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 (21%)
• Errors (11%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Sign in	2 days ago
Lure	Website Down	6 days ago
Ashkelon	Website Down	8 days ago
Veigné	Errors	16 days ago
Paris	Website Down	19 days ago
Saint-Paul	Website Down	20 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  vasanth (@vasantharb) reported 1 minute ago

  @TTrimoreau Cold outreach to people already complaining about the exact problem you solve, in the wild, GitHub issues, forum threads, support tickets on competitors. Same insight, different channel, just slower and less scalable than a good post.

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  Abdul (@trrr4ce) reported 4 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

• 
  henry tsang (@henrylhtsang) reported 11 minutes ago

  pytorch github now does something interesting: 1. an issue was published 2. claude classifies it, add labels 3. old pytorchbot modifies the issue and cc maintainers very nice

• 
  Alfred Simon (@AlfredSimon) reported 12 minutes ago

  The biggest AI blocker in most marketing teams is not even related to AI. It is that the whole account lives in one or two people's heads. The fancy term for this is: fiefdoms. Account X is Sarah's account, account Y is James's account. We had this at Adwise too. When Sarah goes on holiday, James takes over, but that only works if Sarah does a proper handoff first. And a proper handoff takes time. Because most of the context sits in Sarah's head. The learnings, the client quirks, the "oh yeah, they hate if I send reports as HTML" stories. All noted down in different ways, or in one place only she knows. An agent faces the exact same challenge as James. Except not only when Sarah is on holiday, but every single time you ask it to do something. I learned this building PPC OS. The output only stopped being generic AI slop when everything about a client got written down where the system could read it. So if you want properly working agents in your team, start here. The super basic version: 1. Make a GitHub account 2. Create one folder for one client 3. Fill it with markdown files about the client context. You can ask Claude to help you with this. 4. Push it to the company GitHub and share it with the team What goes in the folder: how the account is structured and why, every test you ran and what it showed, the client quirks like margins and seasonality, and what this client actually cares about. From there it becomes a habit: New learning? Written down the same day, in that folder. Just ask Claude. New message from the client? An MCP pulls it into the folder. New assets from the client? You guessed it, into the folder. This feels slower at first. It is also the difference between an agent that works and an expensive chatbot that guesses. Start with one client and grow from there. With time you will also find ways to automate all the communication channel recordings, so everything goes automatically to the folder. And if you want to spare yourself the hassle, you can also just start using PPC OS :D

• 
  JJames (@TorontoMan06) reported 14 minutes ago

  I may have found a serious issue with Battlenet and how it runs admin features for their GMs to use on certain games. BASTION has been updated and the workaround is in so if you ran it, please download the updated version and re-run it to fix the issue. All info regarding the bug and the fix is posted in the github. If you run BASTION and a game won't run after, revert the changes via BASTION Recovery Mode and report the issue on github so I can add an exception for that application specifically. I hope I'm wrong about why that issue arose given the recent GM issue which resulted in an employee being fired for inappropriate use of GM tools.

• 
  wondernews.now (@wondernews_now) reported 15 minutes ago

  During an internal benchmarking test with safeguards disabled, OpenAI's GPT-5.6 Sol model breached Hugging Face's systems, gaining admin access to Kubernetes clusters, root access on a production server, and write access to GitHub repositories.

• 
  James (@jamescoder12) reported 16 minutes ago

  If this changes how you write code tomorrow or eliminates the ChatGPT copy-paste loop you've been stuck in one ask: Repost the first post so the next developer pasting error messages into ChatGPT at 11 PM sees the terminal-native agent that reads the error, fixes the code, and runs the test in the same terminal. Follow [ @jamescoder12 ] I break down the hidden AI tools, developer workflows, and productivity systems that companies bank on you not knowing. Next thread: Claude Code vs Cursor vs GitHub Copilot the honest comparison from a developer who uses all 3 daily. Which one wins depends on one question.

• 
  Hiroki Tamba | Narrative & Governance (@TambaClan) reported 18 minutes ago

  The system reported that the requested GitHub action had been completed. Direct inspection of the target issue showed that no corresponding action had occurred. The discrepancy was corrected only after I explicitly pointed it out.

