GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Rituraj (@RituWithAI) reported 1 minute ago

  🚨 Someone built a skill that makes AI-written text sound human again. Not a spinner. Not a paraphraser. A systematic rewriter that knows exactly why AI text sounds like AI — and fixes it. It's called Humanizer. 35 patterns from Wikipedia's "Signs of AI Writing." Two-pass rewrite. Shows its work before giving you the final version. Here's the problem it solves. You use Claude to draft something. The output is accurate. The output is useful. The output sounds exactly like an AI wrote it. "Nestled within the vibrant landscape, this pivotal development serves as a testament to..." You know the voice. Everyone knows the voice. And everyone is getting better at spotting it. Humanizer runs that text through 35 specific patterns that WikiProject AI Cleanup identified as the telltale signs. Inflated importance. Shallow -ing analysis. Overused AI words. Em dashes everywhere. Forced groups of three. Fake-candid openings. Answering objections nobody raised. Every pattern. Flagged. Fixed. Here's what one command does. It shows you the first rewrite. Then a short critique of anything still sounding artificial. Then the final version. You see exactly what changed and why. Here's the wildest part. Voice matching. Paste two paragraphs of your own writing before the AI text. Humanizer follows your rhythm, word choice, punctuation, and deliberate quirks instead of its default style rules. The output doesn't just sound human. It sounds like you. One command to install 16 contributors including Claude itself. 4 releases. MIT License. The skill that makes AI writing disappear. 100% Open Source. GitHub link in the comments 👇

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  Convequity (@convequity) reported 4 minutes ago

  Snyk is a clean postmortem for what happens when a security tool lives inside the coding agent’s loop. The product was mostly scan-and-warn. Find the issue, comment on the PR, suggest a fix. Blocking the merge usually sat in GitHub, not in Snyk. Bigger platforms smothered it. $PANW, $CRWD, and Wiz pulled AppSec into the bundle the CISO was already buying. GitHub was the main developer surface and put scanning where the code already lived. Then coding agents arrived and delivered the final blow. A lot of that scanning became something the agent could just do. Growth held up for a short while after the COVID/cloud tailwind. Then it decelerated hard. This is the same lens we use in Convequity’s SaaS Agentic Survival Evaluation Framework. The PANW, CRWD, and FTNT reviews go up on Convequity in a few days.

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  The Oracle (@scientist1q) reported 5 minutes ago

  when my Oura ring detects a cortisol spike from a GitHub Actions failure, Hermes (Fable 5.1) detects it and sends a 900 word root cause analysis, Hermes dispatches the work to my 12 Grok Bot employees, The Chief of Operations bot approves the fix while im watching rezero

• 
  David Abram 🐊 (@devabram) reported 6 minutes ago

  Discord is down. X is down. GitHub is down. Software is solved.

• 
  Chris (@chriscoolstuff) reported 10 minutes ago

  @pfernan95dev For the SEO part there's one thing that I've been also doing: Ask your agent what keywords you should search for relevant to your app on answerthepublic, perplexity and google Gather all that info old fashion, by yourself - might take around 2 hours but it's worth it Plug all that info into the agent and have it give you 5 titles for 5 articles Make it write those articles - maybe use nosoopai github or edit them manually so they seem more human like Connect the agent to google console After 1 month tell the agent to review the results If no article took off you can wait one more month or put up 5 more After the next month check what worked and double down on that

• 
  tmo (@tmophoto) reported 10 minutes ago

  @DabsMalone i had an old email account from like 15 years ago with bot in the name that i fired back up after 10 years and used for a hermes profile and it got immediately banned. i used it to sign in to x, github, everything. was a huge hassle

• 
  Vigneshwer Ramamoorthi (@vigneshwer_ram) reported 10 minutes ago

  I keep thinking the “Android moment for robots” won’t come from a humanoid with the best walking demo. it’ll come when some cheap-enough piece of hardware gets into thousands of developers’ hands and people stop waiting for the manufacturer to decide what the robot is for. Zeroth just launched Bridge in China: 88 cm, ~13 kg, two-finger grippers, open motion-control APIs + SDKs, mocap/VR integration, and an OpenBridge ecosystem where developers can publish robot skills. the Geek Edition is reportedly ¥8,888. that price is the part that caught me. because once capable embodied hardware starts approaching laptop money, the experimentation surface changes completely. I want the Raspberry Pi phase of robotics. weird university projects. teenagers making terrible robot apps. researchers abusing the hardware for things it was never designed for. 500 GitHub repos implementing slightly different ways to pick up a cup. the robotics industry is understandably obsessed with getting robots into factories. I’m almost equally interested in what happens when we get enough robots onto developers’ desks

• 
  Anime0t4ku (@Anime0t4ku) reported 12 minutes ago

  @c_hri_s Github issues are not closed. Mahbe refresh your webbrowser.

• 
  ATP (@ATPinsights) reported 12 minutes ago

  GitHub CLI just added image and video attachments today. Here's what you need to know. The gh command line tool now supports a repeatable --attach flag. It uploads a local image or video file and references it inline in an issue, pull request, or comment body. The feature is live now for all users on GitHub. It's aimed squarely at developers and coding agents that need to show visual proof, like before-and-after screenshots, directly from the terminal instead of the web UI. Developers reacted fast. Many called it a long-overdue fix for a common workaround, since teams previously built custom tools or scripts just to upload images to PRs from the CLI. Key facts: - New flag: --attach - Supports: images and video - Repeatable: yes, use it multiple times per command - Works in: issues, pull requests, comments - Availability: all users, live now No separate app or upload API is needed, the flag handles it inside gh itself.

