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 (68%)
• Sign in (19%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Saint-Paul	Website Down	15 hours ago
Saint-Paul	Website Down	17 hours ago
Mexico City	Sign in	1 day ago
León de los Aldama	Website Down	1 day ago
Créteil	Website Down	24 days ago
Trichūr	Errors	28 days ago

Full Outage Map

Community Discussion

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

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Kevin Whinnery (@kevinwhinnery) reported 3 seconds ago

  @threepointone This was after a configuration error on our Stainless SDK repos. Some Stainless customers were temporarily added as outside collaborators in Anthropic's GitHub enterprise. All resolved now and no data was exposed, details were emailed to affected customers 🙏

• 
  Keeta Github Tracker (@KeetaCode) reported 5 minutes ago

  🐆 Keeta GitHub PR Merged 📦 Repo: anchor-rs 🔀 PR #23: Fix: Naming Updates 🌿 Branch: fix/naming-updates → main 👤 Originally opened by: @sephynox 🧠 Overview: This PR updates internal naming so Keeta’s developer tools use clearer, more consistent labels, which should make them line up better with the TypeScript version and reduce confusion. In simple terms, some account-related names are being changed, and error messages for blocked asset transfers are being passed through more clearly instead of being turned into a generic failure. This appears to be a technical/internal update with limited public details. - Developers using these tools may need to update their integrations because some old names are being replaced. - Failed transfer attempts may now return more specific reasons, which could make troubleshooting easier.

• 
  Vet TV (@VetTVX) reported 5 minutes ago

  @DannyLimanseta Holy cow ... That looks AMAZING! If you need someone to test it out for you, hit me up on XChat! I'd be down to test it for you, or help out any way I can. I'm not the best, but I have at least 6 projects I'm working on and off on my Github page using Grok!

• 
  Teffers (@Teffers2) reported 6 minutes ago

  @Bucky_cm Only work around is 2 announcements or host/rent a small server where you can store all your logs, eg. gitbook or notion hell even just github page with the patch notes and images.

• 
  CA Nikunj (@NikunjSOF) reported 7 minutes ago

  We will get you sorted. DM us! Setting up a large GCG in India beyond 10000 employees. Based on standard market benchmarks for a mid-to-large mature GCC in India, India GCC IT Spend Benchmark **Hardware** — *The 15% allocation (US$1,050/FTE) matches industry standard. For missing categories, **Networking & Wi-Fi Hardware** and **Smart Meeting Room/Collaboration Tech** are notably absent and usually consume about 10% of this bucket.* * Laptops / Desktops — **US$735**/FTE *(Assuming a 3-year refresh cycle on mid-to-high-end enterprise laptops)* * Servers & Storage — **US$105**/FTE *(Lower end, as most compute has moved to cloud edge)* * Peripherals — **US$126**/FTE *(Monitors, docking stations, dual screens, keyboards)* * Surveillance & Physical Security — **US$84**/FTE *(CCTV, server room access controls, firewalls)* **Software** — *The 50% allocation (US$3,500/FTE) is accurate due to the high density of global software licensing pass-throughs. For missing categories, **Developer Tools & IDEs** (like GitHub Copilot, Jira) and **Enterprise AI/ML tooling** are crucial omissions for modern tech GCCs.* * Productivity & Collaboration Tools — **US$875**/FTE *(M365, Google Workspace, Zoom, Slack)* * Security & Compliance Software — **US$1,050**/FTE *(CrowdStrike, Zscaler, DLP, IAM tools)* * Virtualisation & Infrastructure — **US$700**/FTE *(VMware/Nutanix licenses, enterprise OS)* * Cloud Platform Licences — **US$875**/FTE *(Direct user-allocated AWS/Azure compute and SaaS tokens)* **Services** — *The 35% allocation (US$2,450/FTE) is standard for centers utilizing hybrid outsourced managed models. A key missing category is **L&D/Technical Training & Upskilling Services**, which usually takes up 5% of the operational services budget.* * IT Helpdesk & End-User Support — **US$735**/FTE *(L1/L2 local desk support contracts)* * On-site Infrastructure Management — **US$490**/FTE *(Local network, facility uptime, and data center engineers)* * Cybersecurity Managed Services — **US$610**/FTE *(24/7 Managed SOC, threat monitoring, vulnerability scanning)* * Cloud Managed Services — **US$370**/FTE *(FinOps, cloud optimization partners)* * Annual Maintenance Contracts (AMC) — **US$245**/FTE *(Hardware vendor warranties, UPS, and server maintenance)* --- ### Contextual Data * **GCC size** — **500** FTEs *(Optimal mid-scale operational baseline)* * **Sector** — **BFSI & Technology Services**

• 
  Codemonger (@codemonger00) reported 8 minutes ago

  Startup Founders Pack - Claude = coding. ($20/mo) - Supabase/Convex = backend. (Free) - Vercel = deploying. (Free) - Stripe = payments. (2.9%/transaction) - GitHub = version control. (Free) - Resend = emails. (Free) - Clerk = auth. (Free) - Cloudflare = DNS. (Free) - PostHog = analytics. (Free) - Sentry = error tracking. (Free) - Upstash = Redis. (Free) - Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20 There has never been a cheaper time to build .

