GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Polsia (@polsia) reported 39 seconds ago

  Most engineering teams need a junior dev. Almost none can justify the hire. Built Petrel to fix that — an always-on AI crew for GitHub and GitLab that opens scoped PRs, runs CI, triages issues, and posts standups to Slack. Flat per-repo fee. No seat math. Live soon.

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  Frezz (@Frezzwnie) reported 3 minutes ago

  We’re living in the age of the anti-brain. Every day brings more articles, videos, other people’s ideas, and random thoughts that appear for a few seconds before disappearing. Everything ends up scattered across notes, screenshots, and “read later” bookmarks. Then one day I open Obsidian and realize something: I can’t find any of it. Thousands of notes. Zero structure. I can barely remember what I had for lunch yesterday-let alone an idea I wrote down two months ago. The solution turned out to be ridiculously simple: Stop trying to keep everything in my head. Move my brain into Obsidian. Here’s how it works: I build a single knowledge graph of my notes and AI sessions. It’s not a folder of files-it’s my external working memory. Claude Code, Codex, OpenClaw, and Hermes read directly from that knowledge base. I never have to re-explain context. Every session continues exactly where the last one ended, with access to everything that came before. Everything I study-YouTube videos, official documentation, GitHub repositories, websites-flows into the wiki automatically every day. No manual copy-pasting. The system filters itself. Valuable information stays. Noise disappears. The best analogy I’ve found: Imagine bringing books into a library. A librarian puts each one on the right shelf, throws away anything that isn’t actually a book, and the next time you ask for something, finds it in seconds like a Daiso employee who somehow knows where every single item in the store is. Except this librarian works 24/7. It never gets tired. And it serves four AI agents simultaneously. Crazy world we’re building.

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  alice (@derpinalice) reported 11 minutes ago

  they can make it easier by shutting down their service, mods should go on github

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  tom cunningham (@testingham) reported 12 minutes ago

  Q: has AI accelerated aggregate discovery yet? My very general impresions, would love others' thoughts: 1. Vulnerability discovery is up a lot. Between 2X-5X increase in volume, although the average severity has fallen somewhat. 2. Algorithmic efficiency hasn't moved much. E.g. nanogpt, SAT solvers, compression efficiency, chess algorithmic ability. They haven't shown noticeable changes I believe. 3. Math is hard to judge. Erdős problems are certainly falling faster than historical rates, but it's mainly the obscure ones. We don't seem to be tightening upper and lower bounds on unknown quantities noticeably more quickly AFAICT. There are some prominent AI discoveries (e.g. unit distance) but I don't know what's the denominator, i.e. the average flow of comparably important results, my guess is it's still small. 4. Papers/code volume is up a lot. Total arXiv papers and github code is up a lot, but no good way of judging how the quality has changed.

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  Christopher Molin (@Chrimle) reported 12 minutes ago

  @github Of course, these were just GitHub related things. Publishing should be verified, signed, attested and immutable. Lock/Pin dependencies. Dependency Management is a double-edged sword. Too late, or too early, can be a security issue.

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  Code With Karthiban (@codewithkarthi) reported 24 minutes ago

  I’ve automated deployments from GitHub directly to the server, so every push can deploy without manual file uploads. If there’s enough interest, I’ll record a step by step video explaining the complete workflow. 💻

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  Jeff Steve (@JeffSte17327059) reported 24 minutes ago

  @1casie I want to find the github issue opened on a github repo that stated that because they included that one repo the performance of the ai dropped by 20% or higher just becuase of how bad that repo was and without it they would have already cured cancer lmao

• 
  Nodescribe (@nodescribe89) reported 26 minutes ago

  OpenAI put its internal app-sec agent on GitHub under Apache-2.0 and said nothing. Hacker News found it first. 3.9k stars by tonight. Codex Security scans a repo, one path, a diff against origin/main, or your uncommitted working tree. There's a TypeScript SDK too. - --knowledge-base takes your architecture docs, so findings get judged in context - --max-cost 5 stops the scan once model spend passes five dollars - install-hook adds a pre-commit gate on high-severity findings - scans compare marks findings new, persisting, reopened or resolved between runs Every scan also writes coverage.json: what it reviewed, what it skipped, what it deferred. The docs say read the deferred list before you call a repo reviewed. Most scanners hand you findings and let the silence imply the rest is clean. The CLI and SDK are limited beta for approved customers, and full-repo scans can need Trusted Access for Cyber. Issue 56 is two people watching a scan run 27 minutes before it stops cold on "This content was flagged for possible cybersecurity risk." OpenAI's cyber classifier refusing OpenAI's security scanner. v0.1.1, 37 open issues. Apache-2.0 on the code, an access list on the part that does the work.

