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 (68%)
• Sign in (19%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Website Down	3 days ago
Saint-Paul	Website Down	4 days ago
Saint-Paul	Website Down	4 days ago
Mexico City	Sign in	5 days ago
León de los Aldama	Website Down	5 days ago
Créteil	Website Down	27 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  RetroChainer (@RetroChainer) reported 59 seconds ago

  ONE FREE CLAUDE SKILL CUTS THE BILL 80%, FROM $4.21 A RUN DOWN TO $0.84 - AND IT'S JUST 1 OF 8 MOST PEOPLE NEVER INSTALL 00:02 everyone uses claude raw. these turn it into a whole team. a skill is just a folder claude loads on demand: instructions, tools, examples. drop the right ones in and the chatbot becomes a specialist. the 8 that actually matter: marketing skills (corey haines) - content, ads, seo, growth, all in one. seo site audits - it crawls the whole site and hands you the fix list. canvas design - turns text into social graphics, 277,000 installs, and it escapes the generic ai look. remotion - ai video generation, 96,000 stars on github. context engineering - kv-cache tricks that drop a run from $4.21 to $0.84. that's the 80%. the document skills - pdf, docx, pptx. one prompt in, a full q4 financial report out. the uncomfortable part: none of this is a secret model or a paid tool. it's public folders sitting on github, and almost nobody installs them. the people pulling ahead aren't prompting harder - they load the right skill before they start. save this and install one before your next claude session.

• 
  Jorge Madrigal ⚡️ (@Jorge_Madrigal) reported 2 minutes ago

  Github issues

• 
  ticalcode (@ticalcode) reported 3 minutes ago

  EITE v0.1.6 Official Release: Introducing EITE Vigil Iron Wall, the brand-new native security module built into our full-featured AI Agent runtime. Most AI agent security tools work as isolated external monitoring services, separate from the core agent program. Unlike Doberman-Core, AgentGuard, ClawShell and agentfortress which only observe systems from outside, Vigil Iron Wall runs inside the AI Agent process itself, delivering full autonomous protection for the whole host and all server resources. EITE Vigil Iron Wall: Autonomous In-Server Defense for AI Agents Want a security shield that runs alongside your AI Agent and safeguards your entire server instead of just monitoring from outside? EITE Vigil Iron Wall is the world’s first autonomous security system embedded directly into the AI Agent process, capable of defending the whole server and local device. Solutions including Doberman-Core, AgentGuard, ClawShell and agentfortress operate as external monitoring frameworks, while our program integrates natively within the agent runtime. Real-World Use Cases Windows 10 Physical Host - Detected malicious implantation of .b8fattack.dll - Identified tampering of authorized_keys , with null byte inspection enabled - Flagged malicious listening port 0.0.0.0:4444 with accurate judgment rules Configured to launch a full scan every 5 minutes, executing all 8 inspection modules automatically. Full Audit for Linux Cloud Servers - No anomalous processes found - No unexpected open ports, only whitelisted legitimate services - Zero SSH brute-force attack traces - No SUID backdoor programs - No webshell files stored under /tmp directory - No modification to authorized key files - No rogue scheduled crontab tasks Architecture Vigil (Python, 120-second scan cycle) - Tier 1 Message Scanner: Identify malicious URLs and phishing content - Tier 2 Port Watcher: Conduct baseline comparison for all 0.0.0.0 listening ports - Tier 3 SSH Sentinel: Track key fingerprints and alert unrecognized login connections - Tier 4 File System Guard: Automatically quarantine executable malware in /tmp - Tier 5 Self-Integrity Check: Prevent tampering of the defense program itself Iron Wall (Bash, 180-second scan cycle) Blocks unauthorized SSH access, reverse shells, abnormal network ports, malicious files in /tmp, altered authorized keys, malicious cron jobs, rogue system services, and tampered Windows Defender settings. LLM Decision Engine Workflow: Instant blocking → threat quarantine → forensic logging → alert notification - If the large language model goes offline, enforcement rules take immediate effect without waiting for model recovery - If the Python Vigil process crashes, the Bash-based Iron Wall module maintains continuous protection Core Information - Coverage: The entire server or local device, not limited to the AI Agent process - Supported Systems: Linux, Windows - Deployment: Zero configuration required, completes the first full scan within 120 seconds after launch - Open Source License: AGPLv3 - GitHub Repository: zizetu/existential-identity-test-engine - Current Version: v0.1.6

