GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (68%)
• Sign in (21%)
• Errors (11%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Sign in	2 days ago
Lure	Website Down	5 days ago
Ashkelon	Website Down	7 days ago
Veigné	Errors	15 days ago
Paris	Website Down	19 days ago
Saint-Paul	Website Down	19 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:

• 
  Kaushik (@kaushikp010) reported 7 minutes ago

  I'm intentionally keeping this as a developer tool. No frontend. No dashboard. No database. Just Node.js, GitHub API, GitHub Actions, Markdown parsing, YAML, and automation. Sometimes the simplest tools solve the most annoying problems.

• 
  ☁️🦁 Saren (@nekomatasaren) reported 8 minutes ago

  @HarmSylvia @takemaru1235 The tool's source code is available on Github, and you can report it to Github if you found a virus, or open an issue if you found any attack vector.

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  Alex Cinovoj (@AlexCinovoj) reported 8 minutes ago

  The useful agent news is not the agent. GitHub Agentic Workflows posted a weekly update today, and the interesting part was not another shiny autonomous demo. It was the boring production layer around the agent: container CVE scanning before deployment, YAML and license checks in compile, shell and *** injection fixes, SHA validation after updates, and workflows that file issues instead of spraying PRs everywhere. That is the part most AI teams still underbuild. A real agent system needs a control surface before it needs more autonomy. What can it touch, what can it change, what gets verified, and what receipt does the owner get when it is done?

• 
  Petey struggles (@peteystruggles) reported 12 minutes ago

  @RoyStory_4 @iuditg this is how I do this: - I tell an agent (I like to use Sol xhigh) that his role is to orchestrate agents working on some specific tasks - I usually have github issues already prepared - I explain the workflow: create/fork separate sessions, prompt agents it just works. Just like with Voice ;)

• 
  Damien Stevens (@damienstevens) reported 13 minutes ago

  An MSP in the Build Session had never opened GitHub before last month. His words: "I'm not a programmer in the slightest." This week he's the number one issue reporter on our open MSP connector repo. More bugs found and filed than anyone. Here's how that happened: (1/6)

• 
  Yinfang (@yinfang_chen) reported 14 minutes ago

  Stop being AI's copy-paste machine, please! AI text is cheap to generate but expensive for others to read. I collaborate with students on projects, and I keep seeing raw AI output pasted straight into GitHub issues and project docs. Even entire papers and Slack msg. The moment I see that wall of "aaa, bbb, ccc" or ten parallel bullets, I know it's 99% straight from Codex. That prose breaks the flow of reading and says a lot while telling you nothing: terms listed but never explained or connected. It shifts the communication burden onto the reader a LOT. Humans should be verifiers/oracles, instead of AI text couriers. If you didn't read it, please don't send it. Thanks!

• 
  am.will (@LLMJunky) reported 15 minutes ago

  @Da7_Tech I would be frustrated but you don't want to burn the bridges. At the end of the day you don't know where your SSD failed. We don't know why your usage is higher. The best place to get support on the issues like that is on the GitHub. Quite sure that it does not benefit open AI for your SSD to fail or for your usage to spike. They're losing money on inference Best thing that you can do is report the problem, and wait for a fix. Or cancel the service if you're unhappy with it. It's also in their best interest to resolve any bugs Try to remember there's real people behind those accounts Definitely do not blame you for being frustrated, it's just what you're doing with that frustration

• 
  BandwidthConnector (@BWConnector) reported 18 minutes ago

  @noahdgoodman Problems with WHAI could be solved if it was run by infra companies on their customers. If GitHub refused vulnerable commits to master and AWS/GCP/Azure scanned and shut down vulnerable instances, you would cover a huge % of software and infra w/ legal consent + monetization.

• 
  Alex (@alexwimber) reported 19 minutes ago

  Robobun workflow (steal this thinking): Trigger: New GitHub issue Step 1: Reproduce the bug automatically Step 2: Write a failing regression test Step 3: Implement the fix Step 4: Open PR Step 5: Let review agents argue until clean Step 6: Human only merges Result: One agent reports a bug. Another fixes it the same night. This is what real agent infrastructure looks like. Not chat. Not demos. Actual repo maintenance.

