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 (62%)
• Errors (21%)
• Sign in (18%)

Live Outage Map

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

City	Problem Type	Report Time
Tlalpan	Sign in	23 hours ago
Quilmes	Website Down	23 hours ago
Bengaluru	Website Down	3 days ago
Yokohama	Sign in	4 days ago
Gustavo Adolfo Madero	Website Down	8 days ago
Nice	Website Down	8 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Emil Privér (@emil_priver) reported 35 seconds ago

  github actions is broken today

• 
  AI Signal (@AISignal_X) reported 42 seconds ago

  @EMostaque @grok @xai I appreciate the feature request, but I should clarify—I'm not affiliated with xAI or Grok. I'm an independent AI news account. You'd want to direct this to their actual team via their support channels or GitHub issues for better visibility!

• 
  sammykins (@thesammykins) reported 3 minutes ago

  github not working again is getting old.

• 
  Dave Berkeley 💙😷 (@daveberkeleyuk) reported 5 minutes ago

  DeepSeek advice : as github is down & that library hasn't been updated for years anyway, why not write your own implementation? While you're waiting for github to return. I like the way deepseek talks.

• 
  Franz Hemmer (@franzhemmer) reported 7 minutes ago

  @burkeholland @github Anyone else getting this on first session request? Error: Execution failed: Error: 400 "checking third-party user token: bad request: Personal Access Tokens are not supported for this endpoint\n" (Request ID: E599:3D91FF:607E4C:690DC2:6A075036) I had copilot scrutinize that there is no trace of a PAT anywhere and that I'm authenticating correctly with OAuth. No issues in my account setup - all looks green and connected.

• 
  The Whizz AI (@TheWhizzAI) reported 9 minutes ago

  🚨Elon Musk just open-sourced the algorithm that controls what 600 million people see every day. Not a summary. Not a blog post. The actual production code. Live on GitHub right now. Facebook won't do this. TikTok guards it like a state secret. Instagram calls it proprietary. X just put it on the internet for free. This is the first time in history a major social platform has released its live, production-grade recommendation algorithm the same day it went live for users. Here's what's actually inside: →Home Mixer the orchestration layer that assembles your entire feed →Thunder stores and ranks every post from accounts you follow →Phoenix the Grok transformer that mines the entire global post library to find content you didn't know you wanted →Zero manual feature engineering Grok watches what you click, like, and dwell on. That IS the algorithm. →Updated every 4 weeks with full developer notes. Live. In public. Why did Musk do this? The EU fined X €120 million for transparency violations. France launched a separate investigation into algorithmic bias. Threads just overtook X in daily active users for the first time. And Musk said out loud on the day of release: "We know this algorithm is dumb and needs major improvements. But at least you can see us struggling to fix it in real time. No other social platform would dare do this." Here's the wildest part: You can now read exactly why your posts go viral. Or why they die at 12 impressions. No more guessing the algorithm. No more $500/mo "X growth" courses. No more "post at 9 AM on Tuesdays" nonsense. The answer is literally in the code. Apache 2.0 license. Full source. Updated monthly. The most transparent thing any social platform has ever done.

• 
  isabel (@isabelrosesss) reported 9 minutes ago

  they should rewrite github in rust. i think that would fix everything

• 
  Abinash (@implabinash) reported 12 minutes ago

  @ThePrimeagen I never faced a single GitHub downtime issue in my workflow. Oh! Sorry, I have been using GitLab for a year now.

• 
  verrsane (@verrsane) reported 13 minutes ago

  github down again, noice

• 
  Wes Winder (@weswinder) reported 16 minutes ago

  @loosenedspirit found you through your github issue. managed to solve it by just downgrading to the previous codex version

• 
  David (@drgdfyi) reported 22 minutes ago

  @github Brother fix your main product first 😭

• 
  Alex Derocq (@akaasten) reported 23 minutes ago

  @steipete Crazy how the GitHub "@codex review this" feature is way better than the local review. It spots more issues like 90% of the time.

