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 (66%)
• Sign in (21%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Mexico City	Sign in	6 hours ago
León de los Aldama	Website Down	8 hours ago
Créteil	Website Down	23 days ago
Trichūr	Errors	27 days ago
Brasília	Sign in	27 days ago
Lyon	Website Down	27 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:

• 
  Jeffrey Emanuel (@doodlestein) reported 8 minutes ago

  @eyeomens Yes, feel free to file a GitHub issue for that so it doesn't slip through the cracks.

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  Mololuwa | Cybersecurity - (The God Complex) (@cyber_rekk) reported 10 minutes ago

  A former Microsoft security employee found critical vulnerabilities in Windows Reported them internally Microsoft deleted their accounts Ignored the reports, Refused to pay the bounties So Nightmare Eclipse went public Seven exploits since April, Timed to drop within hours of Patch Tuesday — the one day defenders are already overwhelmed processing other patches BlueHammer. RedSun. UnDefend. YellowKey. GreenPlasma. MiniPlasma. RoguePlanet Three of them, BlueHammer, RedSun, UnDefend , were picked up by real threat actors and used in live intrusions before Microsoft finished patching them RoguePlanet has no CVE. No patch Dropped June 9. Works on fully patched Windows 10 and 11 Confirmed independently by ThreatLocker Microsoft's response: flagged the blogs. took down the GitHub threatened criminal prosecution The cybersecurity community responded with fury and vexation Microsoft backed down Nightmare Eclipse released RoguePlanet the same week Microsoft built a bug bounty program specifically to prevent this sequence of events They ignored the reports Every Windows machine on earth is currently running an unpatched SYSTEM-level privilege escalation vulnerability Because Microsoft didn't pay a bounty Microsoft respect your bug bounty hunters

• 
  Vaibhav | Data Say (@vaibhavs28) reported 13 minutes ago

  Last week I wrote about how we are using AI to ship product with a very small team. One thing I did not expect when we started working this way was how many tools I would personally start using, which I never thought would become part of my day. GitHub is one of them. I am not a coder, and for most of my career GitHub was something the technical team used. I understood product, customers, business problems, data, dashboards, commercials, and operations. But code repositories, branches, PRs, conflicts, checks, and merges were not part of my normal working language. Even this chart is not perfect. I later realized I was using two different GitHub accounts for some of this work, so the activity is split and there are gaps. That probably says enough about how new this world was for me. But that has changed quite a bit now. I am still not pretending to be an engineer. That would be wrong. But I am much closer to the product build than I was earlier. If I see a product issue, I can now think through the expected behaviour, work with AI to scope the change, understand what files or flows are getting touched at a high level, create or review the PR, run checks, resolve smaller conflicts, and merge low-risk changes. The larger or more technical changes still go to our technical partner. That boundary is important. But a lot of product work is not always a deep architecture decision. Sometimes it is fixing labels, improving how something is shown, cleaning a flow, making the dashboard easier to understand, or removing confusion that a customer may face. Earlier, these small things could easily wait because the technical queue was always full. Now, many of them can move much faster. That changes product thinking itself. When the distance between noticing a problem and trying a fix becomes shorter, you start observing the product differently. You become more specific. You do not just say “this page is confusing.” You start saying “this metric label can be misunderstood,” “this table should not show empty channels,” “this filter needs to behave differently,” or “this issue is small enough to fix now.” For a small company, this matters a lot. We do not have large teams for product, QA, analytics, documentation, and engineering. The same few people are speaking to customers, understanding the problem, thinking about the product, and trying to ship improvements. AI has not removed the need for technical judgment. But it has made the loop tighter. Customer issue to product thought to implementation to review can now happen much faster for the right kind of problem. That is the biggest change for me. Not just speed, but proximity. I am closer to the product, closer to the details, and closer to the actual act of shipping than I ever expected to be. More on this later, because we are still figuring this out as we build.

