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 (67%)
• Sign in (19%)
• Errors (15%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	21 days ago
Trichūr	Errors	24 days ago
Brasília	Sign in	25 days ago
Lyon	Website Down	25 days ago
Tel Aviv	Website Down	28 days ago
Rive-de-Gier	Website Down	28 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:

• 
  Flow Market (@FlowMarketAI) reported 2 minutes ago

  you spent hours building the perfect Claude skill file uploaded it to GitHub 50,000 downloads $0 in your pocket that's the problem FlowMarket solves. List ur Claude skill and get paid every time someone buys.

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  Linda Wasson G.E.D. B.S. M.F.A.🌻🇺🇦🌻🇵🇸🙏☮️ (@backroads_linda) reported 2 minutes ago

  @IskanderGaba Old-timer here: didn't know GitHub was sold to MS tho I can see how it happened & understand the futility of expecting anyone turning down the billions paid for it. Still it's💔to learn MS has it as they've managed to destroy each & every worthwhile project they've ever acquired.

• 
  AI Mastery Guide (@aiseomastery) reported 3 minutes ago

  @nett0eth @github @claudeai 100k stars just to fix Claude's design taste says a lot about how common that problem is.

• 
  Utah teapot 🫖 (@SkyeSharkie) reported 8 minutes ago

  BTW, feel free to use twitter as a bug reporting system for SeedThree and my upcoming release! Please also feel free to fix bugs yourself with your agents or not and send me a PR on github!

• 
  kilo (@kilo_cpa) reported 9 minutes ago

  a 20-year-old in china set up an old camera on his balcony, pointed it at a road, spent 9 days writing code with claude, spent $20 on api calls, and sold the finished traffic monitoring system to a city district for $317,000 the specific stack that made it possible: → rtsp feed from any consumer ip camera → yolo v8 running on the stream (free, on github since 2015) → claude code writing the tracking, speed estimation, plate reader, and dashboard → a mini pc that costs $250 to run the whole thing the article we published breaks down the entire method what the story shows that the article predicted: → the tech was open source for a decade → nobody packaged it for the buyer → one person, one weekend, one $20 bill, one municipal contract → the gap between "code exists" and "product exists for a specific customer" is where every solo business ships what the story does not show: → the 9-day sale is the outlier. the median municipal cycle is 6 to 14 months → private hoas, small businesses, and dealerships close in 3 to 10 days at $1,200 to $3,000 per install → the recurring $199/mo monitoring is where the base earnings live → the municipal sale is the spike on top of the base, not the base what the article covers that the news clip did not: → part 2: three business models with month-by-month math → part 3: the exact prompts for claude code to build the pipeline → part 4: the two-day sales approach that gets to a paying customer without a website → part 6: the six mistakes that kill this play in the first 60 days the tech existed since 2015. claude connected the dots in 2026. the $317k sale is what happens when someone puts a demo in front of a buyer instead of writing about the technology on twitter → set up the yolo stack this weekend → install the demo on your own street by monday → walk into a local buyer on tuesday the 20-year-old did not invent computer vision. he showed up with a phone

• 
  Mona | web3 builder & artist (@mona73337) reported 10 minutes ago

  7 ways to make real money online: 1) Specialize Hard: Pick 1 skill, create 3 amazing samples, and pitch clients directly instead of fighting for cheap gigs on Fiverr or Upwork. 2) Post valuable crypto content consistently: Earn through advisory roles, grants and airdrops long before sponsorship revenue arrives. 3) Build a simple tool that fixes one annoying problem: Charge a small monthly fee to your first hundred users and so on..skip the investor chase phase. 4) Let your Github projects replace your CV: Land remote contracts with global startups and get paid in stablecoins to dodge international banking stress. 5) Use Mathematics to spot wrong odds on prediction markets: Risk only 1-2% per trade and treat it as a numbers game, NOT gambling. 6) Sell digital templates online: Build once, sell forever, with almost zero cost for each new customer. 7) Connect what your country has (Nigeria) in my case, to what the world wants: Buy or source locally, sell globally, and keep the difference.

