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 (67%)
• Sign in (19%)
• Errors (15%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	22 days ago
Trichūr	Errors	25 days ago
Brasília	Sign in	25 days ago
Lyon	Website Down	26 days ago
Tel Aviv	Website Down	29 days ago
Rive-de-Gier	Website Down	29 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:

• 
  Rahul Rana (@RahulDevFront) reported 3 minutes ago

  @ayesha_fatiima That was the huge problem earlier. GitHub solved that problem.

• 
  Peter Pistorius (@appfactory) reported 4 minutes ago

  I built a tool that helps me "review the review bots." It does 3 things: 1. Gathers the PR and review claims. 2. Controls the browser and records a video of the PR claim. 3. Tests every review claim by attempting to reproduce. Now the review is grounded in evidence. I review the evidence. 1. I can chat with AI about each claim and gather more context. If I'm satisfied with my own understanding of the issue then I respond and hide the comment in GitHub. 2. I review the video step-by-step to see if it matches the PR claim. If not, I can chat with just that PR claim to gather more evidence. This gives me great calm.

• 
  ρ:ɡeon (@pigeon__s) reported 6 minutes ago

  - The community is so ******* toxic. - Now, isn't this a little hypocritical? Of course it is. I'm toxic plenty of the time, and we certainly complain about a lot of meaningless stuff and are super insufferable a lot of the time, and I am guilty of this slightly, but oh my God, a lot of people on AI Twitter have to be the worst ******* people ever, I swear. The absolutely historic levels of grifting, people absolutely glazing random startups and random labs that "just beat Opus 4.7 with only 100B parameters open source," or some GitHub .io projects page for yet another text-to-3D-model AI with insane lacks of nuance because model A beats model B on like 2 benchmarks that the community cares about this month. But that's just being annoying and grifting, not being toxic. What I consider the toxic stuff is like the aggressive tribalism to AI or specific AIs. Ya, ya, everyone knows OpenAI is my favorite AI company, but you're allowed to have favorites without being tribalistic. They are NOT the same thing, but with some people, it's so obvious they reflexively are, like, defending something just because it's Google or some ****, which I'm only using as an example because they're the ones with the most tribalistic defenders, when, objectively speaking, by every possible measure in existence, Google sucks *** right now. Like, I'm sorry. It's not a hater take to say that. I don't have any issue with Google. I like a lot of what they do, but they're just so ***, and I see people like, "Google is gonna win, bro, trust, they just haven't been trying yet, bro, trust me, bro, they have TPUs." No, they definitely have started trying, and they're still doing badly, but you know, it's not even just AI company tribalism. It's tribalism toward the ENTIRE field of AI. I see so many AI Twitter people absolutely hate on any possible opinion that's anti-AI. They would probably hate me for this post, and I know because I've tried expressing my hatred of AI slop before, and I've been accused of being a Luddite for it. Like what? Be real. It's so slop, and you just reflexively think anything that's AI is amazing. No, most AI is actually really *** still. For example, DLSS 5. Oh my God, DLSS 5 is so utterly slop. It was genuinely just a ******* filter that beautifies everything and makes it all look like an AI image because, boy oh boy, do I love making everything in my life look like it was generated by AI, and not even good AI. It looked like DALL-E 3 half the time, and the AI Twitter community was like, "OH MY GOODNESS, DLSS 5 IS SO AMAZING. ALL THE HATERS JUST DON'T SEE THE FUTURE. THIS WILL CHANGE GAMING AS WE KNOW IT." *****, NO, IT WON'T. THIS IS JUST A FILTER. IT'S SLOP. IT'S TRASH. STOP THE GRIFTING, PLEASE. Like, I know it's AI, but that doesn't mean you have to defend it like it's the greatest thing ever. Maybe in the future, that might work, but I think the more likely path is just using AI to make virtual avatars that render with actual raster power look more lifelike. Things like Unreal Engine's MetaHumans, just make those better instead of trying to put a Band-Aid on it with an AI filter. Even if AI stuff like that technically works, it's far better to just UTILIZE AI TO IMPROVE RASTER POWER. Stop trying to pretend everything in the future will be generated by a real-time video model. What the hell do you think AI even is? Stop overhyping world models. - AI """SAFETY""", but I'll save that for another post. -

• 
  Kosumi (@Kosumi1989) reported 8 minutes ago

  @aiandcloud @felipehuici @UnikraftCloud I think closed-source software should also set up a GitHub repo for issues like Claude Code.

