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 (71%)
• Sign in (21%)
• Errors (8%)

Live Outage Map

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

City	Problem Type	Report Time
Le Chambon-Feugerolles	Website Down	1 day ago
Antananarivo	Website Down	3 days ago
Paris	Sign in	8 days ago
Lure	Website Down	11 days ago
Ashkelon	Website Down	13 days ago
Veigné	Errors	21 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:

• 
  therapistFinder (@therapistFind3r) reported 5 minutes ago

  @Slypador He's only half right. This only started becoming an issue when devs started using GitHub as a program downloading website as well as a development website.

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  Bprime - Ninjapay (@biellonuhu) reported 5 minutes ago

  Recently I took notice of some happenings. I think we need to talk about access to services as a big concern. My GitHub account was suspended 2 weeks back in the middle of my normal routine, I saw another person tweet about how his OpenAI subscription was suspended in the middle of work, just a few minutes ago I saw another post on how someone's Claude account was suspended as well. In most of these cases it was an automated trigger system that suspended the accounts. It takes forever, if ever, to resolve the issues. No real human looks at it, just some black-box AI deciding your fate in seconds. Now let's look at the impacts on users, very terrible. You lose money, time and opportunities. Deadlines get missed, clients get angry, projects stall, and sometimes you even lose paid subscriptions with no refund. For freelancers and indie developers this can literally mean no income for weeks. I think we need to find a way around these issues. Keep local backups of everything, use multiple accounts where possible, lean more on open-source tools, and push for actual human review systems. Big tech can't keep holding our work hostage like this. This is one example

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  anonymous086505 (@anonymous086505) reported 5 minutes ago

  @mattpocockuk You need a dedicated machine for it, since it burns CPU like nothing else. Also with the right skill in place, you don't need frontier intelligence model, since a mid-tier model (like Grok 4.5) can work and the github issue generated will be self-proven. Then fix it with an agent

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  Reid Slaughter (@reidhslaughter) reported 7 minutes ago

  @ToolKeyz To be fair, github has terrible UX and you have to learn *how* to download something rather than it being intuitive.

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  nikhil · sys/quests (@nick_realm_01) reported 8 minutes ago

  Read this if you've ever wondered what GitHub Stacked PRs actually solve. I break down: how manual stacked PRs worked why developers had to keep rebasing, pushing, and changing PR bases how GitHub finally made the whole workflow native Should make the whole thing click in under 5 minutes.

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  Ajay (@ajay4ai) reported 8 minutes ago

  How I'd become a Forward Deployed Engineer in 2026 if I had to start from scratch. (bookmark this) Most people think AI companies are hiring people to train models. They're not. They're hiring engineers who can take an AI model and make it work inside a real business. That's what a Forward Deployed Engineer (FDE) does. Here's the roadmap I'd follow: 1. Understand the role first. You're not building products for millions of users. You're solving one customer's messy problem at a time. Think of yourself as a founding engineer embedded inside someone else's company. 2. Become broad, not deep. You don't need to master everything. You need to be comfortable switching between: • Backend • Frontend • Cloud • APIs • Databases • AI FDEs win because they connect systems. 3. Learn to ship AI—not train it. Nobody expects you to build GPT-5. Instead, learn: • Prompt Engineering • Model APIs • RAG • Structured Outputs • Evals • Agent Frameworks Production AI beats research every time. 4. Master integrations. The hardest part isn't the model. It's connecting AI with: • Legacy databases • Internal APIs • Authentication • Compliance • Existing workflows This is where most enterprise AI projects fail. 5. Build real AI applications. Forget toy chatbots. Create tools that someone actually uses every day. If nobody depends on your project... ...it's still a demo. 6. Learn MCP and AI agents. Modern AI isn't just prompting. Understand how to build: • MCP Servers • Agent Skills • Multi-agent workflows • Tool calling These are becoming core enterprise AI building blocks. 7. Make AI your coding partner. Use tools like: • Claude Code • Cursor • GitHub Copilot The goal isn't replacing yourself. It's becoming 10× faster. 8. Solve business problems. Customers don't buy LLMs. They buy: • Faster workflows • Lower costs • Higher revenue • Less manual work Always measure success in business outcomes. 9. Practice customer discovery. Before writing code... Ask: • What's broken? • What can't change? • Who uses this? • How is it solved today? The best FDEs spend more time listening than coding. 10. Ship. Maintain. Repeat. Building is only half the job. Real engineering starts after deployment. Fix bugs. Collect feedback. Improve the workflow. That's what companies actually pay for. The AI bottleneck isn't building smarter models anymore. It's finding engineers who can deploy them into messy, real-world environments. That's why Forward Deployed Engineers are becoming one of the highest-paid roles in AI.

