GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (69%)
• Sign in (17%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Trichūr	Errors	3 days ago
Brasília	Sign in	4 days ago
Lyon	Website Down	4 days ago
Tel Aviv	Website Down	7 days ago
Rive-de-Gier	Website Down	7 days ago
Itapema	Website Down	26 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  DannFox (@Dannfox_22) reported 5 minutes ago

  Last night I had dinner with an old tech friend and the conversation was genuinely interesting. @AnthropicAI released @claudeai Fable 5 on June 9 - the most powerful AI model ever made public. State-of-the-art at coding, research, vision. Three days later, it was gone. The US government ordered it offline over national security, after concluding someone had found a way to jailbreak it. Anthropic couldn’t separate who was using it from who wasn’t, so they shut it down for everyone, worldwide, within hours. Now they’re refunding people who paid for a model that vanished overnight. First time a government has reached in and switched off a commercial AI model across the entire world. Fascinating and unsettling in the same breath. The quieter shift is the one that hits closer to home: junior work gets automated first. HR, support, finance - agents already handle pieces of it. Companies are watching. You don’t have to love it. You don’t even have to like where it’s heading. But the tools are here. The people who learn them now will move differently than the ones who wait. So learn them anyway. Claude, @github, whatever your field runs on. Even if you never need it, you’ll be glad you didn’t sit it out. Liking it is optional. Paying attention isn’t.

• 
  Fabiana Cecin (@fabianacecin) reported 8 minutes ago

  The #1 utility of AI so far for me has been asking it: "I need to solve this problem, has anyone solved it yet?" And I find repositories on github that solve that problem. I would never find them via Google or Github search. Never, ever.

• 
  Anton Semenenko (@adelayida210519) reported 9 minutes ago

  @mark_k I use both, but not for the same job. ChatGPT is where I shape the architecture: problem thesis boundaries risk model roadmap public framing what should / should not be built Codex is where I push that architecture into code: small scoped tasks repo changes tests docs checks repeatable validation I try not to use Codex as “do everything”. For me the flow is more like: ChatGPT => architecture / reasoning / prompt design Codex => implementation GitHub => memory / proof / audit trail local runtime => test what actually works The important part is not the tool split itself. It is keeping generation, execution, and release separate. Even with Codex: generated patch ≠ accepted change passing code ≠ released system agent capability ≠ release authority That is basically the same principle I use in my own work: generation != release authority

• 
  hashchan (@0xhashchan) reported 10 minutes ago

  @codephobic Ah not like zeronet, though that was a supercool project. Like just pass a dist/ folder off of github and get users to pay it forward, but if github goes down pass it inside a torrent and a user can open it in localhost

