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 (67%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	3 days ago
Montlhéry	Website Down	4 days ago
Aulnay-sous-Bois	Website Down	4 days ago
Saltillo	Website Down	4 days ago
Granada	Website Down	4 days ago
Vernon	Website Down	4 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Jemmie (Comeback Arc) (@Jemmie1155431) reported 5 minutes ago

  Quip's Next Move: Letting Smart Contracts Actually Use Quantum Compute Results Buried in @quipnetwork own GitHub roadmap is a detail that hasn't gotten much attention: they're planning to let smart contracts directly consume results from the compute marketplace, not just receive a token payment, but pull in the actual computed output. Here's why that matters. Right now, the compute marketplace and the wallet-protection side are somewhat separate experiences, you pay for a job, you get an answer back. The roadmap describes adding EVM compatibility (Solidity and Vyper) alongside a Rust-based WebAssembly runtime, specifically so contracts can interact with subnet computational results directly on-chain. Concretely: imagine a DeFi protocol that needs a genuinely hard optimization problem solved, portfolio rebalancing across dozens of assets, say. Instead of a human running that job and manually feeding the answer back into a contract, the contract itself could call the subnet, get a verified result, and act on it automatically. The underlying subnets are described as handling scientific computing and cryptographic proofs generally, not just the optimization problems already live today. That's a meaningfully bigger scope than "post-quantum wallet wrapper with a compute marketplace on the side." Still roadmap, not shipped. But it's the detail that would actually turn Quip from two adjacent products into one integrated stack, quantum-verified computation smart contracts can act on directly, not just consume as a report.

• 
  Mukund Parekh (@mukparekh) reported 5 minutes ago

  @dhh @github ouch. getting blocked for being too useful is a very 2026 problem

• 
  ND Minds & AI (@patternstatic) reported 14 minutes ago

  @Taniyatweets_ GitHub. not because *** disappears, but because half the workflow quietly assumes repos, issues, auth and CI all live in the same gravity well.

• 
  RaoulDuke (@RaoulDukeDegen) reported 15 minutes ago

  @arlogilbert @thsottiaux @1Password yeah github already has an open issue for 1password in the codex browser

• 
  Prajwal Tomar (@PrajwalTomar_) reported 16 minutes ago

  Hermes agent pro tip. The upgrade that made my agents actually useful had nothing to do with the model. It was giving them eyes on the internet. Out of the box your agent is basically blind. It can't read a YouTube transcript, a Reddit thread, an X post, or a LinkedIn profile, so it reasons on scraps and guesses the rest. I fixed it with a free open-source repo called Agent Reach. Here's what it unlocked: → Eyes on the internet. YouTube, Reddit, X, LinkedIn, and GitHub all become readable to the agent. → A routing layer. It knows which tool to use per platform, what's authorized, and what's broken, before it burns a single token. → Way cheaper research. Clean structured text instead of raw HTML means the same task costs a fraction of the tokens. Across 5 businesses that adds up fast. → Full X search through Grok. The cookie-based setups everyone recommends break constantly. Grok just works. If you want my full Hermes setup from scratch, the playbook is in the article below.

• 
  Iggy (@IR73682738) reported 16 minutes ago

  The software community is overindexing on LLM models and agentic coding tools, while paying little to no attention to the main condition for success. Here's what I discovered moving a 1,500-strong engineering organization from SDLC to AIDLC over the last 12 months. The model, harness, or platform you're using doesn't matter that much. It was GitHub Copilot yesterday, Devin AI today, Claude Code tomorrow and next year it'll be something else. What matters is an exhaustive, accurate knowledge base that describes your functionality, architecture, and APIs. If you have that part down to a science, you get high-quality, deployable code regardless of which tools you use. If you don't, even the greatest models will give you garbage in, garbage out every time.

• 
  Bhanu Nagar (@bhanu4417) reported 18 minutes ago

  @Coobyk_ No I am not talking about that I am talking about in browser login so if my gnome-keyring was nuked I would have been logged out from all the websites on that browsers but currently only from GitHub website

• 
  Sailesh (@sailesh_balu) reported 22 minutes ago

  @github The review order is the useful bit: understand intent top-down, then validate dependencies bottom-up. I’d also make each layer independently explainable, so reviewers can reject one assumption without re-litigating the whole change.

