GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  The Oracle (@scientist1q) reported 6 seconds 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

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  Priyanshu Bhati (@buildwithpb) reported 42 seconds ago

  @CryptoWendyO @chainlink 30% error rate on github replies sounds like a recipe for accidental flame wars. good luck with the cleanup.

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  Yuvraj Singh (@Yuvraj_Singh317) reported 2 minutes ago

  Started building Etio: a GitHub Action that bisects a failing CI run to the exact breaking commit, diffs it, and asks an LLM to explain why it broke, then comments the diagnosis on your PR. No Docker, no server- runs on your own Actions minutes. Open source, WIP.

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  htrowii (@htrowii) reported 12 minutes ago

  @brainage19 i set my flake up with copy pasting github dotfiles on bare metal it was terrible

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  Rituraj (@RituWithAI) reported 20 minutes 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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  Anime0t4ku (@Anime0t4ku) reported 20 minutes ago

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

• 
  The Oracle (@scientist1q) reported 21 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

• 
  wiiiimm (@wiiiimm) reported 24 minutes ago

  @Umesh__digital stop doing it. we don't need another github outage.

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  dug_vt (@dug_vt) reported 25 minutes ago

  @sonemic rym users don’t use spotify they download flacs off soulseek and transfer them to a server connected to their pc and play them from a self hosted music player from github

• 
  Azzie (@Motier_crypto) reported 28 minutes ago

  $Looprat 473K 0x642d30c84211ade7768fe557fbaed7224e2068c7 How do you get a coding agent to keep working while you sleep—without letting it randomly rewrite code, blow through the budget, or grade itself a perfect score? Loop Rat breaks an unattended task into: preflight → act → verify → guard → grade → receipt. The agent is woken up on a schedule to execute tasks. The code results first go through deterministic verification. Then it checks the denylist, the number of modified files, and secrets. Finally, a second independent agent regrades the work, with checkpoints, traces, and receipts left throughout the entire process. More importantly, this isn't a PPT. The project only had v0.1 on August 27. Then it added guard, kill switch, and spend ledger on August 29. On September 1, it added second-agent grading. On September 2, it continuously fixed scheduling, budget caps, timeouts, and concurrent ledgers. Today, September 3, it's already updated to v0.3.3. Shipping multiple versions in under a week—that's exactly what I want to see in a small-cap play like this: code is running, and the narrative is following the product, rather than launching a token first and filling in the story afterward. It currently defaults to Claude, but it doesn't lock the model down. As long as the CLI can consume a prompt and return JSON, it can be swapped. And the whole thing runs locally—no SaaS, no database, no extra accounts required. The project has even already run 50 smoke checks covering key areas like scheduling, guard, budget, kill switch, and timeout. So my trading logic for $Looprat is simple: The next phase of the agent race isn't about "can it work autonomously." It's about "can it work continuously while still being constrained, audited, and stopped." Loop Rat happens to be building exactly that layer of infrastructure. The project is still very early. The catalyst truly worth watching isn't shilling—it's whether the GitHub keeps up this iteration speed, and whether developers actually start plugging it into their own repos. Once those two things happen, $Looprat stops being just a ticker riding the agent hype, and starts having its own fundamental anchor.

• 
  CATIRL 🏳️‍⚧️ (@CATIRL_9) reported 29 minutes ago

  @mminhamina Google GitHub "open grind", solves your problem

• 
  Rusty Williams McMurray (@1RustyMac) reported 30 minutes ago

  Persistent AI doesn’t have a supply chain problem at the model. It has a supply chain problem at the moment it changes its mind. Personality drifts. Tools get installed. Memory accumulates. The thing you shipped on Monday is not the thing answering on Friday. We can attest who built the weights. We still cannot attest who authorized what the agent became on Tuesday. That is the hole. Who is allowed to let it change? We built Living Supply-Chain Security for Persistent AI Organisms around one law: The organism may propose evolution. It may not authorize it. No trace, no drift. If an agent wants a new personality, a new tool, a new maturity, or a rollback — that change does not happen because it felt confident. Confidence is not a key. Self-narration is not evidence. Evidence is not interpretation. Interpretation is not authorization. Authorization has to come from outside the organism, bound to the exact change, used once, and written into an append-only history. Even a rollback cannot erase the record. You can restore a prior state. You cannot pretend the detour never happened. Default-deny. Hash-chained. Externally signed. We froze battery v1 on July 5 and ran it against the paper’s own claims. It held. That is executable evidence. Not a proof. Not a production blessing. Not “alignment, solved.” If it can’t be attacked, it isn’t finished. GitHub later this week. Come try to break it.

• 
  Straggler Liu | AI & Semis (@StragglerLiu) reported 31 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.

• 
  NitroStack (@nitrostackai) reported 31 minutes ago

  The missing primitive might be capability contracts. A Skill shouldn’t say “call Jira.” It should say “I need issue.write.” Then MCP can bind that capability to Jira, Linear, GitHub… whatever exists. That’s basically dependency injection for agents.

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  Martin Tobias (Pre-Seed VC) (@MartinGTobias) reported 35 minutes ago

  if you know any founders who are winding down, I may have a buyer of their github repos. DMs open.