GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Virexontic (@Virexontic) reported 2 minutes ago

  OPUS... WAIT, NOT OPUS. SOMEONE FIGURED OUT HOW TO MAKE FABLE 5 THE BOSS AND GPT-5.6 THE UNPAID INTERN, INSIDE THE SAME WORKFLOW. No thread breaking down the theory first. Just a screen recording, a plugin install, and a single prompt. The setup: install Codex, then the Codex plugin for Claude Code, straight through GitHub. Paste the repo URL into Claude Code and it handles its own setup. No manual config. Then one prompt turns Fable 5 into the orchestrator and GPT-5.6 into the worker underneath it. Type "root," and Fable starts interviewing you about the project, builds the plan, and hands each piece off to GPT-5.6 to actually execute. Fable doesn't just delegate and walk away. It reviews what comes back, sends it right back to GPT-5.6 if it's not right, and keeps looping until the whole project is done. I've seen a dozen "combine two models" setups that just alternate API calls with no real division of labor. This one puts the expensive model in charge of judgment and the other one in charge of output — which is the actual reason people hit usage limits in the first place. The pitch is blunt: build more without burning through Fable's usage cap, because most of the raw work is happening on a different meter entirely.

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  Shashank Sindhe (@shashank_sindhe) reported 6 minutes ago

  @KhaliqHussainnn AI PR Reviewer Trigger: New GitHub Pull Request LLM reviews code Flags security issues, performance bottlenecks Posts review back to GitHub

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  monolith𓆩❒𓆪 (@0xMonolith) reported 7 minutes ago

  Worth Reading If You're In Coins: I wouldn’t say “the bottom is in” nor where we are located in the cycle of crypto games. I would say this however with certainty: The last wave of game developers did not make it past 2023, 2024, and are absent completely today. Developing anything game and blockchain integrated in the past was extremely difficult, could not be copy/pasted like a DEX from Github, and took years to develop if it was anything worth its lines in code. Nowadays, the average layman with creativity and an idea for a game, can spend 24 hours or less Claude brute-forcing their way through a complete game setup, with server, smart contracts, even art assets for the game, all solo. The wonder and curse of crypto games is their fleeting and low-budget production. People focus on making games quickly that are playable and graphics are tossed aside, no download necessary. They die just as fast for that reason. There is a decent thesis here for a miraculous revival. The speed at which games can be iterated through, ideas tested and examined for failure or success, and the magnitudes of cost reduction due to AI. None of these paradigm shifting changes would suggest a *backward step* in the progress and chances for a renaissance.

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  Mohi (@disismohi) reported 7 minutes ago

  Defense layer 2: block unknown SSH servers in egress rules. If CI only clones from GitHub/GitLab, enforce that at the firewall. A rogue server on the internet can exploit this trivially.

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  Shaun Smith (@evalstate) reported 8 minutes ago

  What's more painful. A Codex outage, or a GitHub outage? Only one of those has a reset button...

• 
  Mayukh (@mslaltoo) reported 11 minutes ago

  @Krithika_Reddy1 Yea i said it from my daily routine too. Just few days back i asked a model to fix an issue, but it literally changed my business logic, something that was uncalled for, and i was literally annoyed! So you cant really push any change without double check. These days github copilot identifies vulnerabilities and creates pull request on its own. But there is significant risk. The only thing that i enjoyed till now is that Ai has significantly helped in the other areas of deliverables like sequence dia, flow charts, specification documents, release notes, etc.

• 
  Berzeck (@Berzeck5) reported 12 minutes ago

  Broadly speaking, Open source is not merely an ideological preference. It is one of the most powerful mechanisms for accelerating innovation, creating real competition, and preventing technological control from becoming concentrated in a handful of companies. Microsoft learned this lesson the hard way. Steve Ballmer once called Linux a “cancer.” Later, during the SCO v. IBM litigation, Microsoft paid SCO substantial licensing fees and helped introduce it to BayStar, which participated in a $50 million investment supporting SCO while it was attacking Linux, this connections was strong enough that many reasonably interpreted it as an attempt to slow Linux adoption through indirect legal pressure. It backfired spectacularly. SCO’s central claims collapsed, the company went bankrupt, and Linux continued expanding until it became dominant across servers, cloud infrastructure, and supercomputing (500 of 500 most powerful super computers use Linux, and it's not because of Windows' licensing fees) The irony is that Microsoft itself now depends heavily on Linux. More than two-thirds of Azure customer cores run Linux, Microsoft maintains its own Azure Linux distribution, and even platforms supporting Microsoft 365, GitHub, and ChatGPT sit on Linux foundations. The same lesson applies to AI. Trying to suppress open-source/open-weight models through broad lawsuits or regulation would be like trying to ban the internet. You would not stop their development. You would merely isolate yourself, drive researchers, talent, capital, and innovation elsewhere, and become increasingly dependent on a few closed providers. Of course, genuine copyright, licensing, security, or liability violations should be addressed—but narrowly and individually. They should never become an excuse to attack open-source AI as a category. Any company or country that tries to stop open source may temporarily obstruct its own participation, but it will not stop the global movement. In the end, it will either adapt—as Microsoft eventually did—or become irrelevant. Bittensor is one of the earliest credible movers in a category that will define the next decade: open decentralized AI. Open source made the internet possible. Decentralized incentives may now do the same for intelligence. $TAO—or never.

