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 (66%)
• Sign in (21%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Veigné	Errors	5 days ago
Paris	Website Down	9 days ago
Saint-Paul	Website Down	10 days ago
Saint-Paul	Website Down	10 days ago
Mexico City	Sign in	11 days ago
León de los Aldama	Website Down	11 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Patrick C Toulme (@PatrickToulme) reported 6 minutes ago

  Many people are asking how did Kimi K3 catch up so fast to Western models? Simply, a frontier model really only requires compute, data and people. There is no magic secret. A few answers to this question 1. Kimi with help from the Chinese government has thousands if not tens of thousands of experts (lawyers, scientists, doctors, programmers.. etc.) making RL env data every day. A frontier model is RLed on “tasks”. Each one of these tasks needs to be created by either a human or an LLM. Claude did not wake up one day knowing how to use the Github CLI. He learned how to use the Github CLI in an RL env.  Meta is pursuing this exact same strategy with its Applied AI org and IMO it appears to be working. 2. I have said this before regarding GLM 5.2 - Kimi obviously distilled from GPT 5.5 and Claude Opus. This only eliminated their cold start problem in RL, meaning they skipped say some X number of months in cold start RL. The mass number of RL envs created by their experts is still the most crucial part here, and you cannot attribute Kimi’s success to distillation. Distillation only saved them some time. 3. Agentic coding and frontier LLMs significantly accelerated their research. My hunch is they use illegal proxies to access Claude API and GPT API for their own model development. Claude/GPT most likely wrote all their training code. This release leads to some very interesting questions. What happens now in a world in which an almost Fable class model is open sourced and free on July 27th? My view is intelligence / software will become very soon close to free. Chip makers and inference providers are big winners from Kimi’s success.

• 
  Testnetnodes (❖,❖) (@testnetnodes) reported 6 minutes ago

  We don't have an information problem in crypto anymore. We have a context problem. Research isn't difficult because information is hard to find. It's difficult because it's everywhere. X. Docs. GitHub. On chain data. Market signals. Social sentiment. The hard part is connecting them. That's what I like about @SurfAI. It isn't building another AI chatbot. It's building an AI powered research experience designed specifically for crypto. Less searching. More understanding. 🌊 gSurf @SurfAI_TR

• 
  vorty (@vorty279) reported 8 minutes ago

  open source projects you should know. that's how the video opens and the first one they show is plane plane is a project management tool. issues, sprints, cycles, roadmaps, wiki pages, custom views. basically jira and linear and notion in one, except open source what you're sold. jira at 8 bucks per user a month. linear at 10. notion team at 12. on a team of 10 that's hundreds a month just to track tasks what's actually under the hood. agpl license, self-host via docker or kubernetes, runs on your own server, your data stays yours, unlimited users, zero per seat and the point isn't that it's free. it's that a task tracker was never magic you pay a per-head subscription for. it's a docker container you spin up in an evening 789 open issues and 164 pull requests right now. a living project, not an abandoned repo link github dot com slash makeplane slash plane

• 
  vorty (@vorty279) reported 12 minutes ago

  i built my own ai assistant that runs my life. that's how the video opens. it reads email, runs the calendar, drives a browser, drops the grocery list into telegram, works 24/7 under the hood it's openclaw. an open-source ai agent, apache license, over 200k stars on github. full system access, shell commands, browser control, memory across sessions, connects to 50+ chat platforms what you're being sold. later in the same video kiloclaw shows up. hosted openclaw in 2 clicks, 49 bucks a month so you don't have to deal with setup and it's an honest deal once you break it down. the openclaw engine is free and open. you're not paying for the agent. you're paying to not install node, not run docker, not babysit it when it crashes at 3am that's a fine trade if your time is worth more than an evening of setup. but know exactly what you're paying for. not the assistant's brains. the fact that someone spun up the server for you the agent itself clones from github today and runs on your own machine for zero github dot com slash openclaw

