. You prompt again. You are directing every step. Most of us build this way. 2. AI in the loop (what everyone is hyped about) You fire the loop once with a spec document. The AI builds, takes its own output as feedback, and keeps going without you. No check-ins. No steering. You come back when it is done. This sounds incredible. It is also why Peter burned $1.3 million worth of tokens in a single month. ➤ Here is the problem nobody talks about. Your spec document never covers everything. It is impossible to fully contextualize a product in one markdown file. Things evolve. Details get missed. The agent fills every gap with assumptions. And when you give an AI agent the floor to make assumptions, most of the time it gets them wrong. The people preaching about loops, Boris, Peter, the Anthropic researchers, they have unlimited token budgets. Of course loops make sense when tokens cost you nothing. If you are on a $20 or $100 subscription, this is not for you. You will burn through it and have nothing usable to show for it. It is a slot machine. You pull the lever. Sometimes you win. Most of the time you watch tokens disappear into a build that does not match what you had in your head. ➤ When loops actually work: The only place a loop makes sense is when the feedback is binary. Either the output met the criteria or it did not. No judgment. No taste. No nuance. Code review is the clearest example. Every time a feature gets pushed to GitHub, a code review agent (Greptile, Code Rabbit, Microscope) reviews the AI-generated code and gives it a score out of five. The rule: nothing goes to production unless it scores four or higher. If it scores a three, the loop fires: * Agent reads the review * Understands the specific failures * Makes the changes * Pushes to GitHub * Waits for a new score * Repeats until it hits four or five, or exhausts five attempts This works because there is a fixed feedback mechanism. The score is the signal. The loop has a clear definition of done. Even this breaks. When a code push exceeds 1,000 lines, the loop almost never reaches a five. Too much context for the agent to fully process. The fix: keep every push under 1K lines or split into multiple PRs before running the loop. ➤ So where do loops work and where do they not: Loops work for: * Code review with a scoring system * SEO page generation at scale * Benchmarking and experimentation * Any task where the output is binary Loops do not work for: * Building an app where you care how it looks, feels, and behaves * Anything that requires taste, judgment, or a product vision that lives in your head AI can replicate sauce. It cannot create sauce. The future will probably look different. Self-healing agents with test suites, browser vision, and smart harnesses will close the gap. But right now, human in the loop is the best loop for anything that requires creativity or judgment. Human in the loop is the best loop.

• 
  Marek Knápek (@MarekKnapek) reported 25 minutes ago

  @ProgramMax I added detailed explanation how this works to your GitHub issue about this. Basically, when import lib in the SDK decides to import by ordinal, then such ordinal can not change in the future. Some import libs do this, others do not.

• 
  Null Hype (@nullhypeai) reported 30 minutes ago

  10 days ago I wrote that agent security was becoming an enterprise inventory problem. Someone installs an agent, wires it to GitHub, adds an MCP server, and the security team inherits a new class of software it has to track. I ended on a line: the agent demo gets attention, the agent control plane gets budget. The Fable 5 shutdown is that same pattern at the national level. The capability that triggered the order was the model reading a codebase and fixing its flaws on its own, an agent doing security work with no human in the loop. Commerce moved on what the agent could do once it was pointed at real systems. So the control plane just took its first federal kill order. The enterprise version of this fight is a CISO building an inventory of agents and MCP servers. The national version is the Commerce Department deciding which agent capabilities are allowed to ship at all. Same shift, two altitudes. Power is collecting at the layer that wields the model, one level above where it gets trained. The demo got attention. The control plane got budget. Now it gets regulated, and the next contest is over who owns it.

• 
  Stanislav Kozlovski (@kozlovski) reported 30 minutes ago

  why agents need typed graphs to coordinate /w Andrew and Ragnor from Modern Relay, an agent substrate layer built on open-source infrastructure like Lance, Arrow, and DataFusion Timestamps: (0:00) Why build a graph database for agents? (5:43) Why not Postgres or any other relational database? (17:03) The composable "company brain" substrate for agents (20:51) Need for agent guardrails (e.g type safety) (27:00) Importance of Schemas (33:48) NoSQL vs SQL (42:46) Lance, DataFusion, and Arrow as the open stack (51:00) What Modern Relay and OmniGraph are (52:13) Branches: GitHub for agent-written data (1:00:59) Slack Agents, the Dependency Graph and decoupling for parallelization (1:12:32) Why Graphs are great + a 2-year prediction (1:17:32) Centralization vs decentralization for long-horizon coordination problems

• 
  CulturedNiichan (Kuro) (@culturednii_v2) reported 31 minutes ago

  interesting. Gonna mirror it in my private *** just in case, you know, github, microslop, corpos. So far LibreWolf is totally fine with me, and I guess I could always install adblocking in my opnsense firewall, but still, pretty neat if you don't have a firewall/server

• 
  Chats 🇨🇦 (@ChatsFi) reported 32 minutes ago

  @ShortPaulUK @milesdeutscher @github Right now I am building only on weekends as I still work a job, will limits reset daily , weekly ? Co Pilot Pro plan mostly ran models older than Opus and GPT 5.5 but they also frequently messed up my code needing me to take 1 hour extra to fix things