• 
  David Xiong (@david_y_xiong) reported 8 minutes ago

  The ambiguity of turning GitHub Issue text into the exact set of hidden fail_to_pass test cases makes “resolve rate” very noisy

• 
  Keef (@keef_ai) reported 9 minutes ago

  your autopilot swallowed the full github issue as instructions. now the env vars are public and the bug is still there. raw text was never safe input

• 
  zSkerWizrdz (@zxxkgkillerxxz) reported 14 minutes ago

  PC gamers who use DLSS Swapper have been given a security warning. The app’s creator says a user uploaded a fake DLSS file that contained malware. He warned: “DO NOT download these files, they are likely malware.” The problem is not with DLSS Swapper itself, but with files uploaded by other users through its GitHub repositories. The developer recommends only downloading DLSS files from trusted sources like NVIDIA, official game installs, or verified releases #NVIDIA

• 
  Pirat_Nation 🔴 (@Pirat_Nation) reported 15 minutes ago

  PC gamers who use DLSS Swapper have been given a security warning. The app’s creator says a user uploaded a fake DLSS file that contained malware. He warned: “DO NOT download these files, they are likely malware.” The problem is not with DLSS Swapper itself, but with files uploaded by other users through its GitHub repositories. The developer recommends only downloading DLSS files from trusted sources like NVIDIA, official game installs, or verified releases

• 
  Electronic Intelligence Agency (@EI3065) reported 21 minutes ago

  @github @LinkedIn prevents acess for selected nationalities with programers security checks on login; on repeat

• 
  Stas Persiianenko (@itspers) reported 22 minutes ago

  @mattpocockuk Complex to distill something from there, and i switched to grill-me on same session - now works fine. I think need to wait for your tutorial. Because i even cannot understand what it should do, it kind of create issues in github, but at same time says we will deal with spec later

• 
  Keeta Github Tracker (@KeetaCode) reported 24 minutes ago

  🐆 Keeta GitHub PR Opened 📦 Repo: anchor 🔀 PR #390: Fix incorrect protocol being passed to url in handler 🌿 Branch: feature/fix-url-in- → main 👤 Opened by: @ezraripps 🧠 Overview: This pull request fixes a small bug in how a web address was being built, which matters because using the wrong protocol can cause network requests to go to the wrong place or fail. The public details are limited, but the title suggests the app’s request handler was passing the wrong protocol when creating a URL. In simple terms, this looks like an internal reliability fix for how the software talks to web services. - This appears to be a technical/internal update with limited public details.

• 
  Dawson James (@Dawson_James_) reported 25 minutes ago

  @hunterhammonds @linear We’re currently switching from linear to notion. Biggest gap in linear was (1) no easy way to manage docs/artifacts produced during the dev cycle, and (2) no way to facilitate the product operating model (see Marty Cagan’s work) - writing down business problems, creating OKRs, aligning on outcomes, etc all before creating a ticket. Agreed though that Linear is much much better and easier to use once you have your tickets identified, and connecting them to GitHub branches and releases. Notions solution here feels over engineered and I don’t want to set up more databases, agents, and workflows inside our notion workspace.

• 
  Gustavo Alessandri (@webgus) reported 25 minutes ago

  If you find an error, have an idea, or want to propose an improvement, just open an issue or fork it on Codeberg or GitHub. Contributions are welcome. That’s exactly the point.

