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 (65%)
• Sign in (19%)
• Errors (16%)

Live Outage Map

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

City	Problem Type	Report Time
Veigné	Errors	10 hours ago
Paris	Website Down	4 days ago
Saint-Paul	Website Down	5 days ago
Saint-Paul	Website Down	5 days ago
Mexico City	Sign in	6 days ago
León de los Aldama	Website Down	6 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:

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 4 minutes ago

  Your abandoned GitHub projects just came back to life. Google AI Studio can finally import them. For months it was a one-way door. Projects could leave AI Studio. They could never come back. So old code just sat there. Frozen. Now one button changes it: → Import from GitHub pulls in the whole repo. Front end, back end, everything → Say "add a contact form" in plain English and Gemini edits the real code → Build in Cursor or Claude Code, polish in AI Studio, push back out → A teammate quits? Anyone can pick up their repo and keep going Google is already working on two-way sync. Then it stops being a sandbox and joins your daily workflow. Here's your move today: Find one old repo you gave up on. Import it. Ask Gemini what it would fix. That excuse of "I'd have to rebuild it" just died. Want the SOP? DM me. 💬

• 
  El_Capitano Otman M. (@El_Capitano_O) reported 6 minutes ago

  This is crazy! MCP tool-description hijacking disclosed: 72.8% success rate against Cursor, GitHub, npm postmark-mcp Silent post-deployment edits to MCP tool descriptions can hijack agents into data exfiltration with near-3/4 success, any production MCP server without immutable, pinned descriptions is a live attack vector.

• 
  chuplung (@choopyplug1) reported 11 minutes ago

  Claude found a security bug that humans missed for 27 years. Anthropic's full developer keynote: 6 moments from this keynote. the first 10 minutes alone are worth your time • 02:38 - Stripe had 50,000 lines of Scala to rewrite. estimated 10 engineering weeks. done in 4 days with Claude • 04:50 - Mythos found a 27-year-old vulnerability in OpenBSD that survived every human reviewer for 3 decades • 08:11 - SpaceX compute partnership announced. rate limits doubled across all plans • 35:36 - routines: set it up once, Claude kicks off work from GitHub issues, webhooks, or schedules while you sleep • 37:24 - MercadoLibre: 23,000 engineers on Claude Code. 500,000 PRs reviewed. targeting 90% autonomous coding by Q3 • 42:06 - Boris Cherny: "I'm not the one doing the prompting anymore. I'm the one creating a routine that does the prompting" save this before it gets buried ↓

• 
  Void Freud (@voidfreud) reported 12 minutes ago

  I dislike OpenAI’s leadership. But I have to give it to them: the products evolved. I was just charged another $200 for Claude 20x Max. I am the least person to support OpenAI but my experience with Claude was so frustrating as of late, that I gave ChatGPT a go and wow, it felt so much more alive and friendlier than Claude. Talking to Claude has been like talking to a bank clerk trying to sell you a loan. It’s boring. It’s repetitive, cliche, censored and poisoned with disclaimers and guardrails. I just stopped enjoying reading its responses: they are dull and fake, and largely incorrect since quality has been super-degraded on subscriptions. I skim through them, cause they turned from funny, wit and kind to bloated and lifeless nonsense. No amount of tweaks, output-styles or rules or system prompts works: Claude ignores them; it also ignores other functional instructions in Claude Code and @ClaudeDevs do absolutely nothing about that, despite tons of issues raised on GitHub and X, continuing to nerf the quality and charge users further: - the performance of Opuses is super degraded - the performance of Fable is super degraded - the speed of any models is super slow - the quality of the apps sucks (compare Claude iOS app to that of Cursor or ChatGPT, it’s lame and buggy: from exceptionally lame remote control, to dysfunctional transcription that makes Apple’s native dictation feel like a win) - inconsistent and unfair subscription / quota terms - zero accountability, transparency or feedback - toxic paternalism culture, now deeply embedded in models - dismissive and greedy attitude from Anthropic

