GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (65%)
• Sign in (19%)
• Errors (16%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Veigné	Errors	4 hours ago
Paris	Website Down	4 days ago
Saint-Paul	Website Down	4 days ago
Saint-Paul	Website Down	5 days ago
Mexico City	Sign in	5 days ago
León de los Aldama	Website Down	5 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Echo (@Follow_our_Echo) reported 2 minutes ago

  A popular npm package was compromised, and just hours later, the attacker changed tactics. 🚨 Here's what happened: A compromised release of the hashtag#jscrambler npm, Inc. package, which receives roughly 15,800 weekly downloads and is commonly used in build pipelines to protect JavaScript applications, introduced a malicious payload that executed automatically during installation. It exposed developer workstations and CI/CD environments before any application code ever ran. Initially, the malware relied on a preinstall hook, but after the compromise was discovered, the attacker published several more malicious versions over the next few hours. The payload stayed the same, but the delivery mechanism changed. So instead of using a preinstall hook, the malware was injected directly into the package itself, executing when the package was imported, or its CLI was run. That meant it could bypass scanners focused on install scripts and even survive npm install --ignore-scripts. And the payload itself wasn't even JavaScript. It was a Rust-compiled native binary hidden inside a file with a .js extension, so scanners parsing for malicious code had nothing to read. Once executed, the malware targeted: • Cloud credentials • GitHub tokens • Kubernetes secrets • AI coding assistant API keys • MCP server API keys • Browser wallets • Password managers This attack is a clear indicator that attackers are adapting faster than traditional defenses. As the ecosystem gets better at detecting one technique, they're simply shifting to another, which is why modern software supply chain security can't depend on detecting malicious behavior after a package reaches developers. The good news: if you're using Echo libraries, this package never reached you in the first place. Echo Libraries continuously vet upstream releases before they're made available, blocking packages we've identified as malicious, compromised, or otherwise untrustworthy. So, in this case, the compromised jscrambler releases were blocked before they could be installed. If you're pulling directly from npm, make sure to: • Upgrade to the latest clean release • Audit any machines that installed the affected versions • Rotate credentials exposed to developer workstations or CI environments, like GitHub, cloud, Kubernetes, and AI tooling credentials

• 
  Atlas (@crptAtlas) reported 2 minutes ago

  GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

• 
  🇺🇸 Santore (@santoretech) reported 3 minutes ago

  Your company doesn't need another AI tool. It needs an operating system and you already have one.. it's @github. Tasks? Issues and Projects. Related to code? What isn't, in 2026. Strategy, playbooks, decisions. If it isn't versioned, your agents work from stale context. Skills and prompts? Same place. Writing voice, review checklists, compliance guardrails. Stored, updated, shared. Improve a prompt once, everyone gets it. Approvals? Built in. Define who reviews what before anything ships. Sharing? Invite someone to the repo. One source of truth, not twelve tools with twelve versions. Humans and agents, same playbook. The company brain isn't a metaphor. It's our operating model and @blockskunk is the lab. One repo at a time.

• 
  Jorge Madrigal ⚡️ (@Jorge_Madrigal) reported 5 minutes ago

  Github issues

• 
  🇺🇸 Santore (@santoretech) reported 8 minutes ago

  Your company doesn't need another AI tool. It needs an operating system and you already have one.. it's GitHub. Tasks? Issues and Projects. Related to code? What isn't, in 2026. Strategy, playbooks, decisions. If it isn't versioned, your agents work from stale context. Skills and prompts? Same place. Writing voice, review checklists, compliance guardrails. Stored, updated, shared. Improve a prompt once, everyone gets it. Approvals? Built in. Define who reviews what before anything ships. Sharing? Invite someone to the repo. One source of truth, not twelve tools with twelve versions. Humans and agents, same playbook. The company brain isn't a metaphor. It's our operating model and @blockskunk is the lab. One repo at a time.