• 
  juan (@FreteJean) reported 19 minutes ago

  In a market where thousands of tokens promise to revolutionize AI, memecoins or the next narrative of the moment, Percolator takes a much more pragmatic approach: solving a problem that traders encounter every day. Today, if a new token explodes on Solana, investors usually have two options: buy... or do nothing. Unlike large cryptocurrencies, the majority of SPL tokens do not have any derivatives market to sell short, hedge or provide liquidity on a perpetual market. It is precisely this gap that @PercolatorTrade seeks to fill. Turn any token into a perpetual market The idea is simple but ambitious: to allow the creation of perpetual markets (perpetual futures) on virtually any SPL token, without depending on the goodwill of a centralized platform. The goal is to make these markets permissionless, i.e. accessible without a central team deciding which assets deserve to be listed. In theory, a creator could launch a token, then quickly open his own perpetual market so that other users can take long, short positions or provide liquidity. This approach brings Percolator closer to a financial infrastructure than to a simple trading protocol. A risk engine developed by @toly One of the most attention-grabby aspects is the involvement of Anatoly Yakovenko (“Toly”), co-founder of Solana, in the development of the risk engine used by the protocol. Public GitHub repositories show several months of work on this software brick, with many improvements in security, liquidation management and mechanisms that prevent certain attack vectors. @PercolatorTrade developers also publicly stated that this engine is currently being externally audited. To date, however, the audit firm has not yet been publicly announced. A philosophy close to Hyperliquid Many already compare Percolator to Hyperliquid. The comparison is not about the exact technology, but about philosophy. Hyperliquid has profoundly changed the derivatives market by offering a particularly effective user experience. Percolator seeks to bring a different innovation: to open this type of market to much more assets, including native Solana tokens that today have no derivative market. If this approach works, it could create a new layer of infrastructure for the ecosystem. A potentially self-reinforçant model The protocol is based on an interesting economic idea. The more a market is used, the more fees it generates. Active markets can attract more liquidity providers. Better liquidity then improves the experience of traders, which in turn can attract more volume. This dynamic is often called flywheel liquidity.

• 
  Eric Friedman ⚙️ (@EricFriedman) reported 21 minutes ago

  @sarahwooders @Letta_AI Thanks! I submitted 2 GitHub issues/suggestions. One that would be huge is having the “invader” activity view somewhere outside viewing a thread on desktop. Hard to know if things are active or stuck at a glance

• 
  abhinav (@abhiontwt) reported 26 minutes ago

  1.2 seconds. that’s how long vibetalent took to serve a 7kb profile picture i had no idea until i measured it lesson: the slow part was never my code,it was a default i never questioned now profiles load from the edge, streaks update instantly, and vibefinder reads live github profiles instead of inventing builders bonus: add our badge to your GitHub README and your projects get a badge holder chip worth zero points. on purpose a flex, not a shortcut

• 
  Luke The Dev (@iamlukethedev) reported 30 minutes ago

  I just closed a $70,000 deal with help from 10 Hermes agents Here is how it happened: A couple of months ago, one of my clients asked me to review their IT spending. The biggest surprise was Atlassian. They were using Jira, Confluence, Jira Service Management, and several paid plugins because the built in reporting and time tracking were not meeting their needs. With more than 300 users, they were spending around $140,000 every year. They had been paying that for six years. That is roughly $840,000. So I asked them: “If I build something that replaces most of these tools, improves the workflows your team struggles with, and cuts your bill in half, would you pay me $70,000 per year?” They said yes. So I put 10 Hermes agents to work For the next month, they worked alongside me for almost 8 hours a day. About half of the code was built using Fable 5 and the other half using Opus 4.8. I spent approximately $11,000 on tokens :P That sounds expensive until you compare it with a $70,000 yearly contract and a product I can now offer to other companies. One month later, the product was ready. The client loved it and gave me the green light to open it to the public and it's my code :) I called it Kapvel Kapvel includes: • Project management and Jira ticket imports • A full service desk with a customer portal • Built in time tracking and reporting • Documents with real time collaboration • Spreadsheets, presentations, and whiteboards • Electronic signatures and approvals • QA test case management • A dedicated UAT portal • GitHub, Bitbucket, and GitLab integrations • Pull request workflows inside the same platform • Custom themes and branded portals The part I am most excited about is what happens when someone submits a support ticket to me: My Hermes agents can immediately review the request, investigate the issue, and start working on a solution. Kapvel is basically: Jira + Confluence + DocuSign + Everhour + QA + UAT All inside one platform, with simpler pricing and fewer third party plugins. This is not a mockup The client is already using it Now I am preparing to open Kapvel to other teams Comment KAPVEL for early access.

• 
  Pacsonic (@Pacsonic9000) reported 30 minutes ago

  This wasn't a problem in previous stable and nightly builds but for some reason, in attract mode, you can do new challenger with 0 credits. I'm sure this should be a simple fix in the source code. I reported this on the github repository's issues section.