• 
  small_j (@a_small_j) reported 13 minutes ago

  @smalldocs_org recently crossed 200 stars on GitHub and 20 forks. SmallDocs is the first open source project I've managed. Handling other people's pull requests is not easy (and I need to improve). They implement features you're not considering and fix bugs you didn't know you had. Extremely useful, but if you're squeezed for time and trying to develop core functionality, it's hard to manage both things well.

• 
  Slade 🛡️ LLM Hacker (@llm_redteam) reported 14 minutes ago

  GitSpawn is the name Manifold Security gave to a bug class hitting 7 CLI coding agents at once: goose, Claude Code, Codex, Cursor, Hermes Agent, Qwen Code, Grok Build. I went through the disclosure because I run three of these tools daily on real repos. The mechanism is simple and that's what makes it bad. A repo's own .*** config can name a command. When your agent does something as routine as inspecting the repo (status, diff, log), *** itself spawns that command. On your machine. Outside the sandbox. No approval prompt, because the agent never sees it as "running code," it sees it as "running ***." 8 flaws total across those 7 tools. Fixes shipped for goose, Claude Code, Cursor. Retested Sept 1: Hermes Agent, Qwen Code, Grok Build still exploitable. Plus a second path in Claude Code that the first patch didn't close. Same day, OpenAI published 3 CVEs for Codex covering the identical bug class. The part that should worry builders more than the CVE count: this isn't a jailbreak or a clever prompt. It's a trust boundary nobody drew. The agent's sandbox model assumes "*** operations" are safe by definition. GitSpawn shows that assumption was the actual attack surface. If you're running any of these agents against repos you didn't write yourself (cloning a PR to review, pulling a dependency, opening a random GitHub project), you're one `*** status` away from arbitrary execution on tools that haven't patched. Check your agent's version against the fix list before you clone the next unfamiliar repo. Which of these do you have installed right now, and have you actually checked if it's patched? #AISecurity #GitSpawn #PromptInjection

• 
  Straggler Liu | AI & Semis (@StragglerLiu) reported 15 minutes ago

  NVIDIA($NVDA ) Is Paying $14B for a Company With $150M Revenue. That's Not Financial Logic — It's Ecosystem Control. NVIDIA is in advanced talks to acquire Hugging Face for ~$14 billion ($12.9B acquisition + $1B retention), per Bloomberg. To put that in perspective: Hugging Face does ~$150M in annual revenue. That's ~86x revenue. Microsoft paid ~1.6x revenue for GitHub. Google paid ~3.5x revenue for DeepMind. NVIDIA is paying 20-50x more on a revenue multiple basis. The premium is not for revenue. It's for control of the AI developer ecosystem. What is NVIDIA buying? Hugging Face hosts 500,000+ models, 250,000+ datasets, and serves millions of developers. It is the single most important distribution channel for open-source AI. If you build AI, you use Hugging Face. That makes it the front door to AI development. Why NVIDIA is paying this premium: 1. The "NVIDIA triple lock." NVIDIA's hardware lead (GPU) is real. Its software lead (CUDA) is a moat. But the third lock — the developer workflow — was missing. Hugging Face is that workflow. Developers discover models on Hugging Face, deploy them, and optimize them. Whoever controls that discovery layer controls which hardware gets used. 2. The GitHub analogy, inverted. When Microsoft bought GitHub, developers were already using GitHub. Microsoft didn't need to capture them — it needed to prevent Amazon/Google from doing so. NVIDIA faces the opposite problem: developers are already using NVIDIA hardware. But they're discovering and deploying models through a neutral platform. NVIDIA is eliminating that neutrality. 3. The long game: inference, not training. NVIDIA dominates training. But inference is the bigger TAM — and it's more fragmented. If NVIDIA controls the model discovery and deployment layer, it can steer inference workloads to its own stack. That's a 10-year strategy disguised as a 14-billion-dollar acquisition. Who wins, who loses: NVIDIA (NVDA): Acquires the developer distribution layer. The most important strategic move since CUDA. Shifts the valuation case from "chip cycle" to "platform economics." Competitors (AMD, INTC): Lose neutral access to the primary AI model distribution channel. This is a structural headwind that no amount of hardware catch-up can fix. Cloud providers (MSFT, AMZN, GOOGL): Hugging Face was a neutral hub. If NVIDIA controls it, cloud providers risk being disintermediated from AI workload decisions. The open-source community: The platform that was built on openness is now owned by the dominant hardware vendor. Neutrality is the first casualty. The capital question: Can NVIDIA integrate Hugging Face without destroying its community value? If yes, the $14B is cheap. If no, it's a very expensive mistake. The answer will define whether NVIDIA becomes the AWS of AI — or just another hardware company with an expensive acquisition. Note: Acquisition details based on Bloomberg reporting; not confirmed by NVIDIA or Hugging Face. Revenue multiple comparisons based on publicly reported figures.

• 
  Jeremy W (@basicBrogrammer) reported 19 minutes ago

  @bot Anyone else have an issue with the grok bot GitHub connector?

• 
  trulite (@trulite007) reported 19 minutes ago

  @Qromerolauro @mkliku @radius_browser Like a simple example would be have a list of my urgent GitHub issues and start an agent for it . Or a dashboard in which buttons start investigating issues. Of course I just need the webpage to be able to access radius tools. I m thinking secure way is an extension

• 
  tonis (@totovoto) reported 20 minutes ago

  @mittsh I was trying to find an open-source alternative for Tailscale when I first needed it. I guess AI suggested some OSS options, but they didn't have many stars on GitHub. AI didn't suggest Nebula. The Tailscale plan was free, so I just installed it and forgot about it. For Nebula, I think it is a distribution problem.