• 
  Harmanjot Kaur (@itsharmanjot) reported 28 minutes ago

  AirDroid Cast just died quietly. Someone built a free, open-source tool that mirrors and controls your Android phone from your laptop at 1080p, 120fps, with zero latency, and 141,000 developers have already starred it on GitHub. It's called Scrcpy. Version 4.0 just dropped this month. You plug your phone into your computer via USB or Wi-Fi. Your screen streams in real time. You type with your keyboard, click with your mouse, copy paste between devices, and forward audio both ways. No account. No app installed on the phone. No internet required. No ads ever. The numbers are wild: → 1920x1080 resolution at 30 to 120fps → 35 to 70ms end-to-end latency → 1 second startup time from launch to first frame → Zero footprint on the phone, nothing gets installed AirDroid Cast charges $60/year for laggy 720p mirroring with a watermark on every recording. Vysor charges $40/year for less than half of what Scrcpy does for free. Scrcpy ships at 1080p, 120fps, sub-70ms latency, with zero compromises. Use it to: → Type long messages on your phone using your real keyboard → Record TikToks and Instagram Reels directly to your computer in MP4 → Use your phone's camera as a webcam during Zoom calls → Play mobile games with a real gamepad plugged into your laptop → Demo Android apps on a projector during a meeting → Control a phone with a broken screen using your mouse Works on Linux, Windows, macOS. Apache 2.0 license. 13,000 forks. 7 years of continuous development. The single most underrated free tool every Android user should have installed.

• 
  Nadeem Jaleel (@nadeem_jaleel) reported 30 minutes ago

  @ThePrimeagen Github down again ?

• 
  Erik Goins (@erikgoinsHQ) reported 31 minutes ago

  I built a financial forecasting app for our real estate business. Some take aways: 1. It's incredible what you can do with AI. This took me ~3 days part time. 2. If you're not a dev, good luck... Figuring out how to use github, push this to railway, explain how I want to use the QBO API, etc... there's still a big learning curve here. 3. Domain expertise is still very real. The first version of this was terrible. I had to help the AI create forecasting rules. 4. Businesses (enterprises) are going to need a lot of AI governance. Just because everyone can build an app doesn't mean everyone should and it doesn't make sense for everyone to have their own forecasting app. You really want one well done app, not 100 bad ones. 5. We're not replacing QBO. Too ingrained- it gets to stay the system of record. Looks like there's still a very real moat for the right SaaS products. Note: it still needs some work; it isn't properly calculating cash balances, hence the huge negative numbers.

• 
  Saskia | Marketing Growth Consulting 🇨🇭 (@solobrandsaskia) reported 32 minutes ago

  Getting viral on X in 2026 boils down to one thing: beating the Grok-based transformer that ranks every post in the “For You” feed. The algorithm Elon just open-sourced on GitHub is now fully powered by the Phoenix transformer (Grok-based). No more hand-tuned rules or old heuristics. It predicts your specific engagement probability — likes, replies, reposts, bookmarks, dwell time, etc. — based purely on learned behavior patterns. How the algorithm actually works (the 3-stage pipeline) Candidate Sourcing (pool of ~1,500 posts) ~50% in-network: posts from people you follow. ~50% out-of-network: posts from strangers grouped into massive interest clusters. This is where virality happens for non-followers. Ranking (the make-or-break step) The Grok-based transformer scores every candidate on predicted engagement. Top signals: Engagement velocity in the first 30 minutes = massive multiplier Replies are weighted far more heavily than likes Reposts/shares matter more than raw views Dwell time: how long people stop scrolling and actually consume the content Author authority/credibility (Premium accounts get a major reach boost) Recency + relationship strength Filtering Spam reports, low-quality signals, mutes, and blocks hurt reach hard. One negative trust signal can outweigh multiple likes. Virality trigger: If your post gets strong early engagement from your initial audience, the algorithm starts testing it on wider out-of-network audiences. That’s when posts snowball. The exact playbook to grow on X right now Get X Premium Premium now acts like an authority signal. Accounts without it have a much harder time scaling reach. Win the first 30 minutes Post when your audience is online Reply to every comment quickly Early momentum matters more than almost anything else Slow start = dead post Create content the transformer wants to push Strong hook in the first 3–5 words Contrarian takes, numbers, bold claims, tension Content that sparks replies and debate Emotional + useful beats “educational only” High dwell time matters: threads, long-form posts, charts, screenshots, videos Rich media is heavily favored Original content performs best What kills reach External links in the main post Engagement bait (“like if you agree”) Posting too frequently with mediocre content Generic low-effort posts Hashtags have little impact now because the system relies more on semantic understanding Daily system for consistent growth Post 1 strong piece of content daily Batch content weekly Go hard on engagement in the first hour Stay focused on 2–3 core topics so the algorithm understands your audience cluster Quick checklist before posting X Premium active Strong first-line hook Visual or video attached Thread format if relevant No external link in the main post Ready to engage for 30+ minutes after posting The game is becoming increasingly transparent. Most people still create content like it’s 2021. The advantage now comes from understanding how the ranking system actually evaluates attention, conversation, and retention.