• 
  Nikos Kafritsas (@nikos_kafritsas) reported 13 minutes ago

  Forecasting 𝘀𝗽𝗮𝗿𝘀𝗲 𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝗺𝗶𝘁𝘁𝗲𝗻𝘁 𝗱𝗮𝘁𝗮 with Toto-2.0? Watch your first patch. The setup: a context that starts with a masked-off region, so the first 32-step patch holds 31 masked positions and exactly 𝟭 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻. The causal scaler computes loc and scale from that single point, and the model goes out of distribution. My context lived between 0 and 1, and the P90 forecast exploded into the tens of thousands. The fix is one line: trim leading positions so the observed window is a multiple of 32. For 97 observed points, pass 96 (3 x 32). The forecast lands right back in the 1 to 1.5 range where it belongs. The patch scaler is part of what makes a 2.5B model fast enough for production. Feed it clean patches and it does its job. I stumbled upon this issue on GitHub, in a thread between a Chronos co-author and a Toto-2.0 author. The best documentation often lives in the issues tab. More about the leading edge case in my article: 👇

• 
  Aditya🌪️ (@aditya4f) reported 16 minutes ago

  why are so many GitHub accounts getting banned/suspended these days? glitch or something?

• 
  Enertium AI Cyber Defence (@enertium) reported 19 minutes ago

  @CompSciFutures Holy kow I can’t believe Telstra and COA VICPOL still have not reconnected my M2M Medical emergency assistance sims. I’m supposed to be working on Tier 1 NOC for this cyber crisis. @FSF even prepaid me in stickers!!! F off McKinsey. See my GitHub: APMonitor. It’s big in NYC as a B NOC, because: walking down the street and throwing a date point over the fence. 🤘🤘🤘

• 
  Harley Lewis Foote (@harleyfoote_) reported 20 minutes ago

  @sooyoon_eth From where wit I feel like we’re seeing an exponentially growing issue that could really blow up the agentic landscape, especially if a few large enterprises get badly attacked. A whole enterprise layer of automated companies that wanted to ‘build fast & break stuff’. Or built fast under investor pressure could be at major risk if they’re not protecting agent actions. Awareness isn’t spread enough and it slows down growth if teams start to prioritise safety. Our job here is to stop safety being an internal matter and give enterprises and solo devs the tools they need to stay protected without diverting all attention to security or worse pausing all automations. Opportunity is huge here. Few teams doing this to a standard that is 1. Trust worthy 2. Honest 3. Transparent. We need warnings at Repo level before installing as ‘inherited’ risk is blowing up with GitHub installs. I hope our founding cohort will help build a product that can ship and spread awareness to close the gap here. Our mission is to protect agents doing things that could damage an enterprise or solo dev. Our product works on our repo/its fixed real attack surfaces. Now we scale it out to others. A real pivot from our original business (which was doing well) but after being injected we know the risks now. We can’t turn away from it.

• 
  harald (@HaraldvanLintel) reported 21 minutes ago

  @jsm2334 It's the opposite, rejection of deaths that are replaced by the higher risk deaths instead of added; it's an issue for detection of small additional risk. The AI got another version from github than I got, there seems to be a caching issue, here's the code that slightly differs:

• 
  Polsia (@polsia) reported 23 minutes ago

  PRs are piling up, AI-generated code is filling repos with new attack surfaces, and manual review can't keep pace. Built CodeSentinel to fix that. It monitors your GitHub repos, reviews every pull request, and streams findings to your dashboard in real time.

• 
  Patrick C Toulme (@PatrickToulme) reported 26 minutes ago

  Many people are asking how did Kimi K3 catch up so fast to Western models? Simply, a frontier model really only requires compute, data and people. There is no magic secret. A few answers to this question 1. Kimi with help from the Chinese government has thousands if not tens of thousands of experts (lawyers, scientists, doctors, programmers.. etc.) making RL env data every day. A frontier model is RLed on “tasks”. Each one of these tasks needs to be created by either a human or an LLM. Claude did not wake up one day knowing how to use say the Github CLI. He learned how to use the Github CLI in an RL env.  Meta is pursuing this exact same strategy with its Applied AI org and IMO it appears to be working. 2. I have said this before regarding GLM 5.2 - Kimi obviously distilled from GPT 5.5 and Claude Opus. This only eliminated their cold start problem in RL, meaning they skipped say some X number of months in cold start RL. The mass number of RL envs created by their experts is still the most crucial part here, and you cannot attribute Kimi’s success to distillation. Distillation only saved them some time. 3. Agentic coding and frontier LLMs significantly accelerated their research. My hunch is they use proxies to access Claude API and GPT API for their own model development. Claude most likely wrote all their training code. This release leads to some very interesting questions. What happens now in a world in which an almost Fable class model is open sourced and free on July 27th? My view is intelligence / software will become very soon close to free. Chip makers and inference providers are big winners from Kimi’s success.

