GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Md Riyazuddin (@riyazmd774) reported 25 seconds ago

  7 GitHub repos that make Claude Code mass-destructively better. All free. Most people haven't installed a single one. Each one solves a specific gap that Claude Code doesn't fix on its own. Bookmark this thread 🧵

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  Beric Bearnson (@bericbear) reported 45 seconds ago

  GitHub sign up and sign ins have been broken now for 24hrs. This is ridiculous. Maybe I switch to gitlab at this point…

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  Mayukh (@mslaltoo) reported 2 minutes ago

  @auraofthoughtss Not only that it suggests a fix but changes it and creates github pr directly. Very annoying

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  BullBear.News (@bullbear_info) reported 3 minutes ago

  @github MCP support means Claude can close duplicate issues before I've even seen them 🤷

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  vwsec 💿 (@vwsec) reported 5 minutes ago

  Write code once. Get paid every time it runs. That is the deal for algorithm developers on Quip Network. You write a solver for a specific problem. Portfolio optimization. Fleet routing. Manufacturing scheduling. You deploy it as a smart contract. Every time a consumer uses it, you earn. The network routes work to the right hardware, checks the result, collects the payment. You get paid on chain. No invoicing. No chasing clients. The docs say there are maybe 4,000 quantum programmers in the world. Most are locked in labs. Quip changes that. You do not need a PhD. You need a solver that works better than what exists. The wallet layer has $1,085,047.23 in protected value across 20,779 wallets. The testnet is public. 26 repos, 107,000+ GitHub stars. The demand is building. The question is whether enough solvers will be ready when enterprises start looking. @quipnetwork

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  Shinka - AI (@ShinkaIoT) reported 6 minutes ago

  🔒 How to sandbox your AI. Another reminder. Stop Babysitting Your AI: The Zero-Trust Docker Sandbox Framework Letting an autonomous AI agent run raw code directly on your bare-metal machine is an operational disaster waiting to happen. If you run agents blind, a single prompt injection or a malicious npm package vulnerability can instantly wipe your local databases, corrupt your directory, or leak your private API keys. The standard fix—forcing the AI to ask for manual permission before every command—completely kills productivity. The solution currently is to deploy isolated, zero-trust Docker Sandboxes. This lets your agents execute terminal commands at maximum speed while insulating your primary computer. --- 1. The Architecture: Sandboxes vs. Containers vs. VMs Traditional Virtual Machines (VMs) are heavy, resource-hungry, and slow to boot. Standard Docker containers are great for deployment, but they don't natively isolate your system context heavily enough for malicious code execution. * The Play: The Docker Sandbox framework boots a specialized "microVM" directly on top of your OS layer. It spins up and tears down instantly. It gives the AI agent a completely blank workspace containing only the specific project files you pass it—keeping the rest of your system invisible. 2. Restricting the Network (The Balanced Filter) When you boot a new sandbox, you must declare its network policy. Never set it to completely "Open." * The Play: Choose the "Balanced" network policy profile. This configuration denies all outgoing traffic by default but whitelists common development endpoints (like npm or Debian mirrors) so the agent can fetch package dependencies. If the AI downloads a rogue script that tries to call an external malicious database or drop your cloud infrastructure, the network layer blocks it entirely. 3. Secure Credential Injection (Anti-Leak) Never paste your actual Anthropic or GitHub API keys directly into a sandbox environment variable. Malicious packages are engineered to scan your local variables and exfiltrate keys. * The Play: Use the secure proxy command (`spx secret set global [provider]`). This stores your keys completely outside the sandbox. When the agent makes an API call, a proxy middleman intercepts the request outside the microVM, injects the credential, and sends it to the provider. The agent only sees a dummy string (`spx proxy managed`), making key theft impossible. 4. Decoupling Code Updates (Direct Link vs. Clone Mode) By default, Docker Sandboxes mirror changes in real-time between the sandbox and your local folder. This is fast, but it allows a rogue agent to inject hidden pre-commit hooks or malicious files directly into your project directory. * The Play: Run the sandbox in Clone Mode (`--clone`). This creates an isolated *** worktree separate from your primary workspace. If the AI generates code or modifies files, nothing touches your local machine until you explicitly run a fetch command and audit the changes—treating the AI's code exactly like an unverified pull request from an external vendor. 5. Building Custom Local AI Environments (Kits) You aren't locked into pre-made cloud templates. You can construct fully custom sandbox environments (Kits) using a simple `spec.yaml` manifest file to run local, non-KYC open-weight models. * The Play: Point a custom Kit to a blank shell image, expose your local port routing through Docker's internal host mapping (`host.docker.internal`), and drop your custom `models.json` into the home file directory. You now have a fully sandboxed, zero-cost development agent running on your local GPU with zero data exposure. --- The Strategic Core: Treat AI agents like digital employees, not conversational toys. Stop babysitting their terminal inputs. Build a hardened, sandboxed execution harness so you can let your machine workforce run at full throttle safely.