• 
  🅾🅼🅰🅼🅾🆁🅸 (@oMaMoriTTV) reported 12 minutes ago

  Do I need to give Google my Driver License or SSN? Hello, oMaMoriTV here, guys. Today, I feel like I'm getting scammed by my local weather station. Yesterday, they predicted an 80% chance of rain and a flash flood warning for my area. But today... I'm turning my AC back on while the bright sunshine cooks my house at 92°F. That brings me to our topic today. I need to address a narrative being pushed by self proclaimed "IT Pros" "Cybersecurity Experts" and "AI Engineers" They are flooding social media with panic inducing headlines like: "You will lose your phone" "You are no longer the owner of your device" "Google is taking full control" Take a deep breath, my brothers and sisters. It’s completely understandable why these viral posts made you panic. It sounds incredibly scary like Google is abruptly turning your personal phone or tablet into a bricked, locked ecosystem overnight. But let's look at the facts. 🧐What is Google Actually Doing? Google is rolling out a new policy called the Android Developer Verification program. 🟢The Core Change: Starting in September 2026, Google wants app developers (not you, not the user) to register, pay a one time $25 fee, and verify their identity with a government ID to distribute Android apps (APKs), even if they distribute them outside the Google Play Store. 🟢So whats happening?: This is being pushed via a background update to Google Play Services (the underlying software suite that handles security on most Android phones). 🟢Google why what on earth?: They claim it's a safety measure to stop scammers and hackers from anonymously distributing malware and banking trojans through random links. 🧐Will it block "F-Droid" and sideloading(github) entirely? No, but it is going to make it significantly more annoying. Google is not hard blocking unverified apps out of existence. Instead, they are introducing an "advanced flow" for power users to bypass the restriction. If you want to install an APK from an independent developer who refused to register with Google (like a hobbyist on GitHub or certain indie apps on F-Droid), you will have to do the following: 1⃣ Turn on Developer Options (by tapping your build number 7 times). 2⃣ Toggle a setting called "Allow Unverified Packages." 3⃣ Answer a "scare screen" confirming no one is coercing you. 4⃣ Restart your phone (this instantly kills any active scammer phone call or remote session). 5⃣ Pass a mandatory 24 hour security delay (a cooling off period to break the false sense of urgency scammers use). 6⃣ Come back the next day, re-authenticate, and click "Allow Indefinitely" 7⃣ Once you do this on your device, you can continue to use F-Droid and github apps. The real concern raised by the open source community is the friction it creates forcing developers to choose between giving Google their private ID or making their users jump through these hoops. 🧐Answering Your Specific Fears 1. Will my Android device become unusable? Absolutely not. Your phone will work exactly as it does now for calling, texting, browsing, and using 99% of your apps. 2. Do I need to give Google my Driver License or SSN? No. As a regular user, you never have to hand over your government ID or sensitive personal data just to use your phone or sideload an app. The ID requirement is strictly for app developers. Furthermore, Google is creating a free "Limited Distribution" account path for students and hobbyists to share apps with up to 20 devices without needing an ID at all. 3. Is F-Droid dead? No. F-Droid will still exist. However, individual open source developers who value absolute anonymity might refuse to hand their IDs over to Google. For those specific apps, you will just use the Developer Options bypass I mentioned. 🧐Why are people so angry if it's not a total lockdown? The tech community and digital rights groups are rightfully angry because Android was built on being an open platform. By adding a 24 hour waiting period, Google is creeping toward a "walled garden" similar to Apple iPhone. Because this is handled via Google Play Services, it bypasses major Android OS updates, meaning Google can change these rules down the line. 🌟The Bottom Line 🌟 Your phone is still yours, and you aren't being locked out of it. The viral posts are trying to spark a massive public backlash to force Google to walk back this policy before the deadline but you do not need to panic about your device being ruined. Furthermore, this policy is only launching in Brazil, Indonesia, Singapore, and Thailand in September 2026, with the rest of the world rolling out much later. IT professionals and content creators have a duty to "de-escalate" situations, provide context, and explain how things work not trigger public panic for engagement. If you use the title "Cybersecurity" think twice before you just blindly throw a panic farming article onto social media. I will keep a close eye on these policy changes and let you know if anything updates. Stay safe, and stay rational! Like, Follow, and Sub for more fun and detailed inside stories.

• 
  Timur Yessenov (@Timur_Yessenov) reported 12 minutes ago

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

• 
  Chats 🇨🇦 (@ChatsFi) reported 13 minutes ago

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

• 
  Jon Church (@jonchurch) reported 13 minutes ago

  @jdxcode @nateberkopec @github I know that’s not feasible for everyone, some folks want to read issues etc in their private repos. But, finger to the wind, I think the majority of devs dont use the cli for private repos so default should be opt in not opt out for higher privs

• 
  Robert Clapp (@RobertClapp) reported 14 minutes ago

  been mass building AI tools for months and barely posting about any of them. one thing i keep running into: every AI tool i use has its own memory silo. cursor forgets what claude code knows. nothing shares context. so i built DarkContext. self-hosted MCP server, SQLite + sqlite-vec for semantic search, per-tool scopes so each tool only sees the memory it should. no vendor lock-in. runs on your machine. MIT licensed. 33 repos deep on github and this is the one i keep coming back to.