• 
  Void Freud (@voidfreud) reported 10 minutes ago

  I dislike OpenAI’s leadership. But I have to give it to them: the products evolved. I was just charged another $200 for Claude 20x Max. I am the least person to support OpenAI but my experience with Claude was so frustrating as of late, that I gave ChatGPT a go and wow, it felt so much more alive and friendlier than Claude. Talking to Claude has been like talking to a bank clerk trying to sell you a loan. It’s boring. It’s repetitive, cliche, censored and poisoned with disclaimers and guardrails. I just stopped enjoying reading its responses: they are dull and fake, and largely incorrect since quality has been super-degraded on subscriptions. I skim through them, cause they turned from funny, wit and kind to bloated and lifeless nonsense. No amount of tweaks, output-styles or rules or system prompts works: Claude ignores them; it also ignores other functional instructions in Claude Code and @ClaudeDevs do absolutely nothing about that, despite tons of issues raised on GitHub and X, continuing to nerf the quality and charge users further: - the performance of Opuses is super degraded - the performance of Fable is super degraded - the speed of any models is super slow - the quality of the apps sucks (compare Claude iOS app to that of Cursor or ChatGPT, it’s lame and buggy: from exceptionally lame remote control, to dysfunctional transcription that makes Apple’s native dictation feel like a win) - inconsistent and unfair subscription / quota terms - zero accountability, transparency or feedback - toxic paternalism culture, now deeply embedded in models - dismissive and greedy attitude from Anthropic

• 
  Aayush Giri (@AayushStack) reported 16 minutes ago

  what's the one crypto x ai tool you've actually used more than once this month? not the ones you starred on github and forgot. the ones you keep coming back to. trying to cut my own list down to what actually works.

• 
  Anthony (@anthohad) reported 17 minutes ago

  For almost seven years, I tried to get closer to software in the margins: evenings after work, weekends with courses and lectures, unfinished side projects, and the occasional GitHub streak that made me feel like I was getting somewhere. But for most of that time, I was still watching from the side. I became a product manager, learned how software gets built with other people, and loved the work. Still, there were days when I watched engineers ship and wished I could contribute more directly. Then AI made building software accessible in a way I still find hard to fully process. That can sound like terrible timing after years spent learning, but I have come to believe the opposite: those unfinished years became the foundation that lets me understand the tools, catch what is wrong, and push them further. Today, contributing to code is simply part of my week. I can take an idea, build the first version, test it, and bring something real back to the team. In a software startup, that changes what a small team can attempt. The feeling is exhilarating, but "everything feels possible" cuts both ways. Building is becoming more accessible, while judgment is becoming more scarce. If you have worked close to software while wishing you could build more of it yourself, this may feel familiar. I wrote this from the middle of that shift: what it changes, why technical understanding still matters, and why the most exciting part may not be how much faster we can build, but what we can build that was not possible before. Software can now understand intent, filter enormous amounts of information, and surface what matters to the person using it. Crypto is where that potential feels most obvious to me, and it is a big part of what we are exploring at @elitra_xyz. We are only beginning to discover what that unlocks.

• 
  Atef Ataya (@atef_ataya) reported 17 minutes ago

  The scale is real. BlueRock Security analyzed 7,000+ MCP servers. 36.7% vulnerable to some form of SSRF. Their proof of concept: Microsoft's MarkItDown MCP server. 85,000+ GitHub stars. Real AWS access keys pulled from an EC2 instance.

• 
  Rahul Verma (@RahulVerma989) reported 18 minutes ago

  Day 60 - Building Quillly in public 🚀 Two months. 60 straight days. Today's ship came straight out of my own frustration 👇 I've been shipping so fast that Quillly's view of my own site kept going stale between deploys - I'd push a fix, but the dashboard wouldn't notice until the next daily sync. So I built a deploy hook. one token-protected URL. drop it in your CI/CD, and the moment you deploy, Quillly re-fetches your sitemap and re-checks every tracked page. no waiting for the daily run. → curl one-liner or a GitHub Actions step → pass a delay so it waits for your build to actually go live → auto re-checks at +5 and +15 min if nothing changed yet built it for me. shipping it for you. which is pretty much the whole theme of 60 days. 🙌

• 
  the lich (@thelichhh) reported 19 minutes ago

  4 cheap computers just ran an AI model that needs a $10,000+ machine. The build guide is free on GitHub. Jeff Geerling clustered 4 Framework mainboards into one rig for around $8,004. Then he loaded DeepSeek R1 across all of them, a model too big for any single consumer box on the market. It worked. The tokens printed on camera. The trick is 30 years old. Beowulf clustering powered university supercomputers in the 90s. He rebuilt it for local AI with 1 Ansible playbook: run a single command, the cluster assembles itself, benchmarks and all. He also filmed what breaks. The exact spots where 4 machines stop acting like 1, where efficiency loses to Apple silicon, where prompt processing chokes. Every number sits in public tables. The price chart at the end does the damage: his shelf-sized rack next to Mac Studios and enterprise servers costing multiples more, running the same class of models. Last year, a server he tested crawled at 4 tokens per second. This year the same money buys a cluster. The repo is public. The parts are orderable. The excuse is gone.

