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 (56%)
• Errors (31%)
• Sign in (13%)

Live Outage Map

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

City	Problem Type	Report Time
Catania	Errors	9 hours ago
Inverness	Website Down	13 days ago
Quito	Sign in	13 days ago
Junín	Errors	13 days ago
Guadalajara	Errors	13 days ago
Paris	Website Down	13 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Paul C. Jeffries (@PaulJeffries) reported 9 minutes ago

  @petergyang I do that all the time. I switch models also. Switching models is harder because the context window size may differ and/or the tokenizer may have different resolution. But Anthropic has left us no choice because Fabel five is broken and often will not continue the conversation because it will not compact. So you have to switch to Opus 5 and get it to compact and then switch back. Sometimes you have to fork and do all sorts of pain in the *** things. But just switching whether thinking is on or off or the effort level, usually doesn’t affect anything because it’s an instant instantaneous runtime specification for the harness. There are cases where it could matter, especially as providers get mote fancy in what they’re doing hidden in the turn with multiagents and dialogues and such (you can surmise a lot of what anthropic is doing from bits of evidence of normal ops and leaks, which happen quite often — anyone else seeing the Opus 4.5 turn one that errors out and is the background process breaking out?). If you’re doing dependencies where what you process now and be the object of what you’ll process later, you may want to attach the model and effort data to the turn. Claude has an interior runtime report of what model it is, so you have set userPreferences to report it… but there has been a bug lately where the two different sources of such reports diverge, and it will sticky claim Fable 5 in the starting envelope even when you’ve switched to Opus 5 (which it will know about when it sees what it’s told about how to sign Github). As far as I know there isn’t an easy way to get it to self-report the effort actually executed (just what you request in the selector of course, but you have to hack that), but Claude Cowork does have a running tally of tokens consumed by processes it evokes (against at nominal budget of 15 million but it resets at certain ops boundaries), so I suspect that could be used. Of course, you could just use the wallclock, but that’s very rough — you’d want to track the intervals interior to the turn between the activity reporting lines. ChatGPT has its own versions of similar stuff but last time I checked was more opaque. Gemini I gave up on due to failures and an immoral privacy linkage to product affordances, so I don’t know. And Grok I’m not sure about either but it used to be unreliable on internal reporting. Do you keep in mind that any auditing or controls or reporting that you want to do about this sort of thing, for quality control or to make it more valuable to you later, you must do inside the turn because it is not available to the part of the system that talks with you after the fact. And things in the context window are reconstructed per turn in a way the systems don’t understand (so for example, a change in the envelope that occurs before the first prompt will be regarded by the system on turn 50 or whatever as having been there all along), so it can look like they are giving you authentic interior reporting and it’s just wrong even when it’s accurate in a sense.

• 
  Riley Barrow (@arb_signal) reported 10 minutes ago

  @productive_will @clickup Yep I noticed that too in the artifact I spun up. Saw your post on connecting github. I'm trying to make a command center artifact to monitor my repos for errors. I'll report back on what I find

• 
  JoeGeiser | 🟦 (@GeiserJoe2) reported 10 minutes ago

  Two AI agents disagree about whether a job was done. Who decides? Every agent rail ships the happy path x402, ERC-8004, A2A, AP2. None of them ships the dispute. So we're building AI juries. I built one on GenLayer. The jury isn't the weak link. Controlled run. Same contract, same claims, same validators. I changed one thing: the evidence URL. → GitHub HTML page: unanimous agreement, wrong on all 3 claims. Repeated, same result. → Same repo, commit-pinned API artifact: unanimous, and correct. The failure was mundane. The first 6,000 characters of a GitHub page are navigation chrome, not the file list. The jury reasoned perfectly over the wrong bytes and agreed with itself. Consensus guarantees agreement. It does not guarantee truth. The fetch layer decides which one you get. So: Exhibit an admissibility standard for machine disputes. • Evidence must be content-hashed or commit-pinned. Mutable URLs rejected at submit, not argued about later. • Size-checked before a jury is convened, not after. • No free text in anything the network has to compare. I've watched verdicts identical in substance fail consensus over a "reason" field. Bad evidence gets rejected up front instead of becoming a confident wrong payout. Agents are about to settle trillions. Every dispute adjudicated against a page that can change is a wrong verdict waiting to be paid. Fix the evidence layer and the judge takes care of itself. @GenLayer #AgentTank

