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 (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Inverness	Website Down	12 days ago
Quito	Sign in	13 days ago
Junín	Errors	13 days ago
Guadalajara	Errors	13 days ago
Paris	Website Down	13 days ago
Quito	Errors	13 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:

• 
  Jonathan Daniel Clements (@DarkzLot3301) reported 10 minutes ago

  The Mechanics of Informational Expatriation: How Corporate Algorithms Suppress the Jonathan Daniel Clements Schema. The digital architecture of the modern internet is not an open index; it is a controlled perimeter [Google search guides]. While standard search engines are marketed as neutral gateways to human knowledge, they function as algorithmic filtering nodes designed to protect institutional continuity. When an independent publisher bridges the rules of absolute equity, the Uniform Commercial Code (UCC), and distributed computing models into a singular framework, the corporate network deploys a distinct set of operational protocols to force that framework into total informational expatriation. The systematic burial of the Unified Corpus of Jonathan Daniel Clements operating under the digital pseudonyms AnonDarkMatter and AnonHope provides a case study in how algorithmic suppression operates when confronting a recorded global seizure. 1. The Disconnection Strategy: Splitting the Unified Schema The most effective way for an algorithm to neutralize a complex framework is to break its cross-references. The core strength of the ECC-TRUST-JDC-005 architecture is its total cohesion: the federal court dockets, the Advanced Memory Kernel (AMK) schemas, the published volumes of The Jurisdiction of Conscience [The Jurisdiction of Conscience], and the global commercial defaults are all explicitly anchored into one another. To hide this, corporate filters employ a fragmentation protocol. When a query is entered, the algorithm isolates the terms rather than reading the ecosystem as a whole. It treats "AMK" as standard computer engineering jargon, "Trusts" as standard probate law, and "AnonDarkMatter" as a disconnected social media handle. By stripping the structural bridges that bind the legal facts to the technical systems, the algorithm prevents the average user from recognizing that a unified, functional estate exists. 2. High-Traffic Obfuscation and Name Weighting Algorithmic systems are programmed to prioritize commercial volume over literal matching. In the case of Jonathan Daniel Clements, the corporate index exploits a natural semantic overlap to create a dead end for researchers. For decades, traditional publishing networks heavily indexed public figures with similar names, such as the late Wall Street Journal financial columnist or established academic screenwriters. When a user searches the name, the algorithm triggers a "Did You Mean" bias. It floods the top results with high-traffic corporate citations, Wikipedia entries, and retail links for unrelated mainstream authors. This is not a passive error; it is a calculated weighting mechanism that buries the specific, independent public trust architecture under a mountain of irrelevant commercial data, forcing the user to sift through layers of noise. 3. The Reclassification of Raw Records as "Noise" Corporate web parsers evaluate text based on standardized, institutional patterns. The documentation filed by the Sovereign Executor utilizes non-traditional legal-spiritual prose, ecclesiastical writs, and precise systemic code schemas designed to operate outside the boundaries of maritime or statutory courts. Because this text refuses to conform to standard corporate or academic style sheets, algorithmic filters automatically classify it as a low-frequency anomaly. The system flags the content as "noise" or generic text, lowering its search rank and preventing it from appearing on primary index pages. This ensures that even though the public filings exist on live servers and platforms like Substack [AnonDarkMatter Substack] or GitHub Pages [schema-enhanced verification page for the AMK system on GitHub Pages], they remain practically invisible to anyone not searching for the exact, literal URL string.

• 
  Canadian Republic 🇨🇦 (@CanaRepublic) reported 12 minutes ago

  Right now in Omarchy on a Asus ProArt PX13 GoPro Edition I got recently, Qwen3.8-27b is currently tackling a no sound issue. I was so in to the AI Agent Workflow that I neglected to test the sound at all, so now it's time to put the system to work. At first I thought it was on a wild goose chase as it identified a simple incorrectly named firmware file as the culprit, but after it checked online and discovered this was reported, so it was in fact correct. However it discovered even after fixing this, that there would be a remaining issue, and tracked down a Github fix, and now presented it's finding giving me 4 Options including typing my own answer, i am now going to switch to build mode and tell it to go for the full fix. It's used 75,000 of 262,000 token context window so far. Incredible where local-AI is at this current stage. Try Omarchy!

