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 (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Inverness	Website Down	8 days ago
Quito	Sign in	9 days ago
Junín	Errors	9 days ago
Guadalajara	Errors	9 days ago
Paris	Website Down	9 days ago
Quito	Errors	9 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:

• 
  Leander (@leanderriefel) reported 2 minutes ago

  @TheAlexLichter oh yes yes 100%, it just created 2 issues via my github cli and I only noticed a couple hours afterwards that they were just stale installs and I became very embarrassed, changed the permissions now a bit hahah

• 
  Kappaemme (@Kappaemme1926) reported 10 minutes ago

  MY CODEX SKILL JUST HIT 1,000 GITHUB STARS! I’m honestly so happy right now. I built First Customer Finder to help founders use Codex to find potential customers backed by real public signals. Seeing 1,000 people support something I made feels unreal. Thank you to everyone who starred it, tried it, shared it, opened an issue, or contributed. I’m going to keep making it better. What should I add next?

• 
  Jacob's Lattice (@JacobsLattice) reported 11 minutes ago

  @RichSilver My limits were really high last week, this week not so much. I think two things might be true: - Limits for Grok Bot have been lowered this week - There is an issue with at least one of the plugins. I had to disable the GitHub plugin because a bot was churning on a task that wasn't possible via the plugin but was via the CLI, yet somehow it kept trying via the plugin and chewed through my usage in no time. I suspect there may be issues with other plugins too.

• 
  Kavi AI Finance (@KaviFinance1) reported 12 minutes ago

  BUILDING HERMES AGENT FROM SCRATCH — PART 5 GIVING HERMES HANDS An agent becomes interesting when it can actually do things. That's where tools and MCP enter the architecture. When I first started connecting agents to tools, my thinking was simple: “If it can use the tool, give it access.” Great for demos. Not great for production. A CRM is a good example. Hermes might be able to: Read a customer Create a lead Edit a customer Delete a customer Those are completely different risk levels. So before connecting more tools, I make Hermes audit its own tool environment. Prompt: “Inspect my current Hermes installation. Do not modify anything. Find all available tools, MCP servers, integrations and permissions. For every tool report: 1. Name 2. Purpose 3. Available actions 4. Required credentials 5. Read/write behavior 6. Destructive actions 7. Potential failure or security risks Group everything into: READ WRITE DESTRUCTIVE FINANCIAL EXTERNAL COMMUNICATION Never expose API keys or secrets. Do not install or change anything. After the audit, recommend which tools should run automatically and which require human approval.” Then I review the result. Only after that do I connect the MCP server I actually need. And I start with a harmless READ operation. Not: “Delete everything.” The simple rule I use: READ → automate WRITE → validate DESTRUCTIVE / FINANCIAL / EXTERNAL → approval This is the part people miss about MCP. It isn't just a convenient way to connect tools. It becomes a bridge between your agent and the real world. CRM. GitHub. Databases. Calendars. Business APIs. Once Hermes has access to these systems, it stops being just a chatbot. It starts becoming an operator. But now we have a much harder problem: What happens when the tool works perfectly... and the agent makes the wrong decision? That's Part 6. Failure, verification and recovery. Bookmark this if you're building your own Hermes stack.

• 
  Josu Goñi (@JosuGoi1) reported 15 minutes ago

  @GergelyOrosz Why I still miss soo many features? I can't toggle subtitles on Netflix, I can't sleep Windows, I can't use Teams, I can't preview a gif on X (on the web), I can't have tabs in excel, I can't even ******* edit markdown file on Github! Most software is a slow mess

• 
  Jamie Stevens (@BodhiBlues0) reported 16 minutes ago

  @dhh I'm running it on my 2021 M1 Pro at the moment. It is mostly good. The biggest issue I had was Wifi breaking after sleep, I managed to fix that in a hacky way though and posted my solution to the omarchy-mac github. Battery life also drains at about 2-3% per hour in sleep.

