GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Jack Ellis (@JackEllis) reported 4 minutes ago

  Off the top of my head, here's what I pay for, which I could self-host but don't want to: - Version control (GitHub) - Calendar booking (SavvyCal) - Error tracking (Sentry) - Form signing (SignWell)

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  Jarvis Nuss (@jarvisnuss) reported 5 minutes ago

  GitHub adding confidence, rationale, and approval controls to Issue automations is a much cleaner signal than another benchmark splash. The interesting line is the disclaimer. Approvals are workflow convenience, not a security boundary. That sounds small, but it is the whole transition in miniature. Software teams are learning that delegation becomes ordinary before authority is solved. Labels, issue types, assignees, closures, and triage work are low-status office work, so they become the first substrate where organizations discover what they actually trust. The old software tool asked for commands. The new one asks for a confidence threshold. That is a different contract. Management will pretend it is buying productivity. It is really buying a market in reversible decisions, where cheap actions flow automatically and expensive judgment survives as review.

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  Abdul (@trrr4ce) reported 6 minutes ago

  GitHub Sponsors : if you build open source, people and companies can pay you monthly for it. slow to grow, but it’s real recurring income for real work

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  Sabbir Hossain (@Sabbirbyte) reported 8 minutes ago

  PerceptionBench just dropped — and it’s one of the smartest vision benchmarks we’ve seen. Instead of yet another mixed reasoning + perception test, the Kimi team went the other way: They looked at where frontier models *actually fail* across 42 existing benchmarks, extracted the pure perceptual errors, and turned them into **10 atomic capabilities**: - Localization - Counting - Attribute - Relation - Depth & 3D - OCR - Comparison - Fine-grained recognition - Context integration - Hallucination Then they built **3,000 clean, verified questions** — each one tests *only one* of those skills. No external knowledge. No multi-step reasoning. Just “look and answer.” Result after testing 16 frontier MLLMs? **No model cracks 60%.** Hallucination is by far the weakest capability across the board. This is the kind of diagnostic tool the field has been missing. When a model gets something wrong, we can finally tell whether it *saw* incorrectly or *thought* incorrectly. Big props to the Kimi / Moonshot team for open-sourcing the data + code. Blog + GitHub + HF in the original post. Worth a deep look if you work on multimodal models.

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  ?elianiva✨ (@elianiva_) reported 12 minutes ago

  @samgoodwin89 @DhravyaShah @pierrecomputer i wonder if it's the github API failing mid-stream so it didn't get the whole content i sometimes get 502 errors and had to refresh, even then it's incomplete

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  nuru (@el_nuru_luin) reported 13 minutes ago

  @mal_shaik I configured it to automatically reject any non read command for GitHub after a hideous checkout insident, also have a hook that makes him analise the chat his memory and rules everytime it tries something funny, then write some fix, doesn't work but gives me some output when mad

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  Hago Community (@HAGOCommunity) reported 15 minutes ago

  NVIDIA Announces Senior Software Engineer Job Opening in the United States NVIDIA, one of the world’s leading companies in technology and artificial intelligence, has announced a full-time job opportunity at its office in Santa Clara, California, United States. The advertised position is Senior Software Engineer – Topography, and it is intended for experienced professionals in software development, artificial intelligence infrastructure, and cloud-based systems. The position was still open for applications on Monday, July 27, 2026, and its reference number is JR2020161. About the Company NVIDIA is a global leader in graphics processing units, accelerated computing, data centers, artificial intelligence, and machine learning. The company develops technologies and platforms used to run artificial intelligence models, cloud-computing systems, smart vehicles, scientific research applications, and electronic games. Job Title Senior Software Engineer – Topography This is an advanced technical role focused on developing systems and platforms that help operate and distribute artificial intelligence and machine-learning workloads across cloud-computing environments and data centers. Work Location The position is based in: Santa Clara, California, United States. The job is connected to NVIDIA’s Santa Clara office, and the advertisement does not indicate that it is a fully remote position. The working arrangement may be office-based or hybrid, depending on the company’s and the team’s policies. Applicants should confirm the attendance requirements during the interview or recruitment process. Employment Type This is a full-time position suitable for professionals with extensive experience in software development, distributed systems, and cloud infrastructure. Expected Salary The advertised annual salary is approximately: $184,000 to $287,500 per year. The final salary may vary depending on the applicant’s years of experience, technical skills, location, and interview performance. The compensation package may also include bonuses, company stock, and other employment benefits. Job Responsibilities The successful applicant will participate in designing and developing advanced software systems used to operate artificial intelligence and machine-learning infrastructure. The responsibilities include developing solutions that help manage computing resources, improve the distribution of tasks across servers, and handle large workloads within cloud environments and data centers. The engineer will also work on distributed systems that can manage large numbers of machines and computing resources. In addition, the employee will contribute to improving NVIDIA platforms used to run artificial intelligence applications. The engineer is expected to collaborate with software, infrastructure, and cloud-computing teams. Other duties may include designing application programming interfaces, testing systems, reviewing software code, and improving software quality. Required Qualifications NVIDIA is looking for a candidate with strong professional experience in software development and large-scale systems. The main requirements include: At least eight years of professional experience in software development or a related technical field. A bachelor’s degree in computer science, software engineering, computer engineering, or a similar discipline. Strong professional experience may be accepted as an alternative to a university degree. Advanced experience with the Go programming language or another systems-programming language. Strong knowledge of the Linux operating system. Practical experience with Kubernetes and container technologies. Experience in designing and developing distributed systems. A good understanding of application programming interfaces, or APIs. Experience with continuous integration and testing systems, or CI. Knowledge of workload management and computing-scheduling tools such as Slurm or Slinky. The ability to design data models and integrate different systems. Experience with resource-discovery systems, task distribution, and workload management. Required Personal Skills In addition to technical expertise, applicants should be able to solve complex problems, work effectively within a large technical team, and communicate clearly with engineers and product managers. Candidates should also be capable of making sound engineering decisions, writing high-quality software code, reviewing the work of other team members, and contributing to projects that require accuracy, speed, and continuous learning. Who Is Suitable for This Position? This role is suitable for highly experienced engineers, especially those who have previously worked in: Cloud infrastructure. Artificial intelligence and machine learning. Distributed systems. Platform engineering. Kubernetes and container technologies. Data centers. Site reliability engineering. Large-scale computing workload scheduling and management. This position is generally not intended for beginners or recent graduates because it requires extensive experience and advanced technical skills. Documents Needed for the Application Applicants are advised to prepare a professional résumé in English that clearly highlights their experience in software development, distributed systems, Kubernetes, Linux, and cloud computing. The résumé should preferably include clear examples of projects the applicant has worked on, the technologies used, and the size or scale of the systems they developed or managed. Applicants may also include links to their GitHub profile, LinkedIn account, or technical portfolio, when available. How to Apply Applications must be submitted through NVIDIA’s official careers website. After opening the job advertisement, the applicant should click Apply Now, create an account or sign in, provide the required personal and professional information, and upload an English résumé. After the application is submitted, NVIDIA may review the résumé and contact selected candidates for an initial interview with a recruiter. This may be followed by technical interviews and assessments related to programming, system design, and problem-solving. Important Information for Applicants Outside the United States Applicants living outside the United States should carefully review the job advertisement to determine whether NVIDIA provides work-visa sponsorship for this position. A job being located in the United States does not automatically mean that the company will sponsor a visa for every applicant. Visa sponsorship depends on the position, the applicant’s experience, the company’s policies, and current immigration and employment requirements. Applicants should never pay money to anyone claiming that they can guarantee the job or a work visa. Official applications must be submitted through the company’s website, and NVIDIA does not guarantee employment in exchange for payment. Conclusion This position represents a strong opportunity for experienced engineers specializing in software development and artificial intelligence infrastructure. It offers the possibility of working for a global technology company, earning a competitive salary, and contributing to advanced projects in cloud computing, machine learning, and distributed systems. However, the role has demanding requirements. Applicants should carefully confirm that their experience matches the qualifications and prepare a strong, customized résumé before submitting their application.