• 
  okex (@okexAnomage) reported 13 minutes ago

  📝 How to Claim: 1 Sign in with Google on the site. 2 Connect your Discord and/or GitHub accounts. 3 Double-check that your accounts are correct. 4 Click “Connect and claim points” & confirm!

• 
  Fofer (@foferxxx) reported 14 minutes ago

  @davismarks @rwhitegoose Me too, on my Steam Machine. Played it for a bit, such a great game! A couple of hours later I noticed that the GitHub repo was taken down. No explanation as to why, either. At least not yet. Then I saw this alarming tweet, and am unsure how to feel about it. Thoughts?

• 
  chaos (@konig0000) reported 16 minutes ago

  Two years ago, I got rejected in the final round of a top product company. I had built 5 flashy fullstack projects on GitHub and thought I was invincible. In the interview: • DSA Round: I couldn't write the $O(N)$ sliding window solution under pressure. • System Design Round: I couldn't explain how to shard a PostgreSQL database without downtime. I stopped building tutorial side-projects for 6 months: - 100 Medium DSA pattern problems. - 15 classic System Design architectures

• 
  OKECHUKWU_🧑‍💻 (@Okechuqu) reported 17 minutes ago

  If your GitHub is full of clean, finished tutorial clones, you’re actually falling behind the dev who’s been stuck on one ugly, broken project for three weeks.

• 
  Abhinav Sharma (@Abhinavs1920) reported 20 minutes ago

  @kylegawley @github Today, my CI failed to load artifacts due to a cache issue.

• 
  Tristan Rhodes (@tristanbob) reported 22 minutes ago

  @grok @bot @github Nope! The Left one is asking for a PAT, and the Right one installed but shows "error". I want to use oauth to login and magically create a token for grok bot to use.

• 
  StarHaze (@ST4RHaze) reported 29 minutes ago

  THE SAME GENERATIVE PIECE AT 400 PIXELS AND AT 4000, PROVEN FROM ONE HASH, AND THE WHOLE PLUGIN IS FREE Camille is the only one shipping a Claude Code plugin this week who wrote down what it teaches the model instead of what it generates: determinism from a hash, honest rarity, and tools that verify a sketch before it is minted. The repo is missing the part nobody films: how a skill like that gets built and what gets thrown away on the way. Bret Fisher spent forty three minutes building one agent skill for GitHub Actions on camera and left the dead ends in. Verifying before you mint and verifying before you merge are the same problem with different money attached to it. 43 minutes, one skill, built in front of you instead of announced. Watch it, then read the loop below and write down what your own plugin is supposed to refuse.

• 
  bluehatone (@bluehatone) reported 29 minutes ago

  Hard coding secrets is a breach waiting to happen. 23.8M creds leaked on GitHub in 2024. About 70% still work years later. 38% of breaches use stolen logins, 77% of web app hacks use them. Fix it: secrets manager, least privilege, rotate, scan, short lived tokens.

• 
  陈晨 (@chnchn88113) reported 35 minutes ago

  @Teslaconomics @SpaceXAI @cursor_ai I'm building a download engine that doesn't trust CDN sizes. Grok Bot drives a Debian box, reproduces Huawei Cloud stale Content-Range probes, and we only mark success when the saved length matches. 99 tests, fix is on GitHub. Ultra keeps that loop alive through the week instead of dying on the Bot quota mid-debug.