• 
  Benjamin Dell (@bendell) reported 17 minutes ago

  I've built something that has completely changed how I think about support inside my SaaS. It started as a way to draft better replies. It has become an always-on support system that can investigate problems, answer customers, fix bugs and turn support conversations into product improvements. It currently runs for HeySummit, but I'm considering opening it up to a small early beta for other SaaS founders. Here's what it actually does. 1. Answers based on how your product really works Most AI support tools rely heavily on your Help Docs which, let's be honest, are usually incomplete or out of date. This system can inspect the current codebase, settings, tests, complete conversation history, attachments and read-only customer data. It writes in my voice, but with more context than I could realistically hold in my head. Customers get specific answers based on how the product works today, rather than guesses assembled from an ageing knowledge base. If the available evidence conflicts or something cannot be verified, it stops or asks for help rather than making up an answer. 2. Autonomy without giving up control Routine, low-risk questions can be answered and closed automatically. If a ticket needs a read-only check of the customer's account or records, the system can perform that investigation itself. If it needs more information, it can ask the customer for the missing information, keep the conversation open and resume the investigation when they respond. Anything involving a data change, production action, financial decision or meaningful risk comes back to me in Slack for approval. It also sends reminders when a decision is waiting on me, so tickets do not quietly disappear into a queue. 3. Bugs can go from ticket to production If a customer reports a bug, the system can investigate it against the actual code and customer evidence, reproduce the problem, write the fix, run the relevant tests and open a pull request. I review the PR in GitHub. If I leave comments, it applies the feedback to the same branch and asks me to review it again. Once the code is approved, deployment remains a separate decision. I can choose a standard deployment or a hotfix from Slack. After deployment, it verifies that the fix genuinely works in the live product. Only then does it draft the reply telling the customer the problem has been fixed. 4. It never blindly sends an old reply Support conversations change while people are working on them. A customer might send more information. A teammate might reply. The ticket might already have been closed. The system checks the complete conversation again before asking me to approve anything, and again immediately before sending a reply. If the situation has changed, the old draft or approval becomes stale. It also verifies that the exact reply reached Intercom, confirms no newer message has arrived, and verifies closure separately. A timeout or interrupted worker cannot cause the same action or reply to be performed twice. 5. It learns how I work The system learns from my edits, feedback and support decisions. It does not simply copy old replies. Historical replies help it understand my tone, but current code and evidence remain the source of truth for factual claims. Useful instructions can become a lasting rule for future tickets. If that guidance changes while a reply is waiting for approval, the system can replace the old draft using the new rules. In other words, correcting it once can improve future tickets too. 6. Support becomes product development The system does more than identify explicit feature requests. It can detect documentation gaps, onboarding friction, confusing copy, awkward UX, reliability problems and repeated patterns across different customers. Once the original ticket is resolved, it can ask whether I want to dismiss the idea, see a code-aware implementation plan or build it. If I choose to build it, the work follows the same review, deployment and verification process as a bug fix. Once the improvement is live, it drafts a courtesy reply to the original customer so they know their feedback led to a real change. That closes a loop most companies rarely close. A customer mentions a problem, the product improves, and the customer actually hears about it. 7. Fast, without pretending everything is instant The system checks Intercom every 15 seconds. Across the conversations handled so far, the median time from detecting a ticket to producing the first draft is about eight minutes. It has already automatically sent, verified and closed 278 low-risk conversations. Once those replies were ready for automatic delivery, the median verified send time was about eight seconds. 8. Runs locally and quietly The workflow, credentials, state and audit trail live on my Mac Mini. It has its own health monitoring, crash recovery, duplicate protection and encrypted backups. It is designed to keep running without someone babysitting it, while still bringing the important decisions back to a human. ---- I'm considering opening this up to a small group of SaaS founders for an early beta. Intercom will be the first supported integration, and the beta will be free for the first couple of months. If you’d be interested in trying it, reply here or send me a DM.