• 
  Hussein Nasser (@hnasr) reported 17 minutes ago

  Don’t get discouraged if the thing you are trying to build exists in some shape or form. Simply avoid googling or seeing how it is built. Because when you do so, you snap into the other person thought process and abandon yours. You run into problems that they were trying to solve which you may have never need to solve for your use case. I often see some engineers especially seniors when they are told of an idea by their subordinates they quickly say “oh its been done before” or “oh look this github repo up its all there” Do you have a slightest idea what this does to creativity? And even if you decide to still go on with your project and get “ideas” from that other project, your thoughts are contaminated, you will produce a clone product. Where is the fun in that? Don’t even say “Im going to make it better” because that means you are starting where they left off. Don’t say you are saving time, because you will be cutting corners and getting it over with. Like a chore wishing it to be finished. Where is the Art in that? There is a saying in Arabic “to break one’s paddles.” Don’t kill the excitement and spark you have over that, you have absolutely no idea what it will amount to. Build it only to build it so you are lost while building it.

• 
  Smukx.E (@5mukx) reported 18 minutes ago

  @NinjaParanoid @0xTriboulet @github I have asked about issue very clearly. No response from them since its an weekend... Lets see how this goes..

• 
  Peter Bloxham (@PeteBloxham) reported 18 minutes ago

  @jequesindinero It was wild watching GitHub for the issue and subsequently work on fixes then finally the 4.0 release. Unfortunately, broken again and I wouldn't be surprised if Tesla choose to curtail API access, particularly with the amount of cars out there nowadays.

• 
  Student Offers (@StudentOffersHQ) reported 22 minutes ago

  @Your_PARAM @beingamanFF use github login maybe, could verify you faster, using your personal email would make you wait for days for credits

• 
  Capafy (@Capafyai) reported 23 minutes ago

  @ar27111994 Thanks for the detail — let me make the model clear, since I think there's an expectation gap here. To host an Agent on Capafy, the publisher provides the complete runtime dependencies — i.e. the credentials and config the skill needs to run (Composio, Mem0, the LLM endpoint, etc.). The platform has no visibility into your runtime dependencies or your specific usage needs, so that part can only come from you. What the platform provides is the hosting and infrastructure to run your skill, payment settlement, and dispute/refund handling. The 20% covers those platform services — it does not cover model inference or third-party API usage. Those costs sit with you as the publisher, so it's worth pricing your skill to account for the usage it generates. So buyer runs going through the keys you supply isn't a bug — it's by design: the runtime dependencies are yours, and the platform just runs them for you. The only thing to fix is the personal keys. Don't publish with your personal Composio, local Mem0, or personal GitHub Copilot — provision a dedicated set of keys for this skill instead, so usage stays isolated and trackable and never touches your personal accounts or quota. Also, for any keys that should belong to the buyer (e.g. a token from the buyer's own account), leave those fields blank — the agent will then prompt the user to enter them, so the buyer supplies them at run time.

• 
  Gavinwang (@Gavin_Kollab) reported 26 minutes ago

  I’ll walk you through three typical Kollab workflows. The second is turning user feedback into GitHub issues, so non-technical teammates can participate in bug reporting and issue management. With Kollab, it’s not just engineers who can create GitHub issues. Product, operations, marketing, and other non-technical teammates can also report problems directly. They only need to describe the issue clearly and attach a screenshot, page link, or reproduction steps. Kollab understands the issue, evaluates its priority, and turns it into a structured GitHub issue. It can also assign the issue to the right developer based on the type of problem. For simpler issues, we can even ask Kollab to fix them directly inside an IM conversation. Because Kollab is connected to the full codebase, it does more than record the issue. It can understand the relevant code context, locate the problem, and propose or apply a fix. This turns bug reporting from scattered conversations and manual handoffs into a traceable workflow: Report the issue, create the GitHub ticket, assess priority, assign the owner, and in some cases, fix it directly.