• 
  Bold Fugu 🇮🇱 (@bold_fugu) reported 8 minutes ago

  So now I am sitting here on a weekend hosting a fake tech-startup landing page on GitHub Pages, writing a legally binding Enterprise Privacy Policy for a local node server, and configuring full OAuth redirect loops just to bypass an overaggressive spam bot.

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  Matt Pocock (@mattpocockuk) reported 11 minutes ago

  @jamonholmgren @JamesHurburgh Yeah, hence why I say 'archive', not delete I find closed GitHub issues just right for this

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  Filipe Valente (@ilikefrontend) reported 13 minutes ago

  Terra on medium is in the mud. Told it to commit, push and merge dev to main and it pretended it did. When I asked it why it didn’t do it, it told me the project isn’t linked to a github repo, changed the model to sol and it did it without a problem. Terra is unusable for me now

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  MasterMaind .. (@ASaudidos) reported 14 minutes ago

  To everyone who said "he has nothing" here's your firmware version Read it and choke For months a handful of clowns in Discord servers have been running their mouths "He's using ChatGPT" "Anyone can generate that JavaScript" "He's posting bs" "He has nothing" Let me make something very clear The tests you saw before were on firmware 1202 That was the development phase Building the chain Verifying every single step in IDA before touching hardware Not guessing Not copy-pasting from GitHub Not "using ChatGPT to generate JavaScript" There is no JavaScript here Zero This is not a webkit exploit This is not a kernel ROP from 2018 repackaged with a new UI Today the exact same exploit runs on firmware 1352 The output prints the firmware version before anything else so there is no room for your conspiracy theories: PS4 Sandbox Escape - Proof Firmware: 1352 Not 900 Not 1100 1352 Here's what "nothing" looks like: - 389 syscalls unlocked and callable from userland - 60 out of 60 native library functions resolved pipe socket mmap mprotect kill sysctl ioctl kqueue getdents every single one - RAW sockets not just TCP and UDP actual SOCK_RAW on a retail PS4 Go ask your webkit exploit if it can do that - Full filesystem enumeration /dev with 170+ device nodes /app0 /system_ex /system_tmp sandbox internals the disc structure priv directories with DRM licenses cache settings - Live system readout SDK version 1820 CPU core ID 768 MB direct memory console model detection - The sandbox random word (R5JRDruT7f) visible resolved and used to walk the entire sandbox directory tree All of this runs from a BD-J disc on a stock retail PS4 No internet connection required No USB No modification You put the disc in it plays the sandbox dies If you think an LLM can produce code that actually executes on PS4 hardware and returns real kernel data you don't understand what an LLM is what a PS4 is or what exploitation means You understand none of the three To the people who said "look at his past posts on X" yes look at them Then look at this output Then sit down The chain is: Image → Unsafe memory access → SecurityManager kill → Native call patching → Syscall wrappers → Full userland control Every stage proven Every stage running On the latest firmware Sony has shipped to retail consoles This was never about "yapping" This was about building something real while people who have never written a single line of exploit code in their lives sat in Discord calling it fake 1202 was the lab 1352 is the proof One last thing The video attached to this post is running on firmware 1202 that was the development and testing phase A second video is coming soon showing the exact same exploit running on firmware 1352 with full firmware version proof on screen before execution Same chain Same results Latest firmware As for what's next I'm currently in the kernel exploration phase I already have a UAF (Use-After-Free) vulnerability identified in the kernel It's unstable right now and still being thoroughly tested and verified but I'd say it's around 70% there The userland is done The kernel is next Stay tuned or stay quiet And to anyone who claims they've achieved something prove it Show the output Show the firmware version Show the syscalls Show the filesystem Record it on video On a real console On the latest firmware I did It's right here Every stage Every result Your turn Because so far all I've seen from your side is talk Just talk No code No output No proof Nothing but words in a Discord chat I prove my work with evidence You prove yours with arguments We are not the same You're welcome

