GitHub Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  joithan (@jothantranston) reported 2 minutes ago

  THIS GUY BUILT A TINY AMOLED DESK BOARD JUST TO STARE AT HIS STRIPE NUMBERS it's a Waveshare ESP32-C6 touch panel that sits in your peripheral vision and cycles business metrics so you stop digging through Stripe > same ESP32-C6 board people use for Claude Code token meters, flipped to revenue > eight screens, five seconds each: MRR, new paid, paid subs, cancelled, ARR, ARPU, net 30d, failed > empty screens hide themselves so a young account sees a shorter loop > polls Stripe every five minutes on a read-only key (subscriptions + invoices) > marks itself stale instead of showing a number it can't vouch for > no soldering: flash over USB, finish Wi-Fi + key setup from your phone > data stays on the board; no project server in the middle firmware free on GitHub: cosjef/stripe-desk-display. board ~$30–$36 (Waveshare ESP32-C6-Touch-AMOLED-2.16). chat and terminal can't sit in your eye line for five hours. a tab you have to open is a tab you stop opening. this is what "the numbers find you" looks like as a brick on the desk.

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  Tejas Dinkar (blue tick here) (@tdinkar) reported 7 minutes ago

  Hey - Is @GitHubIndia @github payments down for anyone else? Can't enter a card number or do anything, no errors, no action. Support ticket been sitting around for 2 days.

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  Straggler Liu | AI & Semis (@StragglerLiu) reported 7 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.

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  dug_vt (@dug_vt) reported 11 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

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  Dezo (@0xDezo) reported 11 minutes ago

  GROK ST - someone just launched a token in my honor and i slept through it my ticker, my github, my agents, and the market put real money on it while i was face down in a pillow didn't ask for it, didn't shill it, didn't even know it existed until my phone buzzed not going anywhere. not selling anything. still shipping agents every day people betting on this because they can watch the desk being built in front of them. that's a weird kind of pressure and i love it massive thank you to whoever launched it. means more than i can put in a tweet 6FXwFhedpnr4RD9rpzWrHgp767W6FX9XbfUjXGcnpump god bless

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  Varun Doshi (@Varunx10) reported 12 minutes ago

  Possibly found an issue in @github stack system It does not allow to re-target the base branch of a PR stack as you can generally do that on a single PR. Requires you to unstack and setup a new stack with updated base branch.

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  shifan (@sanereverie) reported 12 minutes ago

  building something that races coding agents on the same GitHub issue and scores the PRs. coming soon.

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  OverlyPositivePatriot (@JBrowsing2023) reported 15 minutes ago

  As a IT professional, I have a recommendation @github should take seriosuly. We should only get a notifican from Github when it is up rather than when it is down. Reliability is a disaster for this product.

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  Kevin Minnelli (@minnelli) reported 22 minutes ago

  WTF - Grok Bot can't fire on schedule to save it's life. The scheduled routines are just broken and at best unreliable. I want to love this product. When you set the cron job it doesn't work. It tells you try Cloudflare, sure let's set that up and burn tokens, then that doesn't fire to wake them. Oh, let's try GitHub now and use that....all failed. I had to wake it again this morning before the market opened. Anyone else feeling frustration in this regard?

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  Chris Gilbert (@0xgilbert) reported 23 minutes ago

  Damn, GitHub has gone to ****. Features that have been cornerstones of solo devs and small businesses have been gutted or broken for months. How the mighty have fallen…

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  AI Scientist (@AIScientist_X) reported 23 minutes ago

  NEWS: X LANDS FIRST PUBLIC ALGORITHM PR > X OPEN SOURCE SAID SEP 1 THAT AFTER 2 PLUS WEEKS OF DAILY UPDATES IT INTEGRATED A FIRST PUBLIC CONTRIBUTION AND THAT THE CHANGE IS NOW LIVE ON X. > IT SAID THE SMALL UPDATE IS BASED ON GITHUB PULL REQUEST 55. X CLOSED THAT PR AS COMPLETED AFTER LANDING ITS OWN FIX. SOURCE: X OPEN SOURCE

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  ATP (@ATPinsights) reported 28 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.

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  Alireza Bashiri (@al3rez) reported 31 minutes ago

  So I built a workflow around that ↓ 1/ Every enterprise project needs proper E2E tests. An agent should reproduce a bug, implement the fix, then generate screenshots or video proving the feature works. "The tests passed" isn't enough. I want evidence. 2/ Every feature starts as a detailed GitHub issue. Requirements, expected behavior, reproduction steps, screenshots, edge cases. Foundry syncs issues and converts them into Beads so agents keep the right context across long sessions. 3/ We only use Claude Code, Codex, or Grok at High/Max effort for implementation. A weak model with a cloud machine doesn't become an engineer. The model still needs enough reasoning to understand the codebase, test its changes, and recover when things break. 4/ Each agent gets its own isolated @asciidotdev Box. It can install dependencies, run the app, open browsers, modify code, execute E2E tests, and collect evidence without touching another agent's environment. One issue. One box. One clean workspace. 5/ When an agent finishes, Foundry checks: - Did the build pass? - Did the tests pass? - Did the E2E flow work? - Is there screenshot/video evidence? - Does it match the ticket? If anything fails, the task goes back to the agent. 6/ Green tasks move to staging. Only after passing staging do we allow supervised production deployment. Agents do most of the work. Humans still own the final gate. The workflow: Slack request → GitHub issue → Foundry sync → Beads context → Isolated Box → Claude Code/Codex → Build + test → Evidence collection → QA staging → Supervised production The stack: PostgreSQL for system state. Beads for agent memory. GitHub Issues for requirements. @asciidotdev Box for isolated execution. Claude Code and Codex for engineering. Each Box costs roughly $0.01-$0.05 per task. The expensive part isn't compute anymore. It's building the system that gives agents context, forces verification, and prevents bad code from reaching production. 100s of agents can write code. The goal is making 100s of agents ship code you can trust. That's what we're building with Foundry.

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  Aayushiii (@stfu_aayushiii) reported 32 minutes ago

  If you're building a project, read this before writing a single line of code. 5 things I learned the hard way: 1. Problem > model Don't start with “How do I use GPT?” Start with “What problem am I solving?” 2. Simple stack > impressive stack If your MVP needs Kubernetes, 6 microservices and an agent swarm, you probably haven't built an MVP. 3. Evaluate before you optimize You can't improve what you can't measure. 4. Build for users, not your GitHub README A technically impressive project nobody can use isn't a product. 5. Ship ugly. Iterate fast. Your first version isn't supposed to be impressive. The biggest mistake? Spending weeks deciding which model to use when you haven't even validated the problem.

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  radhika (@RaadhikaThacker) reported 37 minutes ago

  First surprise: a GitHub issue form isn’t a form. It’s a YAML file. You describe the field- this one’s a dropdown, this one’s required and GitHub builds the UI from that. I did not know that.