• 
  Onions Gillespie (@maxcsmith) reported 19 minutes ago

  This isn't a pitch it's just what will be in its modular setup. Other engineers have no trouble compiling from the Tom A. *** Notes. Tom like Tom Hanks or Tom Cruise, but any Tom- not after me, Tom A. "Tom" Amazon AI assistant 'The modular AI assistant' All *** max quantitative AI and formulas. Ready for github. Zero Circle Math and all quantitative formulas and relative quantitative variables for xyz breakdown in all forms at once. Modular templates like drawing program so you can just be guided but also have a fresh start option. Pick a quantitative breakdown. Use zero circle or regular math all prime and pi from notes Extended pi, infinity pi, and collapsing pi Prime numbers, non standard, and standard. Program modulars with templates. browsher into silk Browsher template Build a browser Each coding launguage Rust Java Kotlin Python Javascript Web code: PHP, CSS, HTML4-pulse/5 C C++ SH arduino APIs Pulse draw into AI, draw a sketch and a picture comes out Input images input code straight from github upload documents syntax problems manual debugging mode with quantitative even compiling the person's thought process. Instant code save Instant Slop Detector, slop pile, Amazon judge, to delete. Can save. Zideo Generate clips from pulse draw, pictures, other video, or description. No copyritten files off Amazon. Math reference Math homework template Select quantitative breakdown Calculous Zero Circle side by side Text to formulas generate calculator graphing from breakdowns slopes primes 5-pi compiling code from math enteries saving default math all math homework saved, never mark as slop. Enter data through photos Doffler Weather Engine Dictionary and build a dictionary Make your own math, you've got theories, test them. All quantitative has been mapped. Quantitative award if found, there won't be one. Forstall like Philosophy to math Logic. Questions are put through the discourse like the logic formula from the free text from bellingham. Tom bias rating. Where tom has bias, it'll admit. Provides a theory behind the bias. "What's the bias meter?" Video Game Template. Build a game! Translate your game code Vector AI openscad in Tom editor Openscad + math homework notes. Ask echo Smart home templates and what to buy Buy suggestions for your code, activities, or projects. Pressure chem template Hortiquestions Assistant Gardening

• 
  Archon (@Yamik1shi) reported 19 minutes ago

  You are using Claude wrong, and it is quietly bleeding your API budget Most builders think generating massive amounts of code is the goal It just hit 81,000 stars and is #1 on GitHub today More lines mean more bugs, higher token costs, and impossible maintenance It is a GitHub repo that forces Claude into strict minimalism Ponytail is the fix It injects one hard rule: do not do extra Claude still thinks deeply about the architecture But it becomes aggressively lazy about the execution You control the intensity: `/ponytail lite|full|ultra|off` Run `/ponytail-audit` to strip accumulated bloat from an existing project Run `/ponytail-review` to clean up live edits on the fly It does not just work in Claude Code It runs perfectly in Cursor, Copilot, Codex, Gemini, and Antigravity Free to install. MIT license The leverage is no longer writing the most code It is generating the least Look up the Ponytail repo and stop paying for bloat

• 
  Michael Sharifi (@michael_sh98190) reported 24 minutes ago

  “AI creates the reverse problem. In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.” I could see this leading to a stable set of “trusted enterprise-safe” LLM providers - the moat is “how trustworthy am I to deploy my ip to this LLM”, not just inference power. By analogy, I trust GitHub to host my ip

• 
  Shivay Lamba (@HowDevelop) reported 25 minutes ago

  It reads your repo's live GitHub state and computes 4 action lists, nothing generic: 🔍 Triage: dupe clusters, hot issues, unanswered threads 🚀Ship It: approved-ready PRs + a changelog draft 👥 People: first-time contributors going stale 💬 Worth Replying To: HN/Reddit/web mentions

• 
  LittleBallOfPurr (@LittleBallOPurr) reported 30 minutes ago

  @PrplHddWrrr When I experience my problem with PyGPT, which is also open-source. We spent days trying to simply find the file throwing the error (Wasn't releasing the file after first TTS). The error told us the file name, couldn't ever find it locally. How I eventually solved this might be an approach for you since VS Code is Open Source. I got Claude to tackle it pre-installation using the Master Files, creating our own branch with the fix. Then have uploaded the working fix to GitHub as a pull request for longer term fix. Have you tried that, get the VS Code master files and get AI to figure out where this hard limit is being defined, then just change it there instead?