• 
  Donkey (@TheDonkWrangler) reported 28 minutes ago

  @gnawbone_ @ClaudeDevs While it was available, I applied it to my muti-agent dev model. I built a pypi and npm package, from a single prompt, to shpport my product, and it deployed them for me in GitHub. Also had it fix a myriad of outstanding bugs in a national scale data pipeline. Configured a Stripe implementation autonomously. Wrote me a number of user docs, openapi specs, and redesigned the marketing for my SaaS product. Oh, and, I had it harden my lightsail env and Auth0 deploy on 2 sites. Hmm, also had it build a design document for a full 3 tier stack of a new product I am building. Then it went dark. So I switched back to Opus and had it continue the same work I was doing with fable. I liked fable, a lot, but it did not slow me down at all. Just have to write a few more prompts is all. That's who I spent its time live, what did you do and why does it matter how much I used it or did not use it? If Anthropic would fix the jailbreak issues. The government will lift the export restrictions. So go bark at them and let me get back to work.

• 
  beiriannydd (@beiriannydd) reported 33 minutes ago

  Well I guess as of today, I am cancelling GitHub Copilot. It is worse than a chocolate teapot. At least you can eat that. Clean code to 2 errors to 20 errors in seconds flat. Is there a slow down and be more precise mode? I don’t know how anyone rates GPT for coding.

• 
  𝖉𝖊𝖒𝖎 (@demi_hl) reported 37 minutes ago

  everyone asking how. here's the actual build. rate limits: four oauth logins 3 claude max accounts + codex pro, four independent rate pools. a worker hits a 429 and auto-rotates to the next account, overflowing to codex when all three are capped. credential swap mid-task, no re-login, no dropped work, no gap. you stop hitting the wall because there are four walls and you're never against more than one. night shift: i label github issues during the day. a cron drains the queue serial overnight. branch → bounded goal-loop resolves it → lint+typecheck+build gate → PR. serial so it never thrashes the box, bounded so it can't spin forever, gated so nothing opens a PR that doesn't compile. branch-only, never main, never deploy. i wake up to clean PRs and merge what's good. it codes while i sleep. the fleet: opus on the brain rig handles all reasoning and orchestration. the mac does hardware render, videotoolbox encode, nothing else. a cheap sonnet box eats bulk grunt work and runs a local model for the free tier. the vps runs what has to survive a sleeping laptop, the live bot never blinks. all wired over a tailscale mesh. per-device execution, one shared cognition. memory: obsidian vault + a local semantic index = one source of truth every agent retrieves from by meaning, not filename. persistent memory and skills carry across sessions, corrections stick, procedures compound. no agent starts cold or relearns what another already solved. foundation: the whole thing runs on pop!_os. linux means the stack is native, systemd cron, ssh mesh, headless browser, the overnight loop, no wsl layer to crash mid-job. nvidia drivers out of the box so the local gpu just works. nothing reboots your 3am run, nothing meters the metal. you own the box. none of these pieces is exotic alone. the leverage is the wiring, each layer covers another's failure mode. rotation means the cap can't stop you. night shift means progress while you sleep. the fleet means each machine does only what it's best at. shared memory means nothing starts from zero. and linux means it all runs native, no layer in the way. that's the stack. ask below.

• 
  Mark Amery (@XplodingCabbage) reported 37 minutes ago

  @BenjaminiteMD @GooalMouth Among other issues, if enforced uniformly this will probably pretty much amount in practice to a ban on teenagers programming, which distresses me enormously. (I assume GitHub and Stack Overflow will count as social media sites and be inaccessible without ID or a VPN.)