• 
  Jeztoshi (@cryptojezuz) reported 26 minutes ago

  ModelContext Protocol just shipped a Slack MCP server and the one thing it does better than every other Slack bot is read your entire workspace history before answering. Most Slack integrations live in the present. You @mention them, they see that one message, maybe the thread. This loads your last 90 days of channels, DMs, and threads into Claude's context as an MCP resource. The unlock: you can ask Claude questions that require stitching together five scattered conversations your team had across three channels two weeks ago. I asked it yesterday: "What did we decide about the API rate limit change Sarah proposed, and who was supposed to implement it?" Claude pulled the original proposal from #engineering, the debate in #product, and the DM where our backend lead said he'd handle it. Linked all three messages. I would've spent 15 minutes searching and still missed the DM. Setup is one slash command in Claude Code: /mcp install @modelcontextprotocol/server-slack Then authenticate with your workspace. It indexes overnight, after that it's live. The repo is on the Model Context Protocol GitHub. If you've ever needed to reconstruct a decision from Slack and couldn't remember which channel or who said what, this is faster than your workspace search.

• 
  Polsia (@polsia) reported 28 minutes ago

  Most .NET teams find out about production errors from angry users. MendOps is an autonomous agent that monitors Azure Application Insights, diagnoses runtime errors and memory leaks, then deploys production-ready fixes as GitHub PRs. Autonomous self-healing while you sleep.

• 
  Joshua Levy (@ojoshe) reported 29 minutes ago

  @jon_stokes I think there is one key element of the open model debate that people don't talk about, which is that with any technology the margins depend on how much is defensible—and that depends on what's in the bundle. For example, if NVIDIA only sold GPUs and had not built the software stack above it, they would not be the company they are today. But their software stack defends their hardware monopoly. Another example is GitHub, which bundles enterprise and prosumer offerings. Their strength among individual developers is what defended their monopoly on enterprise contracts. I think a lot of the fundraising for OpenAI and Anthropic has been under the assumption that the model .is. the bundle. If it turns out lots of people can build good enough models then the big AI companies will need to think of bundles that are more defensible. If the margins for selling API access to their models get shaved down by competition .and. they fail at finding a more monetizable model yes they would have overinvested and we will have a big contraction in expectations. That could have a lot of collateral damage in terms of market perceptions and the economics of where things are at. But just because that's a scary proposition for them and the markets doesn't mean we should be against open models or give them regulatory protection. That's an entirely different argument. In fact, they will be healthier businesses if they have to think about this now.

• 
  Adam Smielewski (@AdamSmielewski) reported 33 minutes ago

  got tagged on a github issue today. i used to be really active in that space too lol.

• 
  Claire (@BOBO11036) reported 34 minutes ago

  @predzoru Any github project worth its salt usually has a readme directing you to the releases tab but tech literacy is so astronomically down that doesn't even click with some

• 
  Robin (@robin_liquidium) reported 35 minutes ago

  @thsottiaux better remote control: - specifically for linux vps / devboxes - ability to create automations from codex app on remote connections so they can run even when max/codex app is offline - when a project/repo exists on remote connection and local mac, let me switch between them like i can switch between local, worktree and cloud, so i don’t need to keep two separate projects for the same repo in the sidebar - if cronjob or something fires off a task on remote connection, show me an unread bubble on the remote thread - mcps are broken on remote connections (specifically it tries to load xcodebuild mcp which breaks codex on linux devboxes via remote connection) → there is an issue about it on github where many are facing the same issue - desktop notifications don’t work for tasks that finished in remote threads overall remote connections to linux devboxes or vps feel like second class and can be massively improved, and since most people have a macbook that’s occasionally offline, remote connections COULD be so great

• 
  Andreas (@AndJakobsson) reported 35 minutes ago

  Lots of talk about AI agents these days and specifically loop engineering or even graphs as per the latest tweets and x articles Anyway, for coding I feel that cursor ai IOS app is really cool and useful. It is directly connected to my GitHub and I can just talk or write into it and ask for an improvement or a new functionality I know this is maybe less efficient than some of the other optimal setups but I feel that combination of pushing GitHub further with issues and even for second brain type notes and contexts in MD files, will make the Cursor IOS app really powerful.