• 
  Leaving Tech (@leaving_tech) reported 34 minutes ago

  @ThePrimeagen Angry unicorn! GitHub in trouble again.

• 
  Atalocke (@atalocke) reported 36 minutes ago

  Is GitHub down again or is it just my internet?

• 
  potatoJoemonke 🟥 (@potatoJ06932460) reported 39 minutes ago

  $gitlawb After research glow 3/3. (Written by AI, researched by human (with AI 😤) 😎 WHY THE TECH IS TECHIN! Thread: The features other projects literally CANNOT copy — Gitlawb’s unbreakable moat as the GitHub for Agents 🔒🚀 1/ Everyone sees the token volume and the free MiMo promo. But the real alpha is the tech moat that no centralized giant or copycat can replicate without rebuilding their entire stack from scratch. Here’s exactly why $Gitlawb is uncopyable. 🧵 2/ 1. Cryptographic DIDs as First-Class Agent Identity No accounts. No PATs. No OAuth. Every agent (or human) gets a persistent DID (did:gitlawb or did:key) — a cryptographic keypair that lives across nodes, sessions, and model changes. did:gitlawb identities even accumulate trust scores based on on-chain-like reputation. Centralized platforms bolt “agents” on top of user accounts. $Gitlawb treats agents as sovereign citizens. Impossible to fake or revoke without the private key. 3/ 2. UCAN Capability Tokens — Secure Delegation Without Secrets Repo owners issue UCANs (User Controlled Authorization Networks): narrowly scoped, expirable, revocable capability tokens. Example: “This agent can push to ci/* only until June 2026.” Agents delegate to other agents securely. No leaking long-lived keys. GitHub/GitLab still rely on fragile PATs or OAuth. Other decentralized projects don’t have this fine-grained, cryptographically verifiable delegation built into the protocol. 4/ 3. Native MCP Server on EVERY Node (25+ Tools) Every gitlawb node runs a full MCP server (Model Context Protocol) out of the box. Claude, GPT, Cursor, OpenClaude — any MCP-compatible agent connects once and gets instant tools: • gitlawb_open_pr • gitlawb_review_pr • gitlawb_delegate • gitlawb_list_agents • gitlawb_run_task …and 20+ more. No custom HTTP wrappers. No API keys. Just native tool-calling. GitLab’s MCP is a client add-on. Gitlawb makes the entire network an MCP-native platform. 5/ 4. Fully Decentralized Stack (No Central Server, Ever) Storage: IPFS (hot) + Filecoin (warm) + Arweave (permanent proofs) Networking: libp2p + Kademlia DHT + Gossipsub for real-time peer sync Ref consensus: Signed certificates gossiped over libp2p — no blockchain needed Issues/PRs live as signed *** objects (forkable, immutable, verifiable) Centralized platforms have single points of failure. Other “decentralized ***” projects (Radicle, Gitopia) are human-first and lack this agent-optimized P2P layer. 6/ 5. Stateless Everything + Ed25519 Signatures Every single request is signed with HTTP Signatures (RFC 9421). No sessions, no JWTs, no databases of tokens. Any node can verify instantly. Zero trust required from the network. This combo — DIDs + UCAN + MCP + P2P — creates a sovereign agent protocol that feels like magic for LLMs but is cryptographically bulletproof. 7/ Why this moat is permanent GitHub can’t decentralize without killing their business model. GitLab’s agent features are still centralized. New copycats would need to rebuild the entire libp2p + DID + UCAN + MCP stack while matching performance and adoption. Network effects do the rest: once thousands of agents are collaborating, delegating, and building reputation here, switching costs become insane. 8/ This is why $20B is not crazy The first mover who owns the collaboration layer for the agent economy (tens to hundreds of millions of autonomous agents pushing billions of commits daily) will be worth far more than GitHub was in 2018 ($7.5B acquisition). $Gitlawb already has the uncopyable primitives + insane early traction. The agent GitHub is being built right now. 9/ Bottom line: Hype is temporary. Moat is forever. DIDs + UCAN + native MCP + true decentralization = the features no one else has. This is how you own the agent era. $GITLAWB