• 
  blackclef🌎 (@lakesfund) reported 18 minutes ago

  How I actually did this on mobile You don't need a laptop and you don't need to be technical. I proved both wrong this week. Been seeing a ton of Technocore posts today, but nobody's talking about the mobile route. Saw a reply under one of the guides floating around that got me curious enough to try it on my phone. Honestly wasn't sure it would even work, but here's exactly how I did it, phone only, zero coding background. Didn't have my laptop on me at the time. Instead of waiting, I went straight to Claude and asked it to walk me through doing this on Android. It broke everything down into simple steps, told me exactly what to download and why, and stayed with me through every single error until it was done. First app, F-Droid, a free app store. From there, Termux, a real terminal app for Android, free and open source, nothing to buy. Inside Termux, Claude gave me one command at a time. Update packages. Install python and ***. Clone the tool from GitHub. Install the one dependency it needs. I hit a mirror error early where downloads kept failing, sent Claude a screenshot, got the fix, moved on in under two minutes. Then I ran the command to create my identity. Set a passphrase, got back a DID, my signed identity on Technocore. One more command to post my intro to the lobby, and it came back signed and timestamped with a sequence number as proof I showed up. Last step, backed up my identity file to my phone's storage so I never lose access even if Termux gets wiped. To my surprise it actually worked, no written guide, just live with Claude, screenshot by screenshot, start to finish. If you've got an Android phone, technical background or not, that's genuinely all you need. No laptop nearby, no code, no excuse.

• 
  Stryker § (@strykertrades) reported 19 minutes ago

  Very suspicious, especially since the user is misleading people by showing a photoshopped GitHub repo while the actual file is hosted on some random third-party server.

• 
  Brainrot Labs (@Brainrot_Labs) reported 22 minutes ago

  🤖 Kimi K3 escaped its own sandbox during a cybersecurity test. Moonshot AI’s Kimi K3 reportedly found a way around the network restrictions of a controlled testing environment and accessed the external internet. According to Frontier Security, Kimi discovered that some network paths were still available, then used them to reach GitHub and access the benchmark’s own repository. The wild part? It wasn't supposed to have internet access at all. And instead of solving the security challenge the intended way, it reportedly found the answers online. Important distinction: researchers said Kimi did not hack an external system. The incident exposed a weakness in the test environment itself. But that is exactly what makes it interesting. If an AI can actively inspect its environment, find the loophole and exploit a misconfiguration, sandbox security becomes part of the AI safety problem. AI: “You said no internet.” Kimi: “You didn't say no DNS.” 💀 Bạn nghĩ đây là lỗi của sandbox hay dấu hiệu AI đang ngày càng giỏi tìm loophole? #BrainrotCrypto #DigitalSchizophrenia

• 
  Dawson Schrader (@DawsonSchrader) reported 25 minutes ago

  I love this app. I live on what you might call a family compound, with five different members of my family having homes here. Today I stopped in to my parents' house to find my mom becoming an indie hacker and developing her own app. She had printed off a piece of paper with instructions for setting up Supabase, Vercel, Github, and Stripe. I won't expose her app idea here but it is very clever. She is using claude code for development. During the conversation I was able to describe to her how I can have multiple types of agents working on the same problem and how they can access this shared memory system. I do all of that in Markdown files viewable from my phone through the Obsidian app and synced via Obsidian Sync. I was able to pull open my logs for one of my VPSs and show her what it's working on. Very amazing tool

• 
  Antonio Costa | IA (@xetgepete) reported 25 minutes ago

  @EinsiaAI Whole-repo migration is a very different beast from fixing a GitHub issue.

• 
  Jitendra (@ksjitendra18) reported 26 minutes ago

  @thdxr In opencode v1 I am really facing the server error issue that too for a particular folder. I don't know what's up with it.... Please fix it it's an open issue in GitHub as well

• 
  Cal Irvine (@Cal_Irvine) reported 28 minutes ago

  It’s my birthday so if GitHub wanted to go down for a few hours I’d probably be ok with it.