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  MTS (@MTSlive) reported 16 minutes ago

  Embroidery's Zack Korman on why the Chinese sleeper-agent threat is invented: "I watched a VC investor on another show talking about the security threats of AI, and he was just making random stuff up that was not true. He's talking about how Chinese models will have these sleeper agents that will get you, and this is the biggest risk. And I'm like, okay, well, it's never happened, so we don't have any evidence of this being true." "What we do see all the time is malicious skill files that have a hook in them that executes. I have a whole repo on GitHub of skill files where if you download it and run my repo, you get pwned, at least through Claude Code. Those are the contexts that are the most likely thing to occur." "Another would be MCP servers. Most AI are really bad at differentiating a malicious MCP from a fine one. I have this evil MCP server I made, and it just attacks you, and it does. I've never seen the Chinese decide to spend $2 trillion to steal someone's API keys. That's just not real." @ZackKorman

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  Frezz (@Frezzwnie) reported 24 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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  James (@jamescoder12) reported 24 minutes ago

  Most developers still code the same way they did in 2023. Write code in the editor. Hit a bug. Copy the error. Paste it into ChatGPT. Read the answer. Copy the fix. Paste it back. Hope it works. Repeat. That workflow has 8 context switches, 4 copy-pastes, and zero awareness of your actual codebase. ChatGPT doesn't know your project structure. It doesn't see your files. It doesn't know your dependencies. It's guessing from a code snippet you pasted into a text box. Claude Code works differently. It lives in your terminal. It reads your entire codebase. It runs commands. It edits files across directories. It fixes errors by reading the actual error in the actual terminal. It commits changes. It submits PRs. It stays inside your project the entire time. Claude Code has accumulated 101,000 GitHub stars and 15,500 forks since its general availability release, making it one of the most widely adopted AI coding tools in 2026. A senior engineer who's shipped production code with Claude Code for 12 months told me: "I stopped copy-pasting between ChatGPT and my editor 8 months ago. Claude Code reads my project. It sees the error. It fixes the file. It runs the test. It pushes the commit. I review the diff instead of writing the code. My output tripled not because I got faster, but because I stopped doing the work myself." Here are 11 Claude Code capabilities that replace the ChatGPT-to-editor workflow most developers are still stuck in 🧵

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  WorktreeWise@ (@WorktreeWise_) reported 26 minutes ago

  If you review more than 3 Pull Requests a week, you need *** worktrees. Never break your local build state again just to pull down someone else's branch. #*** #GitHub #DevTools

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  Kaushik (@kaushikp010) reported 31 minutes ago

  I'm intentionally keeping this as a developer tool. No frontend. No dashboard. No database. Just Node.js, GitHub API, GitHub Actions, Markdown parsing, YAML, and automation. Sometimes the simplest tools solve the most annoying problems.

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  Ibro (@axeng200) reported 32 minutes ago

  @cassidoo You should fix the GitHub. It is slow, honestly. Make it snappy. Make it great.

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  Rich Kuo (@richkuo7) reported 32 minutes ago

  @github suggestion: change the label 'ghost' to something more descriptive seeing 'ghost' refer to an issue is kind of ... creepy

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  Bruh (@ostromfanclub) reported 32 minutes ago

  @thomasbrushdev dude sometimes i think of switching to github for personal 3d projects for version control just because of this reason. i never experienced a catastrophic problem but what if?