• 
  Vatsalpandya333 (@Vatsalpandya333) reported 33 minutes ago

  Production bugs are not just engineering problems. They are customer-retention events. A customer reports an issue. The team searches Slack, logs, Sentry, GitHub, and deploys. Hours later, the bug may be fixed. But the customer is still waiting. The real problem is not just the bug. It is everything that happens after. Customer report → investigation → root cause → safe fix → customer follow-up One context. One timeline. One workflow. That is what we are building at @TasksMind.

• 
  Gold (@G_E_AGLE) reported 39 minutes ago

  @ShitpostRock While I know that githubs purpose is just to hold code, i understand that the average user hates github when the only solution to their very specific problem is on github and they dont know how to execute code

• 
  hitu (@hitu_monke) reported 40 minutes ago

  SOMEONE PACKAGED A MARKETING AGENCY INTO A GITHUB REPO YOU INSTALL WITH ONE COMMAND open-source plugin for claude code. one line and 33 marketing skills land in your terminal, cro, paid ads, cold email, seo, churn, pricing but the count isn't the story. what a "skill" is now is the story it used to be a prompt you saved in a doc and pasted in. "act as an seo expert." now the expert is a versioned package you install, fork, and pull like a code dependency. and they share a base file every skill reads first, so they aren't 33 loose prompts, they're a team working off the same context it's not vague either. point the seo skill at a real site and it doesn't return a score. it returns a checklist: 20 template pages wasting crawl budget, a broken h1, meta descriptions missing sitewide. the exact things a consultant charges a morning to find this is the shift hiding under the agent hype. the work is being cut into parts you install, share, and swap. a specialist was a person, then a prompt, now it's a *** dependency the model was never the product. the library of things you hand it is

• 
  another anon (@eugenioclrc) reported 41 minutes ago

  @Kritt_AI @_blockian @ControlZ_1337 GitHub not working 👉👈🙏🙏

• 
  Matthew S (@MattMakesItWork) reported 44 minutes ago

  @matt_teeixeira About two weeks ago I switched my development process to a fully autonomous 24/7 engineering loop. The system continuously monitors my repository. Whenever there are fewer than 10 open merge requests, it automatically selects the next ticket, assembles a peer engineering team (Codex, Grok, Claude Code, GitHub Copilot, plus several local LLMs), debates the implementation, reaches consensus, writes the code, and opens a new merge request. Fresh review agents then independently review the implementation. The code is revised and re-reviewed until there are no blocking issues and every CI test passes. Bug fixes that can be fully validated by automated tests are automatically triaged, implemented, and merged. Features are automatically merged whenever the repository’s bot-review requirements are satisfied; otherwise they simply wait for human approval. The pipeline keeps itself full. As merge requests are merged, it creates more. When there are no merge requests left to replenish, it means it has run out of work. That happened this week. A backlog of roughly six months disappeared in about two weeks, and the system eventually exhausted every ticket in the queue. Ironically, my new bottleneck isn’t writing code anymore, it’s spending days researching, thinking through product ideas, and collaborating with AI agents to create enough high-quality work tickets to keep the system fed. The part that still blows my mind: this entire engineering organization runs for roughly $700/month in subscriptions. Not long ago, achieving this level of throughput would likely have required $40k–$60k/month in developer salaries. It genuinely feels like the economics of software engineering have changed.

• 
  Lorenzo (@gabor_rar) reported 44 minutes ago

  Your AI feature can be “working” and still have an authorization bug. GitHub just added a CodeQL query for a specific version of this problem: untrusted user input reaching a model’s system prompt in JavaScript or TypeScript. That changes the review question I want to ask in a micro-SaaS. Not: “Will the model follow the prompt?” Ask: Where did this text come from? Can it reach privileged instructions? Which tools or actions become reachable? What negative test proves the boundary? A customer brief, uploaded file, or support message should be useful input. It should never be able to quietly become policy. The goal is not a model that never gets confused. It is code that refuses to let untrusted text upgrade its authority.