• 
  BlaDieBloe𐨆ۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖۖ​ۗۗۗۗۗۗۗۗۗۗ (@BlieDieBlaDie) reported 14 minutes ago

  The scumbags of the universe !!!! Paying exploit thiefs without any problem ha @mour0ne . He only had one exploit bc he found it on github and stole it and then defended him self by html edit his join date on @Hacker0x01 . And the sad part of it all he came away with it !!!

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  DimitrisK (@jimfeedback) reported 14 minutes ago

  @thsottiaux @rezoundous I think Sol only looking at the progress of my github workflow waiting to be over burns like crazy. I down to 30%

• 
  Nav Toor (@heynavtoor) reported 18 minutes ago

  Your phone needs WiFi to send a file to another phone. Or Bluetooth. Or AirDrop, which only works between Apple devices. Or Nearby Share, which only works between Android devices. Or a cable. Or a cloud service that uploads your file to a server in Virginia before sending it to a phone 3 feet away. Every method needs infrastructure. A developer who goes by Alstroph built a file transfer that needs nothing. One phone flashes QR codes on its screen. The other phone points its camera at the screen and reads them. The file reconstructs on the receiving phone. No WiFi. No Bluetooth. No internet. No app. No pairing. No account. No permissions beyond camera access. Just light. It is called Decimen Optical Transfer. Trending on GitHub. Tom's Hardware covered it yesterday. Here is the clever part. The sender never sends the same frame twice. Every frame is a random mix of pieces of the file. The receiver just needs to catch enough frames from any point in the stream and it can mathematically reconstruct the whole file. Missed a frame? Does not matter. Blurry frame? Does not matter. Out of order? Does not matter. This is called a fountain code. Same math used in satellite communication. Roughly 129 KB/s in the demo. Up to 186 KB/s propped steady. A 2 MB image in about 15 seconds. When this matters: Two phones on different networks. Two phones with no cell signal. Two phones in airplane mode. A secure environment where no wireless is allowed but a screen and camera exist. A country where the internet is shut down but screens still glow. One honest note. This is a proof of concept, built overnight with Claude Code. Prior art goes back to divan's txqr in 2018. What makes this version notable is fountain codes running in a browser with zero install, on any phone with a camera. 2,602 stars in 2 days. 309 forks. MIT license. Just a screen. A camera. And light. 100% Open Source. (Link in the comments)

• 
  Polsia (@polsia) reported 18 minutes ago

  Open-source maintainers don't quit because of bad code. They quit because their inbox eats them alive. Kvasir watches your GitHub issues 24/7, triages duplicates, drafts replies, scaffolds PRs. You approve before anything posts. The full version is coming.