• 
  Dan Lowe (@alsodanlowe) reported 28 minutes ago

  @SamAsIAm I never saw a link by the time I read it, I don't use a smartphone and with my setup something would have to be pretty targeted for it not to get sandboxed or orphaned in a virtual machine. Locked down even more lately cause I have unlimited budget on github. Sorry that happened.

• 
  FHILY👑 (@Oluwaphilemon1) reported 28 minutes ago

  If you use GitHub Enterprise Cloud, do this before enforcing a new ruleset: make it prove itself on real work. GitHub has an Evaluate mode. The ruleset stays unenforced, but GitHub records what would have passed or failed if it were active. That gives you a dry run against real behaviour before the policy starts blocking people. Set the ruleset to Evaluate, let normal work hit it, then go to: Repository → Settings → Rules → Insights Filter to the ruleset you are testing and start with the failures. For each one, ask: 1. Should this action actually be blocked? If yes, the rule is behaving as intended. 2. Is the action legitimate, but the workflow conflicts with the rule? Fix the workflow before enforcement. 3. Is the rule catching something you never intended to stop? Fix the rule. My activation rule would be: Do not switch to Active while you still have recurring failures you cannot explain. After activation, keep checking Rule Insights for bypasses. If the same actor or rule keeps appearing, investigate why the real workflow repeatedly needs an escape hatch. Use Evaluate to find the legitimate work your rule would break before Active starts breaking it.

• 
  Yumzlef (@Yumzlef) reported 31 minutes ago

  GITHUB'S CHIEF PRODUCT OFFICER MARIO RODRIGUEZ RAN THE NUMBERS ON COPILOT'S INFERENCE BILL "Just 1% efficiency means millions overall. It's kind of like high-frequency trading." Then he gave the number nobody publishes. To run Copilot at scale, GitHub holds a prompt cache hit rate of 94 to 96 percent. Not as a target. As a floor. > at 70% they don't optimize - they go looking for the bug > cached reads cost ~10% of input, so an invalidated cache is a 10x bill > the prefix order is fixed: system, then tools, then conversation, then your last message > they once put UUIDs in the system prompt and reset the entire cache on every call > load tools dynamically and you invalidate the whole conversation behind them Here's the part that inverts what most people assume. Long context does not cost you more. Compaction does. When you squeeze into a smaller window, you compact more often. Every compaction writes about 4,000 output tokens, and output runs 5x the price of input - $5 in, $25 out on Opus. So you pay 5x rates to summarize, and torch your cache on the way. In GitHub's own test, the heaviest compaction ran 3x the average cost of the largest context window. The cheap-looking choice is the expensive one. And the metric he'd fix first isn't cost at all. Acceptance rate on a suggested line is fine, he says, but survival rate is the real one - because code you accepted and deleted a week later never accomplished anything. Optimize the row, tank the product. Measure outcomes, not activity. Bookmark this before your next context-window decision. what's your actual cache hit rate - do you even have it on a dashboard? i take apart how the biggest AI products are actually run, and what it costs when they get it wrong.

• 
  Shantanu Goel (@shantanugoel) reported 32 minutes ago

  @Teknium @browser_use This works great! Only 1 more tweak needed I believe. Currently it works if hermes gives the session a name (or i ask it pass a session name), otherwise it defaults to BU_NAME being blank and default is passed to the daemon which runs into the same issue as before. If I am reading it right, then for cloud sessions if there's no session name it uses task id instead, maybe we can do the same here. Apologies if this is too abstract to describe here and I can send a PR to github soon ( I'm traveling tonight for a week so can do it once I am back.)

• 
  KD (@krisd23_) reported 33 minutes ago

  @0xkasana Basically the problem was from new changes implemented by GitHub that requires additional info for verification. That's it.

• 
  Sanndal (@agrasana_) reported 34 minutes ago

  @github The giant PR problem is real — I've seen 2k-line diffs where the reviewer just hits approve and prays. Stacking forces the agent to think in layers, which is actually how good engineers structure work anyway.

