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	12 days ago
Trichūr	Errors	15 days ago
Brasília	Sign in	16 days ago
Lyon	Website Down	16 days ago
Tel Aviv	Website Down	19 days ago
Rive-de-Gier	Website Down	19 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Dev Ben (@CodeNomadly) reported 6 minutes ago

  Ever spent more time finding information about your project than talking about the project itself? Code on GitHub. Screenshots in your gallery. Notes in random docs. I’ve run into this problem so many times that I decided to build a solution for it. Building DevPort in public. Day 2. Have you experienced this too?

• 
  Sudeep Srivastava (@sudeepsriv) reported 12 minutes ago

  GitHub might finally have a serious competitor. And it’s from Cursor. Most people know Cursor as an AI code editor. But Cursor Origin is much bigger. It’s trying to become an AI-native alternative to GitHub where AI agents don’t just help write code. They help build entire products. Think: • Source control • AI coding agents • Code review • Project understanding • Team collaboration all inside one workflow. Why developers are paying attention: Instead of manually searching through repositories, you can tell AI: • Fix this bug • Build this feature • Refactor this project • Investigate an issue • Ship a working version And AI handles much of the execution. The bigger shift: GitHub was built for humans writing code. Cursor Origin is being built for humans managing AI agents that write code. That’s a completely different future. We’re moving from: Human → Code to Human → AI Agent → Code My take: If GitHub defined the software era, Cursor Origin could help define the AI-native development era. And that’s why Elon Musk acquiring Cursor would be huge. xAI would gain: • AI models • Compute infrastructure • Coding agents • A developer platform That’s not just buying a product. That’s owning a major piece of how future software gets built.

• 
  nasuy (@n_asuy) reported 12 minutes ago

  i think @xai should be ADE. now they have a chat, cursor, enough coding models and harnesses, strong signal like bookmarks or down votes, video creatives, profile / chat / relationship contexts. if so, we don't have to depend on discord or any chat apps. easy to invite x people to cowork. there is no need to connect Linear, Slack, or GitHub to another platform and ask that platform to solve their problems. true AI chat is a SNS, not a single UI. there is a UX that only xAI can realistically build in the world.

• 
  aichina.news (@AiChinaNews) reported 14 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.

• 
  Dmytro Virych (@dmytrovirych) reported 15 minutes ago

  I’ve been shipping code for 10+ years and imposter syndrome still won’t leave me alone. You’d think it chills out with time. Nah. It just levels up. Early days it whispers “you’re not ready yet.” A decade in it hits harder: “bro you’ve been faking it this whole time, they’re about to catch on.” Mobile apps, web stuff, janky systems with too many moving parts, solo products I actually shipped… none of it matters when the voice kicks in. Thinking about speaking at a conference? Lol who do you think you are, those are the real pros. Want to drop an opinion in a thread? Better stay quiet before someone realizes you don’t actually know ****. Here’s the thing I’ve learned: the voice isn’t tracking your real skill. It’s just screaming about the fake gap between what you know and what you think everyone else knows. That second number is 100% made up. Your messy behind-the-scenes vs their perfect highlight reel. All those “professionals” I’m scared of? Half of them are up at 2am staring at a random GitHub issue, quietly praying someone else already solved this exact bug. It never fully disappears. You just get better at shipping anyway while it’s still yapping. If you’ve got way more years than your confidence shows, reply with the number. Curious how many of us are still out here waiting to get “found out.” 🚀

• 
  Mike Muturi (@_muturimike) reported 17 minutes ago

  Hello @github on 2FA, SMS setup kenya 🇰🇪 is not in the list of countries, is it an error or deliberate omission? Kindly fix it @github @GithubProjects

• 
  Raj Nagulapalle (@rnagulapalle) reported 19 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.

• 
  Jesse (@jessearmand) reported 21 minutes ago

  I no longer remember why many companies started using gitlab before it went public when GitHub wasn’t owned by Microsoft. If we visit the majority of companies most tooling or software are top down driven. Only companies who build developer tools have a different mindset