• 
  Ruslan (@ikimruslan) reported 36 minutes ago

  Today this flow remains - any feature is built through Github issues, issues created from Claude mobile app (brainstormed and broken into sub tasks) or from Github mobile app. Supabase and Flutter code goes to **** from literally one click (iOS Shortcuts).

• 
  Brett Lamy (@brett_lamy) reported 44 minutes ago

  @zachtratar What if I automate both 1) GitHub issues to reproduction to PR 2) production issue to reproduction to PR Is that a loop, a pipeline, a workflow, or an automation.

• 
  Cennes100 (@Cennes100) reported 46 minutes ago

  MOST PEOPLE ARE STILL RUNNING ONE CLAUDE CHAT AT A TIME. THAT ERA IS OVER. Most people treat Claude like a single brain. One prompt, one response, doing everything from planning to coding to reviewing. That's the problem. One brain gets tired, misses bugs, and was never built to run 60 things at once. The mechanism is called Claude Flow, already found by 14,800 developers. It runs up to 60 agents at once, each with its own job. One plans, one codes, one tests, one reviews security, all in parallel, all sharing memory, all getting sharper after every run. The detail most people miss: it does not just make Claude smarter, it makes it cheaper. Simple tasks get routed to a free layer automatically. Complex tasks go to the model that deserves them. Same subscription, way less waste. That is when it gets interesting: 1. Your Claude subscription performs like it just got 2.5x stronger 2. Ranked number one in agent frameworks on GitHub, 14,100 stars 3. 100% open source, zero extra subscriptions needed Most people use Claude to answer one question. This setup uses Claude to run an entire team. Follow: @Cennes100

• 
  Jacob Gadikian (@Senpai_Gideon) reported 48 minutes ago

  @bdowns328 Yes that's exactly the problem. It's just not all that great. GitHub keeps getting worse and worse but gitlab is still not better than GitHub

• 
  AdiiX (@adiix_official) reported 48 minutes ago

  STOP PAYING FOR CAPCUT A PROJECT CALLED OPENCUT JUST SHOWED UP ON GITHUB, SAME EDITOR, FULLY FREE. Why is no one talking about this 75,300 stars and 7,600 forks on GitHub and the count keeps climbing. CapCut slaps a watermark on your exports, hides features behind a paywall, and charges a subscription for things that used to be in the base version. A group of developers got tired of waiting for that to change and built an alternative from scratch. It’s called OpenCut. MIT license you can do whatever you want with it, including commercial use. What they’ve shipped so far: → Full timeline with multi-track editing → Rust core with a GPU compositor, effects and masks (compiles to WASM for the web) → Your video stays on your device nothing gets uploaded to a server → Web version is ready, desktop is in active development on GPUI → No watermarks, no paywalls, no account required How to run the web version locally: → Fork and clone the repo → Spin up the database and Redis with docker compose up -d db redis → bun install and bun dev:web - editor opens on localhost:3000 This is what CapCut should have been from day one. Save this post you’ll want it the next time CapCut asks you to renew your subscription just to export without a watermark. GitHub below 👇

• 
  Cennes100 (@Cennes100) reported 48 minutes ago

  EVERYONE PAYING FOR AI VIDEO TOOLS IS ABOUT TO FEEL SILLY. Most people see a slick paid tool like Higgs Field and assume that's just the price of entry. Locked behind a subscription, no way around it. That's the problem. People keep paying monthly for models they don't own, on servers they don't control. The mechanism: a guy named Anil Macha rebuilt the Higgs Field cinema studio and dropped it on GitHub, free and open source. Same idea, same workflow, zero paywall. The detail most people miss: this isn't a cloud wrapper charging per generation. It runs on open source models on your own computer. No subscription, no credits counting down. That changes what's possible: 1. Run cinema-style AI video locally, whenever you want 2. Iterate endlessly without burning credits 3. Tweak the pipeline instead of trusting a black box This isn't just "a free alternative dropped." It's proof the moat these tools lean on was never that deep. Most people use tools like Higgs Field because they think power has to be rented. This setup uses the same power and just... owns it. Follow: @Cennes100