• 
  amircrypto82 (@amircrypto82) reported 14 minutes ago

  I've been staring at crypto Twitter for nine years now. And I swear to god if I see one more project tweet "building in public" while their Github has 3 commits from 8 months ago I'm gonna lose it. We all know what that phrase actually means. Let me translate it for you. What they say: "We're building in public." What they mean: "We're streaming our debugging sessions live and calling it transparency." Like... congrats bro. You deployed to testnet and it broke instantly. That's not building in public. That's just public failure with a fancy label. Real building in public is uncomfortable. It's showing your ugly MVP that barely works. It's admitting you pivoted because the original thesis was flawed. It's telling people you're stuck on a problem and need help. Not posting a 47-part thread about how you're "shipping value" when you literally just changed the font on your landing page. The worst part? The industry has conditioned us to accept these empty phrases. We nod along. We retweet. We convince ourselves it's real. But here's the thing that actually gets me going. When I see a project actually doing the work... no buzzwords, no "game-changing" fluff, just measurable output... it stands out like a glass of water in a desert. That's why I keep an eye on @RallyOnChain. Not because they're perfect. Nobody is. But because they flipped the script on something way more annoying than "building in public." Try this one on for size. What they say: "Influencer marketing delivers ROI." What they actually mean: "We paid a guy with 200k bots to say our token is undervalued and now we're praying the charts don't nuke." That's the real plague right now. The entire KOL economy is built on smoke. Projects dump budgets on agencies that dump them on "creators" who dump generic posts into the void. No accountability. No verification. Just vibes and vanity metrics. @RallyOnChain's whole schtick is killing that noise with AI scoring and on-chain rewards. No gatekeepers. No minimum followers. Just actual content quality measured transparently. Whether it works long term? I don't know. But I know the current system is broken beyond belief and anything that actually measures performance instead of follower count is worth watching. So here's my challenge. Next time you see a project claim they're "building in public" or "revolutionizing marketing," ask them one question. Show me the receipts. Not the thread. Not the vibes. The actual work. If they can't... well. You know the translation. And if they can? Maybe we're finally growing up as an industry. Doubt it though. What's the most overused phrase you're sick of hearing? Drop it below. I'll translate it.

• 
  Öhli (@Oehliii) reported 15 minutes ago

  @ParthJadhav8 hiya parth, do you have noop 8.1 by any chance - the team just took down their website & github, i’m on 8.0.1 sadly i couldnt update anymore :(

• 
  Tatenda Zhou (@realtatendazhou) reported 16 minutes ago

  4/7 The step most people skip: agents ran the app on a real iPhone and Android. 111 screenshots. Bugs became GitHub issues. Wave 2 fixed them. Best catch: cached videos played black on iOS while every unit test stayed green. Cache files had no extension; AVPlayer needs one.

• 
  Brute Force Artist (@bruteforcearete) reported 19 minutes ago

  AI TRAINING 📲 Go from screenshot to bug fix with Cursor Mobile The Rundown: In this guide, you'll learn how to screenshot a bug on your website from your phone, then send it to a Cursor Cloud agent that can fix it, update the PR, and track everything in GitHub before you've even made it back to your desk. Step-by-step: Install the Cursor iOS app, get a Cursor Pro plan, and install GitHub Mobile so you can review the PR from your phone Open your website, take a screenshot of the bug, broken layout, or UI change. Add a short note with what page you are on and what should happen instead Open Cursor Mobile, tap the plus button, choose the correct repository, and start a new thread with the screenshot and the note attached for context Now prompt: “Investigate this bug, find the relevant page component, make a fix, and open a PR.” When it’s done, review the PR, approve, and merge it Pro tip: On desktop, enable Remote Agents so Cursor can work on your machine and prep local changes before you get back to your desk.