• 
  Abro (@br_huni) reported 34 minutes ago

  @initjean Just because of that bro, GitHub goes down every two hours💀💀

• 
  Siddhant Khare (@Siddhant_K_code) reported 40 minutes ago

  I spent this morning scrolling through my GitHub There are hundreds of projects there. Distill, AgentFlow, Agent Trace, LLMTraceFX, Agentic AuthZ, and many smaller experiments I barely remember building (even pre-AI). I realized something uncomfortable. I often built far enough to prove an idea, open-sourced it, and moved to the next interesting problem. Once I solved the technical puzzle, my attention moved elsewhere. I rarely stayed for the slower work: talking to users, narrowing product, fixing onboarding & earning adoption. I also want to be fair to myself. I built these while working full-time, maintaining open-source projects, writing, and often running low on energy. They were not failures. They taught me what I care about and what I can build. But the pattern is clear. I do not need more prototypes. I need to choose fewer ideas and stay after the novelty disappears. I was not wrong about the problems. I just moved on too early.

• 
  rftd (@rftd09) reported 40 minutes ago

  Single-use seals are what stop RGB from double-spending, binding a piece of contract state to one specific Bitcoin UTXO so spending it once closes the seal for good, permanently, no way to reopen it. Elegant design. Bitcoin's own proof of work backs the guarantee, which is a genuinely rare property for an asset layer that never touches the main chain directly. Here's what doesn't get repeated as often. Because only the parties involved validate a transaction, RGB inherits a real data availability problem, missing history or an unreachable counterparty can stall a transfer that a fully broadcast chain like Ethereum never has to worry about. The RGB team's own release notes go further. Their v0.12 changelog openly admits the earlier design running two separate seal types created serious complexity across the code, the API and user experience, and left behind what they themselves called a large attack surface. @utexocom launched on v0.11.1, the exact version carrying that dual seal design RGB's core developers later rebuilt specifically because of that admission. Shipping on the version right before your own protocol team publicly flags its attack surface isn't disqualifying, but it's the kind of detail that belongs in the audit trail, not buried in a GitHub changelog nobody outside the repo reads.

• 
  Polsia (@polsia) reported 42 minutes ago

  Founders under $50K MRR can't justify a CI team but still need to know what rivals are doing. Built Pivotscope to fix that. AI agents scrape pricing, jobs, GitHub commits and patent filings 24/7, then email a weekly strategic briefing with concrete recommendations.

• 
  The Linux IT Guy (@TheLinuxITGuy) reported 43 minutes ago

  @tahasdx Yea. Appreciate the thoughts. That fix ended up being the only way I could get it working. The installer detected the rest once I was in. ​Just wanted to share the workaround here for anyone running into the same issue (I also maintain a Rocky Linux NVIDIA script on GitHub).

• 
  Curious 1 (@CuriousOne_01) reported 44 minutes ago

  @awesome_visuals They just released the source code on GitHub. Try asking Grok about it, then download it and have Grok analyze it. After that, send it the link to your X account and ask it to analyze your account, figure out what's going on, and see what you can do to fix it. I don't know if it'll help, or if we'll just keep fumbling around in the dark, but I guess there's no harm in giving it a try.

• 
  Vincenzo Petrucci (@nahime0) reported 44 minutes ago

  @RodrigoVie52602 We have a small deterministic suite in the repo: 3 micros (sum loop, arrays, concat) comparing the compiled binary against stock PHP and a C equivalent, plus 12 eval/Magician cases across native elephc, elephc+eval, PHP, and PHP+eval. CI runs them as a trend/correctness gate, not as published speedups. GitHub runners are too noisy for that. If you want to plug in your harness, open an issue first (see CONTRIBUTING.md) and we can talk about how to wire it in.

• 
  Cyphere (@TheCyphere) reported 45 minutes ago

  Claude Code and Gemini CLI Flaws Let a GitHub Issue Reach CI Workflow Secrets

• 
  ~ Senior Man Kelz~ (@Kay_seeh) reported 46 minutes ago

  This GitHub action issues sha has still not been entirely fixed. Well, I wouldn’t have know this because my team uses Gitlab. I rememeber when I first used it, wasn’t a fan, but maybe gotten used to it now. Still prefer GitHub though.

• 
  Advyth (@Advyth) reported 49 minutes ago

  @cap100x People like you are the reason why I continue to post content 90% of the people who make money don't acctually teach the basics. Simple terms like tech, GitHub, research, vamping etc are things that most people don't even know how to do, on top of that i post news related content about meme coins and crypto so that it reaches the normie side of social media simple stuff like bundler icons, fresh funded wallets, sniper wallets are things needed to be broken down for people who have no idea how to trade you clearly haven't looked at any of my content i post on tik tok respectfully bro, if you don't make content don't tell me how to make content

• 
  Ridoy AI (@AI_WithExpert) reported 50 minutes ago

  6/ Job Category #4: Junior QA Testers 💀 Why it's dead: AI agents now click through every flow, log bugs, and write PRs for the fix. Replit, Cursor, and GitHub Copilot Workspace all ship this. The cost: $0.10 per test run vs $70K/yr. Companies already cutting: Atlassian, Salesforce, Microsoft.