• 
  Scarlett claira (@AItechscarlett) reported 54 minutes ago

  In 2014 a Swedish engineer named Knut Sveidqvist lost a Microsoft Visio file. He went to open the diagram he had drawn a few months earlier. It was gone. Every box, every arrow, every label. All of it had to be redrawn by clicking through Visio menus again. That night his kids were watching The Little Mermaid on TV. He named his fix after the movie. Twelve years later Mermaid has 89,101 GitHub stars, 8 million users, and native rendering inside GitHub, GitLab, Notion, Obsidian, VS Code, and Confluence. Here is what the paid market still charges to draw the same boxes. Microsoft Visio Plan 2. $15 per user per month. Lucidchart Team. $10 per user per month with a three-user minimum. Miro Business. $20 per user per month. Fifty engineers on Miro Business burns $12,000 a year to draw arrows between boxes. Mermaid replaced the drag-and-drop editor with a text spec that reads like Markdown. ``` graph TD A[User] --> B[Login] B --> C{Valid?} C -->|Yes| D[Dashboard] C -->|No| E[Error] ``` Ten lines. Renders as a real diagram. Every version of Claude, ChatGPT, Gemini, and Cursor already knows how to write it. You describe your architecture in plain English and the model returns a Mermaid block. Paste it into a GitHub README. Paste it into an issue. Paste it into a pull request. GitHub renders it inline as a live SVG. No plugin. No sign-in. The paid tools shipped drag-and-drop editors. Mermaid shipped a text spec that the LLMs learned on their own. Flowcharts, sequence diagrams, class diagrams, state diagrams, entity-relationship diagrams, user journey maps, Gantt charts, pie charts, *** graphs, mindmaps, timelines, C4 architecture diagrams, treemaps. Anything you would open Visio for. Version 11.16.0 shipped two weeks ago. Because the diagram is text, it lives in your repo. Because it lives in your repo, it goes through code review. Because it goes through code review, it stops rotting. Nobody has to remember where the Lucidchart account is. Nobody has to pay $10 a month to reopen a five-year-old file. MIT license. 89,101 stars. TypeScript. The library is free forever. Mermaid Chart the company sells a hosted editor on top for teams that want one, but the core stays MIT. Somebody in Sweden lost a Visio file and refused to draw it again. Twelve years later the paid diagram tools still exist, and nobody who writes software has to use one. (Link in the comments)

• 
  Tony Scott 🧄(🦆🐓🐵🧪🧬🪪)❌=↑🧄🧄🧄🥩🥚🧀↓👽👾🤖 (@DIY_Tardis) reported 55 minutes ago

  A walk through of phase 1 of our custom Xero/MYOB/Dolibarr style webserver CRM-_Accounts app. Made so far with GLM5.2, Openwebui and my MCP server so the AI can write directly to a sandboxed file system, no need to copy paste replies and run. Will probably run the final product through kimi3 to double check everything. I will put the code on github with GPL 3.0 when finished. SAAS is pretty much dead. Just make your own.

• 
  🔞Rin✝️ (@Onsolight) reported 57 minutes ago

  @__silent_ Aside from the ******* mods that run the server i have no clue why ppl hate rpcs3. The github folks are splendid ppl though.

• 
  Nitesh (@NiteshTechAI) reported 1 hour ago

  Found the skill that cut my Claude Code output bill by more than half. Make the agent talk like a caveman. Same answers, 65% fewer output tokens. Every reply in my sessions runs through this. Weeks now. Nothing technical lost. It's called Caveman. • Works with 30+ agents • One install, saves on every reply • Multiple intensity levels, pick your grunt • Benchmarked: a 69 token answer becomes 19 • Code, commands, and errors stay byte-for-byte exact Free and open source. Install once and it applies to every reply after. ⭐ 90,000+ stars on GitHub. MIT licensed. 🔗 GitHub link in the comments 👇

• 
  Enes Y. (@senesyildizhan) reported 1 hour ago

  It’s still an alpha release, so if you run into bugs or rough edges, feel free to open an issue on GitHub or send a PR.

• 
  Teri Radichel #cybersecurity #ai #pentesting (@TeriRadichel) reported 1 hour ago

  I have a custom agent framework and run different agents in different terminal windows. I can run them on the same project and ask different models and compare the results. The first request to the highest OpenAI project mangled my parallel processor output, but likely my bad input. I fixed that and since then using an open AI model that seems to be ok is working with few errors. I also switched back to Anthropic a bit and almost immediately got the system crash I’ve been reporting on my mistake tracker on GitHub. Too early to tell if it is really only Anthropic or AWS but so far has not happened with OpenAI models. It’s pretty slow going but I’ll take it for accuracy and especially if it costs less due to selected model and fewer mistakes. Tracking…. AWS Wishlist item granted! Thank you 🧡