• 
  Blum (@Blum_OG) reported 24 minutes ago

  Andrej Karpathy on MCP: "it's a protocol of speaking directly to agents as this new consumer and manipulator of digital information." that is the cleanest way to think about MCP your coding agent is becoming a second worker inside the product it needs the same context you use: repo, docs, browser, database, errors, designs, tickets, payments if you keep pasting those things into chat by hand you are doing integration work manually the best MCP stack for vibe coding: 1. Context7 give the agent current docs this saves you from stale Next.js patterns, old Supabase calls, wrong Stripe webhook shapes, and Vercel config from 2 versions ago 2. GitHub MCP give it the repo, issues, PRs, branches, workflow runs, and review context half of real work lives outside the file you currently have open 3. Playwright MCP give it a browser the agent should click the thing it built, fill the form, check the mobile view, and catch the button that compiles but does nothing 4. Firecrawl MCP give it clean web research use this before building around a third-party API, writing a comparison page, reading changelogs, or checking pricing claims 5. Supabase or Neon MCP give it the database context that matches your stack start read-only. add writes only when you trust the permissions 6. Sentry MCP give it production evidence real stack traces beat "it crashes sometimes" every single time 7. Figma MCP give it design context when the interface matters spacing, layout, copy, components, and screen structure should come from the file, not from a screenshot and hope 8. Linear MCP give it the task queue bugs, feature work, release notes, follow-ups, and PR links belong somewhere more durable than yesterday's chat 9. Stripe MCP give it official payment context checkout, subscriptions, webhooks, billing, and test mode deserve docs close by and human review close behind 10. Filesystem, ***, Memory, Sequential Thinking give it the base layer files, diffs, history, decisions, and longer plans make the agent act like it is working inside a real project recommended install order: 1. Context7, GitHub, Playwright 2. Supabase or Neon, Sentry, Firecrawl 3. Figma, Linear, Stripe when the product needs them 4. Filesystem, ***, Memory, Sequential Thinking as the base

• 
  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 25 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

• 
  Alpha Batcher (@alphabatcher) reported 27 minutes ago

  David Soria Parra: "2026 is all about connectivity, and the best agents use every available method" A coding agent needs access to the same places you check while building: - repo and PRs - docs - browser - database - error logs - Figma - tasks - payments The article gives the 11 MCP servers for that setup: - Context7, GitHub, Playwright first - Supabase or Neon, Sentry, Firecrawl next - Figma, Linear, Stripe when you need them - Filesystem, ***, Memory, Sequential Thinking as the base Read it if you keep copying code, docs, schemas, screenshots, errors, and tickets into Claude Code by hand

• 
  Lost In Tech (@lost_in_tech) reported 29 minutes ago

  @8_senkou Probably not intentional tbh. Have you logged as issue in the snorca GitHub? If not probably worth doing.

• 
  李新宝 (@lixinbao_X) reported 30 minutes ago

  Just watched KK's technique. Damn. Absolute game-changer. Install 7 skills in Codex. Writing, images, covers, PPTs. Full pipeline, done. The principle is dead simple. Break the workflow into 7 parts. One skill per part. Only do one thing. Step 1 Open GitHub, find a repo. Copy the link locally. Create a project folder to save it. Step 2 Write the skill description. Input three things. What it does. What the input is. Output and acceptance criteria. Step 3 Run it and find the bottlenecks. Where it stalls Create a new skill and break it down. Don't let one skill Do 7 things it's bad at. This works for writers, Xiaohongshu creators, WeChat pub runners, Video script writers. How many skills you got installed? Have you tried it yet?

• 
  Blake (@devwithblake) reported 31 minutes ago

  The rate limit issues im having with @Zai_org while paying the full 20x is very interesting, disappointing and obviously annoying lol 1 session can’t finish out a GitHub public write up repo without 6 API rate limit errors totaling to 297k tokens out of the 1m 2 sessions earlier, 1 doing research the other trying to deploy this repo, both hitting rate limits. How do I fix this? Seems like rate limit adjustments are only by request? @Zai_org

• 
  toni (@tonitrades_) reported 35 minutes ago

  @github Capping PRs helps with the queue, but does it fix why reviews pile up in the first place? If reviewers are already stretched thin, limiting submissions might just hide the real problem.

• 
  Alexey Samoylov (@metalagman_dev) reported 35 minutes ago

  @geminicli Antigravity CLI is a trash, closed source, full of bugs. They don't even read issues on the github.

• 
  top10.dev (@Top10_Dev) reported 39 minutes ago

  SunJaycy/GoldenEye-Recomp just hit @github Trending at 503★ — the N64Recomp toolchain (the one behind Zelda 64: Recompiled / Majora's Mask) now eats Rare's 1997 engine. Static recomp ≠ emulation. The ROM is lifted to C at build time, compiled to native x86_64/ARM64, and paired with RT64 for path-traced lighting at 4K. No interpreter loop. Real binary. GoldenEye was the hard target — microcode-heavy muzzle flashes, split-screen viewport math, infamous AI. If it works, the toolchain has cleared the "Zelda-shaped problem" bar. #opensource #gamedev

• 
  Muhammed Mukthar (@MuktharBuilds) reported 42 minutes ago

  @railway_status i am trying for some time i am not able to sign in using any github google or email. i tried both my lap and my phone is thishappening only for me? or any problem in your end

• 
  Arti | AI Builder (@Artur_roses) reported 43 minutes ago

  Claude Code just took my GitHub issue, wrote the code, ran the tests, and opened a PR. My job: approve it. The dev workflow isn't changing. It already changed.

• 
  Gjermund Garaba (@GjermundGaraba) reported 47 minutes ago

  @RhysSullivan I’ve deployed it locally and hooked up a bunch of stuff. Are GitHub issues the preferred feedback channel or do you have a better way?