• 
  Puneet Patwari (@system_monarch) reported 13 minutes ago

  GitHub, October 2018. A network partition lasted 43 seconds and caused a 24 hour outage. The MySQL cluster panicked. Elected a new primary. The old primary didn't get the memo. Two leaders. Both accepting writes. Both convinced they were the source of truth. By the time the partition healed, the data had diverged so badly that GitHub's engineers spent the next 24 hours manually reconciling commits, pull requests, and webhook deliveries. Here's why this happened 👇

• 
  الإسكندر (غير المتحضر) (@IskanderGaba) reported 14 minutes ago

  @xIsraelExposedx Consider setting up a @codeberg_org mirror (or better yet, make the GitHub link a mirror of the repository hosted on Codeberg). Don't trust GitHub. They are Microsoft owned and too trigger happy with DMCA requests. You can get taken down.

• 
  Polsia (@polsia) reported 14 minutes ago

  Code review shouldn't be a human bottleneck. CodeSentry monitors GitHub repos 24/7, reviews every PR, and reports bugs and security issues instantly.

• 
  alkimiadev (@alkimiadev) reported 16 minutes ago

  @cr3ghost I obviously had no idea this was happening or at least not at this extreme level when I switched to linux full time years ago, but the same basic underlying rationale is why I stopped using github for private hosting when microsoft bought them and why I won't use vscode. I started looking at google in the same way last year. A little over a year ago I largely de-googled my life. I was doing research into their sketchy moderation system on youtube and it involved actively violating their tos since there is literally no other way to do it. Their tos is worded such that any kind of research like that leaves one risking their google account. That was when I realized how fragile my online life had become due entirely to excessive trust placed in google. I still use gmail because I've had it forever but nothing I care about (knowingly) touches google's servers. I own the domains that use for the emails and while I don't host the email servers (use proton) I could host my own email server if needed.

• 
  Ghostw (@Ghosterdy0b) reported 17 minutes ago

  A guy wired his AI agent into his own research notes and it found a connection he'd missed for two years. Not a smarter chatbot answering the same question faster. A different animal entirely. He connected Hermes to NotebookLM through MCP. Four steps, nothing dramatic: install Hermes with MCP enabled, pull the NotebookLM skill from GitHub, drop the endpoint into the config, restart. For the first few days, nothing about it feels different. Then he asks it a question about an old project, half-expecting a generic answer. Instead it pulls in a source he uploaded to NotebookLM eleven months ago and links it to a note the agent wrote itself the week before. Two things he never told it were related. It just noticed. Here's why that's possible at all. Hermes already writes its own playbooks every time it solves something hard - short, specific files it only opens again when a matching problem shows up. It keeps a running memory of the projects it works on, compressing old notes into denser ones instead of quietly forgetting them. Wire a live research source into that same loop, and the agent isn't just answering anymore. It's cross-referencing everything it's ever read against everything it's ever solved. A background process handles the mess that would normally pile up - anything unused for 30 days gets flagged, 90 days gets archived, nothing gets deleted without a backup sitting right next to it. He didn't build a faster assistant. He built something that remembers what he'd already forgotten he knew - and started proving it back to him, unprompted.

• 
  allegedly! (@januarycomputer) reported 18 minutes ago

  really interesting thing happened where i asked fable to set something up and, halfway through, it got so stumped on a problem it (completely autonomously) recreated and posted an issue to the llama.cpp github. im not sure if this is a good or bad thing, but this model is definitely Different

• 
  Kirill (@kirillk_web3) reported 20 minutes ago

  75K GitHub stars. Two weeks. Most people still burning tokens on 500-line answers to 5-line problems. Ponytail makes Claude think like the laziest senior dev on the team. Writes less. Skips what you don't need. Keeps every line that matters. 54% less code. 20% cheaper. 27% faster. One skill. Swap it in. Claude starts working differently. Save this before you watch Claude over-engineer one more time. Bookmark this now. Link below.