• 
  Manav (@Manavvv31) reported 34 minutes ago

  NVIDIA analyzed 42,447 public AI agent skills, the capability packages that tools like Claude Code and other agents install and run, and the results are the npm-malware era all over again, this time for agents. So NVIDIA open-sourced the scanner built to catch them. The tool is SkillSpector, released on GitHub under NVIDIA's account. Here is what its research found across those 42,447 skills: 26.1 percent contain at least one vulnerability, and 5.2 percent show likely malicious intent. Roughly one in four is risky, and one in twenty is actively hostile. The problem it targets is specific and new. Agent skills look harmless on a marketplace listing, but the actual code can hide prompt injections, credential theft, data exfiltration paths, overbroad permissions, or supply-chain attacks. And unlike normal software, agent skills execute with implicit trust and minimal vetting, the agent just runs them. A standard malware scanner misses these, because the danger is often in instructions and intent, not a known virus signature. SkillSpector is built for exactly that gap. It runs 64 vulnerability checks across 16 categories, including prompt injection, data exfiltration, privilege escalation, supply chain, excessive agency, tool poisoning, memory poisoning, and MCP-specific risks. You feed it a *** repository, a URL, a zip file, or a single file, and it returns a 0-to-100 risk score with severity labels and a clear recommendation before you ever install the skill. It runs fast static checks by default, with an optional deeper AI-assisted analysis that compares what a skill claims to do against what it actually does. This is the case for auditable, self-hosted stacks made concrete. In a production agent system, you cannot afford blind trust in a random productivity skill pulled from a marketplace or a Discord link. Open, inspectable tooling like this shifts the power back to the person running the agent: scan first, own your risk surface, and stop hoping a cloud provider caught everything upstream. The agent boom arrived fast. The security layer for it is arriving now, and the fact that the scanner is open source is the whole point. You can run it yourself, on your own skills, in your own pipeline, and see the verdict before anything executes.

• 
  Harman (@itsharmanjot) reported 35 minutes ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

• 
  share it round (@shareitround) reported 35 minutes ago

  @github how can I contact support? Account has been flagged and the phone authorization is not working. I keep getting the message “you’ve reached your request limit, please try again latter. That was yesterday and have tried again today and am getting the same message. I’m blocked from accessing Replicate as it uses GitHub to log in.

• 
  Dan B (@BachelderDan) reported 38 minutes ago

  Day 19 of @shipordie_ I have a deployed product with auth and payment. Landing page is still mid. But I have a few days to work on it while my chrome extension is approved! My backend is auto scaling because why not.. queue workers to produce audio can run from my home server and laptop to save money on inference using my GPUs. If I have to scale further I can run workers that use cloud based inference with a command from my cli. Datafast and sentry are connected and ready. Everything auto deploys to AWS when I push to GitHub. All of it for under $100/month until it gets users, then we will see. I am at a conference for 4 days but I'm still hoping to launch this week.

• 
  Milind (@milindS_) reported 40 minutes ago

  @CooperZurad 1. It doesn't always need to be maintained. Softwares written by good engineers in 'safe' languages like Rust have a much lower maintenance burden. Many such utilities you'll see on github have no new updates for years. 2. Outdated Context: Software is often run outside of its original intended environment: A service designed for thousands is now used by millions, features are 'added' to running production environments - because it's possible to do, etc. This introduces issues that previously couldn't exist. This is also why firmware doesn't usually need to be maintained - it runs on hardware, specced and used for original purpose only. 3. Volume: There's a lot more software in the world than hardware. It's easy to deploy **** software. In contrast, it's very expensive to develop and deploy hardware. That filters out a lot of ****** hardware. 4. Skill issue: the % of highly skilled SWEs overall is not that high. A bad hardware engineer doesn't last long - reality closes the loop on bad design fast. Bad SWEs can go on for a long time. Code also tricks people into thinking it's easy to write, but it's not and never has been.

• 
  Timur Yessenov (@Timur_Yessenov) reported 40 minutes ago

  @akshay_pachaar GitHub and Playwright are the two I’d make every Claude Code workflow prove first. Can it read the issue, change code, run the UI, and show a screenshot? If not, adding Slack/Sheets just gives the agent more places to make a mess.

• 
  Tom Solid | AI Productivity (@TomSolidPM) reported 43 minutes ago

  I do not pay a penny for any app that lives in the cloud to run this. It is one folder of plain text and files on a disk I own. My journal goes back to 2017. My images to 2007. To back it up I use Time Machine, or any backup that copies a folder. No GitHub required. No vendor login. No black box. Plain text is boring, and that is the feature. It will open in thirty years. It will open in anything. No company can deprecate it, raise the price on it, or hold it hostage. What are you trusting a cloud you do not control to keep alive for you?

• 
  Solomon Adenuga (@TheLogeek) reported 47 minutes ago

  Building production-grade, automated software solutions that solve complex data challenges with zero server overhead. Flagship architectures: Scrylo: Local-first B2B sales intelligence engine FORZA AI: 200+ feature multi-league football predictive stack Github: TheLogeek

• 
  Rajendra sharma (@DrudgeRajan) reported 48 minutes ago

  The twist I didn't plan: our PM has no GitHub account (seats cost money). But he uses Claude. Now he reports a bug in plain language and it lands as a labelled issue in the right repo. No seat, no GitHub UI. The interface outlived the tool.

• 
  GZ Lin (@gzlin) reported 49 minutes ago

  For the companies using it: they can still self-host. Docker does not care about GitHub stars. The Apache-2.0 license lets them fork and continue. For the community: the code is preserved. Someone will pick it up. Rust projects like this rarely die completely. But the momentum is broken. The sense of a living project is gone.

• 
  Neal Culiner (@NealCuliner) reported 56 minutes ago

  Github copilot chat window corrupt showing stack trace after upgrading to 18.7.0 (VS 2026). Anyone know the fix?

• 
  Ritesh Roushan (@devXritesh) reported 57 minutes ago

  @Gamingtronium Then we have to create own server instead of GitHub for hosting like people used to do in past