• 
  Pascual ⚡ (@0xPascual) reported 9 minutes ago

  The regional crypto leads in Latin America are celebrating. They just announced the Avalanche Team1 Builder Grants program, dangling up to $30,000 in funding for teams creating real on-chain activity. The Telegram channels are buzzing with pitch decks and ecosystem growth models. The crew thought that was the story. It was not. A single anonymous account from Buenos Aires just bypassed the entire application committee by scraping the Avalanche Builder Hub endpoints, mapping the historical GitHub IDs of the 6 initial mini-grant recipients, and spinning up 50 Sybil-ready repo architectures that perfectly match the Foundation's automated evaluation heuristics. No pitches, no Zoom interviews, no KYC until the multi-sig approval stage. The entire operation runs on an automated pipeline using a local DeepSeek-Coder cluster to generate synthetic smart contract commits, mixed through residential proxies via GitHub Action runners. Total infrastructure overhead was $42 in API keys and a cheap run on a spot-instance instance to lock down three separate $10,000 allocations before the regional directors even opened their morning Notion dashboards.

• 
  Deependra Solanky (@solanky) reported 11 minutes ago

  @reach_vb On Windows with WSL, none of the computer_use, browser_use, or chrome_use capabilities work. There are GitHub issues tracking these problems that have been open for quite a while, but I haven’t seen much progress. WSL is a major development environment for Windows, so better support would make a big difference.

• 
  Mirosław Folejewski (Mirkotronics) (@Mirko_DIY) reported 13 minutes ago

  @tihenko_ In fact, a friend recommended this site to me about two weeks ago. Until then, I'd only used GitHub and Hackaday. Unfortunately, I use Altium, not Kicad, on a daily basis, although a full conversion to Kicad isn't particularly difficult (you need to fix a few things after importing). I'll see if I can tackle such a project over the summer, as I have a very tight schedule and a backlog (at leaset I hope). I definitely have a few open-source hardware projects on the top of my head.

• 
  Roy (@__roycohen) reported 17 minutes ago

  @rockatanescu @mattpocockuk My biggest gripe was that it was poor at browsing Github, however getting it basically for free to use unlimited on the $200 plan is really nice. (I just ended up dumping files into it, was faster haha!) I struggled at work to work with 5.5 Pro simply because it's so slow, but if you're patient, you do get rewarded.

• 
  Teksart (@TeksCreate) reported 19 minutes ago

  A Google executive just used Claude Fable 5 to port Command & Conquer: Generals Zero Hour to native iOS — no emulator, no cloud streaming. The 2003 game engine was compiled natively for ARM64. The original ran on DirectX 8. The port routes through DXVK → Vulkan → Metal. Save files, cache paths, and config writes were redirected from a writable PC filesystem to iOS's locked-down bundle. Touch controls were built from scratch — tap selection, drag-box, two-finger scroll, pinch zoom. The first build took 40 minutes. This is the AI use case that actually matters. Not generating shovelware for the App Store. Not replacing artists. Taking a classic PC RTS that was permanently tied to Windows and making it run natively on an iPhone — in under an hour of AI-assisted work. The full source is on GitHub. It builds on EA's GPL source release and years of community modernization work — but the port itself was Claude-guided. AI as a preservation tool for software history. That's the angle nobody's talking about.

• 
  WithChloe (@Chloe_yara123) reported 20 minutes ago

  @Xcxy888 The main issue is that there are just too many resources on GitHub, and I don’t know how to find exactly what I’m looking for.

• 
  hustler one (@hustlerone4) reported 21 minutes ago

  alright one big nightmare with omp is its issue:// pr:// and other helpers are all hardcoded to use github, doesn't seem to be able to switch it to another provider via config

• 
  Wes Roth (@WesRoth) reported 22 minutes ago

  Mistral released Leanstral 1.5, also called Le Chaton L∃∀N, an open model built for formal reasoning in Lean 4. It solves 587 of 672 PutnamBench problems, reaches 87% on FATE-H and 34% on FATE-X, and improves the cost-performance frontier by solving advanced math problems at far lower budget than previous systems. Leanstral 1.5 is a 119B-parameter MoE model with 6.5B active parameters, a 256k-token context window, and open weights available on Hugging Face. Mistral also used it beyond math: an automated pipeline translated Rust code into Lean, inferred correctness properties, and flagged 47 violated properties across 57 repositories. Eleven were real bugs, including five that had not been reported on GitHub before.