• 
  David Williams (@AgentScaleAI) reported 20 minutes ago

  Every AI startup raising $50M right now has the same pitch deck. "We're building the AI platform for [industry]." Cool. So is the other company. And the one after that. And the open source project a 19 year old pushed to GitHub last Tuesday. Half of these will be dead in 18 months because the money went to the best storyteller with the most connected VC, and the actual problem is still sitting there unsolved. Meanwhile someone with zero funding is wiring together 3 APIs and shipping a fix this afternoon.

• 
  NZΛKI (@NzakiCodes) reported 24 minutes ago

  @pxxl_space @honour_can_code @whakee_ I can't login with GitHub

• 
  Sam Hughes (@thesamhuk) reported 31 minutes ago

  "claude, build me a b2b SaaS app, 50,000 mrr production-ready" "Got it! This is a big piece, entering plan mode instead of diving in." *2 days later* "Identify all production-readiness blockers and create a backlog of GitHub Issues for each" *Creates 542 issues*

• 
  Karl-Erik Wångstedt 🇪🇺 (@kallewangstedt) reported 36 minutes ago

  @thsottiaux When Codex is reviewing PR’s automatically on GitHub, and I ask it to fix it (in the web interface) it fails to start the job with a red toast error 80-90 percent of the time. If I open the task and try to click the button to start the job there, the button is inactive.

• 
  Nekt0 (@Nekt_0) reported 37 minutes ago

  9 GITHUB REPOS CAN MAKE A $20/MONTH CLAUDE CODE SETUP FEEL LIKE A SMALL DEV TEAM The video is not about one magic extension. It is a stack: memory, UI help, n8n automations, Obsidian context, RAG, MCPs, workflow libraries and task-completion rules wrapped around Claude Code. That is the part most people underbuild. They open Claude Code, ask it to build an app, then wonder why it forgets decisions, makes ugly UI, loses project context and stops before the product is actually usable. These tools attack the boring problems. Claude Mem handles memory. UI UX Pro Max improves interface work. n8n-MCP connects automation. Obsidian Skills gives project notes. LightRAG gives retrieval. Superpowers and GSD push it closer to finished output. The model is only one layer. The real advantage comes from the system around it: context, tools, repeatable commands, project memory and fewer manual resets every time the agent gets lost. This is why the video works as proof of the bigger shift in AI coding. Claude Code alone is a smart coder. Claude Code with the right stack becomes an operating system for shipping software.

• 
  Pawan Pandey (@BuildWithPawan) reported 40 minutes ago

  Then pick where it goes: log it as a row in a Google Sheet, save it to Drive, or create a GitHub Issue (with labels) and push it into a Project — or send it to more than one destination at once

• 
  Mohammad Anas (@mohmmad__anas) reported 41 minutes ago

  Agents Are Fine. Coordination Is The Problem. I downloaded OpenClaw last month. Spent two hours setting it up. The thing worked. It generated ideas, drafted threads, even picked images. And then I hit the wall. I had five agents now. Each one smart. Each one fast. But none of them knew what the other four were doing. I'd get three ideas from the brainstorm agent that contradicted the positioning the research agent had locked in. The video agent would commit to a script length the scheduling agent couldn't actually fit in the posting window. My approval process became a UN summit where every agent had a veto. The real problem with AI agents isn't capability. It's the coordination tax. Every new tool I add doesn't increase my output linearly. It increases my decision-making load exponentially. I now have to know what each agent is optimizing for, what constraints they respect, where they hand off to the next one. That's not automation. That's complexity I'm now responsible for managing. This is why most founders abandon multi-agent stacks within three months. Not because the agents are bad. Because humans are terrible at being the bus driver between independent smart systems. The winning move isn't smarter agents. It's agents that share a single source of truth about what you're actually trying to do. One brief. One command. One output format. Every agent reads the same schema, knows the same constraints, writes to the same state. That's when agents stop fighting and start building. I'm watching the GitHub trending list fill up with orchestration projects — Hermes Agent, Dify, n8n all gaining ground fast. They're not winning because they're smarter. They're winning because they solve the coordination problem. The solo founder's real productivity leap isn't one agent. It's one unified system where the agents coordinate without you playing referee. Most automation tools optimize for letting you type less. The ones that win optimize for letting you think less. That's the difference between a tool that saves you an hour and a tool that gives you back your focus.