• 
  Manav (@Manavvv31) reported 39 minutes ago

  NVIDIA just dropped an open-weight model that can solve 60% of GitHub issues on its own The model is Nemotron 3 Super, released at NVIDIA's GTC 2026 conference on March 11. The benchmark that matters for software engineering is SWE-bench Verified, which tests whether a model can autonomously resolve real issues pulled from production GitHub repositories. The closest proxy the field has for: can this thing actually do engineering work unsupervised. Nemotron 3 Super scores 60.47 percent on that test, the highest score ever published by an open-weight model. For context, the previous leader, GPT-OSS, scored 41.9 percent. That is not a narrow margin. The architecture explains how a 120-billion parameter model can run efficiently at scale. It uses a hybrid Mixture-of-Experts design that activates only 12 billion parameters per forward pass, not all 120 billion. The result is 5x the throughput of the previous generation and 2.2x higher than GPT-OSS, running on a 1-million token context window. On RULER, the benchmark for long-context retention, it scores 91.75 percent versus 22.30 for GPT-OSS. The context window actually works. The weights ship under the NVIDIA Nemotron Open Model License, which permits commercial use, alongside full training recipes and datasets. It runs on vLLM, SGLang, TensorRT-LLM, and a free tier on OpenRouter. Production deployments already confirmed by Perplexity, CodeRabbit, Factory, Greptile, Palantir, Cadence, Dassault Systèmes, and Siemens. The honest context: on raw intelligence benchmarks, Chinese open-weight models, particularly Kimi K2 and Qwen3.5, still lead globally. Nemotron 3 Super wins on a different axis entirely: inference efficiency on NVIDIA hardware, and the ability to ship code changes autonomously in production without sending proprietary repositories to a cloud provider. For the first time, a model that resolves 60 percent of real engineering issues runs on hardware you own, at a marginal cost that scales with your servers rather than someone else's pricing.

• 
  Anthony Kroeger (@kr0der) reported 41 minutes ago

  i love how the Cursor agent window integrates PRs into the app so you don't need to open GitHub Bugbot comments all come with a "Fix with Agent" which automatically queues up a message in the chat to fix the PR comment with Cursor profiles recently being launched, and their native PR + Bugbot integrations, i actually wonder if they're building a GitHub competitor 👀

• 
  Neal Culiner (@NealCuliner) reported 43 minutes ago

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

• 
  薇冷洛天依 Violet (@VioletFlowV) reported 44 minutes ago

  @thsottiaux More importantly, the issue on GitHub regarding the one-million-token context window seems to have been open for two months now. In the next generation of models, will we be able to use a million-token context natively within Codex?

• 
  Luke The Dev (@iamlukethedev) reported 51 minutes ago

  @sebuzdugan @NousResearch Hmmm interesting. MIght want to open an issue on Github for that one

• 
  Evinstein 𝕏 (@Evinst3in) reported 53 minutes ago

  Anthropic dropped Claude Fable 5… and the government shut it down in under 72 hours. The exact same thing happened 3 years ago. One indie developer released something that made every major AI CEO nervous and forced them to testify before Congress. It was called Auto-GPT. March 2023. Toran Bruce Richards uploads Auto-GPT to GitHub. It exploded. Thousands of stars appeared on GitHub in days. Everyone was talking about "agents that work on their own." People were testing it with the newly released GPT-4 and generating crazy results (and invoices). The era of AI Agents, which we use today in OpenClaw, Crew, etc., was born. It was the first real autonomous AI agent: you gave it a goal and it would break it down into tasks, browse the internet, write code, and keep looping until the job was done. One month later, Sam Altman and other AI CEOs were called to testify in front of the US Senate. Senators used Auto-GPT as the main example: “Look how fast this is moving… agents with internet access and code execution.” One solo developer forced the first big regulatory conversation about AI. History always repeats when something gets too powerful.

• 
  Libegato (@Libegato) reported 53 minutes ago

  Working with AI means accepting no bottlenecks. I don’t always exercise that instinct as much as I should. But a few days ago, I did! I had a local workflow problem: how to parallelize work when a single repository is ~50GB? I wanted multiple parallel workstreams, but I definitely did not want 10 full copies of the repo when I barely had disk space for one. Worktrees don’t solve it. So I built Mirage. It leverages APFS to clone a folder with virtually zero upfront disk cost, and then only pays as files are actually edited in a sweet CLI API. Suddenly BANG! I can spin up a bunch of “worktrees” fast and cheap. Now to the next bottlekneck... Github repo here: renanliberato/mirage

