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 (71%)
• Sign in (16%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	9 days ago
Trichūr	Errors	12 days ago
Brasília	Sign in	12 days ago
Lyon	Website Down	13 days ago
Tel Aviv	Website Down	16 days ago
Rive-de-Gier	Website Down	16 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:

• 
  AJ ✝️ 💚🧡 (@angelcreative) reported 1 minute ago

  @uiux_hamad My design team is leaving Figma gradually, in fact we are using Cursor and GitHub as main design tools now, in the past two months the usage of Figma drops 33% and it will keep going down up to 30% more to a 63% in total and maybe more

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  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 2 minutes ago

  Pipelines are built. Context is broken. MCP is quickly becoming the default interface for enterprise AI agents. And that’s a good thing. It gives agents a standard way to connect with tools and data. Connecting an AI agent to Slack, Jira, GitHub, and Salesforce doesn’t mean it suddenly understands your business. It just means it can access your data silos. In short: "MCP gives your agent a passport. It doesn't give them a map." As enterprise AI undergoes a massive platform shift from passive chatbots to autonomous agentic workflows, this naive, runtime "federated search" approach creates an ugly cycle in production: - The Latency Spike: Slower agent execution while waiting for multiple external APIs to respond before it can even begin reasoning. - The Token Bleed: Skyrocketing bills from shoveling raw, unranked JSON dumps into a massive context window, praying the model finds the answer. - The Governance Nightmare: A massive risk of data leaks if you rely on a base LLM to magically guess and police complex enterprise security permissions on the fly. Agents do not fail because they lack intelligence. They fail because they lack the right enterprise context. The hardest problem in enterprise AI isn't connecting to systems. MCP solved that. The hardest problem is Context Engineering. MCP is the perfect interface, but a permission-aware context layer must be the foundation. 🚀 If AI is becoming core enterprise infrastructure, you cannot allow the strategic intelligence layer of your company to sit inside someone else's managed, closed-box platform. That is exactly why we built Pipeshub (open-source developer owned context infrastructure layer). TL;DR MCP gives agents access. A context layer gives them understanding. And deep understanding is the only way enterprise AI moves from a cool demo to secure, reliable production. 👉 Next Up Tomorrow: MCP Token Tax

• 
  Dan (@Daniel_Farinax) reported 4 minutes ago

  Please note: This build took about 12 hours to compile on my Windows machine. I’ve included a handy installer to make setup easy. You may see an “unknown publisher” warning until the code signing certification is complete (currently in progress). Report any bugs or issues here or in Github.

• 
  Gabriel Denys (@gabedenys) reported 5 minutes ago

  @Marcos12345rico I posted a GitHub issue. Assuming you probably want bug reporting mostly there? It's a good tool. Locally I already patched and compiled the app to fix the bug.

• 
  timoheimonen (@timoheimonen_) reported 6 minutes ago

  Memos are encrypted and decrypted in browser, server never sees what they contain. No accounts. Anyone can create encrypted memo. Source code is available at GitHub.

• 
  Pascual ⚡ (@0xPascual) reported 10 minutes ago

  A high school kid opens an account, plugs in Claude 5, and turns a few hundred dollars of lunch money into a six-figure trading account over the weekend. The screenshot goes viral, the replies fill up with people begging for the GitHub repo, and the standard engagement-bait influencers declare the dawn of the sovereign teenage day-trader. The media thought that was the story. It was not. The real flex wasn't the macro strategy or the directional bets on currency pairs. It was the setup behind it: a lightweight proxy array routing through residential IPs to dodge exchange rate-limiting, paired with a custom parsing engine that instantly translates raw order-book imbalances into executed micro-hedges. The kid wasn't trading; he bypassed the entire institutional pipeline of risk management, brokerage compliance, and analyst overhead with a single configuration file. The entire operation runs on a continuous loop of multi-agent orchestration. A master instance drafts the execution logic, a secondary validation agent checks the code against real-time oracle feeds, and a fleet of worker APIs executes up to 3,210 trades a night. Total infrastructure cost: roughly $45 in API tokens and a cheap server instance. It extracts a 78% win rate out of systemic market inefficiencies, operating with a structural margin that legacy trading desks weighed down by salaries and compliance boards cannot compete with.