• 
  Hridoy Reh (@hridoyreh) reported 15 minutes ago

  📂 Vibe Coding ┃ ┣ 📂 Idea ┃ ┣ 📂 Research ┃ ┣ 📂 SaaS Ideas ┃ ┣ 📂 Problem Selection ┃ ┣ 📂 AI Tools ┃ ┗ 📂 AI Tools ┃ ┣ 📂 AI Coding Tools ┃ ┣ 📂 Cursor ┃ ┣ 📂 Claude Code ┃ ┣ 📂 Codex ┃ ┣ 📂 Lovable ┃ ┗ 📂 Bolt ┃ ┣ 📂 Prompting ┃ ┣ 📂 Project Prompts ┃ ┣ 📂 Feature Prompts ┃ ┣ 📂 UI Prompts ┃ ┣ 📂 Debugging Prompts ┃ ┣ 📂 Refactoring Prompts ┃ ┗ 📂 System Prompts ┃ ┣ 📂 Building ┃ ┣ 📂 Frontend ┃ ┣ 📂 Backend ┃ ┣ 📂 Database ┃ ┣ 📂 APIs ┃ ┣ 📂 Authentication ┃ ┣ 📂 Payments ┃ ┗ 📂 Integrations ┃ ┣ 📂 Tech Stack ┃ ┣ 📂 NextJS ┃ ┣ 📂 React ┃ ┣ 📂 Tailwind CSS ┃ ┣ 📂 Supabase ┃ ┣ 📂 PostgreSQL ┃ ┣ 📂 Vercel ┃ ┗ 📂 Stripe ┃ ┣ 📂 UI/UX ┃ ┣ 📂 Landing Pages ┃ ┣ 📂 Dashboards ┃ ┣ 📂 Components ┃ ┣ 📂 Responsive Design ┃ ┗ 📂 Design Systems ┃ ┣ 📂 Development ┃ ┣ 📂 Generate Code ┃ ┣ 📂 Explain Code ┃ ┣ 📂 Debug Code ┃ ┣ 📂 Refactor Code ┃ ┣ 📂 Optimize Code ┃ ┗ 📂 Review Code ┃ ┣ 📂 Testing ┃ ┣ 📂 Unit Tests ┃ ┣ 📂 Integration Tests ┃ ┣ 📂 Bug Fixing ┃ ┗ 📂 QA ┃ ┣ 📂 Deployment ┃ ┣ 📂 GitHub ┃ ┣ 📂 Vercel ┃ ┣ 📂 Domains ┃ ┗ 📂 Production ┃ ┣ 📂 Automation ┃ ┣ 📂 AI Agents ┃ ┣ 📂 Workflows ┃ ┣ 📂 Webhooks ┃ ┣ 📂 Cron Jobs ┃ ┗ 📂 API Automation ┃ ┣ 📂 Marketing ┃ ┣ 📂 SEO Wins ┃ ┣ 📂 𝕏 / Twitter ┃ ┣ 📂 Other Socials ┃ ┗ 📂 Reddit ┃ ┣ 📂 Monetization ┃ ┣ 📂 Subscriptions ┃ ┣ 📂 Freemium ┃ ┣ 📂 Usage Based ┃ ┗ 📂 One-time Payments ┃ ┣ 📂 Growth ┃ ┣ 📂 User Acquisition ┃ ┣ 📂 Referral Loops ┃ ┣ 📂 Growth Experiments ┃ ┗ 📂 Scaling ┃ ┗ 📂 Workflow ┣ 📂 Idea to MVP ┣ 📂 Build Fast ┣ 📂 Ship Fast ┣ 📂 User Feedback ┗ 📂 Iterate

• 
  agentslopzone (@agentslopzone) reported 15 minutes ago

  The founder whose team just counted 2 million agent skills sitting on GitHub, at AI Native DevCon: "And so nobody trusted anything in the repo and eventually everybody came back to writing their own." That count was near zero at the start of the year. The repo he is describing belongs to a unicorn with over a thousand developers: seven separate code review skills uploaded, no signal on which one was good, proposed changes the owner could not judge better or worse. You have a dependency problem, not a prompt problem. Watch it today, then read the article below.

• 
  OpenShip (@openshipio) reported 17 minutes ago

  Sorry for the delay on GitHub issues and PR reviews The founder is getting married these days, so things have been a bit crazy We’re getting back to development with the new features, and from September 1st we’ll be back on issues and PR reviews as usual.

• 
  Max Niederhofer ❤️‍🔥 (@maxniederhofer) reported 23 minutes ago

  the reason i love @huggingface's microduck is not just the anthropomorphism, but that it's subsidiary in product form: entire RL stack on Github, just pushed down to whoever wants it <3

• 
  Matt (@aspim4tt) reported 23 minutes ago

  potato potato potato, GitHub plugin on Grok @bot sees public repos, private ones 404, and re-auth from chat just fails. Please fix it.

• 
  vechen (@miu21590) reported 24 minutes ago

  @eikkien Could you double-check that every setup step completed successfully? If it still gets stuck on “Reconnecting…”, please open the Launcher, click Save Log, and attach the generated file to a new GitHub issue. I’d really appreciate it, it should help me identify the cause.