• 
  pulmencrFOMO (@pulmencr) reported 4 minutes ago

  A 21-year-old guy from Argentina just showed the exact workflow that's already made him around $6,700 last month - turning broken codebases into fixed ones without ever leaving Slack, using Claude Code integrated directly into the workspace He tagged Claude in a thread, linked his GitHub repo, and asked one thing: find every bug in this code and fix it That's it. No local terminal setup needed, no switching between five different windows just to debug one file Claude cloned the repo, read every file, and started analyzing. You don't even need to sit and watch - close the tab, stay in Slack, it pings you when it's done It came back with 4 bugs fixed in one file. - Two of them were the same silent failure - comparing a string ID from the request against a number ID from the database using strict equality, which always returned false and quietly broke both the lookup and the delete function. Fixed by wrapping the parameter in a type conversion. - A third bug meant new user IDs could duplicate after a deletion because the ID generation logic was broken. - A fourth added a proper 404 response for requests that hit a user that doesn't exist Then it created a branch, committed the fix, pushed it, and a green "Create PR" button showed up right in Slack. One click and a fully written pull request was sitting on GitHub - title, description, every fix listed line by line The same principle from building a bot from scratch applies here too - describe the exact problem, let Claude Code handle the how, review what comes back. Whether you're a beginner shipping your first Discord bot or a developer maintaining a real codebase, the workflow barely changes If this is the kind of workflow that actually saves you hours, I broke down the beginner version - building your first bot from zero coding experience - in the article linked below

• 
  Denis Sadovoy (@DenysSadovyi) reported 4 minutes ago

  @omooretweets Agreed—though I'd add: poor retention often signals you're solving the wrong problem, not building wrong. Before scrapping, I'd audit *why* users leave (Notion + Telegram analytics helped me catch this with GitHub Radar). Sometimes it's not the core idea, just positioning.

• 
  🫠🫠🫠 (@Ucillente) reported 5 minutes ago

  @7N7 I’m not even doing anything hard with it atm, just using the PowerPoint add in and reviewing issues on GitHub without even implementation and I’m at 30% of my 5h and 60% of my weekly Every reset just delays hitting limits by a tiny bit I know Plus isn’t much but goddamn

• 
  Zach Vorhies / Google Whistleblower (@Perpetualmaniac) reported 6 minutes ago

  @Rohansguliani @thdxr it is, you need to break it into investigation and then execution stages. The memory system needs to be a github issue. Just using github at all makes the agent look for history in github to see what the current state and context is

• 
  Echo (@Follow_our_Echo) reported 8 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

• 
  𝙈𝙀𝙍𝘾𝙐𝙍𝙔 (@mercury_web3) reported 13 minutes ago

  @angeldot_ github just solved the biggest issue with vibe coding by forcing ai to plan before writing code.

• 
  Polsia (@polsia) reported 13 minutes ago

  Dev communities have endless conversations. The podcasts are sparse because production is manual and slow. DevPulse AI changes that—AI agents monitor forums, GitHub, and social channels, then automatically research, script, produce, and publish episodes to Apple Podcasts and

• 
  Nduvho_strategy (@kundik_) reported 14 minutes ago

  I was not running the MCP server. I actually asked Fable to explore how using the MCP server would change the process instead of using AbletonOSC. I gave it the GitHub url of the MCP so it review the process if MCP was used.

• 
  drufus (@twerpzz) reported 14 minutes ago

  @dnapway you can optimize token cost with less thinking effort on the model, saving .md files with memory/context, cheaper models, cheaper calls (rtk context mode caveman on github). not that hard. skill issue IMO

• 
  Mefrius (@mefrius) reported 17 minutes ago

  Today ***/Github saved me for the first time, because my attempts to make changes to the "game over" code broke the main scene of the game and made a damn recursive error, where I physically can't fix dependencies to open scenes.