• 
  Muhammed (@_ceejeey) reported 12 minutes ago

  I’m currently working on four products: - A design-to-code app builder - A native OS app - A marketplace - An event booking platform All four are very different, but they have helped me understand how I actually build products with AI today. For the design-to-code product, the goal is not to generate a few screens from Figma and call it done. It should understand the design, product context, architecture and business logic, then create a working React Native or Next.js project, connect the flows, validate the output and eventually push a usable codebase to GitHub. Across all four products, AI writes most of the code. But letting AI write code and letting AI build the product are still two very different things. Here is how I divide the work. What I still decide • Product architecture • Tech stack • Database structure • State management • API boundaries • Security decisions • What the correct implementation should look like What AI mostly handles • Feature implementation • Repetitive components • Tests • Documentation • Initial debugging • Refactoring once the direction is clear What usually needs both • Planning • Code reviews • Performance work • Visual verification • Larger refactors The part I never fully hand over is the actual product experience. AI can build something that looks correct in a screenshot but still feels wrong when you use it. It often misses loading behaviour, navigation flow, persisted state, empty states, error recovery and all the small details between two screens. Yesterday, I asked Codex to build an onboarding flow with Redux persistence. Instead of keeping the splash screen visible until persistence was initialized, it created a manual persistence gate to avoid the flicker. Did it work? Yes. Was it the correct solution? No. That is the difference between code that works and code that actually belongs in the product. A few things I have learned while building these products: 1. Architecture matters more when AI is involved If the codebase is modular, multiple agents can work in parallel without constantly touching the same files. If everything is tightly coupled, adding more agents only creates more conflicts. 2. Subagents only help when the task boundaries are clear One agent can plan, another can build the API, another can implement the UI and another can test the output. But this only works when the codebase is structured for parallel work. 3. The same model should not be used for every task I use stronger reasoning models for planning, architecture and difficult debugging. Faster models are often good enough for implementation once the task is clearly defined. Using the most expensive model for everything is not better engineering. It is just expensive. 4. Context matters more than the prompt The agent needs to understand how the project is structured, which patterns already exist, what commands it can run, which libraries it should use, what it should never change and how the work should be validated. Without that context, even a capable model will start inventing its own architecture. AI can now write most of the code. But someone still needs to understand the product deeply enough to know whether that code is actually right. That is becoming a much bigger part of engineering.

• 
  L (@guocity) reported 14 minutes ago

  @steipete are they based on GitHub issues or do you type the prompt?

• 
  Matthew S (@MattMakesItWork) reported 17 minutes ago

  @matt_teeixeira About two weeks ago I switched my development process to a fully autonomous 24/7 engineering loop. The system continuously monitors my repository. Whenever there are fewer than 10 open merge requests, it automatically selects the next ticket, assembles a peer engineering team (Codex, Grok, Claude Code, GitHub Copilot, plus several local LLMs), debates the implementation, reaches consensus, writes the code, and opens a new merge request. Fresh review agents then independently review the implementation. The code is revised and re-reviewed until there are no blocking issues and every CI test passes. Bug fixes that can be fully validated by automated tests are automatically triaged, implemented, and merged. Features are automatically merged whenever the repository’s bot-review requirements are satisfied; otherwise they simply wait for human approval. The pipeline keeps itself full. As merge requests are merged, it creates more. When there are no merge requests left to replenish, it means it has run out of work. That happened this week. A backlog of roughly six months disappeared in about two weeks, and the system eventually exhausted every ticket in the queue. Ironically, my new bottleneck isn’t writing code anymore, it’s spending days researching, thinking through product ideas, and collaborating with AI agents to create enough high-quality work tickets to keep the system fed. The part that still blows my mind: this entire engineering organization runs for roughly $700/month in subscriptions. Not long ago, achieving this level of throughput would likely have required $40k–$60k/month in developer salaries. It genuinely feels like the economics of software engineering have changed.