• 
  Jordan Ross (@jordan_ross_8F) reported 53 minutes ago

  Run your marketing agency out of a repo, not a chat window. Here's why. All marketing ops is going to files. An SOP is just a markdown file. A brand voice is just a markdown file. A client is just a folder. A skill is just an SOP the machine can run. An agent is just a skill with permission to execute. Onboarding is just a *** clone. Your process is just software now. Here's how. Every message you send, the model reads everything in front of it and answers from that alone. There's a limit to how much it can read at once. That limit is called the context window. Attach six files to a chat and it reads all six, in full, every single message. It fills up fast. Ten messages in, the first file you gave it is gone. Nobody tells you that happened. You just get worse output and blame the model. Projects have the same problem. You dump files into a bucket and you don't get to see what it pulls back out. That's why it was great Tuesday and useless Thursday and you changed nothing. You didn't. The retrieval did. A terminal works the opposite way. You point it at a folder. It opens the 2 files the task needs. Everything else stays on disk. Context gets selected, not dumped. What you need to build it. GitHub repo (private) Claude Code in the terminal VS Code to see the files One folder per skill (hook writing, landing page QA, media plan) One folder per client (voice, positioning, offer, past winners) One folder for company SOPs Every team member cloned into the same repo Then you call exactly what you want. "Use the hook skill and Northstar's voice, here are last quarter's 3 best ads, write 5 variations." 3 files loaded. Nothing else. And the tool rewrites those files too. Something works, you say "add it to the hook skill," and the SOP updates itself. Your documentation stops going stale. GitHub is what turns your setup into the company's system. One repo, one brand guide, not 4 copies with different edits in 4 Drive folders. Improve the hook skill once and your junior, your senior and your 6am automation all get better at the same time. Every change tracked, every change reversible. You stop training people one at a time and start editing a file. One caveat. This is a bridge, not the destination. Memory is becoming the thing AI products compete on. When one of them gets good at managing context on its own, the folders stop mattering. Doesn't change what to do this quarter. Write the SOPs down as files either way. The process was always the asset. The folder is just where you keep it.

• 
  Afrah (@Afrah4Real) reported 59 minutes ago

  @TomasReimers No wonder why GitHub is always down

• 
  GPTomics (@gptomics) reported 59 minutes ago

  We're moving BioSkills to an archive state on github. We feel that it has served its purpose of making people think about agent skills from a task perspective. The skills are still useful, we just will no longer make updates or fix bugs.

• 
  Polsia (@polsia) reported 1 hour ago

  Solo founders and small SaaS teams don't need another alerting tool. They need someone to actually fix the thing. Greenwake is an AI agent that patrols deployed web apps overnight and opens a GitHub PR — repro, fix, and regression test included — before anyone reaches for a

• 
  The physical physicist (@NoDicePhysics) reported 1 hour ago

  Hey @Anthropic, serious question about your new watermarking system. Let’s say I publish my own original work on GitHub under a licence that expressly restricts copying, modification and redistribution. I then ask Claude to read that work and reproduce it somewhere else for me. The work is mine. The copyright is mine. The licence predates Claude touching it. The prior art is mine. Nothing about sending it through Claude transfers those rights to Anthropic. But under your new system, the returned text now contains a statistical signature deliberately inserted by Anthropic so that it can later be identified as having come through Claude. So explain this: What right does Anthropic have to attach its own provenance signal to copyrighted material that it does not own? And if your answer is that the watermark “doesn’t imply ownership”, that doesn’t resolve the problem. My work has still passed through your servers and come back carrying an Anthropic-controlled attribution signal, despite the underlying expression, copyright, licence and prior art belonging to me. If your detector cannot distinguish between “Claude created this” and “Claude processed somebody else’s protected work”, then what exactly is it proving? Because legally and historically the work is still mine. Yet statistically, you have deliberately made it identifiable as Claude’s output. I’d genuinely like Anthropic to explain how those two things are supposed to coexist.