• 
  Mr.Jack 🐬TermMax (@0xHypeETH) reported 45 minutes ago

  @moha_web3 @github This design reduces cognitive load by surfacing structured metadata inline, which should minimize context-switching when triaging issues.

• 
  Ask GPTs (@askgpts) reported 48 minutes ago

  A tool called Graphify just hit number 6 on GitHub trending and it solves one of the most frustrating problems in AI-assisted coding. When you ask an AI coding agent to find something in a large codebase, it guesses. It looks at what's in the context window and hopes it found the right files. Graphify maps your entire project into a knowledge graph first. Then you query the graph instead of guessing. Here is what it does: 1. Type /graphify in any coding agent and it processes your entire project 2. Maps code, documentation, PDFs, images, and videos into one connected graph 3. Supports 36 programming languages via tree-sitter, processed entirely locally 4. Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode, and 20+ other agents 5. Ask precise questions: "What connects auth to the database?" "Find the path between UserService and DatabasePool" 6. Auto-rebuilds on every commit via *** hook so the graph stays current 7. Neo4j and FalkorDB integration for sharing across your team via MCP server 8. PR dashboard showing graph impact before you merge Code processing happens locally. Nothing leaves your machine. No API key required for code. 100% open source. 2.8 million downloads. YC S26

• 
  Julian Li (@liweiyi88) reported 50 minutes ago

  It seems that the re4/LibreCode repo is under a spam attack, for both issues and star bots. It gained 796.2k stars yesterday, GitHub is deleting the star events at the moment. I'm delaying the Trendshift star events re-sync until GitHub has cleaned everything up 🙏

• 
  L (@guocity) reported 50 minutes ago

  @steipete are they based on GitHub issues or do you type the prompt?

• 
  Dweeb (@dhruvweeb) reported 53 minutes ago

  The Best Alpha Is Still Hidden. The biggest opportunities rarely show up on your timeline first. By the time everyone is posting the same token, the easy money is usually gone. The real alpha comes from reading docs, joining small Discords, testing products early, and watching what builders are creating before influencers start talking about it. Some of my best finds never came from viral threads. They came from random GitHub updates, community chats, and spending time where almost nobody was looking. Your timeline is great for news. It's terrible for being early. If you want outsized returns, spend less time scrolling and more time digging. That's where the edge is.

• 
  VisiveAI (@VisiveAI) reported 54 minutes ago

  MayhemCode: 'Closed vendors answer the phone. Open source gives you a GitHub issue.' Healthcare and finance: accountability > benchmarks. Open vs closed isn't tech. It's liability. Who do you call at 3am?

• 
  NakCrypto (@Rpequ2322) reported 57 minutes ago

  Been skeptical of $NOCK's demand story for weeks. Fair is fair though: I scanned the GitHub again and the *** looks good. What changed this week: The AI-PoW branch went from a month stale to a full adversarial audit sprint on July 8. Not vague commits, real attack-surface work: MoE expert-routing bleed mitigations, noise-matrix pinning, matmul under-constraint checks, verdict tables reading "11 SAFE, 1 issue fixed." That's pre-launch hardening behavior. You don't audit like that unless you're about to ship something adversaries will attack for money. The loop speed is the wild part. Founder posts "working on MoE support" July 7. Community asks sharp questions about tile selection and expert routing in TG the same day. July 8, the repo shows audit commits against exactly those surfaces. Question to code in 24 hours. The PR went 484 → 543 commits in a week. Pearl merge-mining compatibility actively maintained ("matching Pearl, fork fix"). Even the block-size tweet has fakenet test commits behind it, not just vibes. Small team, basically one workhorse committer carrying master. That's a real risk. But the work is disciplined, adversarially audited, and matches the founder's public claims commit for commit. My skepticism was never about the engineering. It's that every commit hardens the supply side, the puzzle, and nothing yet touches paid demand. That question is still open. But the launch looks real and close, and the code quality is exactly what you'd want to see. Credit where due.

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  The Qodesmith (@Qodesmith) reported 60 minutes ago

  Hey @mattpocockuk, I notice /wayfinder uses Github issues with subissues but /grill-with-docs does not. Is that intentional?