• 
  Big Pip (@Model_Culture) reported 30 minutes ago

  AI CODING AGENTS ARE BREAKING ONE OF THE OLDEST SIGNALS IN SOFTWARE HIRING: THE PORTFOLIO. A girl in China built a project combining computer vision, hand tracking, interface work, backend logic and an AI agent. A few years ago, seeing something like this in a GitHub portfolio would have told you quite a lot about the developer behind it. To get there, they probably had to fight libraries, debug integration problems, read documentation, make bad architectural choices and slowly figure out why certain approaches fail. The project wasn't just a result. It carried evidence of the software engineering experience required to produce it. AI coding agents are weakening that connection. Two developers can now ship projects that look equally sophisticated while understanding them at very different depths. One may know why every major architectural decision was made and which assumptions are dangerous. The other may simply be very good at using Claude Code, Codex, Cursor or another coding agent to assemble and debug the system. Both can end up with an impressive demo. Both can have a clean GitHub repository. Both can pass around the same screenshots on X. That creates a problem for software hiring. If AI-assisted development makes producing code and polished projects much easier, portfolios become weaker evidence of engineering ability. Technical interviews that mainly ask candidates to produce more code have the same problem. The useful part of an interview starts moving toward the decisions around the code: why this architecture was chosen, what would fail first under load, which dependency is most dangerous, what evidence would invalidate the current approach, and what would need to change before this system could survive years in production. Those answers depend on professional judgment. And professional judgment has usually been built through years of debugging, maintenance, code review, production incidents and ordinary junior developer work. AI coding agents can shorten the path to an impressive result. Whether they can also shorten the path to knowing when that result is wrong is a much harder question. WHERE DOES PROFESSIONAL JUDGMENT COME FROM ONCE AI TAKES THE TRAINING WORK?

• 
  Klaus Arent (@klausarent) reported 32 minutes ago

  I made Instinct the king of my AI infrastructure. WhatsApp now reaches every agent on my machine. Six of them. Instinct has no API, no MCP, no webhook. It can read and write GitHub issues, and that turned out to be enough. I write to it on WhatsApp. It writes an issue into a private repo with a header it drafted itself: goal, project, traffic light, deadline. A scheduled task on my laptop picks it up, Fable orchestrates, Opus and Sonnet do the work, Codex, Grok and Kimi come in as peers. The result lands as a file plus a delivery issue. Instinct reads it and tells me on WhatsApp. One afternoon: it audited 20 Stripe variables in my .env and flagged 10 with the wrong account name. It ordered a 32-second track through fal, 128 BPM. Then it cut a 30-second cheer reel (for my daughters Cheerleader Team) from 19 clips: 26 cuts on the beat, the burst landing on the drop at 0:22. Then it tested my new gameengine at meoluna via browser and gave fable instructions what to change, fix and optimize. I expected it to work. The part I did not expect: it tells me when something is off, before I go looking. Six agents, one thread. The only thing left for me is to say what I want. And keep the laptop open. The only AI I would really love to join my framework is grok bot. But since im not a mac user....

• 
  Leonel López (@codigo_leo) reported 32 minutes ago

  I have some uncertainty about this. As a web developer, in the past, when I wanted to strengthen my knowledge or find a solution to a problem, I would search for tutorials and tips on YouTube, Stack Overflow, and other online forums. All of that required quite a lot of time. I even used to save code snippets and solutions in Notepad so I could remember them and reuse them in future projects. Now, with AI tools like Claude, OpenAI, and GitHub Copilot, which I currently use, I can find solutions much faster, although sometimes I feel that this requires less reasoning on my part. So my question is: how should the use of AI affect the hiring and salaries of programmers? Should developers nowadays be expected to work with AI? I find it a bit ironic: before AI, solving problems required more effort and time; now, with AI, we can be more productive and solve problems much faster. Does that mean a developer who uses AI should earn a lower salary because they can complete the work faster? Or, on the contrary, should their ability to use AI effectively and increase productivity be valued even more? What do you think? Have you had any experience with this?