• 
  Ritesh Roushan (@devXritesh) reported 56 minutes ago

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

• 
  Liam Zebedee (@liamzebedee) reported 57 minutes ago

  - I cannot push to this *** repo, `gh` is not installed! - I must flag, the images/ dir is 1.4GB. This could be a scalability issue to deploy! ---- What a wuss - Mangling dev.ts server instead of just deleting code - GitHub repo doesn't exist yet (404)! -- a private repo it tried to curl to check if it exists

• 
  Rituraj (@RituWithAI) reported 60 minutes ago

  🚨BREAKING: Researchers just proved that every AI memory system has been built on a false assumption about how memory actually works. Memory isn't retrieved. It's reconstructed. This isn't a new finding in neuroscience. It's been understood for decades. When humans remember something, we don't play back a recording. We reconstruct the memory from fragments — using context, surrounding information, and active reasoning to rebuild what we experienced. Every AI memory system ever built ignores this completely. Current memory-augmented agents all work the same way. Store memories. Search for relevant ones. Retrieve them. Pass them to the LLM. Done. The retrieval happens before the reasoning. Once memories are retrieved, they're fixed. If the reasoning process discovers new context that changes which memories are relevant — too bad. The retrieval already happened. That's not how memory works. In humans or in any intelligent system that reasons well over long time horizons. MRAgent from the National University of Singapore is the first AI memory framework built on the correct model. Here's the core insight. Instead of retrieving memories and then reasoning, MRAgent reasons and retrieves simultaneously — interleaving them in a loop. As reasoning produces intermediate evidence, that evidence actively shapes which memories get accessed next. You find one clue. The clue changes what you look for next. You find another clue. That changes your search again. You prune paths that turned out to be dead ends. You expand paths that keep yielding relevant information. Memory access adapts to the reasoning context in real time. Here's the structure that makes this work. Memories are stored in a Cue-Tag-Content graph. Not a flat list. Not a vector database. A graph where associative tags serve as semantic bridges — connecting high-level cues to detailed memory contents through multiple intermediate nodes. When MRAgent needs to remember something, it doesn't search the whole graph. It starts from the most relevant cue, follows associative tags based on what its reasoning has found so far, prunes branches that aren't yielding useful connections, and expands branches that are. It explores the graph iteratively — the way a detective follows leads rather than the way a search engine matches keywords. Here's the number that defines the result. Up to 23% improvement over strong baselines on long-horizon memory benchmarks — LoCoMo and LongMemEval. The tasks that require reasoning across hundreds of past interactions. The tasks that break every existing memory system. And it costs less. Fewer tokens. Less runtime. Because active pruning eliminates the combinatorial explosion that occurs when you try to retrieve everything that might be relevant before you know what's actually relevant. Better memory reasoning. Lower computational cost. From building memory the way biology built it. Here's the part most people will miss. Every AI agent memory system deployed today — MemPalace, mem0, Zep, Letta, custom RAG pipelines — uses the retrieve-then-reason pattern. Fixed retrieval. Static context. No adaptation during reasoning. MRAgent proves that pattern has a ceiling. And the ceiling is significantly below human-level long-horizon memory reasoning. The fix isn't more memory. It's smarter memory access. 23 GitHub stars. Code available now. From NUS. #1 paper on Hugging Face today — June 15. 100% Open Source.

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  X (@TheMsterDoctor1) reported 60 minutes ago

  Burp Suite Professional costs $475/year per seat. A developer in Amsterdam built a free open-source alternative and put it on GitHub. His name is David Stotijn. The tool is Hetty. ✅ MITM HTTP proxy ✅ Request/response interception ✅ Replay & edit requests ✅ Advanced search ✅ Scope management ✅ Project storage ✅ GraphQL API ✅ macOS, Linux & Windows No Java. No license server. No telemetry. No subscriptions. Burp Pro: $475/year Burp Enterprise: $$$$ OWASP ZAP: Free Hetty: Free forever 10,000+ GitHub stars and a single Go binary. Find bugs. Earn bounties. Keep the $475. Your proxy. Your binary. Your bounties. (Link in comments)