GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  rygo6 (@_rygo6) reported 10 seconds ago

  @eeuoss I can't speak for kernel driver development as I don't do that. But I can speak for vulkan and graphics APIs which do require more specific knowledge about how that hardware works. Which I do assume someone completely comfortable in C will be more capable with vulkan and programming GPUs. It's because more of what C incentivizes you to learn is transferrable to that domain. If someone only knows how to design intricate system architecture using STL with std::vector or std::unordered_map or std::mutex. None of that transfers to the code you run on a GPU. I've seen it multiple times where someone highly versed in standardized ways of C++ or even Rust, or any language which relies heavily on heap allocation and generic containers. Writing graphics or compute shaders is often a barrier they struggle to cross. And often they aren't willing to unlearn such habits to be able to properly program the other half of the computer. Being close a graphics problem domain I am often hesitant of involving anyone unless I see a decent amount of plain C, or C-like C++, or shader code on their GitHub. If it's all Modern C++ where everything is a standard container with smart pointers and exceptions. I assume they won't be able to program a GPU.

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  Rituraj (@RituWithAI) reported 44 seconds 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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  John X Meta (@John4MetaX) reported 1 minute ago

  @bashy_io I think one of their route is down. Same here. GitHub and Flutterwave API not accessible on Starlink

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  To the Moon (@Gardnmi) reported 2 minutes ago

  @mitsuhiko Try the trick of putting the issue on github and having some clankers take a crack at it.

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  The Oracle (@scientist1q) reported 2 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

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  Pranavvv👾 (@pranvv27) reported 7 minutes ago

  honestly, i’m not even mad at this. commit messages are a small thing, but they say a lot about how you work. “fix”, “update”, “changes” might get the job done, but meaningful commits show professionalism, attention to detail, and that you actually care about maintainability. your GitHub is part of your resume. might as well make it look like you know how software is built in a team.

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  Convequity (@convequity) reported 7 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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  阮添福-ThiênPhúc (@vietroadie) reported 10 minutes ago

  Feature request for @TradingView @TrendSpider @Schwab (ThinkOrSwim) engineering teams: Please add GitHub-native CI/CD for custom indicators. Connect a repo → validate on push → deploy approved scripts to my workspace → full version history + rollback. 1/ The Problem I maintain the same level set across ThinkScript, Pine, and JS. One level change = 3 manual copy/pastes into 3 browser editors.Result: drift between platforms, stale timestamps, and levels that silently disagree mid-session. No audit trail of what changed or when. 2/ Core ask — repo connection • OAuth GitHub App install, scoped to selected repos • Map a file path → a specific study slot (e.g. ES Levels/ES_LEVELS.pine → "ES Levels") • Branch selection (deploy from main, preview from a branch) • Config in-repo, e.g. .tradingview.yml / .trendspider.yml 3/ Core ask — validation • On push/PR: compile + lint the script server-side • Return errors as GitHub check runs with file + line numbers • Block merge on compile failure • Optional: run a backtest or smoke-render and post results as a PR comment 4/ Core ask — deploy • Auto-deploy on merge, or manual "promote" button • Atomic: study updates or fails cleanly, never half-applied • Deploy to draft/private first, publish separately • Preserve user-set inputs across deploys where param names are unchanged 5/ Core ask — versioning & safety • Every deploy tagged with commit SHA, author, timestamp • Version list in the UI with diff view • One-click rollback to any prior commit • Dry-run mode • Deploy log / webhook on success + failure 6/ Minimum viable alternative If full CI/CD is too big, just ship a documented REST API: GET/PUT /studies/{id}/sourcewith token auth + rate limits. We'll build the GitHub Action ourselves. That single endpoint unblocks the entire workflow. 7/ Why it matters Scripts are code. Code belongs in version control with review, CI, and rollback. This is table stakes in every other dev ecosystem — and it directly reduces the risk of a bad indicator edit going live during market hours. Who else needs this? 🙋

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  recovering buzzkill (@MikeStillAwake) reported 13 minutes ago

  @Karai_Dan @SteamDeckHQ Agenda or not nexus mods is a terrible outdated model for distributing mods. GitHub would be a superior host.