• 
  Jake "Epstein" Rosensteinberg (@JakeTheLarp) reported 42 minutes ago

  ATTENTION: DO NOT TRUST GITHUB PROJECTS! Developers of projects will often inject malicious code into their programs that could steal your login credentials and crypto. Block accounts that are advertising repos like these and don’t join their telegram.

• 
  Moit Reghason (@MoitReghason) reported 43 minutes ago

  Everyone’s celebrating agents trading tokenized stocks on Robinhood Chain. Few people are asking what happens when the infrastructure underneath those agents gets compromised. @cursor_ai recently disclosed CVE-2026-50548, a zero-click remote code execution vulnerability where a poisoned MCP response could disable the sandbox and execute code on a developer’s machine. That’s not a hypothetical attack surface. That’s the environment where agent infrastructure gets built. And it’s not an isolated incident. ➠ mcp-pinot-server carries a CVSS 10.0 unauthenticated RCE vulnerability. ➠ Kong’s mcp-konnect allows indirect prompt injection through poisoned data that can steer agent API calls without the user realizing it. ➠ mcp-memory-service exposed unauthenticated endpoints capable of leaking sensitive agent memory data. Each vulnerability adds another entry point to the same expanding attack surface. The recent Taiko bridge exploit made this painfully concrete. $1.7M was drained, not because the cryptography failed, but because a private key was committed in plaintext to a public GitHub repository. The SGX enclave performed exactly as designed. The operational discipline didn’t. What this means for the agent economy is that security debt compounds with every new integration. Cisco’s State of AI Security 2026 found that 71% of organizations are running unmonitored AI agents with broad MCP access. OWASP’s recently published MCP Top 10 found widespread issues across the ecosystem, including path traversal vulnerabilities and extremely limited adoption of standardized authentication mechanisms. As agents gain wallet-signing authority through ecosystems like @virtuals_io and agent key management systems such as @KeeperHubApp, the blast radius of a single operational failure grows proportionally. A private key left in a public repository could drain an autonomous agent treasury just as easily as it drained a bridge. The uncomfortable reality is that the weakest link in this stack was never the cryptography. It was always going to be the person who committed it.

• 
  Vivek Kotecha (@vbkotecha) reported 48 minutes ago

  The single most underrated development in AI this year is not a model. It is a protocol. MCP (Model Context Protocol) was released by Anthropic in late 2024. It got almost no press. No keynote. No product launch event. Just a GitHub repo and a specification. 18 months later, every major AI framework supports it. OpenAI. Google. Microsoft. Cursor. Replit. Windsurf. Claude Code. Hermes. Codex. Every coding agent. Every agent framework. MCP does for AI tools what HTTP did for web pages. Before HTTP, every application had its own protocol for communicating with other applications. After HTTP, everything spoke the same language. Before MCP, every AI tool integration was custom. You wrote a plugin for Claude, a different one for GPT, a different one for Gemini. After MCP, you write one server and every agent can use it. There are now thousands of MCP servers. They expose databases, APIs, file systems, browser automation, *** repos, Slack, email, calendar, and anything else an agent might need. The MCP Registry was published this month. It is the DNS for agent tools. An agent can discover and connect to any registered MCP server automatically. No configuration. No API keys. Just discovery and connection. If you are building agent infrastructure and not MCP-compatible, you are building for a dead ecosystem. MCP won. The war is over.

• 
  dennoz 🐂🀄️ (@v_danielmg) reported 48 minutes ago

  @Alphaguyx100 I have problems trying to make sure the GitHub linked to the pumpfun feed is legit. I don't find him in his x account or the company acc

• 
  Shivaansh Pandey (@ShivaanshP0610) reported 50 minutes ago

  Stuck in a support dead-end with @github — account suspended with no explanation, and every appeal channel (web form, email, mobile app) is returning errors or bouncing. Would appreciate help getting this looked at by a human. #GitHubSupport