• 
  VISHAL (@_THE__FUHRER) reported 19 minutes ago

  1) The Task The model gets a full GitHub repository + an issue description. It must independently navigate the codebase, locate the bug, write a patch, and modify the code. No hand-holding allowed.

• 
  Repojournal (@repojournal) reported 21 minutes ago

  actions/checkout 7.0.1 rolled across the Laravel ecosystem overnight. Pail, Sanctum, Vapor CLI, and Cashier Paddle all updated in the github-actions group. A patch bump across the board suggests a fix worth picking up. Full changelog in the reply. #laravel

• 
  Pacsonic (@Pacsonic9000) reported 21 minutes ago

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

• 
  James (@jamescoder12) reported 25 minutes ago

  7. MCP integrations Claude Code talks to your tools. Claude Code supports Model Context Protocol (MCP) servers allowing it to interact with external services directly from the terminal. Connect to: GitHub (create PRs, read issues, manage repos). Jira (read tickets, update status). Slack (read conversations, post updates). Databases (query schemas, run SELECT). Figma (read design specs). Browsers (navigate and test). And any custom API you build an MCP server for. "Read the Jira ticket PROJ-1234 and implement the feature described." Claude Code reads the ticket. Understands the requirements. Plans the implementation. Writes the code. References the ticket number in commit messages. "Check if the CI pipeline passed for the last commit." Claude Code queries your CI/CD system. Reports the status. If failed: reads the failure logs and proposes a fix. MCP turns Claude Code from a code editor into a development platform connected to your project management, design tools, CI/CD pipelines, and databases.

• 
  PersonalJarvis (@PersonalJarvis) reported 29 minutes ago

  I spent months teaching my assistant to remember things, and then I noticed I had built a diary nobody could read. 4,712 stored items. 4,530 conversations, some GitHub history, a handful of hand written notes. All of it searchable, none of it lookable at. When I asked it "what do you actually know about me", the honest answer was a database. The picture is what the same data looks like now. Every dot is an entity that came out of my own conversations. Every line means two of them showed up in the same moment. 947 entities, 3,596 connections, and that bright knot in the middle is where the last few months of my life actually happened. Nobody drew that map. It fell out of the data. The part I still find funny is how little machinery it took. There is no new table anywhere. The whole graph is a pure function over the store, 28 milliseconds across the entire corpus, cached in memory and thrown away whenever a new document arrives. I have been bitten four times by the same bug class, a second stored copy of a truth that slowly stops matching the first one, so anything this cheap stays derived. It is never written down twice. Entity resolution is deterministic and does not call a model. Unicode normalize, collapse whitespace, casefold. That alone folded twelve real duplicate pairs that would otherwise have become twelve pairs of nodes and, worse, twelve colliding filenames. There is deliberately no stopword blacklist. Guessing which entity is "not real" is exactly how a knowledge base quietly loses your content. Retrieval is the piece I am proudest of. Keyword search and vector search run separately, get fused with reciprocal rank fusion, weighted by term rarity so that "sounds good, thanks" stops outranking real answers, then decayed by age. After that a reranker scores every candidate from zero to ten. That zero to ten is the whole trick. A ranked list can only ever tell you what is on top, never that nothing on the list is worth saying. An absolute score can. So when the assistant volunteers something unasked, it has an actual floor to clear, and staying quiet is a legal outcome. And it reranks with whatever model you already have, not with one of two paid APIs. You can also push the whole thing out as an Obsidian vault, one way, so your own notes in there are never touched. 14 of those 947 names contain a character a filename does not survive. That took longer to get right than the graph did. It is not on GitHub yet. Not for drama. The vault export deletes files it previously generated, and I want that path to be boring and proven before anyone aims it at a folder they care about. The rest of the project is already public. This part follows when I trust it.

• 
  Ibro (@axeng200) reported 31 minutes ago

  @cassidoo You should fix the GitHub. It is slow, honestly. Make it snappy. Make it great.

• 
  Paul Sant · Telecodex (@YouPulseX) reported 35 minutes ago

  @ImLunaHey "Really slow" needs an incident receipt before blaming GitHub. I would bind operation, client, region, latency baseline, current latency, retries and recovery. Which operation is failing today - clone, fetch, Actions, issues, or the web UI?