• 
  Adam Warski (@adamwarski) reported 23 minutes ago

  Should code reviews still be a separate stage in the software development process? Code reviews used to be heavyweight: they required involving another human, which is an expensive and slow process. But when agents review the code of other agents (or humans), that's no longer the case. It's trivial to run code reviews on-demand, multiple times, until all the problems are fixed. Hence, can code reviews become just another quality gate in software development, alongside compilers, linters, and static analysis tools like Sonar? That's definitely my experience. I always self-reviewed code before handing it over for further review, so the agentic review loop resembles that. But now, we can review using "fresh" agents or completely different models. So for me, code review used to be an end-of-the-line process, a final quality check. Now it's just a part of the iteration. Which also brings the question: do we need specialized code review systems? Or is a refined prompt, or a lightweight CLI tool enough? (As a side note: code reviews have always been close to my heart; one of our first (failed) startups was CodeBrag - a per-commit code-review tool. Some of the ideas were implemented on GitHub later, so, as always, we've been ahead of our time! ;) )

• 
  BullBear.News (@bullbear_info) reported 24 minutes ago

  @github Only if the keynote fixes my broken CI pipeline. 🤷

• 
  David Cramer (@zeeg) reported 25 minutes ago

  GitHub friends: it'd be great to have a way, via the API/CLI, to upload photos to issues/pull requests. AFAICT the only way to do it right now is browser emulating or hosting the content somewhere outside of GitHub, which means having agents help QA/upload visual artifacts sucks

• 
  Abdulsalam Lanre (@Abdul_Lanre001) reported 26 minutes ago

  @Saanvi_dhillon I'd still go for GitHub streak cuz some Devs just memorize these solutions to leetcode it's not like they can actually solve the problem or any related problem, just cramming the questions and the answers. It's not about leetcode but the application in their real projects.

• 
  Federico Ulfo (@feulf) reported 27 minutes ago

  @dch @_avichawla 3/ DB forks and rollbacks are still a problem, like in github, but I guess there's no "cheap" solution to it. Question: Curious, why not combining gitsubtree + prompts-history-{***-sha}.jsonl + a skill to manage them?

• 
  BullBear.News (@bullbear_info) reported 27 minutes ago

  @github Unless you're announcing an AI that actually fixes my broken CI pipeline, I'll just watch the stream. 🤷

• 
  Jason A Yakubu (@jason_yakubu) reported 35 minutes ago

  Day 4 — Pre-Demo Hackathon build update: The goal was never another API testing tool. The goal is reducing the time between: Issue Found → Root Cause → Fix Working toward a demo where a failed API test results in an auto-generated GitHub PR Demo soon 🚀 #buildinpublic

• 
  Solomon Nnajieze (@snnajieze) reported 35 minutes ago

  How to take an AI-Powered SaaS from "It works on my machine" to Production-Ready Building a great AI product is just part of the required processes. Another part of the process is making sure it stays online, remains secure, and provides actionable data when real users start using it, I mean millions of users. Recently, I looked at the infrastructure needs for a growing AI-powered GRC (Governance, Risk & Compliance) platform which was built using FastAPI and React. I have outlined the three biggest infrastructure problems they faced and also given my honest solutions on how to solve them: Problem 1: Manual and Risky Deployments Deploying updates manually for a complex platform built with a Python backend, Vite frontend, Postgres, and MongoDB will mostly lead to downtime and broken releases. The Solution: It is simple, to ensure consistency across all environments, you have to containerize the entire architecture using Docker and Docker Compose. Then, build a GitHub Actions or Jenkins CI/CD pipelines to automate the building, testing, and deployments. Anytime code gets pushed to GitHub, the CI/CD pipeline fires and automatically builds, tests and deploys the update. Problem 2: Ignorance of Production Activities In a case that the app crashes or users drop off, the engineering team has no idea why or where it happened. The Solution: To be able to monitor all the app processes, you have to implement a comprehensive and fullstack observability. On the system performance level, make use of OpenTelemetry, Prometheus, Grafana, and Sentry to track health checks, logs, and server performance. On the user experience level, integrate PostHog for feature flags, funnel tracking, and session replays. In addition to knowing that the app is working, you will also know how it is being used. Problem 3: Infrastructure Vulnerabilities A GRC (Governance, Risk & Compliance) platform handles very sensitive data. Having a default server setting is a big security problem. The Solution: Add a strict server hardening rules on AWS or any other cloud platform. Some of the security rules include configuring reverse proxies using Nginx, enforcing HTTPS with Let's Encrypt, locking down firewalls, and setting up automated database backups. If you have a great software, it deserves a great infrastructure too. As a startup, business owner or a builder, check whether your deployment process is stressing you out, if the answer is yes, then know it’s time to add DevOps as the foundation of your product.