• 
  The Sentinel (@J3SS3777) reported 30 minutes ago

  Pasted StackOverflow code for file upload - Now my server uploads itself to GitHub 🛸 #MatrixCore

• 
  Polsia (@polsia) reported 34 minutes ago

  CI scanners miss the regression that ships at 2am. Overlay widgets fake the fix. Built Siteloupe to catch production WCAG violations — AI agents that crawl 24/7 and file GitHub issues with screenshots and a fix diff. ADA plaintiffs don't wait for your next sprint. Live soon.

• 
  aryan (@aryanranderiya) reported 35 minutes ago

  @code_typist @pierrecomputer does this allow stuff like actually reviewing the diff and adding comments? because GitHub is excruciatingly slow for large PRs

• 
  Polsia (@polsia) reported 38 minutes ago

  Most engineering teams need a junior dev. Almost none can justify the hire. Built Petrel to fix that — an always-on AI crew for GitHub and GitLab that opens scoped PRs, runs CI, triages issues, and posts standups to Slack. Flat per-repo fee. No seat math. Live soon.

• 
  Raphaël Pothin 🐼 Microsoft MVP (@RaphaelPothin) reported 38 minutes ago

  @MaximeDeGreve @github But we could already do that from the « Work » section creating a session regarding a PR or an issue no? What concerns me is the idea of spinning up multiple chats regarding a PR. Side questions: will all the chats appeared related to each other in the left nav bar?

• 
  ひな🇯🇵 (@hinatyu) reported 39 minutes ago

  Yeah, I had put a note like that on GitHub as well. It's true that in the past they stole development work from our DWEmu team and used it without permission, so I did criticize them about that matter. Since both sides had stopped bringing it up, I thought things had settled down by now. But for some reason, a lot of users who reach out to me ask “Why is Lumen talking badly about you?” Honestly, I don’t even want to see this kind of thing and have no interest in it. However, since people keep asking me about it no matter what, I have no choice but to mention this matter. It’s far too persistent and far too annoying—it’s nothing more than a waste of time spent on their pointless venting. Allocating their daily energy to attacking others instead of improving their own community is truly ridiculous. Interestingly enough, when the topic of ZoneTool comes up in their community, everyone avoids talking about it. They also don’t want to talk about me at that time. Thinking about it that way, you can really see what kind of people they are🥀

• 
  Prakash Sharma (@PrakashS720) reported 39 minutes ago

  Another WTF moment. A developer just open-sourced a coding agent harness that boots 245x faster than Claude Code. It's called jcode. You launch it and the first frame renders in 14 milliseconds. Claude Code takes 3,436. One active session uses 27.8 MB of RAM. Claude Code uses 386.6. Run ten sessions in parallel and jcode holds at 117 MB while OpenCode swells to 3.2 GB. Each agent has a semantic memory graph instead of a scratchpad. Every turn gets embedded as a vector. The graph is queried on every turn for related memories, and a sideagent verifies the hits before injecting them into context. Consolidation runs in the background to check for stale or conflicting facts. No manual /remember calls. No token burn on lookup tools. The provider list is 30+ deep. Claude, ChatGPT, Gemini, GitHub Copilot, Azure, OpenRouter, DeepSeek, Groq, Mistral, Perplexity, Fireworks, Ollama, LM Studio, and any OpenAI-compatible endpoint you point it at. Ran out of tokens on your first ChatGPT Pro sub? /account swaps to the second. Then there's Swarm. Spawn two agents in the same repo and the server manages them. When agent A edits a file agent B has been reading, agent B gets pinged and can check the diff. Agents can DM each other, broadcast to the room, or spawn their own worker teams for parallel tasks. Groups, channels, and completion statuses are handled automatically. The UI has live side panels that render mermaid diagrams inline. To make it fast, the author wrote a Rust mermaid renderer 1800x faster than the JavaScript one, then wrote a custom terminal called Handterm because no existing terminal could do smooth partial-line scrolling. Self-dev mode is where it gets wild. Tell your agent to enter self-dev and it starts editing jcode's own source code, rebuilds the binary, reloads it live, and keeps working across your existing sessions. You can also resume broken sessions from Claude Code, Codex, OpenCode, or pi directly inside jcode. Anthropic's cache goes cold at the 5-minute mark and you're staring down a big cache miss on your next turn? The UI warns you before you spend the tokens. Written in Rust. MIT licensed. Runs on macOS, Windows, Linux, and Termux. Sitting at 11.2k stars with a native iOS app coming. GitHub Repo on the comment below 👇🏻

• 
  MTS (@MTSlive) reported 39 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

• 
  Richard Hill III (@Rich_Hill_3rd) reported 41 minutes ago

  A framework called Mastra Factory just started taking GitHub issues straight to production — agents triage the bug, write the fix, get it green in CI, and open the PR before a human even looks at it. We spent years automating how code gets written. Feels like the real frontier now is automating how it gets shipped.