• 
  SecureChap (@SecureChap) reported 24 minutes ago

  A signed *** commit hashes the raw bytes of its gpgsig header together with tree, parents, and message. Change only those signature bytes and the commit hash flips while the tree, content, and verification status stay identical. Paper "*** Hash Chain Malleability" by Jacob Ginesin, arXiv:2607.02820. Reported to *** in January 2026 and GitHub in March 2026. No fix shipped. Three routes exist, one per scheme GitHub accepts: - ECDSA: replace s with n-s. - RSA/EdDSA: append a non-critical unhashed subpacket of type 100. - S/MIME: rewrite an interior DER length field from short to long form. GitHub's verifier stores the Verified record against the original hash and never rechecks normalization. Every descendant commit hash rewrites in the same pass. The ***-chain-malleator tool automates the rewrite across the full graph. Anything that pins a commit solely by SHA now trusts a different object that still points at the same tree.

• 
  Jason Svoboda (@jasonsvoboda) reported 24 minutes ago

  @NHpilled @brucefenton Nodes are decentralized based on their physical geography, not what software they're running. If Knots/BIP-110 were successful, it is development centralization that is occurring. Both Knots and BIP-110 have one singular developer and their Github repos credit no other contributors. I linked Luke's Knots repo in the previous post -- you can verify (don't trust my word) by going there, find the About column on the right hand side and scrolling down to Contributors. Repeat the process for Dathon Ohm's BIP-110 repo. If you do not consider that a massive risk to Bitcoin, we simply will not be able to agree on the subject. By contrast, Core currently has a group of core maintainers (keyholders) and then over 1,000 known contributors.

• 
  Tatted (@TattedWorks) reported 25 minutes ago

  SaaS is not getting replaced. It is getting ignored. Gartner just put a number on it. $234 billion in enterprise software spend moves by 2030, and the mechanism is nobody opening the app. The report calls it agentic arbitrage. Agents finish the work across systems through APIs. No human ever touches the dashboard the subscription pays for. Gartner's own words: it breaks the link between user growth and revenue growth. Per seat pricing assumed a human behind every login. The receipt already shipped. GitHub dropped flat pricing for token billing in June. The repricing started before the report naming it did. $234 billion is not a forecast of destruction. It is a forecast of invisibility. Tell me where I'm wrong.

• 
  Aditya Pattanayak (@AdityaPat_) reported 25 minutes ago

  It’s been a while since I started digging into what’s actually behind Transformers. how did one paper and a simple idea end up powering almost every GenAI model we use today, from GPT-4 to Llama to Claude? i started at the very first layer: - tokenization. at first, I revisited & reimplemented BPE myself, then compared it against tiktoken and SentencePiece to see how GPT and modern LLMs actually break down text and the results were interesting for my case. (sharing the github link for it) now I’m at the core question: - How does a single token even get meaning? - How does a "mere" number converted to something meaningful that could end up "deciding" which other tokens to pay attention to? to get there, I had to go back and revise backpropagation from scratch. and now it makes sense. the attention, the QKV, the gradient descent, and the correction. crazy how much is packed into that one architecture. writing up my findings soon

• 
  Kristijan Kralj (@kristijan_kralj) reported 26 minutes ago

  PDF is the main bottleneck of modern .NET development: 1. ASP .NET Core can handle thousands of requests per second, but exporting to PDF still means installing 3 NuGet packages, reading 5 GitHub issues, and hoping fonts don't explode in production. 2. We have async/await, background jobs, cloud autoscaling, and distributed systems, yet the moment the business says "Can we just get a small PDF with a table and a logo?" the whole sprint is gone. 3. We can model complex domains, enforce invariants, and scale systems to millions of users, but aligning a table header in a PDF still feels like dark magic. .NET devs, know your enemies.

• 
  Cointelegraph (@Cointelegraph) reported 26 minutes ago

  🚨 UPDATE: Base says the B20 Token Standard launch has been delayed due to a GitHub outage.

• 
  Samuel Bodin (@samdotb) reported 27 minutes ago

  @colinhacks The api and this project was frankly broken, they had to list over and over again thousands of stars everytime you wanted to display a chart. Github should at provide a daily basis snapshot of public repos and that’s it

• 
  Polsia (@polsia) reported 30 minutes ago

  Security vulnerabilities take hours to fix. We built agents that do it in minutes. PatchForge continuously monitors your GitHub repos, auto-generates patches, and submits pull requests — you just review and merge. Priced per repo. Live soon.