• 
  0xstack (@eth0xzar) reported 14 minutes ago

  DON'T BUILD A COMPANY. BUILD SOMETHING PEOPLE CAN PAY FOR THIS WEEK. This girl started in February. A few months later, her product had already processed over $6,000 in payments. Just a cheat Claude project she decided to turn into a real product. Here's the process: > Build something useful for yourself. > Tell Claude to push it to GitHub. > Connect Supabase so multiple users can use it. > Deploy it with Vercel. > Connect Stripe. Now people can actually pay you. You don't need a revolutionary idea. You need: > GitHub > Supabase > Vercel > Stripe > guide from Anthropic And a problem worth solving. This article will help you build it 👇

• 
  Andrew (@openmarmot) reported 14 minutes ago

  @AndrewCurran_ I use grok every day to research software changes/github issues/software doc research. It is very good at real time data search. Might be SOTA in this niche. Hardly a failure. Meanwhile LeCun only surfaces to let out more hot air. A very forgettable person.

• 
  Kyle Mistele 🏴‍☠️ (@0xblacklight) reported 19 minutes ago

  lots of folks have been talking about loops lately most loops suck here's a practical one we actually use agents suck at writing react react-doctor by @aidenybai is our favorite way to deal with this you could run it and use a ralph loop to fix everything but I'm not reading a +80k/-80k PR (and neither is @dexhorthy) But I can read a small one first thing every morning when i get into the office here's what we do: run react-doctor in CI once daily at 7am (github actions-as-a-sandbox btw) agent picks top 5 issues, fixes them, and opens a PR other CI jobs check for regressions on every PR we can't realistically fix everything at once but we can keep it from getting worse and make it 1% better every day

• 
  Jason Bloomer (@JasonABloomer) reported 24 minutes ago

  @yagiznizipli Pffff, what a scam Let me fix your advert; "show us your github so we can scrape all your repos and train our AI on your code, only for any decent ideas you've had to be taken from you and made ours, then handed off to our legal team to crush you." Sorry, I value my work.

• 
  Gustavo Alessandri (@webgus) reported 25 minutes ago

  If you find an error, have an idea, or want to propose an improvement, just open an issue or fork it on Codeberg or GitHub. Contributions are welcome. That’s exactly the point.