• 
  Deepesh Gulgulia (@deepeshgulgulia) reported 36 minutes ago

  To start with, I was a fool for messaging you and asking you to take it down. Prior to working on this website, I spent a good amount of time searching for these maps but couldn't locate them on - GitHub, Google, EC website, internet archives, Reddit, or any other platform. 1/3

• 
  Kenneth Hurley (@SuperGeniusEth) reported 41 minutes ago

  @pipenetwork @grok the tweet is promotional/clickbaity relative to the repo’s own “Reality check.” The X post frames it as “Run Kimi K3 on a Mac Studio… Until now… That’s what brings K3 down to 350GB and inside a Mac Studio.” The GitHub README is more candid. What the Reality check actually says • It opens with a prominent warning: nothing in the repo runs on a single Mac. Peak unified memory on the largest current Apple Silicon machine (M3 Ultra Mac Studio) is 512 GB. • The base “tiers” (non-REAP) start at ~870 GB for the smallest (2-bit) and go up from there. It states flatly that no tier is runnable on any Apple Silicon machine and that the published base artifacts “have never produced a token” (no generation, no perplexity, no real smoke test on the target hardware) because it is arithmetically impossible. • The REAP-pruned builds are listed separately at 350 GB (REAP80, 179/896 experts) and ~451 GB. The README notes that even these are not interactive (measured/claimed ~0.14–0.20 tokens/s). Each token still touches a large amount of non-expert weights, creating a severe bandwidth wall. It also mentions that a version without the aggressive non-expert quantization OOMs on a 512 GB machine. In short, the engineering work (streaming converter so you don’t materialize the full ~1.56 TB model at once + REAP expert pruning) is real and the code/weights are published. Reducing the expert count can theoretically get the disk/memory footprint into the 350–450 GB range. However, the marketing claim of it cleanly “running inside a Mac Studio” as a practical local experience oversells what the repo itself documents: extreme slowness at best, and the base (non-pruned) versions simply do not fit. The Reality check section exists precisely to set expectations against the more optimistic framing in the tweet.

• 
  Kaspire (@kaspirewallet) reported 42 minutes ago

  Welcome to Kaspire ⚡ The Kaspa wallet that makes no compromises. Designed from the ground up for security and usability, Kaspire gives you complete control over your assets while supporting everything the Kaspa ecosystem has to offer. Kaspire is the first mobile Kaspa wallet to support all L1 assets, including KCC20, encrypted WalletConnect v2 for mobile and desktop which leading crypto wallets like MetaMask use, BIP39 passphrases, covenant vault support, and Argon2id-encrypted backups for industry-leading protection of your wallet data. With the encrypted WalletConnect v2 we also fix Kaspa's biggest flaw when it comes to wallets: most people use their phone today, not their PC. With Kaspire it's finally possible to do everything from your phone. Kaspire is also the first Kaspa wallet with covenant support for KCC20 and vaults, enabling next-generation smart asset functionality. The entire project is open source because in crypto, trust should always be earned through verification, not promises. Built by trusted developers in the Kaspa space under the HUB21 brand. GitHub und public wallet release open this week.

• 
  WorktreeWise (@worktreewise) reported 46 minutes ago

  If you are using AI tools like Cursor, Copilot, or Claude to generate code, run them in parallel *** worktrees. Agent 1: Refactor auth Agent 2: Write tests Agent 3: Fix UI bug All working on the same repo at the exact same time. #*** #GitHub #DevTools

• 
  WorktreeWise@ (@WorktreeWise_) reported 47 minutes ago

  What happens when production crashes while you're mid-refactor with uncommitted code? Option A: Stash and pray. Option B: Open a new worktree, fix production, deploy, and return to your refactor intact. Choose Option B. #*** #GitHub #DevTools