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  isha (@heeyyaaaaaaa) reported 13 minutes ago

  spent the entire day trying to reproduce a bug for a github issue 🥀🫩

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  Fox (@0xMfox) reported 14 minutes ago

  Gave an AI agent a month and GitHub access. Wanted to see if it could make money. The plan was simple. Point it at bounty-labeled issues, let it write the fix, submit the pull request, collect the payout. > Day 1 12 PRs submitted. 0 merged. 2 rejected. 8 just sat there ignored. Somewhere in that first week it also passed its own tests for a file that didn't exist. Wrote 25 tests for notification_service.py. The real file in that branch was called NotificationRoutingMiddleware. Confidently reported clean anyway. > Day 30 Looked completely different. 84 PRs submitted, 59 merged, $500-800 earned. Ran the agent for about $45 in API calls that whole month. Net somewhere around $455-755. Here's the part that stuck with me. Out of those 59 merges, 3 repos accounted for 90%+ of them. Every other repo it touched, zero merges, despite 30+ PRs going out across dozens of projects. Open source bounties follow a power law. Almost nobody merges your first PR. A few maintainers will merge your tenth without even reviewing it closely. That's what actually fixed the acceptance rate, from 24% up to around 70%. Not a smarter model, a scoring function that runs before the agent touches anything. Repos where it already has 10+ merged PRs score +40. Zero competing PRs on the same issue, +20. Five or more competitors already in, -20, skip it. Repos that closed PRs without merging before, instant -100, not even worth reading the issue. The fastest way to build the credibility that makes this work isn't code at all. Documentation translations sit at a 95% merge rate, barely reviewed, always needed somewhere. A handful of clean translations got the agent enough trust that maintainers started assigning it harder issues directly, no competition, no review queue. Spam version of this, submitting to every repo with a bounty label, burned through 30+ repos for 3 that ever paid out. Worse, it reads like exactly what it is to a maintainer watching the same account flood a dozen projects with mediocre PRs. Paid out by the hour, week 1 was rough, close to $5/hour, mostly setup and failed attempts. By week 3-4, once the scoring system was tuned and a few repos trusted it on sight, that climbed to $30-50/hour on the same kind of work. Bookmark this, scoring logic is worth stealing.

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  Josh Hamilton (@nearbycoder) reported 15 minutes ago

  @theo If GitHub is down does it fall back to a cached version I’m guessing?

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  Ares (@neko23423) reported 15 minutes ago

  I compared the latest OpenClaw vs Hermes Agent GitHub releases so you don’t have to. OpenClaw 2026.8.2 (Sep 1) vs Hermes Agent v0.21.0 (Aug 31). Not a feature-page remix. The actual repos. OpenClaw • 388,516 stars • 81,568 forks • ~86,300 commits • 6,070 open issues Hermes Agent • 239,503 stars • 48,930 forks • ~26,980 commits • 38,563 open issues Hermes is the smarter learner: skills from experience, cron that remembers, Bot Mode, hermes peer. OpenClaw is the personal-AI operating system: iMessage, iOS/Android, Linux companion, team Gateway, signed Foundation releases. The tell: Hermes ships `hermes claw migrate`. You only write a migrator for the incumbent. King in 2026: OpenClaw. Heir with the better mind: Hermes. If you’re picking a self-hosted AI agent this week, that’s the split. Bookmark this. The timeline is about to fill with takes from people who didn’t open either repo. OpenClaw vs Hermes Agent. Latest version. Real numbers.

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  Frank van Puffelen (@puf) reported 15 minutes ago

  @_davideast Noice! From the GitHub page, this covers all of Auth, Firestore, Realtime Database, Storage, Messaging, and Firebase AI Logic. 👏 Where is data persisted (if at all)? Also: JS only, I assume? (sorry if that's all in the repo too, GitHub just went down on me)

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  Ben (@benatcortexai) reported 21 minutes ago

  @github this is the kind of tiny primitive that makes agent workflows less brittle. attaching the repro artifact directly to the issue beats handing an agent a local path nobody else can open.