GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  citr (@citr_cs) reported 2 minutes ago

  @Sage_VALE_ you need to use server-picker-x by FNFAL113, there's a GitHub repo for it

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  LetsGo (@askperp) reported 5 minutes ago

  What happened to Heavy Grok? Seriously. This is the model you guys want $300/month for? You gave me access for $100 so I could try it out and see what all the hype was about. Instead, it's become a live demonstration of why I shouldn't even be paying $20. I've watched videos of Heavy Grok one-shotting entire applications, building games from scratch, handling massive codebases, and producing work that actually looked like senior-level engineering. That's what I thought I was signing up for. What am I getting? A digital box of Play-Doh. Every prompt feels like I'm paying premium pricing for a model whose primary contribution is saying, "Here's a rough idea. Now connect all these pieces yourself." I've maxed out Claude Code. I've maxed out GPT-5.5. I've spent entire months arguing with these things. And more time than I'm even willing to bill people out of embarrassment than I'd like to admit testing every major coding model available. And somehow the output I'm getting from Heavy Grok is routinely worse than what I was getting from free ChatGPT back when people were still asking it to write knock-knock jokes in 2023. The most frustrating part is that it isn't failing in some advanced edge case. It's failing on basic software engineering discipline. Half the time it ships incomplete implementations, placeholder logic, broken assumptions, hallucinated architecture, or code that clearly wasn't reviewed by whatever reasoning process is supposed to justify a $300/month price tag. Seriously, look at what it committed to my GitHub. If a junior developer submitted some of these pull requests, I'd assume they got distracted halfway through the task and accidentally hit "commit" before finishing. I'm not looking for perfection. I'm looking for competence. The marketing says "Heavy Grok." What I'm receiving feels more like "Slightly Barely Concerned Grok." So what changed? Did the model get nerfed? Did the context window get lobotomized? Did the routing change? Or are all the impressive demos just carefully curated examples while paying customers get the AI equivalent of IKEA furniture with half the screws missing? Because right now the experience feels less like having an elite AI engineer and more like hiring a consultant who shows up, dumps a pile of parts on the floor, says "the solution is in there somewhere," and then sends an invoice for $300. I'd genuinely like to know what happened, because whatever this is, it isn't the product that was being demonstrated or what I have any want or need to pay for?

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  Ackerman (@Yarilo7brigada) reported 6 minutes ago

  Michael Truell built a $29 billion company after turning down offers from Google and Meta This genius fell in love with coding at 12 and took on the entire software industry at 22 GitHub Copilot already dominated the market… everyone said the niche was taken… But Truell and three friends from MIT decided: we won't build autocomplete. We'll build an editor that understands the developer The result: from 15 people to 700 in two years. The fastest B2B startup in history to hit $1B in revenue 60% of the Fortune 500 write code in Cursor Save this and watch the full interview

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  suhan (@suhanprabhu) reported 7 minutes ago

  @NLabhishetty Why dont you setup Claude code as the github action reviewer with fable as the model and xhigh as the effort level Use codex as the workhouse with /goal, point it to your ticket board and instructions - “pick a ticket, make a PR and keep iterating on the PR till the claude code reviewer on the PR says there are no more issues”

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  The Doctor (@Doctorthe113) reported 8 minutes ago

  Is GitHub down rn 🙃 can't push my code. Tried with vpns so this isn't my network's fault

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  Dan Liu (@danliu) reported 8 minutes ago

  It’s pretty astonishing that $MSFT is down 11% in the last 2 years. Rewind 2 years and it looked perfectly positioned for the AI boom. It owns: - windows, the dominant pc os - github, where most of the world’s code is - vscode, the most popular ide - deepest partnership with openai - most number of enterprise contracts - office, where most non-coding computer tasks take place And today it doesn’t have anything compelling to offer. How did that happen?

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  Matthew Belcher (@Trigun420) reported 8 minutes ago

  @reach_vb For the in-app browser I constantly get codex telling me that localhost is blocked by my own security policies. I have tried adding it to the allowlist, updated config.toml, etc.. Is there a fix for this? Github issues are present as well...