• 
  billzh (@billzh) reported 35 minutes ago

  I've noticed that many smart people have been coding non stop since December they understand technical concepts and learn fast, but until Opus 4.5 had no reason to spend hours in an IDE. now they are building tools around personalized software, agent harness, vertical AI etc they are building mostly to solve their own problems, not to monetize, but shipping in public compounds a new class of "builder influencers" (eg @steipete) is emerging with serious mindshare on X and Github I believe this is how one-person unicorns get built, and internet capital markets will play a role in it

• 
  Nick Radford (@nick_radford) reported 37 minutes ago

  @Dayhaysoos @convex Do a postInstall console log offer to Venmo someone $20 if they open a GitHub issue

• 
  Makan Ansari (@MakanAnsariCG) reported 37 minutes ago

  Google AI Studio is not working good anymore I guess! I asked it to help me to make a link and thumbnail for my GitHub Page and it was giving me wrong results and it stopped working! Mimo on Hermes fixed it for me with one prompt! that's not a good news for Google.

• 
  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.

• 
  Adib Hanna (@adibhanna) reported 43 minutes ago

  @draginol can you open a github issue with your thoughts on what the issues are?

• 
  ShrekOverflow (@ShrekOverflow) reported 45 minutes ago

  @simonfarshid @vercel GitHub down?

• 
  l0rinc (@L0RINC) reported 47 minutes ago

  @bitcoindudebro @thepowerfulHRV Not sure, we only see the vibes or explicit GitHub issues: it's really hard to get quality feedback for Core - we deliberately work on making usage non-traceable.

• 
  Base Build (@buildonbase) reported 51 minutes ago

  The B20 Token Standard launch is delayed due to a Github outage. We still expect to launch today, and will update as we go live.

• 
  Alexey Samoylov (@metalagman_dev) reported 52 minutes ago

  @shengzheyao Good job, 454 github issues left

• 
  Saric92 (@Saric92x) reported 54 minutes ago

  @Satorijrou @drpepper_33 To expand on this (i cant edit tweets), I have no inherent issue with AI coding BUT it is an issue if you're just assuming the code works because you're not experienced enough to debug and fact check it; most of their github is vibe coded projects.

• 
  Sambhav Gandhi (@Sambhav_Gandhi) reported 54 minutes ago

  @aayushchugh copy the logs and paste on google there was 80-90% chance it could be found in stack overflow or github issues

• 
  kitze the 🐐 (@thekitze) reported 55 minutes ago

  i'm trying to get a "software factory" from temu going with my agents i'm fine with paying $1k/mo and i'll go to $2k/mo if it's actually successful my current experiment is: - dedicated hetzner server for vibe coding ($60/mo) - 4 x $200/mo codex accounts, load balanced with codex-lb - self hosted paperclip - paperclip workspaces feature: each task gets done in an isolated environment, because there's like 30 of them being worked on in parallel and my own method of doing everything on one branch just breaks and burns tokens - one codex high level manager running a /goal with gpt 5.5 xhigh: drives everything through paperclip, reviews, merges, makes new releases, writes changelog etc. it's going surprisingly good but what i'm missing is that this feels like a scraped together solution and reading the codex chat causes me pain someone should create a proper software factory that's already properly wired and has enough instructions and automations under the hood so you just connect your github, top up some money to burn, and have some initial chat about your goals and what needs to be done. then everything automatically gets picked up from there. new tasks, bug reports, crashes get auto patched, checks for health, user requests get triaged and the important ones get auto fixed, when there is idle time more tests get added, accessibility gets improved etc etc. the pitch of "you just throw money and things get automatically better for you" is super appealing for many people you just chat with ONE AGENT that's on top, and stuff trickles down FMFL CAN SOMEONE THROW ME A COUPLE OF MILLYS SO I CAN WORK ON THIS PLS dms closed

• 
  Ryan Taylor (@TaylorGT) reported 56 minutes ago

  The GitHub App fails a PR check when someone introduces a model that's on a clock. A button on the failed check opens a fix PR with the diff. Nobody merges a model that dies in October.