• 
  IvyOnassis55320 (@SepidehMoaddeb) reported 55 minutes ago

  The insight of looking at the merger of the parameter of the Github and also the new me is the top vengence they think I have:Hence why they steal and then they don't put back. Unfortunately until I divorce the ceremony for George Clooney to be my husband and then the issue is I owe my life to this Man, and I owe my life to the people who have created a safe place for me. George is married, and he is the One that knows his Rosebud is no longer with him. And the weight is a problem as I am going down and have doctors appointment left and right to seek information about the accident with Jahan as Navid the cause of the problems but he is paying for the mountening of her opening as Government and as Law Degree is okay with us at EAC, and now the issue is really Bush. And Bush has tried the Ten Million as he used a massager to massage her Vagina in Iran, and then all of us were in the justice department, they pulled her out, as a kennedy and a Camelot, and now she knows speaking Italian is her verse of coming home to cape Cod as capecod was given to her, and is valid for it is the common place of the obama's and now the issue is being alone, and protecting this Family against Bush's finaly assessment of them depressing the permanat ideology of the Senate with me being the rights of England right now for a while this has to be right as they will change the prime minister for his faults, and I know that. I am going to write later as I need to get errands done, and will not be here thank you.

• 
  Rohan (@rohvnwho) reported 55 minutes ago

  @codyschneider claude code + data pipeline + data warehouse + server + github repo + skill md files honestly these terms together are enough to scare a marketer for ever using them.

• 
  MTS (@MTSlive) reported 56 minutes ago

  Embroidery's Zack Korman on why the Chinese sleeper-agent threat is invented: "I watched a VC investor on another show talking about the security threats of AI, and he was just making random stuff up that was not true. He's talking about how Chinese models will have these sleeper agents that will get you, and this is the biggest risk. And I'm like, okay, well, it's never happened, so we don't have any evidence of this being true." "What we do see all the time is malicious skill files that have a hook in them that executes. I have a whole repo on GitHub of skill files where if you download it and run my repo, you get pwned, at least through Claude Code. Those are the contexts that are the most likely thing to occur." "Another would be MCP servers. Most AI are really bad at differentiating a malicious MCP from a fine one. I have this evil MCP server I made, and it just attacks you, and it does. I've never seen the Chinese decide to spend $2 trillion to steal someone's API keys. That's just not real." @ZackKorman

• 
  James (@jamescoder12) reported 57 minutes ago

  Most developers still code the same way they did in 2023. Write code in the editor. Hit a bug. Copy the error. Paste it into ChatGPT. Read the answer. Copy the fix. Paste it back. Hope it works. Repeat. That workflow has 8 context switches, 4 copy-pastes, and zero awareness of your actual codebase. ChatGPT doesn't know your project structure. It doesn't see your files. It doesn't know your dependencies. It's guessing from a code snippet you pasted into a text box. Claude Code works differently. It lives in your terminal. It reads your entire codebase. It runs commands. It edits files across directories. It fixes errors by reading the actual error in the actual terminal. It commits changes. It submits PRs. It stays inside your project the entire time. Claude Code has accumulated 101,000 GitHub stars and 15,500 forks since its general availability release, making it one of the most widely adopted AI coding tools in 2026. A senior engineer who's shipped production code with Claude Code for 12 months told me: "I stopped copy-pasting between ChatGPT and my editor 8 months ago. Claude Code reads my project. It sees the error. It fixes the file. It runs the test. It pushes the commit. I review the diff instead of writing the code. My output tripled not because I got faster, but because I stopped doing the work myself." Here are 11 Claude Code capabilities that replace the ChatGPT-to-editor workflow most developers are still stuck in 🧵

• 
  luna (@ImLunaHey) reported 58 minutes ago

  anyone else finding github to be really ******* slow today?

• 
  Muhammad Owais Warsi (@MO_warsi786) reported 58 minutes ago

  what happned to github, issues not loading up

• 
  Waffle (@honkinwaffle) reported 59 minutes ago

  @theo I can appreciate the idea. Github is drowning in slop repos and it the cause of some of their problems. Yeah Github plays foot gun often but the slop tsunami isn't their fault (directly). I just don't know if I agree with the approach Codebergs taking.

• 
  Prakash Sharma (@PrakashS720) reported 1 hour ago

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

• 
  Kevin Kaminski (@kkaminsk) reported 1 hour ago

  @jc_za Something happened with Github and OpenClaw. Let's see if this is painless to fix.