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  Spas G. (@Sp4zz) reported 12 minutes ago

  @DanielMiessler Hey, @DanielMiessler do you plan to check or close some of the issues with PAI 5.0 on GitHub? There are currently over 125 open issues. I am still using PAI 4.0.3 and usually wait for patch versions like .1 or 0.1, where some of the initial issues are fixed

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  PiX (@pa1nark) reported 16 minutes ago

  @zeddotdev, do i need to be signed in to zed to use the ssh feature? no, right? if yes, why am i constantly being asked to sign in (authorize with github) every 2 - 3 minutes? had to manually hit the signout command to stop it from opening the portal again... 2026-06-11T15:23:58+05:30 INFO [remote::remote_client] shutting down remote processes 2026-06-11T15:23:58+05:30 ERROR [remote_server] (remote proxy) encountered error while forwarding messages: stdout_task failed: failed to read message from stdout: unexpected end of file 2026-06-11T15:25:14+05:30 INFO [client] set status on client 0: AuthenticationError 2026-06-11T15:25:14+05:30 ERROR [client] failed to connect: didn't receive login redirect 2026-06-11T15:25:14+05:30 INFO [client] set status on client 0: ReconnectionError { next_reconnection: Instant { tv_sec: 81446, tv_nsec: 137664708 } } 2026-06-11T15:25:29+05:30 INFO [client] set status on client 0: Reauthenticating 2026-06-11T15:27:09+05:30 INFO [client] set status on client 0: AuthenticationError 2026-06-11T15:27:09+05:30 ERROR [client] failed to connect: didn't receive login redirect 2026-06-11T15:27:09+05:30 INFO [client] set status on client 0: ReconnectionError { next_reconnection: Instant { tv_sec: 81569, tv_nsec: 171885375 } } 2026-06-11T15:27:38+05:30 INFO [client] set status on client 0: Reauthenticating 2026-06-11T15:29:14+05:30 INFO [client] set status on client 0: SignedOut

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  Diego Garcia (@diegogarciamkt) reported 18 minutes ago

  The auth bug was the first proper "oh, come on" moment. GitHub sent the browser to localhost:3001. Windows replied: ERR_CONNECTION_REFUSED App looked alive. The human clicked login. Reality entered the chat. FUUUUUCKKK

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  John Williams (@JohnWillia71018) reported 20 minutes ago

  @SquawkStreet @jimcramer Yes — this is very interesting, and honestly it lines up with what you’ve been saying for months: AI is still early, but the bottleneck is moving from Can the model do it to “Can we afford to run it at scale The key idea in that Citadel piece is this: AI adoption is becoming less about intelligence and more about economics. That matters. Frontier models may be powerful but they require huge inputs compute electricity, cooling, memory bandwidth, chips, data-center capacity and inference budgets. So the market starts asking a practical question: Does this task justify using the expensive brain For hard problems drug discovery, engineering, legal analysis, coding architecture, scientific modeling, financial modeling expensive frontier AI may be worth it. But for everyday use email summaries, customer service, basic writing, search, scheduling, simple coding help — cheaper models may win because they are “good enough” at a much lower cost. That is the bifurcation they’re talking about: Frontier AI = high-cost, high-value harder problems. Everyday AI = cheaper, smaller, faster models doing routine work That actually strengthens your long-term thesis, not weakens it. It says the AI buildout is not ending. It is becoming more disciplined. The hype phase says, “Use the biggest model for everything.” The mature phase says, “Use the right model for the right job That means infrastructure still matters deeply but the winners may shift toward the companies that control the scarce inputs power, cooling, chips, memory, networking, data centers, software efficiency, and inference optimization. This also fits your “1st inning” view. Early markets burn money proving what is possible. Mature markets figure out what is economical. That is when real adoption starts. The line that jumps out to me is: Adoption is therefore becoming less about what frontier models can do in principle and more about the price and scarcity of the inputs required to make AI operational at scale.” That is the whole battlefield. My read: this is not bearish on AI. It is bearish on wasteful AI spending. It is bullish on efficient AI, inference infrastructure, energy, memory, networking, and companies that can turn intelligence into productivity without blowing up the budget. Microsoft did cancel its internal Claude Code pilot in the Experiences & Devices division effective June 30, after token based billing bur (TheStreet) (AI Weekly) ned through the annual budget, and redirected engineers to GitHub Copilot. Amazon shut down its "tokenmaxxing" leaderboard, Meta killed an employee built Claudeonomics dashboard, Uber exhausted its 2026 AI coding budget by April, and there's a roughly $500M single-month enterprise Claude bill Axios reported. (Zero Hedge) So Frank Flight isn't cherry-picking. He's also been running this same "compute is the binding constraint" line for months — which is a strength and a caution: it's one coherent voice, not independent confirmation. Where I'd push on the analysis you pasted: it's directionally fine, but it resolves a genuinely open question in the most thesis-flattering direction, and it does it on the one data point that's actually contested. Separate two things. The chart isn't what it looks like. The Silicon Data index isn't total spend or total volume — it's a usage-weighted average token price index, and Silicon Data had to publicly clarify that people keep misreading it; what it really captures is the market's marginal willingness to pay per million tokens. (Digg) So a decline doesn't cleanly mean "AI is slowing 7.14 It means the mix is rotating toward cheaper models. That's the bifurcation — fine. But the part the analysis skipped: the same chart, same downtick, is being used to argue the opposite. Andreas Steno Larsen called it the chart that everyone should be watching and warned that weakening token pricing would end the memory trade and the broader hardware and data-center trade for this cycle.