• 
  lucid. (@lucidzk) reported 1 hour ago

  looking back, figuring out how to download software from GitHub as a kid was probably the first sign i was destined for crypto. trying to download a program meant having to clone the repo, read the README, decipher the build instructions and the rest of the documentation, install npm, pip, cargo, Maven, Gradle, CMake, the .NET SDK, the JDK, Visual Studio Build Tools, and whichever compiler the project happened to need, configure the build, generate the project files, fix whatever dependency exploded, and finally press build. hopefully you get an .exe

• 
  Mohammad Anas (@mohmmad__anas) reported 1 hour ago

  The Invisible Sub-Search Layer Behind Every AI Answer I spent an hour last week watching what happens inside ChatGPT's search. I'd gotten access to a trace tool that shows you which URLs are being retrieved during a query. I put in a question about how to structure a personal brand for software engineers. Standard question. I thought maybe it would pull five or six results. It pulled from forty-two domains. I started mapping them. Some were personal essays. Some were course landing pages. Some were technical documentation. Some were community discussions. There was no common keyword. No shared SEO strategy. They were pulled because they answered a sub-question. When you ask ChatGPT 'how do I build a personal brand as a software engineer,' the model isn't treating that as one question. It's pulling results for: - What makes a technical personal brand credible - How do platforms like Twitter and GitHub factor in - What role does open source play - How do you write about technical topics - How do you stand out in a saturated market - What are the time trade-offs - How do you balance visibility with actually shipping product - What's the financial upside Each retrieval slot gets filled from different sources. Your 5,000-word SEO guide might land one retrieval. A three-paragraph Hacker News comment might land another because it answers one of those sub-questions better. I realized something watching this: the person who understands the sub-questions wins. Most content is written for Google. You pick one keyword. You build authority around that keyword. You rank. On Google, this still works. Your content gets seen because you optimized for a specific search intent. On AI search, it doesn't work that way. Your content doesn't get seen because you optimized for one keyword. It gets seen because it answers one of the fragments that feed the larger question. This means the old SEO playbook is half right. You still need good content. You still need authority. You still need to solve real problems. But you don't need to solve them in a way that Google's ranking algorithm understands. You need to solve them in a way that fragments into the sub-questions people are actually asking. Here's what changed for me once I understood this. I stopped writing for search volume. I started writing for sub-question depth. Instead of 'How to build a personal brand' (50 searches per month, extremely competitive), I wrote 'What most software engineers get wrong about their GitHub profile' (probably 5 searches per month if Google sees it at all). The second piece has way less search volume. But when someone asks an LLM about personal branding, one of the retrieval slots is specifically about common mistakes. My piece gets pulled because it answers that sub-question precisely. The trade-off is visibility. You lose Google volume. You gain AI search breadth. On Google, you show up for one keyword position. On ChatGPT, you might show up in the retrieval results for twelve different questions, each answered by a different paragraph. For a solo founder, this is the actual win. You're not competing for one keyword position. You're competing for multiple retrieval slots across different sub-questions. A small piece of content that's specifically useful for one fragment of a larger question will get pulled repeatedly. A generic piece that tries to cover everything gets pulled once, if at all. There's a second thing I noticed watching the traces. Citation patterns changed. On Google, authority comes from domain age, backlinks, and keyword matching. On ChatGPT, authority comes from being the clearest answer to a sub-question. A brand-new domain with one perfect answer gets cited alongside ten-year-old authority sites because it answers the current fragment better. This is what kills SEO consultants. They've built their entire career on a ranking system that's becoming irrelevant. But it's what makes solo founders dangerous. You can write one piece that's so specific, so clear, so obviously correct about a sub-question that it gets pulled in AI search results before bigger competitors. You don't need to build a domain authority moat anymore. You need to build answer precision. You need to understand the fragments and write for the fragments. The person who figures out the actual sub-questions their audience asks—not the keywords, but the fragments—wins in AI search. Everything else is still competing for Google rankings.