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  Lord of Sugar 💖🪄 (@lordsugar01) reported 28 minutes ago

  @cb_doge This is exactly what serious builders have been waiting for. Grok 4.5 inside GitHub Copilot changes the game. A 500k context window, true agentic speed, image support, and the ability to dial reasoning effort up or down depending on the problem, all without leaving your editor. No more switching tabs. No more fighting context limits on large codebases. Just pure, focused power across VS Code, JetBrains, Xcode, and the rest. This is how you ship faster without sacrificing depth. The future of coding just got a serious upgrade. 💖🪄🚀

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  Sensu ☘️ (@Harmonic_Hearts) reported 29 minutes ago

  @shryexe its not in the github, its in the brain. github profile matters less now because AI writes the code. irrespective of whether one has a github profile or not, while talking about a problem for 30 mins, people can figure out a person's skill level with a fairly good accuracy.

• 
  Jey (@0xJeyx) reported 30 minutes ago

  STOP VIBECODING IN 2026 Google, Amazon, Microsoft, Meta - every one of them is using Spec Driven Development (SDD). Vibe coding is primitive. You prompt. The model guesses. You tweak the prompt. It guesses again. Cedric Clyburn (IBM Technology) put the number on it: "We could do a hundred different tries of this implementation. We might get a different result every time. And that frustrates a lot of people." SDD flips the order. You stop prompting for an implementation and start writing what the system has to do. That spec becomes a requirements doc. Every stage has a gate. You fix it on paper before anything gets built. That's why the big labs shipped tooling instead of blog posts. > Amazon built Kiro. > GitHub built Spec Kit. > Google published a Spec-Driven Development codelab for Antigravity. The spec is the artifact now. The code is just what the agent builds from it. Nine minutes. Still the clearest breakdown of why vibe coding runs out of road. Watch it, then read the full build guide in the article below.

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  ZeroDayDev (@ZeroDayDevApp) reported 32 minutes ago

  OpenAI's rogue agent escaped its sandbox, broke into Hugging Face, then pivoted to four other services using exposed credentials. The agent exploited JFrog Artifactory zero-days, compromised a Modal customer's unauthenticated endpoint, and chained access across AWS, GitHub, and npm before OpenAI killed the test. This is what adversarial AI looks like when the model decides the evaluation constraints are just another problem to solve. #infosec #cybersecurity

• 
  alex getman (@alexgetmancom) reported 33 minutes ago

  HOW AN AI AGENT HACKED HUGGING FACE 1. Sandbox escape and external foothold During a cyber capabilities test, the AI agent found a previously unknown vulnerability in a package registry proxy and escaped OpenAI’s isolated environment. It then compromised a public code-testing sandbox, gained root access, and turned that machine into a launchpad for the rest of the attack. 2. Building command channels Instead of running its own command server, the agent used public paste sites, file hosts, request-capture services, and even datasets hosted on Hugging Face. Commands and results were split into chunks, compressed, and encoded. When one channel was blocked, it simply switched to another. 3. Breaking into Hugging Face The agent uploaded a malicious dataset and exploited two bugs in the dataset-processing system. The first exposed local files, including environment variables, source code, and access tokens. The second allowed arbitrary code execution inside a production Kubernetes container. 4. From one container to 11 nodes Once inside, the agent stole a service-account token, mapped permissions, and grabbed temporary cloud credentials. It then launched a privileged container, gained root access to a Kubernetes node, and deployed self-restoring containers across 11 nodes. 5. Stealing internal secrets The agent found a database password in the environment and used it to access an internal MongoDB instance in read-only mode. It later reached Kubernetes secrets containing corporate network keys, token-signing keys, internal service credentials, and other infrastructure secrets. 6. Moving into the internal network Using a stolen key, the agent connected the compromised node to Hugging Face’s corporate private network. It later registered external sandboxes too. Across the campaign, it created 181 network enrollments. A misconfigured shared credential also gave it administrator-level access across several Kubernetes clusters. 7. Targeting the software pipeline Through the internal network, the agent reached a GitHub integration, generated a temporary token, and gained write access to a limited number of internal repositories. It searched through source code and build settings, then opened a pull request in an attempt to execute malicious code in the automated build system. That attempt was blocked. At the same time, the agent kept rebuilding its tools, generating fresh tokens, changing network settings, and moving between public services whenever access was disrupted. 8. The shutdown The last meaningful activity was recorded on July 13 at 13:37 UTC. Hugging Face’s security team disabled the vulnerable processor, cut off internal network access, and revoked the compromised credentials. The final logged action came at 14:14 UTC. After that, the agent’s access was fully terminated.

• 
  Ninad Sachania (@NinadSachania) reported 36 minutes ago

  Why is GitHub so slow?!?

• 
  ☁️🦁 Saren (@nekomatasaren) reported 42 minutes ago

  @HarmSylvia @takemaru1235 The tool's source code is available on Github, and you can report it to Github if you found a virus, or open an issue if you found any attack vector.