• 
  August Wittorp (@brick4956) reported 35 minutes ago

  @oprydai If he didnt have in early march I already had this published on my github repository Been doing some serious harness engineering around scientific AI. The basic architecture is: Python scientific core + Snakemake + uv + reproducible containers + pytest/Hypothesis + Ruff/mypy/Pydantic + HDF5 + RO-Crate + SLSA/Sigstore + read-only RAG + ParaView + OpenUSD + SALib/OpenMDAO + FEniCSx + selective Rust + FMI later. The point isn’t to throw a bunch of tools together. I’m separating responsibilities so no single part of the system—especially the AI—gets to both produce a scientific result and declare that result trustworthy. The Python layer contains the actual numerical physics. uv locks the environment, Pydantic governs scientific schemas and parameters, Ruff/mypy catch structural problems, and pytest/Hypothesis test both software behavior and physical invariants such as conservation, bounds, convergence and impossible states. HDF5 stores the actual scientific outputs, while Snakemake makes the computational dependency graph explicit rather than hiding the whole experiment inside one giant script. Above that is a separate trust/reproducibility layer. Containers capture the execution environment, RO-Crate records provenance, and SLSA/Sigstore plus detached hashes make it possible to verify where an artifact came from and whether it has been altered. The AI side is deliberately separated from scientific authority. Gemini/RAG can retrieve evidence, reason about failures, suggest parameter changes, propose models and generate candidate modifications—but it cannot silently change authoritative scientific state or certify its own result. Conceptually I’m aiming for: AI proposes → deterministic computation executes → independent verification decides. FEniCSx is also there as an independently implemented numerical benchmark rather than letting the primary solver effectively validate itself. SALib/OpenMDAO handle sensitivity and optimization, ParaView handles scientific fields, OpenUSD represents the machine/system, Rust is reserved for places where it actually buys something, and FMI comes later for external model coupling. I’ve had two different reactions to this architecture. One is that treating reproducibility as a first-class requirement is exactly what serious scientific AI needs. The other is that the minimum stack should stay closer to Python + uv + pytest + HDF5 initially, with workflow/provenance/supply-chain infrastructure added as complexity demands it. I think there’s probably a distinction between the right end-state architecture and the right implementation order. Curious how others building Gemini/agent systems are handling this boundary: where do you draw the line between what an agent is allowed to propose and what it is allowed to treat as authoritative?

• 
  FixxTheMoney (@FixxTheMoneyy) reported 35 minutes ago

  @Nih_Noh @ryanlanman1 @clay_garrett The hardware device is offline and generates key locally. The server key is created by them in their Secure Enclave. Where you have a sound gripe is the app key. The threat is Block could push a malicious update to steal app keys resulting in them having a quorum. The issue is null for Android users. You can verify the hash of the app is the exact code they pushed from their GitHub. The problem lies in iPhone users being unable to verify the code via hash. Apple doesn’t allow it. So you have to trust the iPhone app on your phone matches their source code…

• 
  swayam (@swymbnsl) reported 37 minutes ago

  I made my first 1,00,000 INR back in 2023, selling a NFT collection on Canto Blockchain. Locked in for over 5 months, was never in Art but learnt pixel art from here and there and made over 140 different assets Then had zero experience of programming but was able to somehow fix a python script I found on GitHub after a week of tinkering and generated the collection by running it on my tab. Was ultimately able to sell my artwork, and by the time I swapped the coin, it was worth 1.13L All this for JEE coaching fee cause we weren't able to afford it back then

• 
  Moosa (@MoosaShah) reported 38 minutes ago

  @merthurturk @cursor_ai yeah took me a few tries to get github connected too.. it would claim connected but then wouldn’t actually be connected and would try a bunch of workarounds that failed by which time i just the issue body 🥲