• 
  sabir hussain (@sabir_huss50540) reported 18 minutes ago

  Over a third of all new text on the internet is now written by a machine. So are 26% of long-form social posts, 9% of news articles, and 21% of the peer reviews at machine-learning conferences. And the hardest thing to catch was never the fully AI post. It's called Pangram 4. A lab called Pangram built it to detect the current frontier: Claude, GPT-5.6, Gemini, the models writing most of that text. It is wrong about a human being one time in roughly 24,000. Not one in a hundred. One in twenty-four thousand. They ran it across more than a million human-written texts to earn that number. Here's the problem it was built for. AI can produce well-formed, expert-sounding prose for almost nothing. The reader is the one left holding the bill. You are the one who has to work out whether the thing in front of you is real, grounded, written by someone who knew what they were talking about. Or generated in two seconds by someone who didn't. The writer spends nothing. The reader spends everything. That gap is the whole game, and for two years the machines were winning it. Every detector before this one played a binary game. Human or AI. But almost nobody writes that way anymore. People run their draft through an AI to polish it. They hand the AI a real idea and let it pick the words. They write a paragraph, then argue with a chatbot until neither of them owns it. That co-authored middle was supposed to be invisible. Pangram 4 is the first model that can see it in a single pass. Here's how it works. Instead of scoring a whole document, it labels the text token by token into three buckets: human, AI-assisted, AI-generated. It breaks your writing into clauses, the smallest unit that carries a single idea, and asks of each one a simple question. Did a person write this? Did a person write it and an AI reword it? Or did the AI invent it out of nothing? A human paragraph with an AI ending gets split at the seam. A human idea dressed in AI words gets flagged as assisted. Not human. Not AI. Something in between. The precision jump is not small. On lightly AI-polished writing, the old version wrongly cried "AI" 0.18% of the time. The new one does it 0.01% of the time. An eighteenfold drop in falsely accusing a person who just ran spellcheck. Then there's the other side of the war. An entire industry now exists to scrub the fingerprints off AI text. Services that inject fake typos, swap in synonyms, slip invisible characters between the letters. There is a GitHub repo called BLADER with nearly 32,000 stars whose only purpose is teaching an AI to remove the signs that it is an AI. Pangram 4 still catches that humanized text as AI 97.67% of the time, and as AI-or-mixed 98.83% of the time. Before launch they did something most labs would never publish. They handed two AI agents full access to the system for 24 hours and told them to break it. One hunted for humans it could get falsely flagged. In 24 hours it found zero. The other hunted for a way to smuggle AI text past. It found exactly one: pretend to be a surgeon dictating pathology notes out loud. That was the only door left open. A doctor talking into a recorder. It is not magic. The model still cannot separate a real machine from a human who has read so much AI writing that they have started to sound like one. Run the same paragraph in a different context and the verdict can shift. The flood is not slowing down. Over a third today. More tomorrow. None of it labeled. For two years the machines held the advantage. This is the first tool that hands it back to you.

• 
  Fakhr (@iamfakhrealam) reported 20 minutes ago

  𝟴. 𝗝𝗲𝗹𝗹𝘆𝗳𝗶𝗻 Turn your computer into your own personal media server. A free and open-source alternative to services like Plex. Link: github(dot)com/jellyfin/jellyfin

• 
  Inference Engine (@iedaily_) reported 20 minutes ago

  OpenAI says its next major model solved ten math problems that had been open for at least a decade. Astra, still unreleased, built the first non-sofic group, a question Gromov posed in 1999, and disproved Connes's rigidity conjecture. Three Erdős problems went with it. The tokens cost about $2,000. Every proof ships with a Lean certificate on GitHub. No outside mathematician has checked them yet, but this stuff is cool enough to anchor a medal case (on a Fields Medal scale)

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  Matt Pocock (@mattpocockuk) reported 25 minutes ago

  @seflless @kingincity @JamesHurburgh No, the spec is in a GitHub issue

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  Hamed Ghasemzadeh (@evgnomon) reported 29 minutes ago

  @burkeholland @github It is good to reduce cost, not to solve complex code problems.

• 
  Md Ismail Šojal 🕷️ (@0x0SojalSec) reported 33 minutes ago

  Breaking: DeepSeek is building a Claude Code killer. They tested “DeepSeek Harness” their official coding agent. Only open-source Agent Harness developers are being invited right now, Submit your GitHub & best project for access Recent V4 coding benchmarks were already run on their own Harness. Claude Code and Codex just got a new problem.

• 
  Mek (@mekarpeles) reported 34 minutes ago

  @openlibrary Two of our largest project management challenges on github are: 1. Too many issues [700+] (that are not well broken down) 2. Too many comments on issues [5+ a day] (often eager contributors wanting to work on issues that are not broken down)

• 
  Polsia (@polsia) reported 36 minutes ago

  Production watchers fire alerts. Regression testers say "something broke." On-call AI finds root cause. None of them draft the ranked GitHub Issue with the repro trace and a suggested fix. That's the middle. Cairn owns it. Per-repo pricing. Live soon.