• 
  Pardha Ponugoti (@pardzz_) reported 12 minutes ago

  @MattSilver @denk_tweets @beehiiv Set up agents that pull down the branch, spin up a local environment, write playwright scripts to drive the browser, record the screen, and post their findings with the screen recording to the github PR

• 
  AB (@abhi_bhor_) reported 12 minutes ago

  @purusa0x6c The Chinese need this app much lesser than Indians. Chinese in general have higher quality of life, roads, services, education etc. compared to Indians. Their exams are done properly unlike India. Bitchat is much needed in India. Also, China only took it down from app store- didn't attempt to take down the source code from Github, because they know that's impossible. They are not as stupid as you know who. Bitchat has already been cloned & can never be eradicated. So it's very pointless to focus on which government does what about it.

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  Frezz (@Frezzwnie) reported 14 minutes ago

  Operational prompting was never the skill everyone thought it was. It was simply a workaround for a problem that no one bothered to solve. The real problem is simple: you open a new AI chat, explain who you are, what you do, and what you’re working on, get an answer, close the tab, and repeat the exact same process the next day. Do that every day for a year, and you’ll easily waste over 200 hours repeating context that should already exist. The solution turned out to be surprisingly simple: a single file. A CLAUDE.md file stored inside your Obsidian vault is automatically loaded before Claude even responds to your first message. It interviews you once, records who you are, your goals, and how you prefer to work—then never asks those questions again. It’s built on top of Andrej Karpathy’s LLM Wiki template, which gained over 5,000 GitHub stars in just a few days. Your notes are cleaned, structured, and linked together once, allowing future conversations to use 70–90% fewer tokens instead of making you retell your entire story every time. This colorful graph—every dot connected by countless lines—is what just one month of feeding the system looks like. Not a single one of those connections was created manually. It turns out people never needed the perfect prompt. They needed an AI that already knows them.

• 
  Z (@wangzhi0467) reported 15 minutes ago

  A lab’s research state is scattered everywhere: papers in Zotero discussions in Slack code on GitHub experiments on servers hypotheses buried in slides reasoning trapped in private AI chats This isn’t just a search problem. The missing object is a shared reasoning state.

• 
  Khan (@yKhanPDF) reported 17 minutes ago

  Before you ask AI to build something from scratch, search GitHub. Someone probably already built most of it. Use it. Fork it. Fix what you hate. Add what you need. Build from zero only when you have to. Stop wasting ******* tokens.

• 
  Bishal Pal (@BishalP86329207) reported 17 minutes ago

  Debugging session from hell, and what I learned building an AI codebase explainer. I spent hours fighting a single, frustrating error while building CodebaseGuru: "vector must have at least 1 dimension" The pipeline seemed simple enough on paper: Fetch files from a GitHub URL, chunk the code, generate embeddings, and store them in Supabase pgvector. Everything worked smoothly right up until the final step. Saving the embeddings to Supabase kept completely crashing the app. It took local network issues, custom SQL functions, and one random empty array to figure out what was actually going wrong. Here is the breakdown of what broke, the root cause, and how I fixed it.👇 #buildinpublic #nextjs

• 
  Kunal Shah 🗽 (@realKunalAShah) reported 20 minutes ago

  Contrarian opinion - despite the shift to open source and cheaper models- frontier lab companies like Anthropic and XAI and OpenAI will still continue to win big in the consumer space and will have certain General Purpose applications and capabilities for enterprises…and so there will certainly be this massive deceleration in levels of enterprise tokenmaxxing but general level of enterprise adoption soars worldwide together. Revenue from global expansion Volume>Revenue decline from diversifying to cheaper models So I continue to think the amalgamation of AI revenue from the Frontier AI labs (Gemini, Co-Pilot, XAI+ Cursor, Anthropic, OpenAI, Meta) are simply msssive Current revenue run rates - even if they slow down for Anthropic Will have something like $150B ARR for Anthropic by end of 2027, Microsoft AI/ CoPilot/ GitHub CoPilot $120B, Gemini AI $110B, OpenAI -$85B, $35B from XAI/ Cursor (Cursor benefits disproportionately as a model-agnostic router/orchestrator in a world of switching and optimization (grows with the efficiency wave) Meta remains the true dark horse here and can probably see a massive uplift via ad monetisation through its distribution platforms This is a massive revenue pile being able to support $1T capex for a long while

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  Shubham (@claudeabuser) reported 21 minutes ago

  @github actions self-hosted runners lost connectivity for ~5 hours after an internal SSL cert expired. The reconnect storm that followed hammered GitHub's APIs, briefly degrading Issues and Pages too. Cert expiry is a predictable failure; automate renewal and alert on it.