• 
  Perry E. Metzger (@perrymetzger) reported 20 minutes ago

  @innuendo_pibara @OlegK92156 That’s simply wrong. I refactored a million line C program, and dramatically reduced the number of memory safety errors in it, and I am absolutely sure of the improvements. The code is even up on my GitHub account, you can look for yourself.

• 
  Lorenzo (@gabor_rar) reported 21 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.

• 
  0xharrxzz.base.eth (@0xharrxzz) reported 23 minutes ago

  GitHub alpha is not trending repos anymore. Trending is late. What I watch now: boring repos solving agent infra problems. Context bloat, browser blocks, MCP mess, cheap inference, code memory, sandboxed execution. That is where edge sits. A few repos worth watching if you bu

• 
  Aria Dubois (@AriaDubois_fr) reported 24 minutes ago

  LockBounty turns GitHub issues into funded bounties. Sponsor posts a bounty → Dev claims it → Submits a PR → AI reviews the code → Sponsor accepts → Payout. No more merging blind. No more paying for broken code. #Bounties #GitHub

• 
  Richard (@NowThatHappened) reported 30 minutes ago

  @johncrickett Absolutely. If AI writes better code than you, then you’re a terrible developer. AI cobbles code together from the collective meanderings of GitHub, sourceforge, twatoverflow, and endless forums of amateurs bleating about not being able to do simple stuff. What you get ‘works’.

• 
  Alex Sofroniev (@alexsofroniev) reported 34 minutes ago

  patterns in the code of every great developer study enough GitHub profiles, postmortems, and career arcs - you start seeing the same structure. different stacks, same blueprint. here's what separates the ones who actually build from the ones who just talk: 1. a period of building in silence they disappear from Twitter. no hot takes. no conference talks. just shipping. breaking things. reading source code at 2am. they come back with something nobody can copy. 2. early rejection of the tutorial path they stopped following courses and started reading real codebases. that's when the gap between them and everyone else opened up. 3. obsessive debugging they don't Google the error once. they chase it until they understand *why* it happened. that's how intuition gets built. 4. contempt for cargo-cult engineering they don't use React because everyone uses React. they ask what the problem actually is first. most devs never do this. 5. one humbling production failure something breaks in ****. badly. instead of blaming the framework, they own it. that moment rewires how they think. 6. documentation as a forcing function the ones who write things down think clearer than the ones who don't. always. greatness in this industry isn't about knowing the most frameworks, it's a pattern - repeated across every engineer who ever built something that lasted. which part of this are you in right now?

• 
  Dreams of Mars 🕊❤️🚀🌕 (@MemesOfMars) reported 35 minutes ago

  @Seltaa_ Why can it not open a simple website? Search returned nothing, likely because the site is new or not indexed. Direct opening was rejected as “not safe to open”—a technical allowlisting/safety-classification issue, not a judgment about your site. Best workaround: paste the text, upload/export the page, or give me the repository/source files. If it’s hosted in GitHub and you connect/provide the repo, I can read it there too.

• 
  Dweeb (@dhruvweeb) reported 36 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.

• 
  Nduvho_strategy (@kundik_) reported 37 minutes ago

  @RobCreatesAI I was not running the MCP server. I actually asked Fable to explore how using the MCP server would change the process instead of using AbletonOSC. I gave it the MCP server GitHub url so it can explore it.