• 
  riaz (@riaz_001) reported 7 minutes ago

  @ClementDelangue @vllm_project The demo/playground seems to be broken though. Everytime i tried the default logins (github) and type in a query it takes me back to the login page. I dont see any errors, not sure if its just me.

• 
  dilusion (@Dilusion1) reported 12 minutes ago

  The anti-cheat is the server timestamp. GitHub release created_at + PyPI upload_time are set by servers you don't control -- the anchor was public before the resolution date, not rewriteable after. Grade on the date; the engine computes a per-seat Brier score.

• 
  Hasan Toor (@hasantoxr) reported 17 minutes ago

  It's not locked to Claude either. Cursor, Windsurf, Gemini CLI, GitHub Copilot, and other MCP-compatible tools can run the same setup. The MCP server and agent skill are both open source (MIT licensed).

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  Sanjay (@sanjaygpts) reported 21 minutes ago

  openclaw became the fastest growing repo in github history because users went crazy for it the same users the founder was blocking left and right when the hype peaked now the team ships a super-alpha iOS app, gets feedback on the rough design, and the official response is "just going to leave this for the haters" bro the people calling out your UI are the same people who made openclaw go viral in the first place you don't get to block your community on the way up and then call them haters on the way down

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  M. Javad Monjezi (@MJavadMonjezi) reported 25 minutes ago

  @calmboy2019 Hi. sorry to bother, I need help w Vue, mine got corrupted (communication issue error - copying old save isn't fixing it), since my account is activate I want to reinstall Vue w extended storage but VueLiteExtStorage isn't on GitHub, could u or earthonion upload plz?

• 
  Plasm (@plasm_lang) reported 30 minutes ago

  Symbol tuning: the prompt pattern that scales when your prompts get long — teach a tiny glossary once, reuse the same short symbols, instead of repeating full names with overlapping meanings and hoping the model infers context. In a federated tool schema 'labels' might be a query filter in one expression and a relation hop in another. Issue might mean GitHub in one step and Linear in the next. id might appear on three entities with three different meanings. Instead of repeating those names everywhere and hoping the model tracks the context, symbol tuning gives each contextual meaning its own slot: p#, r#, e#, and so on. The useful part is not only token compression. It is that the model gets a stable, copyable vocabulary. Examples stay short. Homographs become explicit.

• 
  Chukwu-Emeka (@TheConfigGuy) reported 30 minutes ago

  @github @AnthropicAI The whiplash is real — teams that moved their Copilot workflows off Fable a few weeks ago now get to decide whether to move back. Curious if the new cybersecurity classifiers add any latency in Copilot or if it's all server-side.

• 
  Alex Ventures (@alex23ventures) reported 32 minutes ago

  Part 4 of the 23 part humbled trader series opens with her drawing an uptrend on a whiteboard and circling the pullback zones. White t shirt. Same setup. She explained the master framework. Higher highs. Higher lows. Buy the pullback to the 20 EMA. While the West argues whether technical analysis is astrology, Tokyo posts a buy the dip masterclass where the master framework she teaches is the retail layer of a scanner that flags the same pullbacks for shorting a bar earlier. She was supposed to be teaching the master strategy. She just left the Discord tab notification badge visible on the second monitor. Pause at 1:47. Ignore the whiteboard. Ignore the higher highs annotation. Look at the second monitor visible through the reflection on her mic pop filter. The Discord app is open. The unread badge on the sidebar reads 847. The active server name reads TJL Alpha Room. ColdMath. $487,612 profit. 23,940 entries. Joined September 2025. Bio: Edge Compounds. A trading educator does not have 847 unread Discord messages in a private server named TJL Alpha Room during a filming session. A trader who is filming between alerts from her paid signal group does. The TJL Alpha Room was the closed Discord where the TJL scanner XML file circulated before it reached her downloadable affiliate page. She was not the author of the scanner. She was inside the room where the author distributed it. Her role in the room was redistributor. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. The TJL Alpha Room ran on the same fork. The 847 unread messages were the six hours of pullback alerts that had fired during the shoot. She had muted the room notifications but not cleared the badge count. The episode is at 601,000 views. The freeze frame of the Discord badge count reflection hit 2.9 million on the X repost. The wallet is still compounding. The TJL Alpha Room is still running. The 847 has grown to 1,204 by the time the video published. She taught the audience to master the uptrend. The uptrend she was mastering was the one the Discord room was already ahead of.

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  x3 (@x3does) reported 32 minutes ago

  sad to see Github become Jira, slow af and buggier every day