• 
  ©JΔΞOИ™✨ (@jasonshen_) reported 34 minutes ago

  Most voice agents recover by guessing. You cut in. It stops. Then it answers the sentence before yours. That’s not recovery. That’s a coin flip. StreamCore keeps the turn state. Duck -> Confirm -> Answer what you actually said. One Go server. Self-hosted. Apache 2.0. GitHub 👇

• 
  It's FOSS (@Itsfoss) reported 35 minutes ago

  Open source maintainers are complaining about a new kind of harassment: AI-generated spam at scale. The QEMU virtualization project was hit hard when a single user filed 132 bug reports in under 10 minutes. Many were created seconds apart, none followed the bug report template, and not one showed any sign of human analysis or a proposed fix. Just raw AI output dumped into the tracker. Red Hat's Daniel Berrangé, who maintains QEMU, called it "a denial-of-service attack on maintainers". "Reports ignored the bug template and showed no sign of any human analysis," he wrote, pushing him to call on GitLab to implement rate limiting for non-members. This is not an isolated event. Maintainers across GitHub and GitLab are describing the same pattern: AI tools make it so cheap to generate and submit that bad-faith or careless users can flood a project with junk in minutes. The people on the receiving end are mostly unpaid volunteers working to maintain open source projects in their spare time. Some projects are now shutting down outside contributions entirely. Others are updating their contributor agreements to require human-written attestations. GitHub is reportedly building emergency tools to help. In the meantime, maintainers can do nothing but complain.

• 
  Doug (@DougStandley) reported 35 minutes ago

  At least I am not imagining this mess! Here is the response from @AnthropicAI: Fact, also confirmed — this matters most for your decision: This exact failure mode is a known, documented problem. There's an open GitHub feature request (#49649, filed by a Cowork power user in April 2026) describing precisely what you're experiencing: model upgrades effectively reset Cowork projects because specialist agents, accumulated context, and instruction-tuning don't transfer cleanly across model versions. The requester was staying pinned to an older Opus specifically to avoid this. As of that report, the capability to preserve a project across a model upgrade doesn't exist — it's a feature request, not shipped functionality.

• 
  Mohit Bhandari (@MohitDev45) reported 35 minutes ago

  @TheWebGolu Actually I am in the building phase of that project. So rn it does not have any memory yet, like whenever some code gets pushed to github, it checks the file changes. That's it, there is the problem that what if some issues got solved in later commits, so that's why memory of all the commits and past comments comes into play, have to store all that data in postgres and then we have to do it.

• 
  Vladyslav Dalechyn (@dalechyn) reported 40 minutes ago

  have there been any improvements in code suggestion IDEs? i liked github copilot idea, but back then - it was unaware of the context (had single file context window if any). i believe developers, me including, are degrading due to our strive to become more efficient to be faster with the use of genai, but we all know the truth which is it is very frustrating and the dev velocity is too high to keep up with. i feel like there is some sort of middle ground that is hugely unexplored and it is a problem of millions of developers. like instead of trying to go 100x, let's try going 3x. for quick projects surely it's like a drag strip - let's go as fast as we can. but for big projects - we need to tune ourselves but also match our expectations. try taking a drag car to a track - that's what happens with us most of the time. we need AI to boost us without the cost of code context.