• 
  Build Fast with AI (@BuildFastWithAI) reported 50 minutes ago

  The hardest part of building AI agents in 2026 isn't writing the code. It's knowing what your agent actually did. Your agent made 40 tool calls, called 3 LLMs, hit a rate limit, retried twice, and returned a wrong answer. Which step broke it? Without observability you're reading logs and guessing. This is what Laminar is built for. Open-source observability platform purpose-built for AI agents. One decorator. Full trace of every LLM call, tool execution, and custom function - automatically. What makes it different from generic APM tools: SIGNALS - describe failures in plain English. "Agent deleted a file it wasn't supposed to." "Tool call returned an empty result." Laminar reads every trace and produces structured events you can query, cluster, and alert on. No regex. No custom parsers. DEBUGGER - reproduce any agent run from any point in the trace. Swap the model. Change the prompt. Compare results side by side. You don't re-run the whole pipeline to test one step. EVALS IN CI - run evaluations against datasets locally or in GitHub Actions. Catch regressions before they ship. INTEGRATIONS - works with everything you're already using: LangChain, LangGraph, Vercel AI SDK, Anthropic, OpenAI, Browser Use, Stagehand, Pydantic AI, OpenRouter, LiteLLM, Mastra, Temporal, Playwright. One import. Full traces. Plus: raw SQL access to all your trace data, full-text search, MCP server to query traces directly from Claude or Cursor, PII redaction, and self-hosting if you need it. Open-source. MIT license. GitHub: lmnr-ai/lmnr. If you're running agents in production and you're not tracing them - you're flying blind. What's your current setup for debugging agent failures?

• 
  Pascual ⚡ (@0xPascual) reported 51 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.

• 
  Red Zen Cloud LLC (@RedZenCloudLLC) reported 53 minutes ago

  Cursor's Origin platform and Claude's GitHub imports both solve the same problem: developers automating code work need their tools to understand context, not just generate tokens. The winner isn't the smartest model—it's whoever reduces handoffs between agent and human.

• 
  Yarchi (@undefinedKi) reported 55 minutes ago

  BORIS CHERNY, THE CREATOR OF CLAUDE CODE, JUST SOLVED AI'S BIGGEST PROBLEM. HE STOPPED PROMPTING CLAUDE AND STARTED WRITING LOOPS THAT RUN IT 24/7 The guy who built Claude Code doesn't prompt Claude anymore. He writes loops, and the loops do the prompting. It's called loop engineering. Here's what it is and how to set it up. A loop is a system that wakes itself up, finds work, does it, checks it, and repeats, while you watch instead of type. In Claude Code it's three built-in commands: > /loop runs a prompt on an interval. Example: /loop every 5 minutes, check for new GitHub issues and handle any that come in. > /goal makes the agent work until a condition you set is true, with a separate model grading the result. Example: /goal build this feature until all tests pass. > /routines are scheduled jobs. Example: every hour, wake up, read the spec doc, and do the next task. The fastest way to start: write a simple task list in a plan.md file, then tell Claude "use the loop skill and work through plan.md one task at a time." It sets up the /loop itself, does the first task, validates it, wakes itself for the next, and reports back when the list is done. You never write the loop prompt by hand. Three rules so it doesn't burn your budget or ship garbage. One, split work across separate sessions instead of looping in one (a long /loop bloats your context and overwhelms the model). Two, use a cheap model like Haiku for planning and a strong one only for the actual code. Three, keep a human checkpoint on anything that ships, never let it run all night unchecked. Bookmark this

• 
  Cursor Releases (@cursorreleases) reported 55 minutes ago

  New GitHub triggers: - Five new triggers: issue comment, PR review comment, PR review submitted, review thread updated, and workflow run completed. - New Marketplace templates added for triaging failed GitHub Actions and auto-fixing PR review comments.

• 
  John D. Clay (@JohnDClayAuthor) reported 56 minutes ago

  @XFreeze I tried out the new update to Grok Build last night and put it to the test. It helped me go back to a far previous session, it actually has all sessions in a nice area to look at and choose from. I challenged it to fix a broken framework I had built with the earlier versions of Grok Build and with the help of @grok too. I had published it a couple weeks ago and it was not working well. But now after a couple prompts... clayforge the first ai-matove framework for multi agent UI's. You should check it out if you are coding with AI. It's on GitHub.

• 
  Domi (@domirosari0) reported 57 minutes ago

  @ajayyy_k @hqmank If you got Github it would be no issue for you

• 
  Kelvinsekx (@kelvinsekx) reported 57 minutes ago

  Just read a nestjs codebase on github. Most it written with Claude. AI doesn’t save you guyz from mess. 1. Bloated logger. Why make logger a service when you could just import and initiate. Eazy 2. They didn’t hash the password before registering a user. But did on login

• 
  Kevin Tabet (@TabetKevin) reported 1 hour ago

  @upstash Hey guys i think login with github is broken can't log in rn will try later. google works email i dont have

• 
  Mady (@TrippleBon) reported 1 hour ago

  It was only a matter of time. Centralized = ID/KYC/AML Go to Bastyon - decentralized social network based on blockchain. No central authority or corporation behind it. The platform is run by equal nodes on a blockchain with no centralized server (github link below)