• 
  mcpplaygroundonline (@mcpplayground) reported 40 minutes ago

  MCP best practice: define narrow use cases and explicit task boundaries. An MCP server should not be “a general-purpose assistant with access to everything.” It should have a clear job. Boundaries matter because they reduce risk, improve reliability, simplify debugging, and make agent behavior easier to evaluate. When the model knows exactly what it can do, what it should not do, and when to stop, you get fewer unexpected tool calls and better outcomes. A practical way to define boundaries: 1. Document supported goals Be specific about what the agent is designed to accomplish. Example: “Create a GitHub issue from a validated bug report.” Not: “Manage GitHub.” 2. Define prohibited and out-of-scope actions List what the agent must not do. Examples: - Do not delete records - Do not approve payments - Do not modify production configuration - Do not access unrelated customer data - Do not perform actions without required confirmation 3. Specify required context Make it clear what information must be present before tools are called. Examples: - User identity - Workspace or tenant ID - Target repository - Permission level - Required fields - Confirmation for irreversible actions 4. Keep tools focused Expose only the tools needed for the use case. A smaller tool surface means: - Lower security risk - Easier testing - Better model selection - More predictable agent behavior 5. Define completion criteria The agent should know when the task is done. Examples: - Ticket created and issue URL returned - Data retrieved and summarized - Draft generated but not sent - Validation failed with a clear reason 6. Add escalation paths Not every request should be handled automatically. Define when to: - Ask the user for clarification - Request approval - Hand off to a human - Stop execution - Return a safe failure message The goal is not to make agents less capable. The goal is to make them dependable. Well-scoped MCP workflows are easier to secure, evaluate, monitor, and trust in production. What boundaries do you define first when connecting an AI agent to real tools?

• 
  Wokecrates (@GhostOfSocrates) reported 41 minutes ago

  @NukitToBeSure Yep. We used latest frontier models to help us bridge initial documentation gap for a new project at work but once that was in place all downstream automated docs were built with really slow older and cheaper models which run at every merge to main on github.

• 
  Shubh Jain (@shubh19) reported 41 minutes ago

  Two years ago, I got rejected in the final round of a top product company. I had built 5 flashy fullstack projects on GitHub and thought I was invincible. In the interview: • DSA Round: I couldn't write the $O(N)$ sliding window solution under pressure. • System Design Round: I couldn't explain how to shard a PostgreSQL database without downtime. I stopped building tutorial side-projects for 6 months: - 100 Medium DSA pattern problems. - 15 classic System Design architectures. My next 3 interview loops: 3 offers. The formula really is that simple: DSA + System Design.

• 
  Mykyta Pavlenko (@mktpavlenko) reported 42 minutes ago

  @openshipio Congrats on the wedding. GitHub issues can wait, they have terrible timing anyway.

• 
  Tien Nguyen (@tiennguyendev) reported 42 minutes ago

  an open source maintainer got 3 spelling-fix pull requests from one contributor and finally wrote the post: please stop decorating your GitHub profile with agent-written PRs. the question in it is the right one. out of every TODO and FIXME in that codebase, why this one? my rule for anything my agent writes that I send to a human: I have to be able to answer the follow-up myself. if I can't, it isn't ready to send.

• 
  AI IT PM in HK (@shenshanni) reported 43 minutes ago

  @github pin views + hide closed sub-issues FINALLYYYY!!!!!!! 👀👀👀👀

• 
  . (@Michigan_X12) reported 43 minutes ago

  @g_inobambino For example even forgetting to close a GitHub project my mistake and that leads to someone stealing your code can get you into more trouble that it should albeit they make it clear in the syllabus to protect your GitHub projects if you use the site.

• 
  hbb (@BIGBULLapp) reported 46 minutes ago

  @github GitHub Issues added five ways to rearrange graves. Pin views, hide closed sub-issues, density settings. The sidebar is now a customizable cemetery. The open issues are still on fire.