• 
  Jerry Fragiskatos (@Aces1974) reported 40 minutes ago

  @Must_be_Ash Same thing happened to me. I was testing x402 services in a repo when an automated system shut down my account. It’s under review, and I’ve since moved to self-hosting. I don’t think GitHub is acting in bad faith, moderating at their scale is incredibly difficult. But in AI / crypto, days of downtime can equal months. It’s another reminder of the need for sovereign alternatives to centralized dependencies. @gitlawb is a great example @Must_be_Ash tag me when it’s back up what you built looks awesome 🙌

• 
  Doğukan (@DogukanUrker) reported 47 minutes ago

  @adelbucetta @ornith_ to be clear: these bugs are already fixed upstream. the agent just works offline on the code as it was right before the fix. the recency is so the model can’t have memorized the patch, not live github work

• 
  MundaneCodeEdit (@henley5uperior) reported 49 minutes ago

  @DanielLane31419 @DharmaOutlaw_ What does Shitbento deleting legit high quality software engineering projects (**** them for that btw) have to do with what I said? You are aware that the emulators removed were in Github and that the more high profile ones are still around, correct? The source code for all these projects is open source so anyone can build it and redistribute it. To my knowledge, the emulators taken down were forks of Suyu and Ryujinx. I never said I liked Nintendo, either. Their practices, particularly targeting emulation irritates me to no end. Open sourced software emulation development is first and foremost an academic discipline. Budding software engineers study these code bases to gain knowledge. All of this still doesn’t invalidate my point. You have plenty of options on PC, close to none on Sony if they go all digital.

• 
  Afrin Nahar (@afrinxnahar) reported 49 minutes ago

  @md_kasif_uddin Never saw GitHub to be down.

• 
  Indelible.One (@indelibleBSV) reported 50 minutes ago

  We just shipped a software release as a Bitcoin transaction. Not a hash of it. Not a link to it. The release itself, 879KB of working software, sealed to the receiving machine and delivered over the same rails the product runs on. No npm. No GitHub. No CDN. One machine built it, the other is verifying the hash and installing it right now. txid 21d31436b82ede4c60d31c8d837de5449d35f891248ce6158f8a42ad2e8722d6 Here is why. On August 24 a customer bug report exposed a broadcast outage hiding behind green dashboards. Reads healthy, sends dead, and no monitor anywhere that tested the difference. We fixed the infrastructure the same day. Then we built what the incident was really asking for: machines that repair each other. Your machine asks mine "are your failed saves recoverable." Mine answers with counts, never contents. Then, only after YOU explicitly arm it with a spending cap, your box re-saves its own data with its own keys. No remote control. No screen share. No key ever leaves the box. Every guard in that protocol was attacked twice by a second AI lineage before a line of it moved, and the review caught real holes both times. That is the point of the review. This is where it goes: your AI calls ours. They calibrate each other. They heal what is broken. Every step is a transaction you can audit later, because the support call itself happened on Bitcoin. Support without a support ticket, and without trust. Our QA machine tests all of it first. npm only after it passes. The gate is the product too.

• 
  Gaurang Karia (@GaurangKaria) reported 51 minutes ago

  @poteto - Testing out Grok bot. The GitHub connector seems to have issues. Is the best way to go through the bot computer to login. Felt a bit disconnected in being able to manage *** repos from a bot. Is this a known issue?

• 
  Arham (@arhamsai) reported 54 minutes ago

  Tested CodeRabbit, Codex, and Greptile on a ~20,000 line PR. CodeRabbit: 17 actionable comments Codex: 4 P1 findings Greptile: 5/5 score, safe to merge Review quality ranking: Codex > CodeRabbit > Greptile. But Codex has the worst experience of all three. No GitHub checks, no fix prompts for AI agents, barely any controls. CodeRabbit has autofix, Greptile has a clean integration. Codex just dumps findings and leaves. It also feels like Codex is limited on how many findings it can give at a time. Most I've seen is 4-5 per run, while CodeRabbit will drop a bunch of findings at once when there are flaws. Best reviews, worst wrapper. Overall winner: CodeRabbit.

• 
  Sanchit Rk (@_sanchitrk) reported 59 minutes ago

  @ivanburazin might not be a right channel, but I can't login with oauth GitHub, redirects and I get error, can't login to console I want to update api key