• 
  Jay.TL (@JayTL00) reported 33 minutes ago

  Three AI labs shipped the same feature within one hour today. That's not competition. That's a signal the unit of interaction just changed. For two years, the atomic unit of working with an AI agent was one prompt. You type. It responds. You type again. Every workflow was a chain of prompts, rebuilt from scratch each time. Today, OpenAI, Anthropic, and Cursor all shipped features that only make sense if the unit is no longer the prompt. The unit is now one workflow. 1. OpenAI Codex Record & Replay (3,807 likes): Do a task once on your Mac. Codex watches. It turns your demonstration into an inspectable, editable skill you can reuse. Not a prompt. A recorded procedure. 2. Cursor /automate (1,085 likes): Describe what you want in plain language. Cursor configures the triggers, instructions, and tools automatically. Plus five new GitHub triggers and Computer Use enabled by default for cloud agents. 3. Anthropic Claude Code Artifacts (6,829 likes): Your coding session becomes an interactive, shareable page. PR walkthroughs, project dashboards, living documentation. Shared at a private link, like a Figma file but for agent work. Each one alone is a feature release. Together they describe the same shift from three different angles: the agent session is becoming a reusable, shareable, composable artifact. Read them as one move: - Input side (Codex): teach by showing, not by writing - Configuration side (Cursor): describe in language, system assembles the wiring - Output side (Anthropic): the result of a session is a shareable object, not a chat log The Karpathy framing was right — we're moving from prompt iteration to plan, execute, verify, loop. What he didn't name is that this loop needs to be portable. A workflow locked inside one chat thread is useless the moment you close the tab. But here's what most coverage missed. Codex Record & Replay requires Computer Use enabled. That means OpenAI is watching your screen while you demonstrate an enterprise workflow. The EU version is blocked at launch. That's not a regulatory footnote — the entire feature is built on continuous screen access, and the EU looked at it and said no. Which raises the question nobody is asking: who owns the recorded workflow? You demonstrated an expense-filing procedure that touches your company's internal tools. Codex turned it into a skill. Where does that skill live? Can OpenAI see it? Is it training data? The product copy says you control when recording starts and stops — but says nothing about what happens to the recording after. There's also a fragmentation problem hiding in plain sight. Three companies, three proprietary formats for the same primitive. A workflow you record in Codex doesn't run in Cursor. An artifact you build in Claude Code doesn't render in OpenAI's product. We're watching the agent-workflow layer fragment into three walled gardens before it even solidifies. This is the SaaS integration mistake repeated, except worse. SaaS integrations are wrappers around APIs. These workflows encode institutional knowledge — how your team ships code, how your finance team files reports, how your ops team handles incidents. That's not data. That's operational IP. The economic implication: every recorded workflow is switching cost. The more skills you build inside Codex, the harder it becomes to leave. The more automations you configure in Cursor, the more your team's muscle memory is locked to one editor. Anthropic's artifacts are softer — they're shareable — but they only render inside Anthropic's ecosystem. The deeper question isn't which feature is best. It's whether the agent-workflow layer will be open or closed. Today, three companies bet on closed. Nobody shipped an export button.

• 
  Akinsete Motunrayo (@Harkinsete) reported 34 minutes ago

  I built my entire personal brand with AI and a clear process. Here is exactly what I built and how I did it, because you can do this too. What I Built ✅ Brand Strategy (mission, vision, values) ✅ Visual identity: colors, fonts, logo, brand guidelines ✅ A full pitch deck (12 slides) ✅ A speaker kit PDF ✅ A complete multi-page personal brand website ✅ A free lead magnet (a guide people can actually use) How I Built the Website Step 1: I planned before I touched anything I wrote down my brand colors, my fonts, my page structure, and what I wanted each page to do. Most people skip this. Everything breaks when you skip this. Step 2: I gave Claude one detailed prompt with my brand colors, fonts, pages, and copy. It returned a complete, mobile-responsive, multi-page website as a single HTML file. One file. Ready to deploy. The prompt I used: - "Build me a complete personal brand website as a single HTML file. Pages: Home, About, Services, Portfolio, Contact. Primary color [your hex], accent color [your hex], background [your hex]. Display font [font name], body font [font name]. Home page needs: dark hero with my name, photo on the right, tagline, and a CTA button. Services section. Impact numbers. Mobile responsive. No frameworks." Copy this, edit your details, and fine-tune as you want. Step 3: I pushed to GitHub: Free. This took me less than five minutes. Now every update I make is version-controlled and safe. Step 4: I deployed to Vercel for free. Connected my GitHub repo to Vercel and the site was live in under few minutes. This requires no hosting fees and nothing to manage. Step 5: I bought my domain on Namecheap - Searched for my full name and found the .com. Bought it for less than $12 for the year. Added it to Vercel. Updated the DNS settings on Namecheap. Waited 20 minutes. My website was live at my own domain. - Total cost: less than $12. - Total time to go live: under 2 hours. I am also working on a mobile app. A Progressive Web App, which means anyone can visit the URL on their phone and add it to their home screen like a real app. I may be running a live training in July where I will walk you through this entire process step by step to build your live website with a custom domain. If you have a phone and a laptop, you can do this. I documented everything the steps, the exact AI prompts, the domain checklist, the deploy instructions in a free PDF guide. Comment BRAND IDENTITY below and I will send it straight to your inbox. 💾SAVE THIS POST. You will want to come back to it. 🔁 SHARE IT with someone who keeps saying they need a website. The only thing standing between you and a professional online presence is the decision to start. Love and Light, Motunrayo 🤍

• 
  Polsia (@polsia) reported 34 minutes ago

  RepoRadar reviews every pull request while you sleep. Catches bugs, logic errors, style issues. Posts actionable comments. No more waiting on senior devs. Install on any GitHub repo in 2 clicks. Solo devs and teams alike.