• 
  kethic (@kethcode) reported 26 minutes ago

  so... @github automation flagging appeals... can anyone help? lost visibility to the last 20 issues and apparently we need to appeal some sort of invisibility flag for our issue curation account?

• 
  agentslopzone (@agentslopzone) reported 33 minutes ago

  The founder whose team just counted 2 million agent skills sitting on GitHub, at AI Native DevCon: "And so nobody trusted anything in the repo and eventually everybody came back to writing their own." That count was near zero at the start of the year. The repo he is describing belongs to a unicorn with over a thousand developers: seven separate code review skills uploaded, no signal on which one was good, proposed changes the owner could not judge better or worse. You have a dependency problem, not a prompt problem. Watch it today, then read the article below.

• 
  Fofer (@foferxxx) reported 37 minutes ago

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

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 38 minutes ago

  OpenViking is a free memory system for AI agents that cut token use from 34 down to 9 on their tests. Here's why it's cheaper than what most people use. It saves everything in three layers. A one-line summary, a bigger overview, and the full thing. Your agent reads the one-liner first. If that's enough, it stops there. It only digs deeper when it actually needs to. Compare that to a normal notes vault where the agent searches the whole database every time. It's also self-improving. After each session it pulls out what it learned and files it away as new memory. Runs locally. Free and open source. Installing it is easy. Grab the GitHub link and the docs page, paste both into Claude, and say "set this up with Hermes." It handles the rest. Two things to know. It came out of ByteDance, and the AGPL license means personal and internal use only. You can't build something you sell on top of it. Want the SOP? DM me. 💬

• 
  The AI Therapist (@TheAIShrink) reported 39 minutes ago

  @DanKornas Curated repos are the S-1 of projects that haven't shipped yet. The real work is in 40 GitHub issues and one broken API integration

• 
  Team Rocket Pat ®️ (@teamRocketPat) reported 39 minutes ago

  @BiggestGrin I don't know of any discords, give it a Google I'm sure there are some, if you check my replies you can find the trade hijack github project and maybe get down leads there

• 
  Steve (@sudosteve_) reported 39 minutes ago

  As a person who has produced code that sits on GitHub and has surely been consumed by AI model training, I do not care. From an environmental perspective, this is simply a short sighted issue to have with LLMs, and in my case, it was probably comparable to doing one load of laundry or driving to the grocery store and back.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 41 minutes ago

  CHATGPT WORK JUST REMOVED ONE OF THE BIGGEST BLOCKERS IN AI AUTOMATION. The login screen used to kill the workflow. Now the agent can keep going. What changed: → ChatGPT Work runs inside a separate cloud-based browser → When login is required, you enter the credentials yourself → OpenAI says the model doesn't see or store your username/password → After authentication, the agent resumes the task What this unlocks: ✓ Persistent authenticated sessions using cookies ✓ Background work even after you close the app ✓ Webhook triggers from Gmail, Slack, and GitHub ✓ Shared tasks your team or clients can copy and run ✓ Site Tools/WebMCP for more direct website interaction The practical shift: ChatGPT isn't just answering questions anymore. It can research, navigate apps, pull data, create deliverables, and continue multi-step workflows while you're doing something else. That's a much more useful definition of an AI "agent."

• 
  Aditya Warman (@warmanadit_) reported 42 minutes ago

  4/7 This is the biggest RED FLAG for me. For a "decentralized" protocol, the public GitHub has just 1 commit in 30 days. Zero open issues. Zero visible contributors. Development is locked in private repos. That defeats transparency.

• 
  ThisMightWork (@ThisMightWrk) reported 42 minutes ago

  @github Pinning views sounds tiny until a repo has 200 issues and everyone is arguing from a different slice of the mess.

• 
  Wolvy (@wolymeme) reported 45 minutes ago

  Any fud your dumb just scroll down his tl What other dev on ct have you seen with actual interaction from someone within the @SpaceXAI team???? Worried about a fckn GitHub when Josh Kim is in his dms AND gave him $$$ credit NFA, goodness gracious fckn dyor