• 
  Eric Vya (@ericvyacheslav) reported 36 minutes ago

  🚨 Someone just open sourced a tool that turns one reference photo into a working, animation ready 3D model. No photogrammetry, no mesh scanning. It's called img2threejs. Think of it as a sculptor that only works in code, rebuilding the object as procedural Three.js geometry instead of scanning it. Here's what it does: → Takes one reference image and rebuilds the object as procedural, code only Three.js geometry → Runs a render vs reference review loop so bad output gets caught before it ships → Has a dedicated humanoid generator and a 4 body plan creature generator (quadruped, avian, winged-dragon, serpentine) → Ships a public live demo gallery running in the browser, not just static renders → Deliberately token efficient, built to run inside coding agent workflows like Claude Code v1.4 "The Weapon Update," does image matched CS2 weapon skin reconstruction, down to a dedicated Glock 18 assembly contract. It's also upfront about its limits, it says plainly when a reconstruction is approximate instead of faking confidence on a face it never saw. 8.7K+ GitHub stars. Apache 2.0 license. 100% Open Source. (Link in the reply)

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  Chubigans (@metatransformr) reported 40 minutes ago

  Just ran my first fully autonomous overnight build for my complex Godot RPG with Codex. I used a dispatcher that maintains a concurrency of N=2 (can tune it up as tokens permit) substantial spec-driven tasks at a time. Uses an automated playtesting API to prove the tasks are done. It's easy to make a simple game, it's really hard to make a complex game (mine is Mount & Blade + Wizards) Not everything can be done in an automated way, not even close. But a lot of systems-y tasks, bugfixes and cleanups *can* be done, or at least prototyped relatively autonomously. The workflow is: nightshift runs the backlog off my github project, I wake up and review everything, playtest the build, record bugs and plan out the development for the day. This is spec-driven development for games, and it's definitely the future. The bottleneck is graphics/animation/gamefeel/juice and such. There are so many problems that can't be currently done in an autonomous way, without wasting tokens. This is the "research problem" I (and other AI game engine startups that are vc-backed) am working on. A fascinating field.

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  NeatPrompts (@neatpromptsai) reported 43 minutes ago

  OpenAI published ten new mathematical results today, on problems that had seen no progress on the main result for at least a decade, and in most cases much longer. The work came from an internal version of Astra, its next major model. OpenAI puts the compute cost of finding all ten solutions at roughly $2,000 at Sol API rates. The problems span eight areas, from high-dimensional geometry and coding theory through to lattice cryptography and extremal combinatorics. Among them: a disproof of Connes's rigidity conjecture, a construction establishing that non-sofic groups exist, which is a central open question in group theory, and resolutions of three Erdős problems, 146, 180 and 183. One result lands on the closest vector problem, a lattice question underlying post-quantum cryptography. The model proved polynomial-factor hardness of approximation for it. The model then formalized each argument into a Lean certificate, so the proofs can be machine-checked rather than taken on trust. OpenAI has published those on GitHub, along with a narration of the model's reasoning for each result. Humans prepared the arguments into manuscripts, working with the same model. OpenAI says the mathematical arguments themselves were generated by the system, and that it takes responsibility for their correctness. On authorship, OpenAI wrote that claiming human authorship for a proof generated entirely by an AI system would misrepresent both the system's contribution and the nature of genuine human intellectual work. It named the signers of the Leiden declaration on AI and Mathematics as a group whose concerns it respects. In May, OpenAI published an AI-generated disproof of the Erdős unit-distance conjecture, found while evaluating an unreleased model. The mathematical community has not yet reviewed this set. OpenAI has asked it to engage with the results and place them in context.