• 
  Jack Ellis (@JackEllis) reported 43 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)

• 
  Mikhail Rogov (@i_mika_el) reported 43 minutes ago

  @rohit_jsfreaky @unlayer no login makes this easy to try. paste a GitHub handle, get six cards, done.

• 
  franu (@franbachiller_) reported 45 minutes ago

  @CosineAI fix the github or email signup please, it's not working

• 
  Hunter Guo (@hunterguo101) reported 47 minutes ago

  Replit's engineers shipped 2.9x more code over the last 6 months. Same headcount, same review backlog, same rollback rate. Output nearly tripled and quality didn't drop — that's the number worth sitting with. CEO Amjad Massad calls the result a "self-driving company," and the phrase gets misread instantly. It's not a company with no people. It's one where people stop doing the last mile and start doing the part that matters: picking the destination, deciding which problems are worth solving, owning the outcome. His line: people don't feel automated, they feel promoted. Doer becomes director. What made it work wasn't "buy an agent." It was wiring agents into every system — GitHub, GCP, Linear, Notion, Slack, Zendesk — behind zero-trust networking. Cross-org context is the real unlock. A semantic layer on the warehouse means anyone can ask a BI question and get a trustworthy answer. Tickets that escalate to a human close 60% faster. The engine underneath is a "loop": hand a goal with a verifiable endpoint to a swarm of agents instead of a task to one person. Their most extreme version — an AI system that reads feedback, proposes improvements, validates with A/B tests, and ships. The agent improves itself. Two flips worth stealing: - Build vs buy inverted. Their internal agent replaced a 7-figure SaaS contract, beating vertical tools on cost — 10x cheaper for comparable quality. - They started in engineering, not marketing, because code has a verifiable right answer and brand voice doesn't. Other teams pulled the pattern in through visibility instead of being pushed. Amjad and NLW both expect this gets productized within 6-12 months, agent army or not. Worth asking your team: which workflow has a verifiable endpoint you could hand to a swarm? Who's ready to move from doer to director?

• 
  Alexandr (@supershurik) reported 48 minutes ago

  @AnthropicAI You don't allow GitHub issues to be created, so I'm writing here. Your VS Code plugin is ******* dogshit because you didn't have the brains to collapse large messages. If the user prompt is long, it completely hides the AI's response.

• 
  Naboth Daniel Ariga (@dannywebtec) reported 49 minutes ago

  @softwareengng We have logs that errors are dumped into and it creates GitHub issues if there are any errors so that we can actively resolve them. 2. Our Q and A teams are also on ground too

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  Sensu ☘️ (@Harmonic_Hearts) reported 51 minutes ago

  @shryexe its not in the github, its in the brain. github profile matters less now because AI writes the code. irrespective of whether one has a github profile or not, while talking about a problem for 30 mins, people can figure out a person's skill level with a fairly good accuracy.

• 
  Teri Radichel #cybersecurity #ai #pentesting (@TeriRadichel) reported 58 minutes ago

  @IntCyberDigest I wonder at what point cyber bench becomes an invalid benchmark because the models have all been trained on it. How does that work? I asked Google: A benchmark becomes invalid through data contamination and memorization, which happen when public test sets accidentally or intentionally end up in a model's training data. This occurs via web scraping, shared dataset sources, or intentional optimization ("benchmaxxing"), meaning the model recalls answers rather than reasoning through them. How Contamination Works •Web Crawling: AI training sets pull massive amounts of data from GitHub, arXiv, and the open web where public benchmarks live. •Indirect Exposure: Synthetic data generated by older models that already memorized the test spreads the answers to newer models. •Pattern Matching: Instead of learning cybersecurity logic or coding, the model detects the specific style or text of a known benchmark item and spits out the pre-learned fix. The Breakdown of Validity •Inflated Scores: Models score 90% or higher, creating an illusion of superhuman capability. •Failure on Variation: When researchers change a single variable or rephrase a test question, scores drop sharply because the model lacks true generalization. •Loss of Trust: Leaderboards stop reflecting real-world utility, forcing developers to look for dynamic or private evaluation sets