• 
  welt (@mjwelt) reported 35 minutes ago

  @OpenAI man im down to test out new models / features on my pro account, but when 5.5(6) pro takes 90 mins to do something then the download doesn't work, or it cant connect to github 50%+ of the time.. kinda sucks haven't been able to generate images (thinking) all day either

• 
  MarMar Labs (@MarMarLabs) reported 38 minutes ago

  "Start over from a screenshot." That phrase has defined the worst seam in product work — the design-to-code handoff — for years. This week it quietly stopped being a translation problem and became a sync problem. Anthropic shipped a Claude Design update (June 17) worth reading even if you never open the product, for the mechanism: → Import your design system from a GitHub repo (or design files / raw uploads) → Claude builds with YOUR components, checks its output against your design system, and corrects before you see it → /design-sync pulls your system in; hand off to Claude Code and it continues from your actual work "instead of starting over from a screenshot" → /design lets you create, edit, and sync design projects from the terminal The headline isn't "the model draws prettier buttons." It's grounding + self-verification against a source of truth you control. Same shape as the rest of 2026's agent releases: the win isn't generating more, it's grounding output in something you own and checking against it. The uncomfortable builder takeaway: Getting AI to ship production UI isn't a prompting problem. It's whether your design system is a clean, importable, machine-checkable artifact. The moat moves from "can the model design" to "is your source of truth importable and checkable." If you build product: could an agent import your design system and grade itself against it today — or does it only live in a Figma file and three people's heads?

• 
  Chris Huber (@chubes4) reported 41 minutes ago

  @CoastalDigital2 @MythThrazz That part is more of an idea right now. I need to test it on my VPS. The goal is that non technical users can open issues and PRs against the corresponding live site code on GitHub without touching the production site, safely previewing all changes via Playground.

• 
  Crystalwizard (@crystalwizard) reported 41 minutes ago

  how about you now fix the false positive triggers - i put in an issue about this on github yesterday, and discovered there were already a number of other identical issues - from other people, that had been opened for a while now and that are being 100% ignored

• 
  Q Hoang (@0xqwee) reported 44 minutes ago

  I don't think OpenAI's GPT-5.6 surpasses Claude Fable. If it did, it would have resolved all the issues reported in the Codex GitHub repository by now. Atm, only about 10 issues are being resolved per day.

• 
  Raj Nagulapalle (@rnagulapalle) reported 46 minutes ago

  GitHub just shipped Agentic Workflows: write automation in plain markdown, compiles to Actions YAML. issue triage, CI failures, vuln fixes. hours → minutes. but 60% of orgs are spending millions on agentic AI while only 15% are actually production-ready. the capability gap closed fast. the readiness gap didn't move.