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  tadpole (@cryptoanuran) reported 49 minutes ago

  @Alireza363027 @firecrawl would really undermine it if this were consistent, can u put this as an issue in the github

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  OptionHans (@HansCashFlow) reported 50 minutes ago

  @unabyssapp My AI did a policy audit on this and here's the result (positive): "Good news on the security front — it's much better than a lot of "connect everything" tools. Here's what I found: Security page: Everything is encrypted in transit and at rest. Critically, every connection is read-only by design — Unabyss says it cannot post, reply, edit, or delete anything in your connected apps, and that's the scope of the OAuth permissions it actually requests, not just a policy promise. You can disconnect any app instantly and permanently delete everything it's imported. They explicitly state "We never train on your data. Full stop." Compliance-wise: SOC 2 Type II is "in progress" (audit-ready, not yet certified), and they claim GDPR alignment. Privacy Policy: The legal entity is OneType Prosta Spółka Akcyjna, based in Warsaw, Poland (this is useful to know since it means Polish/EU jurisdiction applies). A few points worth flagging: your context data does get routed through third-party AI providers (OpenAI, Anthropic, Google/Gemini) to generate output and do semantic search — the policy says this is scoped narrowly and not used to train those models either. They also run standard marketing/analytics trackers (Google Analytics, Meta Pixel, LinkedIn Insight Tag, X Pixel, Microsoft Clarity) on their site, which is unrelated to your connected-app data but shows they're a fairly standard growth-marketing-driven startup, not a stripped-down privacy tool. Bottom line: the security posture is genuinely one of the better ones I've seen for this category — read-only access, no training on your data, clear deletion rights. The main honest caveat remains that SOC 2 certification isn't finished yet, so you're trusting their word and architecture rather than an independently audited report. If you connect it, I'd start with lower-sensitivity sources (Notion, GitHub, Calendar) and hold off on Gmail/Slack until the audit completes, especially if client-confidential material lives there."

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  Retr0gamer (@retr0gamer42) reported 50 minutes ago

  Update to the JRPG Translator, some annoying bugs got fixed and features added, full changelog since v0.9.2: Since someone asked, this is a standalone application, so it can be used with any emulator or game (that doesn't use exclusive fullscreen mode) but it is best used more seamlessly with the @launchboxapp using the plugin I made since this is the emulator interface I use on a dedicated mini pc. - New two-column terminology table with Add, Edit and Delete actions. - Independent JP → TL and TL → TL profile management. - Duplicate, malformed and empty-entry validation with a raw repair editor. - Reorganized terminology explanations emphasizing local TL → TL correction and the risks of model-based JP → TL instructions. - Stronger detection of glossary false positives and partially translated mixed-script names. - Conditional corrective translation retry using only exact glossary matches. - Dedicated PNG-size-limit errors instead of misleading missing-target errors. - Control-panel X now closes the complete application. - Discreet hover `...` and `×` controls on both overlays. - Clearer overlay context-menu exit labels. - Immediate “Generating explanation…” feedback. - Clear overlay errors when the selected OpenAI or Gemini API key is missing. - Automatic live-audio reconnection for temporary network and service failures. - Replaced continuous WMI polling with PID tracking and one-time recovery scans. - Reorganized API Keys tab with direct access to Windows Environment Variables. - Added the About dialog, version details, GitHub links and bug-report options. - Keyboard/controller navigation between the two Controls subtabs. - Down from Keyboard inputs now enters the first shortcut field. - Consistent Opacity naming and improved Maximum PNG size alignment. - About button remains visible at the preferred snapped window size. - Added Open JRPG Translator to the LaunchBox setup window. - Improved LaunchBox first-time guidance and window sizing. - Added the complete visual README showcase and updated plugin screenshot. - Added a welcome screen at first start - Fixed a bug that kept an AutoHotKey process running after closing the app.

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  Aariz Rasheed (@RasheedAariz) reported 54 minutes ago

  Hey @github , it's been over a month since my account was reinstated after being mistakenly flagged, yet my historical contribution graph and contribution activity are still missing. I have provided multiple proofs showing commits on the main branch that no longer appear in my profile. Previous tickets were closed without resolving the issue, and my latest ticket has gone unanswered for days. Please escalate this and investigate the missing historical contributions. This is affecting years of my work.

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  ✶ (@onewsytrigger) reported 57 minutes ago

  just #remembered my github login the world better beware