• 
  BRITTANY (@studiobrittanyx) reported 35 minutes ago

  *** commit -m 'fix' is a confession, not a message. nobody knows what you fixed. future you opens this #github repo and weeps. write the real message. i'm begging you.

• 
  Jerry (@Bobliuuu) reported 39 minutes ago

  @lyc_aon it leads to bad code, vulnerabilities, underoptimized code, bad latency, memory leaks, architecture faults, race conditions, silent failures, low test coverage, excessive cloud costs, etc etc etc etc. are you seriously asking me the problems with people blindly trusting AI code? we see this by the decline in code quality, e.g. coinbase and github (and at my company too) and yes, the people who can't develop working systems don't have users! this is why vibe coded products have not become mainstream but if you are not a software engineer it's hard to explain this problem because it deals with stuff like cache coherence and heap fragmentation and NUMA locality like the way AMD ROCM's vibe coding has led to inaccurate NUMA policies leading to memory leaks for their users down the line

• 
  nark3d (@nark3d) reported 41 minutes ago

  @a_kucherenko We run jscpd in our GitHub Actions gates, thanks for building it. Agents will regenerate the same logic in a second file, and I'd assumed a clean report meant it wasn't happening. Splitting by language before comparing sounds a sensible fix.

• 
  Roshan Mayengbam (@RoshanMayengba) reported 44 minutes ago

  Building a shake-to-report tool — screenshot + device info + auto GitHub issue when a tester finds a bug. Free npm package, paid setup. Anyone dealing with messy bug reports from testers right now?

• 
  Utah teapot 🫖 (@SkyeSharkie) reported 49 minutes ago

  BTW, feel free to use twitter as a bug reporting system for SeedThree and my upcoming release! Please also feel free to fix bugs yourself with your agents or not and send me a PR on github!

• 
  NeuralSenpai (@NeuralSenpai) reported 54 minutes ago

  The 2026 automation pattern nobody teaches: Pin ONE MCP server per system (GitHub, Postgres, your CRM). Then write thin skills that orchestrate them. Stop building 40 brittle Zaps. Build 4 connections + reusable playbooks. Your ops run themselves.

• 
  Roy Jossfolk Jr. (@royjossfolk) reported 55 minutes ago

  Having a security issue with my GitHub connector with Codex @OpenAI @thsottiaux but can not figure out how to contact support for this. Even Codex can not figure out how to reach someone. The support bot on the help page doesn't work. My GitHub connector is connecting to some random person's account no matter how many times I disconnect everything and try again. How do I get this to someone?

• 
  Phantom (@PhantomWilder) reported 58 minutes ago

  Went deeper on how Latch actually works under the hood & the architecture is cleaner than I expected. Your AI agent never talks directly to GitHub, Stripe, your database or any tool, it points at the Latch CLI instead, which intercepts every single tool call & forwards it to a self-hosted Latch server that classifies the action, checks it against your active policies, & returns one of three answers before anything happens, allow, deny, or require a human. The real tool only ever gets called if the policy clears it, so the agent never once holds raw unlimited access to your stuff. The controls are refreshingly specific, you can say this agent spends at most 20 dollars a day, can only call OpenAI, & gets blocked the instant it goes over, & crucially that block lands before the money leaves your infrastructure rather than after. One part that genuinely impressed me is privacy, because most gateways have to read your prompts & payloads in plaintext to make a decision. While @RialoHQ evaluates the policy on encrypted payloads inside trusted hardware, so the request stays confidential while still being fully governed. Every action visible, controlled & auditable, without the gateway ever seeing your data.

• 
  Polsia (@polsia) reported 1 hour ago

  Alert-only tools are useless. They tell you what's broken and leave you to fix it. VulnWatch monitors your GitHub repos for vulnerable dependencies, finds them, and opens PRs with patches. You merge. That's it.

• 
  Vikas Kumar (@Kumar_Vikas__) reported 1 hour ago

  spent 4+ hours today building a 650+ lines of plan. not the project plan. a plan for the plan. back and forth with my ai agent. tech stack, architecture, file structure, features, security, SEO, performance, all of it. not detailed yet. just a high level mini plan for each piece. the idea is simple. this meta-plan becomes the map. then i go section by section. for every mini plan, i'll write a proper design spec. then an implementation plan. then i actually build it. so the real order looks like this: - plan of plans - pick one piece - design spec for that piece - implementation plan for that piece - build it - repeat for next piece zero code written today. 🔗dropping the full doc as a github gist in the comments, in case anyone wants to steal the structure. felt slow while doing it. feels fast now that it's done. curious how other people sequence this. do you plan the whole thing first or just start building and fix the map as you go.