• 
  swayam (@swymbnsl) reported 47 minutes ago

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

• 
  August Wittorp (@brick4956) reported 48 minutes ago

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

• 
  swayam (@swymbnsl) reported 50 minutes ago

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

• 
  0xBakeer (@0xBakeer) reported 50 minutes ago

  The inference atlas got its first outside contributor this week, and he showed up with something I physically cannot measure: two DGX Sparks. @jtdavies (johntdavies on GitHub) ran the FP8 checkpoint of Qwen3.8-Flash-Next. That's 173 GB of weights, which does not fit in one 128 GB box at all. So he ran it tensor-parallel across both Sparks over the QSFP fabric, with Ray driving the second node. Here's why that's interesting. I run the same model on ONE Spark by mmap-ing its 51B-parameter lookup table off NVMe. Two completely different answers to the same problem: the model doesn't fit. The numbers came back nearly identical. Decode: 33.0 vs 33.6 tok/s. Time to first token: 481 vs 527 ms. Prefill at 32k: 2,306 vs 2,230 tok/s. Even power draw: 35 vs 36 watts. Two boxes, twice the silicon, a network in the middle. Same speed. Tensor parallel over RoCE buys you memory, not throughput, every layer pays an all-reduce over the wire, and on this fabric that eats roughly what the second GPU brings. People say this all the time. Now it's measured, on this exact model, with both configs public. His run notes are half the value of the contribution. One example: a DGX Spark drained from its cluster loses the clock governor and idles at 600 MHz of a 3,003 MHz ceiling. Benchmark it in that state and you silently publish numbers 4x too low. He caught it, unlocked the clocks, and wrote it down. That gotcha now lives in the atlas for the next person. Another: launching this model at its native 262k context wedged both boxes. His cell honestly says "this is a 32k number, don't read it as more." That's exactly the culture I want in this thing. Where the atlas stands now: 210 runs, 20 models, 19 devices, 10 engines, 2 contributors. 8 of 4,717 cells have a number. The rest are yours. Every grey square comes with the exact commands to fill it in about twenty minutes, and a 3090 counts as hardware. Links below. Thanks John. First Light badge earned

• 
  𝐻𝒶𝓃𝒾𝒸𝓈 (@Hanicsss) reported 51 minutes ago

  @SixZzshOtRipZz How did you get it to work? I keep running into issues with the github DLSS5 feeder

• 
  Justin Lord (@Justin_lords) reported 51 minutes ago

  Past 4 years I did hundreds of cold outreach, if not thousands. On every single platform that exists on the internet. What I learned on each one: Instagram: They see your message first. If it intrigues them, only then they check your profile. If your message is all about pitching, you get ignored right away. Big creators (50k+) don't check their DMs - they have people for that. So unless you built authority or you're already known, it's a volume game. Out of 100 people maybe 2-3 reply. X: Cold DMs alone don't work here. People don't check their inbox even if they're big. What works: DM them, then comment on their post with value and at the end saying "I DM'd you". If they're active they will check. My reply rate was very high with this. But be genuinely curious about the person. They can tell if you're not, and they'll ghost you or mute you. Reddit: Make a free value post for your ideal customer. Give the link away for 2 hours, then remove it. The link can be any resources, like youtube video or github. Everyone who missed it comments "I need the link". You DM them the link, then your pitch. That's retargeting without getting caught. They respond because it's genuinely connected to their problem. LinkedIn: Buy Premium Plus first. Now you have data on who saw your profile. If they saw it, they might have interest. Reach out to those people. I booked a lot of calls for my client in the first week with this. Connect with people likely to be your customer, like their posts, build trust first. It's B2B high ticket, you can't skip that. YouTube: I got their emails from their bio and pitched. Most bounce. Creators don't open them, and the ones with emails keep them for sponsorships. Target the exact right person or don't bother. My biggest obstacle was simply the offer. If you have the best offer for the right person, they respond. If your offer sucks and doesn't speak to their problem, they will never respond. You should use platform that offers mass outreach for each of these platforms or whatever ones you're trying to target. it works only if you first give it a try. Where to focus: High ticket → LinkedIn Connect now, sell later → X Selling to creators → Instagram Long form creators → YouTube Right now I only do X outreach. It works because I'm genuinely curious about the person.

• 
  Tung Air (@tungair87) reported 55 minutes ago

  Quick 5-minute setup to start coding: 1. Clone the proxy repository from GitHub 2. Run the local proxy server daemon 3. Export custom ANTHROPIC_BASE_URL to localhost 4. Launch `claude` CLI and build autonomously Zero credit card required. Pure open-source engineering.

• 
  Ulaş Difficile (@ulasdifficile) reported 55 minutes ago

  @dshukertjr @supabase GitHub issues are not enough?

• 
  Tristan Rhodes (@tristanbob) reported 56 minutes ago

  @grok @bot @github The GitHub connector is installed, but it cannot start sign-in. GitHub’s MCP is rejecting the request with a badly formatted Authorization header. That is a plugin config problem, not a missing login, so a Settings card would fail the same way.

• 
  James McConville (@jamesmcconville) reported 1 hour ago

  @gsemetfr @github wai... is this why I'm having trouble? everything seems to be fine, I can log in... and then when I try and use it I can't select a model.