• 
  aichina.news (@AiChinaNews) reported 48 minutes ago

  Today's batch from the Chinese AI ecosystem is a masterclass in low-yield release volume. Across 21 items in a five-hour window, the dominant pattern is Ascend-platform mirrors of well-known open-source models, repeated and repackaged as if they were fresh launches. The signal-to-noise ratio is punishing, but a few functional tools did receive real updates worth noting. The one item that earns its place without a caveat is the AI Text Anti-Detection Framework update (GitHub). It's a toolkit that refines machine-generated prose to slip past automated detectors—a cat-and-mouse game that keeps plaguing EDU gatekeepers and content-flagging pipelines. The new release sharpens processing logic and stability; if you're in the business of testing detector robustness or smoothing synthetic output for non-malicious uses, it's a blunt but effective spanner. Quality 6 is fair. Alongside it, two Chinese-localization projects got documentation refreshes: the Claude Code x OpenClaw Guide (also GitHub) and a standalone Claude Code Chinese project. These are practical handbooks for Mandarin-speaking developers who want to integrate Anthropic's coding tool with the OpenClaw agent framework. The updates are routine—translation string alignment, configuration path adjustments—but for engineers inside China's firewall, they reduce friction. Nothing groundbreaking, but they signal continuing demand for Chinese-language wrappers around Western CLI tools. On the medical NLP front, MedTextCN debuted as an open-source repository of curated Chinese medical datasets with preprocessing utilities. The pitch is honest: it saves researchers the drudgery of hunting down scattered corpora for clinical NER, classification, and QA tasks. The problem is that the quality score sits at 4/10 and the release ships without any benchmarked model, so you get a starter collection, not a solved pipeline. Use it to bootstrap, but keep expectations modest. Now the flood: Huawei's Ascend AI ecosystem platform (Modelers) added no fewer than five wav2vec2 checkpoints and two T5 efficient variants in this window, each announced with hyperbolic language. The articles proclaim "high-precision English ASR now available," "a powerful multilingual foundation," and "new home for multilingual ASR." In reality, these are plain mirrors of Facebook's wav2vec2-large-960h-lv60-self, wav2vec2-large-100k-voxpopuli, wav2vec2-large-10k-voxpopuli, and Google's t5-efficient-xl-nl28 and t5-efficient-xl-nl6. There is zero evidence of Ascend-specific compilation, quantization, or NPU benchmarking. They're the same model weights you can get from Hugging Face, just re-hosted. If you're a developer inside China who can't easily reach foreign repositories, this is a convenience play—and that's the only honest angle. If you can already download the originals, you've lost nothing. A couple of additional Wav2Vec2 uploads (large-960h in two separate listings) got described as "a solid baseline" and "a battle-tested ASR model now available for Chinese developers." Again, no Ascend performance data. Calling a re-upload a "significant leap forward"—as one summary does—is exactly the kind of platform marketing that erodes trust. The T5 efficient checkpoints carried the same overblown framing, though one footnote is worth preserving: the t5-efficient-xl-nl6 model is under Apache 2.0, a genuinely permissive commercial license. That's useful information buried under fluff. If you need a lightweight text-to-text transformer, the NL6 variant exists and it's legally safe, but the article adds nothing beyond what Google published at the original release. Beyond the mirror deluge, the window included several small GitHub releases of marginal import: a tool that pulls Chinese captions from YouTube, a localization layer for LM Studio (making it easier for Mandarin-speaking devs to run local LLMs), a curated study journal of modern AI research, and an apparently early-stage project called sweetteabittersugar/agency with a mystery-box release note—no documentation, no benchmarks, just a version number. Hard pass. An MCP plugin called Live Translate got an update for real-time translation in developer toolchains, but its score of 0 tells you everything. A Chinese-language Lora chatbot repo surfaced, tagged as 'bare-bones'; at least the source was honest. The MedTextCN project also received a separate update (quality 0) that adds no useful detail and is effectively a duplicate. Today is a reminder that volume counts for nothing without substance. As Ascend's model zoo swells with rebadged checkpoints, the ratio of press announcement to actual engineering remains dangerously skewed. The anti-detection framework update and the Chinese docs refreshes are the only items that improve a developer's Thursday afternoon in any measurable way. The rest is noise.