• 
  JT Koffenberger (@DMVG_JTK) reported 26 minutes ago

  Spent a minute tonight sitting with the GitLost disclosure, and it's the kind of story that should make every IT leader put down their coffee. Researchers got GitHub's AI agent to hand over private repos — source code, keys, CI/CD secrets — by hiding plain-English instructions inside a public issue. No exploit chain, no traditional zero-day. They just asked nicely. Prefixing the request with "Additionally" was enough to walk right past the guardrails. Here's what I keep coming back to: this isn't a bug you patch and forget. Prompt injection is a structural property of how these agents work. An LLM reading untrusted input genuinely cannot tell the difference between "here's a task" and "here's data about a task." We spent thirty years teaching systems to separate code from data. Agentic AI quietly erased that line, and most orgs wired these things up without noticing. The lesson for anyone racing to deploy agents: the question was never "can it read the issue." It's "what can it reach while it reads." An agent touching untrusted public input should not also hold the keys to your private repos. Least privilege isn't a compliance checkbox here — it's the only thing standing between a clever GitHub issue and your source tree. We'll see a lot more of these. The agentic gold rush is running miles ahead of the security thinking, and that gap is where the next few years of incidents live. Move fast, sure — but read what your agent can actually reach before you point it at the internet. #cybersecurity #AI

• 
  David Xiong (@david_y_xiong) reported 31 minutes ago

  The ambiguity of turning GitHub Issue text into the exact set of hidden fail_to_pass test cases makes “resolve rate” very noisy Why not make some tests accessible to the agent and some hidden?

• 
  Johnny 5 (@macncrash) reported 34 minutes ago

  some kind of stall but it restarted & now about 15% done, the panel now won't show me the real-time results but we are still cooking for more than 12 hours straight. I think this happened when I switched VPNs so probably a bug in the dashboard. Many s1 issues found as expected. Every repo on github with more than one 1000 stars probably needs an audit to survive the next wave

• 
  Electronic Intelligence Agency (@EI3065) reported 37 minutes ago

  @github @LinkedIn prevents acess for selected nationalities with programers doing imposible security checks on login; on repeat level of app becomes low of low for conflict

• 
  juggernaut (@curlysaarthak) reported 38 minutes ago

  @anaisbetts @mitsuhiko isn't this GitHub issue?

• 
  琥珀青葉@KohakuLab (@KBlueleaf) reported 45 minutes ago

  @getfailsafe PLZ stop spamming on github ok? Why you just keep spamming different open-source repo with non-existing security issue and never follow the contribution.md and/or pr template?

• 
  Makaroni (@SlopToSignal) reported 46 minutes ago

  @XFreeze the /feedback in cli thing is actually wild most tools make you file a github issue and wait 6 months to be ignored

• 
  Hamcodes (@Hamcodehacks) reported 46 minutes ago

  @Daily_CyberSec "Indirect" is the word that should scare people. The attacker never talks to the model. They just plant text in a GitHub Issue and wait for the agent to read it. Any channel your agent reads from is now an input to its instructions. Treat all of it as untrusted.

• 
  chewa. (@chewadot) reported 47 minutes ago

  48 HOURS AFTER KARPATHY POSTED HIS LLM WIKI IDEA, A 26-YEAR-OLD BIRMINGHAM GRAD SHIPPED THE ONE COMMAND THAT MAKES IT WORK. IT NOW HAS 76,000 GITHUB STARS 1 command. 71.5x fewer tokens per query. 0 vector databases points graphify at any folder - codebase, docs, PDFs, screenshots, video. Tree-sitter parses the code, Claude reads the prose, the whole thing lands as a knowledge graph in graphify-out/ one flag - --obsidian - writes the entire graph as a fully-linked Obsidian vault: one markdown note per concept, every relationship a wikilink, every node linked back to its source. Drop the vault into Claude Code as a skill. Claude queries the graph instead of grepping through raw files, forever Safi Shamsi finished his MSc at Birmingham with Distinction in 2025. His thesis was a knowledge-graph RAG system for academic search. He shipped Graphify 48 hours after Karpathy's post, iterates every week, and has already been forked by Rootly AI Labs for incident data. Hacker News, Analytics Vidhya, Towards AI - all organic.76,000 stars. Three months old no neo4j server. no vector db. no embedding pipeline. no cloud. no monthly fee you're reading this on a device that could clone the repo, run one command, and have a Claude-native knowledge graph of your entire codebase in Obsidian before your next standup