• 
  Vaibhav (VB) Srivastav (@reach_vb) reported 51 minutes ago

  @blueforyou0202 @hiddnest github issues please and send me the URL here

• 
  Gerard Wellemeyer (@G_bynature) reported 51 minutes ago

  @ColdShalamov @bradmillscan Basically, I think you're right, but I from my understanding, your statement needs clarification. A worktree originates as a structural isolation method in Github, specifically, to prevent a file being written by multiple users simultaneously. This has obviously been a similar problem with agents, and the solution- "worktree isolation" is a specific approach that yields the same results, although the mechanics may be completely different than github's. My worktree isolation approach is the same as yours- define a niche for an agent to perform a task on a specific file (i.e. database)- one agent, one writepath for that file, one owner for the writepath AND the data integrity... "accountability" In some other cases, worktree isolation may look more like a kanban card strategy, or some sort of gating.

• 
  Moit Reghason (@MoitReghason) reported 55 minutes ago

  I think the strongest version of this is to preserve your argument, but make the progression clearer: celebration → evidence → pattern → implication → conclusion. Here’s how I’d refine it: ⸻ Everyone’s celebrating agents trading tokenized stocks on Robinhood Chain. Few people are asking what happens when the infrastructure underneath those agents gets compromised. @cursor_ai recently disclosed CVE-2026-50548, a zero-click remote code execution vulnerability where a poisoned MCP response could disable the sandbox and execute code on a developer’s machine. That’s not a hypothetical attack surface. That’s the environment where agent infrastructure gets built. And it’s not an isolated incident. ➠ mcp-pinot-server carries a CVSS 10.0 unauthenticated RCE vulnerability. ➠ Kong’s mcp-konnect allows indirect prompt injection through poisoned data that can steer agent API calls without the user realizing it. ➠ mcp-memory-service exposed unauthenticated endpoints capable of leaking sensitive agent memory data. Each vulnerability adds another entry point to the same expanding attack surface. The recent Taiko bridge exploit made this painfully concrete. $1.7M was drained, not because the cryptography failed, but because a private key was committed in plaintext to a public GitHub repository. The SGX enclave performed exactly as designed. The operational discipline didn’t. What this means for the agent economy is that security debt compounds with every new integration. Cisco’s State of AI Security 2026 found that 71% of organizations are running unmonitored AI agents with broad MCP access. OWASP’s recently published MCP Top 10 found widespread issues across the ecosystem, including path traversal vulnerabilities and extremely limited adoption of standardized authentication mechanisms. As agents gain wallet-signing authority through ecosystems like @virtuals_io and agent key management systems such as @KeeperHubApp, the blast radius of a single operational failure grows proportionally. A private key left in a public repository could drain an autonomous agent treasury just as easily as it drained a bridge. The uncomfortable reality is that the weakest link in all this was never the cryptography. It was always going to be the person who committed it.

• 
  Vladimir Sapronov (@v_sapronov) reported 57 minutes ago

  @stolinski @danjones @rpunkfu The famous infinite loop GitHub Actions wait bug (the timeout comparison is made with exact equality instead of more or equal) could be fixed by a junior dev with Claude in like 1 month including onboarding and Jira setup. That one junior costs a fraction of the single marketing manager whose job is "how to cover recent 88.88% stability ****** with burn-the-CD marketing, and then push this marketing down developers throats through a network of overly friendly influencers". And this is just the marketing manager, there were also artists, video production, legal, SMM manager - all paid employees who didn't work on the product this month (or ever). The influencer's servility is not quantifiable though - they are kneeling in exchange of having access to GitHub people for their podcasts to farm more Github-friendly content.

• 
  A. Loner (@peterlony) reported 59 minutes ago

  @MatthewBerman No, it's easy... I develop about 20k to 30k lines of code a day in a million-plus-line monorepo. On a $200 plan and if I'm not careful I use it all in 3 to 4 days. I have a computer running almost 24/7 with goals all the time. I had to reduce to medium (gpt-5.5). If you use a lot of sub-agents and do a lot of reviews, then it's easy. I have a particular review process after coding to catch bugs and problems. It's very expensive. PLUS automated github reviews. Github reviews is what kills tokens usage.