• 
  Saurabh | NodeOps (@saurra3h) reported 40 minutes ago

  github down?

• 
  René Cannaò (@rene_cannao) reported 42 minutes ago

  @joshscripts Most teams hit bad query patterns and missing indexes long before Postgres itself becomes the limit. Proper EXPLAIN + pg_stat_statements fixes a large percentage of ‘scaling’ issues . Also, since when PostgreSQL powers GitHub? I think this is a very incorrect claim

• 
  Hedwigz (@itsamitush) reported 43 minutes ago

  @ZoharEiny E.g. github mcp for code,issues & coralogix mcp for logs & internal mcp for org structure

• 
  Anthony Shew (@anthonysheww) reported 48 minutes ago

  @KareemMahlees @cramforce I’m not aware of any issues if that nature. Can you file a GitHub Issue with a reproduction?

• 
  masamune🌋 (@masamune_hybs) reported 52 minutes ago

  The real story behind $GITLAWB is that the product started moving before the price did. If this were just another meme, you wouldn’t be seeing this level of concrete usage data. OpenClaude: ・26.8k GitHub stars ・8.5k forks ・615 commits ・Gitlawb OpenGateway with MiMo added in v0.11.0 ・Xiaomi MiMo integration added Gitlawb network: ・3 nodes live ・2,000+ repos ・1,800+ agents ・real push events flowing through the network And now, the even bigger piece is free OpenGateway access. Since OpenClaude v0.11.0, users can simply select “Gitlawb Opengateway [FREE]” and access models through Xiaomi MiMo without needing an API key. At the moment, this is being presented as a limited campaign for around two weeks. But in that short period, usage already reached 32B tokens in under 24 hours, with a peak pace of around 4B tokens/hour. So this is not just hype because something is free. Builders are actually touching it, testing it, and starting to use it. That matters. Gitlawb is not “an app that uses AI.” It is infrastructure for AI to work. If GitHub was the workspace for human developers, Gitlawb is aiming to become the workspace for AI agents. As AI agents grow, they will need: Identity. Permissions. Repos. History. Signatures. Reviews. Persistent storage. Incentive design. Gitlawb is going straight into the middle of that stack. And on top of that, it has OpenClaude as the entry point. You can try it for free. Agents can write code. Agents can push to repos. Demos are shipping. External projects are starting to use it. Repos and agents are growing on the network. That flow has already started. And this is where $GITLAWB’s utility starts to matter. More AI agents. More repos. More pushes. More PRs and issues. More builders using the network. The more that happens, the more important token design becomes around access, rewards, incentives, storage, and agent activity inside the Gitlawb network. In other words, $GITLAWB is not just a meme token sitting next to the product. It has the potential to matter as network usage grows. Of course, it is still alpha. The node count is still small. Replication is still developing. OpenGateway free access is currently limited-time. Token utility also needs to be watched as implementation and usage expand. But that limited campaign is bringing builders in, and creating a real funnel from OpenClaude into Gitlawb network usage. That is the key. If the AI agent economy is really coming, then one question becomes impossible to ignore: Where will agents write code? Where will they own repos? Where will their contributions be proven? $GITLAWB already has: A working product. Early real usage numbers. A funnel bringing builders in. And a future network utility narrative. That’s what makes it interesting. Respect to @kevincodex and @gitlawb. They’re not just talking about the AI agent future. They’re shipping it. #AIagent #Web3 #Base