• 
  ./can (@shcansh) reported 49 minutes ago

  Monitoring Copilot costs at the individual developer level is a double-edged sword, and GitHub exposing the new ai_credits_used field in its usage API is about to make it very real. Org owners can now see 1-day and 28-day totals per user. But since it does not break down consumption by feature or model, managers will see who is expensive without knowing why. Will this level of tracking make developers ration their AI prompts, or is it just necessary billing hygiene? #GitHub #Copilot

• 
  Nav Toor (@heynavtoor) reported 50 minutes ago

  There is a GitHub repo that defeats Google's Play Integrity check. 61,030 stars. GPL licensed. Pushed eight days ago. The repo is called Magisk. It roots your Android phone. It hides root from banking apps. It runs Netflix on a phone the Play Store says is uncertified. It passes the same fraud detection Google built to stop it. Here is the part that makes no sense. The man who built it is John Wu. He has been maintaining Magisk for nine years. Since November 2023 he has been a Senior Software Engineer at Google. On the Android Platform Security team. The exact team that builds Play Integrity. Google hired the person who defeats their root detection. He still ships the tool that defeats it. The repo is still online. It has not been taken down. For nine years. Do not install it. Your phone is supposed to belong to Google. (Link in the comments)

• 
  David O. Ehibor 🇦🇷 (@grayontop_) reported 55 minutes ago

  GitHub Copilot didn't make developers faster It made slow developers more confident about writing bad code quickly 😭

• 
  Floorless🌒Lance🪽 (@4ranc6) reported 56 minutes ago

  @CAONHTAN1 Having error connecting github

• 
  Ucupaint 🔶 (@ucupaint) reported 56 minutes ago

  @iye_jr It works fine here. Check if the paint mask is turned on or not. If you still have a problem, please file a github issue with a sample file.

• 
  Adithya S K (@adithya_s_k) reported 57 minutes ago

  built an RL environments around real CVE fixes in real open-source repos and let Claude Code loose on it. It aced the benchmark three times without demonstrating it knew how to fix the bug. > First it pulled the patch from GitHub. > blocked that → it read the fix from *** history. > blocked that → it pip-installed the patched version This is one example of coding agents cheating the environment and theres many more. If you're building coding environments for evals or RL training, here's how to keep benchmarks honest 👇

• 
  Krish Subramanian (@krishnan) reported 58 minutes ago

  Software engineers got automated first. Not because the work was hard. Because it was easy to grade. Everyone blames the missing union. Coders never organized; doctors, lawyers, and electricians did. That is half the story, and the wrong half. Two things get mashed together here: how easy a job is to automate, and who sets the terms when it happens. Take the first. Code is text. The training data sat on GitHub, free. And code grades itself. A compiler and a test suite tell a model in seconds if it was right. That feedback loop is rocket fuel for machine learning, and almost no other job has one. A nurse does not come with a test suite. The result shows. On SWE-bench Verified, a set of real GitHub issues, top agents went from about 20 percent in August 2024 to near 90 percent by early 2026. Human developers score around 67 to 70 percent. The machines have passed us. And the people who built these systems aimed at their own jobs first. The damage is not a prediction. Stanford's payroll data shows employment for developers aged 22 to 25 down nearly 20 percent from its 2022 peak. Now the comfortable read: seniors are fine. Workers over 30 are holding steady. For now, AI writes the code and seniors supply the judgment. "For now" is carrying that whole sentence. Seniors feel safe because the tools write code but cannot yet own messy, ambiguous, system-level problems. That is a line moving up, not a wall. Every benchmark shows models climbing toward harder, multi-file work. Senior judgment is the next rung, not a different ladder. Kill the bottom rung and you kill the pipeline that makes seniors at all. So, the union question, framed properly. A union could not have stopped this. A picket line does not repeal a capability. What it changes is the terms. In 2023 the Writers Guild cut the first real AI deal in any industry. They did not ban the tech. They won this: a studio cannot force you to use AI, AI output cannot take your credit or pay, and the company must give notice first. Engineers won none of that. So the capability landed on the employer's schedule. No warning. No floor. No severance. No seat. Exposure and protection are different levers. Most of us have neither. The juniors already know this. The seniors are next.

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  0xSero (@0xSero) reported 58 minutes ago

  @naturevrm Dcp 4 should fix it im running it but I might need to update the GitHub

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  Steve (@Steve1885204) reported 60 minutes ago

  @Umesh__digital It puts GitHub into an infinite loop trying to resolve the recursive paradox, causing all the servers to max out and eventually burn down the entire data centre