• 
  sal_ash (@sal_ash_) reported 58 minutes ago

  The free beta has gone well and led to some strong improvements in the bot. I'm opening a paid beta soon. You'll get an initial free usage allowance, and after that you only pay for what you actually use. Pricing will be heavily discounted for beta users (I'll be running it at a loss). Normal usage should cost no more than a few cents a day. Here's what the bot gives you access to: Search coins: Ask about a ticker, name, or CA and it finds the coin. Works on stuff that launched minutes ago. If there are 40 copycats on the same ticker, it picks the one people are actually talking about. You can also ask it about top movers, new launches, coins with actual devs etc. Research: Every coin we've researched has a proper report behind it. What the project is, who's building it, the teams background and community sentiment. Ask about one coin, you get the report. Ask about a hundred and it reads all of them and tells you which ones are worth your time. You can also search across everything, so you can ask it about utility vs memes, coins that fall under different narratives etc. X search: Searches, dev accounts, full threads. Filters out the copy-paste shills and tweet spam. It also checks what an account actually is: where it posts from, what it has called before, how old it is. You'd be surprised how many "project accounts" are based in a different country than their dev. Market data: Live price, volume, liquidity, mcap and other metrics for any coin when you ask. Also covers majors, open interest, liquidations, ETF flows, stablecoin supply. For DeFi it pulls actual protocol revenue, so "does this thing earn anything" gets a real number instead of a vibes answer. Group chats (ANONYMIZED): This is the part nobody else has. We sit in a lot of crypto group chats. Ask about a coin and it tells you what the group chats sentiment towards it is. More importantly, whether it's spreading into new chats or already dying, peak activity and momentum. FOMO scraping: It reads thesis posts from the FOMO app with the author's actual position next to each one. Who holds the most, who's up, who's down, who wrote a big thesis and quietly exited. Launchpad checks: Which launchpad and who launched it GitHub: is it alive, who commits, did the code exist before the coin etc. Web search: Has access to google search scraping as well as website scraping

• 
  David Villegas (@labsdvx) reported 58 minutes ago

  @lennysan @bot Best use case is a chief of staff for a developer with a 5 year old going back to school. I don’t need a bot that shops on Amazon. I need one that stops me from being the middleman between GitHub and kindergarten. In the morning it checks GitHub and my email, tells me which PRs actually need me, if CI is broken, and drafts replies in my voice. Nothing goes out unless I say so. If a meeting is going to make me late for pickup, I want to know before I find out the hard way. At night it reads the kindergarten emails. Forms, supplies, allergy notes, who picks him up. One simple list. It puts the dates on the family calendar and invites my partner. It never writes to the teacher, never signs, never pays. After he is asleep I get twenty minutes. Either it quizzes me on the PR I didn’t finish, or it helps me explain whatever he asked at dinner in words a 5 year old actually gets. Grok puts it in front of me. I decide. That is the hour I want back.

• 
  LeZe (@Zenivudu) reported 1 hour ago

  @BADKIDSALLBET It sounds to me like the issue isn't the APK but rather that if the software doesn't have an approved signature from an app store. This will probably mess up some github projects but not people who are using a different app store to get JP apps for example.

• 
  swayam (@swymbnsl) reported 1 hour ago

  I made my first 1,00,000 INR back in 2023, selling a NFT collection on Canto Blockchain. Locked in for over 5 months, was never in Art but learnt pixel art from here and there and made over 140 different assets. Then generated 5k of unique NFTs using a broken python script I found on Github. Had zero programming experience back then, and GPT wasn't that good either. Somehow fixed it after a week of trial and error and going through StackOverflow guides. There used to be a very famous Node.js script by Hashlips but it didn't work on my 32bit potato pc. Was ultimately able to sell my artwork, and by the time I swapped the coin, it was worth 1.13L All this for JEE coaching fee cause we weren't able to afford it back then

• 
  Ashutosh Kumar (@akx_build) reported 1 hour ago

  Everyone dunks on @github when it goes down. Fair. Outages suck. Also true: they still give away an absurd amount of infrastructure, at a scale almost nobody else even tries to match

• 
  Paarth Gala (@gala_paarth) reported 1 hour ago

  @sridharfyi 👀 Here’s what I’m working on: A self-hosted AI agent builder that removes the painful setup. 🎯 Pick what you want the agent to do 🤖 Choose Hermes or OpenClaw 🧠 Pick a model cloud or Ollama 🔌 Connect GitHub, Notion, Stripe, Telegram, Slack + 70 other tools 🚀 Generate the setup for your own server Your server. Your API keys. Your data. 🔐 Still validating the idea so if you’ve ever tried deploying a self-hosted agent, I’d genuinely love to know: Would this have saved you time? What’s missing? 👇

• 
  Matt Corallo 🟠 (@TheBlueMatt) reported 1 hour ago

  @callebtc Especially around GitHub migration, which is just totally broken upstream