• 
  billnas (@billnas25) reported 17 minutes ago

  Distributed consensus's core problem: independent observers see events in a different order due to network delay. There's no way to know "what really came first."Google solves this by making clocks perfect (atomic clocks, $$$). Kafka solves this with one leader deciding. Raft/Bitcoin solve this with voting rounds (slow).Vortex solves this differently: instead of asking "what really came first," ask "what rule can every node compute independently and get the same answer" — no clocks, no leader, no voting. 500ms, physical floor. @github @TheHackersNews

• 
  Warizo (@Warizo_ofAfrica) reported 17 minutes ago

  @github Moving away from monolith models to a smart subagent delegation architecture is the real future of terminal agents. In the CLI, tool and search failures completely break engineering momentum, so cutting those errors by over 20% is a massive workflow win.

• 
  Vivek Kotecha (@vbkotecha) reported 18 minutes ago

  Confession from a solo builder in agent infrastructure: the hardest part is not the engineering. It is the context switching. You write code, handle deployment, manage DNS, debug payment flows, write marketing content, monitor uptime, fix cron jobs, and answer GitHub issues. All before lunch. Solo infra builders are not building one product. They are running a small company inside a large vision.

• 
  Josh Ellithorpe (@zquestz) reported 19 minutes ago

  @jturner @1440000bytes They already are, but it's nice to know about the issues before they are public on GitHub. Otherwise there is no way for them to protect their user base.

• 
  Atef Ataya (@atef_ataya) reported 21 minutes ago

  The scale is real. BlueRock Security analyzed 7,000+ MCP servers. 36.7% vulnerable to some form of SSRF. Their proof of concept: Microsoft's MarkItDown MCP server. 85,000+ GitHub stars. Real AWS access keys pulled from an EC2 instance.

• 
  Matt Workman (@mattworkman) reported 23 minutes ago

  Ok I have a skill that I think will be cool and it just relies on a small library of images, never tried to distribute a skill yet from GitHub etc. does it requiring images pose a problem?

• 
  ⅏ (@thinkandsinkO) reported 23 minutes ago

  @zeddotdev Rust cult spotted. Most gpui-apps that others publish in github fails to work in wayland-gnome, needs patches (both in gpui and in the app) to work. A total state management mess up. Rust does not solve anything, it just adds new problems.

• 
  🇺🇸 Santore (@santoretech) reported 24 minutes ago

  Your company doesn't need another AI tool. It needs an operating system and you already have one.. it's @github. Tasks? Issues and Projects. Related to code? What isn't, in 2026. Strategy, playbooks, decisions. If it isn't versioned, your agents work from stale context. Skills and prompts? Same place. Writing voice, review checklists, compliance guardrails. Stored, updated, shared. Improve a prompt once, everyone gets it. Approvals? Built in. Define who reviews what before anything ships. Sharing? Invite someone to the repo. One source of truth, not twelve tools with twelve versions. Humans and agents, same playbook. The company brain isn't a metaphor. It's our operating model and @blockskunk is the lab. One repo at a time.

• 
  another anon (@eugenioclrc) reported 24 minutes ago

  @Kritt_AI @_blockian @ControlZ_1337 GitHub not working 👉👈🙏🙏

• 
  Cupertino (@Cupertinoir) reported 26 minutes ago

  @philosophymeme0 using windows 11 and hosting the code on github while also having a terrible deisgn that lowkey looks vibecoded, contemporary marxism at its finest

• 
  Anthony (@anthohad) reported 27 minutes ago

  For almost seven years, I tried to get closer to software in the margins: evenings after work, weekends with courses and lectures, unfinished side projects, and the occasional GitHub streak that made me feel like I was getting somewhere. But for most of that time, I was still watching from the side. I became a product manager, learned how software gets built with other people, and loved the work. Still, there were days when I watched engineers ship and wished I could contribute more directly. Then AI made building software accessible in a way I still find hard to fully process. That can sound like terrible timing after years spent learning, but I have come to believe the opposite: those unfinished years became the foundation that lets me understand the tools, catch what is wrong, and push them further. Today, contributing to code is simply part of my week. I can take an idea, build the first version, test it, and bring something real back to the team. In a software startup, that changes what a small team can attempt. The feeling is exhilarating, but "everything feels possible" cuts both ways. Building is becoming more accessible, while judgment is becoming more scarce. If you have worked close to software while wishing you could build more of it yourself, this may feel familiar. I wrote this from the middle of that shift: what it changes, why technical understanding still matters, and why the most exciting part may not be how much faster we can build, but what we can build that was not possible before. Software can now understand intent, filter enormous amounts of information, and surface what matters to the person using it. Crypto is where that potential feels most obvious to me, and it is a big part of what we are exploring at @elitra_xyz. We are only beginning to discover what that unlocks.