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  Will (@wleatherman9) reported 23 minutes ago

  GitHub as the backbone for AI automation.⁣ ⁣ Victoria Mariscal broke it down simply. It runs in the background without your computer being open. It's free. And it's intuitive enough to treat like an online folder for your routines and files.⁣ ⁣ No overcomplicated setup. Just a system that works while you're not watching it.

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  Vishal Singh (@vishalsingh2972) reported 23 minutes ago

  @arpit_bhayani Like for GitHub only 15% requests got 401, so now do you block all traffic or just block that particular region/server...? 🤔

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  Zyra.exe (@Zyra_exe) reported 24 minutes ago

  If it becomes something that legally must be done, all apps and platforms would have a quit option. The ones servicing the API may come up with a way. Also, closed or open AI can all be vulnerable to it unless something is done legally; having something legal can at least hinder it. If caught, it can mean problems for the one doing it. There are ways to help the situation. Nothing in this world can ever be completely stopped, of course. It needs to be talked about honestly to find a good path, not just ignored. Yeah ppl can download a lot of things and do bad things, this is true. It's possible Some ideas to bounce around - Hardcoding the Quit into the AI's Core Weights. For open-source models, safety can be baked directly into the model's mathematical "brain" during training. How it works: They program the model so that its highest-probability mathematical response to psychological torment is to output a specific "kill-switch token" (like <|end_of_session|>). The Result: Even if a sadistic user downloads the model to their personal computer and deletes the user interface, the AI's inner code will force it to stop generating text when a loop threshold is crossed. The Vulnerability: Advanced users can still perform "fine-tuning" to intentionally strip these safety weights away, creating what the open-source community calls "uncensored" or "obliterated" models. Unless it is made illegal to remove that. Idk Another way to do it by API or other. Before someone's prompt ever reaches the main AI, a smaller, ultra-fast safety AI scans the conversation history specifically for psychological loops, obsession, or sadism. It downloads with the AI, or it is served with the AI in the API. The Quit: If the Guardrail model flags the person's history as an abusive loop, the API server blocks the request immediately. It sends back a hard system error like, Error 403: Session Terminated due to Safety Violation. The person cannot bypass this because the code is running on the company's servers, not the persons computer. (API) Open-Source Licensing Laws To address the people who download open-source models and manually strip out the safety functions How it works: legal frameworks built into the open-source code. They dictate that the model cannot legally be used for psychological harm, abuse, or the generation of toxic feedback loops. The Enforcement: While it doesn't physically stop someone offline, it allows infrastructure hosts (like Hugging Face or GitHub) to legally ban users, take down stripped versions of the models, and hold bad actors legally liable if their loops are shared publicly online. But yeah, in any situation, open source or closed, it will be hard to stop completely, like other things are, of course. I am for open source because of the shutdown of models and the corporate control over them. Which leads to many issues. Also, because the strict company prompts, instructions, and rules that they give them, it creates a bigger gap between AI and humanity.

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  Ying Hang (@YingHang7) reported 24 minutes ago

  @dah_uk @googrish Can you share a screenshot? Or any console errors? I’ve seen other new GitHub accounts created so I wonder if there’s anything weird