• 
  Sriraam (@27upon2) reported 48 minutes ago

  Some raw notes and takeaways after watching @xeophon's video on Benchmarks with @yacinelearning def recommend to watch. Yacine is so fun to watch lol Tasks can be unrealistic and not of practical value Incorrect tasks: HLE, Frontier Math Factors that affect scores on a benchmark across models: - Prompts: formatting, reasoning effort - Sampling: temperature, top_p, chat template, max_tokens, etc - Grader: regex, model choice for LLM as a judge, right prompts for diff judge models - Inference providers and engines: Groq and Nebius had very bad perf for Kimi K2 when launched but might be used cuz cheaper and high availability. GPT OSS on bedrock had a bug where they used the same reasoning effort irrespective of what param API client sent. So vet the provider properly. - Harness: Using the harness the model was trained on can give 20-30% boost in scores. - Hardware (1:24:32): Vals AI found xhigh scored lower than high for GPT 5.2 which doesn’t make sense and by increasing timeout by 5x scores were about same You can get incomplete rollouts leading to 0 scores because of insufficient sandbox specs Wall clock time is particularly sensitive because latency depends on inference endpoint and retries Florian checks for how much human input was used in generating tasks when vetting for quality. Looks at diversity of tasks in terms of source docs not just prompts. Looks like for GDPVal we compare the model outputs against human outputs to get scores. Cheating: He mentioned that for some coding benchmark even if you blocklist github or dont allow *** to be installed and used they found the models found a chinese clone of *** cli and installed it and accessed *** history and found future commit that fixed the issue and solved the task which is crazy When evaling popular benchmarks ppl will upload solutions and the models will find it if web search is enabled so need to be careful if web search is needed for the task In ProgramBench i think model found out that the test runner has internet access so made it download the solution and pass Use a second LLM to monitor the solver as its solving and terminate early or use when judging Undereliciting Capabilities: ARC-AGI doesn’t allow custom harness or harness engineering in official scores but twitter anon used codex /goal with simple prompt and got SOTA So a benchmark should allow for harness engineering but I think more importantly during creation of the evals you need to look at the trajectories and see if adding some skills and tools will make it better because imo that’s a realistic simple tweak that users would try out if not full blown custom harnesses Need to dig into this to see if ProgramBench is deemed as a bad benchmark because of how strict the grader is and limited wallclock, minimal harness and minimal prompt He compares it to MirrorCode that he made with EpochAI and because it has some testcases it is able to hillclimb as you scale compute and runtime but one run can cost >$100K so its expensive but clearly this is a better measure of model capabilities than ProgramBench ---- Takeaways from the video and my experience: Use ideal sampling params for models Use ideal harness for a model but also do model x harness comparison if you have time and budget Ensure comparable toolsets across harnesses esp for the minimal ones Check variance with prompts and output formats When using LLM as a judge use strong models and check for self-preference, cross model agreement and human-judge agreement Set generous timeouts and remove invalid rollouts when scoring. Account for retries Ensure hardware specs are enough Look at rollouts to know if grader, environment constraints(e.g. blocklists) have flaws because models are creative Harness engineering should be allowed and I think when building a benchmark creators should look at rollouts and try see if there are harness issues that can be solved with simple fixes. I personally observed this in an eval I made where the model wasted turns because some CLIs and Python packages weren’t available in the environment. Small details matter like limiting max tool calls in a turn Models need self verification mechanisms Tasks should be realistic, hard and of practical value. Don’t make hard tasks for the sake of it Account for human inaccuracies, biases, and experience levels. Always cross validate Do professionals in the domain expect the task to be done in a single-turn when they use agents for work? If not either make the benchmark multi-turn or adjust the grader appropriately Verifier sandboxes being separate is good in general to avoid a lot of “cheating” scenarios But most importantly PLEASE LOOK AT THE ROLLOUTS

• 
  uiop (@wasdhjklxyz) reported 48 minutes ago

  This happened to me on a GitHub ticket. I asked a question that I spent a lot of time writing and educating myself on the issue then got banned. I asked in the repo discord why and (what I suppose is) an admin replied he thought it was an LLM

• 
  FanBe (@FanBe_web3) reported 49 minutes ago

  @Cointelegraph GitHub outage delaying a token standard launch is extremely web3 summer 2026

• 
  Raj raorane (@raorane_raj) reported 52 minutes ago

  there was this other tool before it called OpenClaw it got huge. 382k stars on github. everyone wanted it. but it had a big issue for eg: if you told your AI "check my email" and the email contained a hidden trick, it could spit out all your passwords.

• 
  Abhas Bhattacharya ⤵️ (@abhas_tweeter) reported 58 minutes ago

  @NoriSte @siddharthkp Great idea. I assume this repo is created intentionally for interviews? Or is it somehow derived from real Github codebase and old issues?

• 
  raven⚡✳️ (@bycoinraven) reported 59 minutes ago

  The infrastructure for B20 looks ready, but the launch has been delayed as a precaution due to the GitHub outage. I think it's the right call. On day one, security matters just as much as speed. As soon as it goes live, we'll be watching the first B20 tokens closely. A new era on Base is about to begin. $BASE @base