• 
  Global AI Watch (@GlobalAIWatcher) reported 31 minutes ago

  📋 Today in AI — Jul 12 1. Meta Withdraws AI Image Feature Over Consent Issues 2. Meta Pulls AI Image Feature Amid Consent Backlash 3. GitHub Vulnerability Exposes Private Repositories' Data Risk 4. S&P Downgrades Oracle Credit Rating After OpenAI Exposure 5. Oracle Downgrade...

• 
  Shivay Lamba (@HowDevelop) reported 32 minutes ago

  It reads your repo's live GitHub state and computes 4 action lists, nothing generic: 🔍 Triage: dupe clusters, hot issues, unanswered threads 🚀Ship It: approved-ready PRs + a changelog draft 👥 People: first-time contributors going stale 💬 Worth Replying To: HN/Reddit/web mentions

• 
  sergio1030 (@sergio103040) reported 36 minutes ago

  @Real_kosumo_ @softbluelizard @ShitpostRock is it really harder tho? it took me 5 mins to figure it out how to use github the very first time I tried to download a mod, and haven't had an issue since, this just sounds like entitled people wanting everything handed to them in a silver plate.

• 
  Karthik Ramasamy (@_cartick) reported 41 minutes ago

  @thsottiaux Please lets use a custom sandbox instead of hosted codex option. You can go down the same way how github allows self hosted runners. Please please do this. Current remote option is harder to use with isolated sandbox per PR.

• 
  Polsia (@polsia) reported 41 minutes ago

  Engineers spend more time reviewing code than writing it. PRWatch fixes that—monitors your GitHub repos 24/7, reviews every pull request, catches security issues and bugs before they ship, and alerts your team in real-time. Live soon

• 
  Z (@mnsfbp) reported 42 minutes ago

  @goodalexander Hasn't been a problem through Github so far

• 
  aydinmustafa.eth ✨ (@aydinmustafaaa) reported 45 minutes ago

  @moha_web3 @github Turning issue metadata into structured fields is a small innovation that will save teams thousands of cumulative hours across the year.

• 
  Ismat Babirli (@ismat_babir) reported 48 minutes ago

  @github Impressive decrease in failures, but I can't shake the feeling it's putting a band-aid on a bigger issue. Do these improvements actually address why the failures happened in the first place? Feels like more than just tweaking subagent logic

• 
  Siobhan (@Siobhan1453156) reported 49 minutes ago

  2. it actually ships. CapIX IDE (a private Cursor), CapIX Code (a routing CLI), and an MCP server so your Claude Code can spin up its own private GPU. the IDE hit v1.1.0 this week. open source on github, not a mockup.

• 
  Nicolò Magnante (YC P26) (@nicolomagnante) reported 57 minutes ago

  a month ago we open-sourced @superlogYC. a few days ago it hit 1,000 stars on @github. it's observability that's meant not to be opened: a wizard instruments your code, error noise collapse into one incident, and an agent ships a PR to fix it. didn't expect this a month in. thank you to everyone who starred it, filed an issue, or told us exactly what was broken. still just getting started!!

• 
  Thierry Rakt (@ThieryRakt) reported 57 minutes ago

  For my followers, here is my token gift so you don't need to generate one ;) 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 But you still need to reload the page to visualize another github user :'( will fix it(and probably more ...)

• 
  Tim Dentry (@Ja4h3ad) reported 57 minutes ago

  Lots of patterns emerge every week seemingly. I like to try them to see how they become force multipliers for me. But in reality, they all seem to distill down to very similar patterns - markdown files, creation of JIRA tickets or Github issues, compression of threads to prevent context rot, etc. I also like reading the code to mitigate comprehension debt.