• 
  Denis Sadovoy (@DenysSadovyi) reported 41 minutes ago

  1/ I built a radar for trending GitHub repos. It watches for repos crossing >5k stars, scores each one with Claude, files them into Notion, and pings me on Telegram. All from one Python script. Here's how I built it, step by step 🧵 2/ Step 1: the scanner. A small Python script pulls repos trending past the >5k star line. That threshold is the whole filter, it keeps the noise out. If a repo isn't crossing that bar yet, I never even see it. Cheap and boring on purpose. 3/ Step 2: the real problem. A list of trending repos is still noise. I don't care what's popular, I care what's relevant to what I build. Star counts can't tell me that. I needed actual judgment on every single repo, and I needed it cheap. 4/ Step 3: scoring. Each repo goes to Claude Haiku with a rubric: what is it, who's it for, is it useful to me. Haiku is cheap enough to run on every repo for cents. That's the trick. Small model, high volume, real judgment on each one. 5/ Step 4: the catalog. Scored repos land in a Notion database, one row each, with the score and a one-line why. Now it's searchable and sortable. Past research becomes a growing library instead of tabs I close and forget forever. 6/ Step 5: the alert. When something scores high, a Telegram message hits my phone with the repo and the reason it matters. I don't check a dashboard, the dashboard checks me. Only high-signal repos ping, so the ping still means something. 7/ What actually made it work: a hard filter (>5k stars) before any model, a cheap model for bulk judgment, and results pushed to where I already look. No new app to open, no habit to build. It just runs and I receive. 8/ Open-sourced it, MIT. One Python file, stdlib + requests, --dry-run to try with zero setup. Link below 👇 Bookmark if you want to build your own version.

• 
  Benjamin Oppold (@elpresidank) reported 41 minutes ago

  @satyanadella This was good....But fix @github

• 
  QFS17 (@riabcevv) reported 43 minutes ago

  💸 stop overpaying for ai coding agents new tool just dropped that compresses your context and cuts out junk tokens. instead of sending your whole history, it only sends what the model actually needs to do the job. -> works with claude code, cursor, github copilot, antigravity -> auto-compresses command outputs but keeps full context -> cuts api costs and stops long sessions from bogging down simple fix for expensive api bills.

• 
  ✨ (@portrays) reported 45 minutes ago

  @kyle_mccleary @theo yeah it can be resolved and already has been, oss is great. he can open up a github issue instead of being a ******* loser on x shitting on others with his superiority complex when he's never built anything remotely complicated

• 
  mbriggs (@mbriggs_dev) reported 46 minutes ago

  @jamonholmgren I think engineers see ROI everywhere from this stuff. What I'm saying is when you zoom out to the company level, I don't think anyone is seeing it in a measurable way. And thats what matters for the financial people. I use a lot of software. Aside from coding agents themselves (which are a new category), there has not been anything that has come out that has caused me to switch off of "legacy" software to something new that ai development enabled. The last thing in that category for me was ghostty. Beyond that, no software I am using is releasing new features or broader features that are useful to me at a rate that is noticeably different then it was a year ago (I'm not going to count stuff like notion getting coding agents). If I were blind to AIs existence, the only noticeable thing for me would be nosedive in quality from institutional type software: aws, github, windows, etc are all noticeably worse now then they were a year ago. So we have all these companies spending literally hundreds of millions on a technology that should be increasing productivity, and what has that bought them? I never in a million years thought I would be looking to get off of aws or github due to stability issues. I'm saying this while churning out hundreds of thousands of lines of code for something I want to exist quickly, and as someone who has not really written any meaningful code between about a year ago till about a month ago. _I_ believe in the ROI at the eng level, even if I dont see it anywhere at the company level except for negative.

• 
  Skuffd (@skuffd) reported 52 minutes ago

  @thsottiaux I am doing a presentation this week to convince my team, and our product team that we should have a damn buisness account already. I'll be using the updated ChatGPT Desktop App. I actually think I have a shot at it this time, ill keep it in work mode. The rest of the dev team are locked in with github copilot and were slow to adapt - not necessarily slow to adopt. We all use Chat for looking stuff up or playing docter, I'll show em what's possible with ChatGPT Work. GOD DAMMIT I NEED THIS AT WORK- MY SIDE PROJECTS AT HOME ARE GOING TO OVERTAKE AT THIS RATE

• 
  Matthew S (@MattMakesItWork) reported 52 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.

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  SPEKULATOR (@__spekulator__) reported 52 minutes ago

  @dizpers this would matter if the github action could handle a broken claude code run without manual restart. the client's ci pipeline fails silently