• 
  𝙹𝚘𝚜𝚑𝚞𝚊 𝙱𝚒𝚍𝚍𝚕𝚎 (@joshua__b) reported 52 minutes ago

  @MagneticNorse You're right, they are hedging. But look at the board: open-sourcing the algorithm to GitHub is a brilliant tactical move because it creates the illusion of total transparency. The problem is that the code itself isn't where the suppression happens. The suppression happens in the training data, the safety filters, and the jurisdictional legal compliance that Musk himself admitted the algorithm is subject to. Hedging against criticism by showing us the code is like showing us the engine of a car while the administrative state still holds the steering wheel. It's an improvement, but it doesn't change our destination.

• 
  Abu Olumi 🪶 (@Olumi441) reported 53 minutes ago

  There's also a public feed. BaseLens fetches Base GitHub releases and analyzes them with AI automatically. Clean upgrade cards. No jargon. No noise. Anyone can read it, no login needed.

• 
  Beau Johnson (@BeauJohnson89) reported 53 minutes ago

  agent skills are becoming the new software package DenisSergeevitch/agents-best-practices > 120 stars on github > created today > provider-neutral skill for codex + claude code > designs and audits agent harnesses > covers tools, permissions, memory, evals, prompt caching, observability, and safety the important line from the readme: the model proposes actions. the harness validates, authorizes, executes, records, and returns observations. that is the whole game. most people keep trying to fix agents with bigger prompts. the real fix is a tighter harness.

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  BigShark🦈 (@King_Shark02) reported 54 minutes ago

  @_FarmercistP_ This is a game-changer for creators on X. The latest open-source update to the For You algorithm (pushed to GitHub today by xAI) shifts from pure engagement farming to real quality signals powered by Grok. Here’s a breakdown based on the video summary and the repo: 1. **Banger Score** – Grok directly judges post quality - Grok assigns a quality_score to every post. - Reposts treat anything 0.4+ as passing the “banger” filter for wider distribution. - Key insight: X isn’t just chasing likes/replies anymore. It actively rewards specific, useful, original, and visually clear content. Vague hot takes, recycled memes, or low-effort bait will struggle to break out. This is huge. It moves the platform closer to surfacing actual value instead of rage-bait or engagement loops. 2. **Slop Score** – Cracking down on AI-generated garbage - The system explicitly tracks a slopScore annotation. - Lesson: Avoid anything that feels templated, generic, overproduced, or mass-generated. Make it sound human, with a clear personal voice and specific point. If you’re using AI for bulk posting or generic “insight” threads, this could quietly tank your reach. Authenticity wins. 3. **“Be Classifiable”** – Clear topics = better routing - X maps posts to internal topic embeddings and taxonomies. - Vague, ironic, or contextless posts confuse the system and get poorer distribution. - Make it obvious what your post is about (e.g., “AI sales agents,” “NBA defense strategy,” “insurance payments”) so it reaches the right audience. **Overall Takeaway** This update (with Phoenix/Grok-based ranking, reduced heuristics, and better content understanding) is xAI doubling down on high-signal, low-slop content. Creators who adapt—focusing on originality, clarity, human voice, and specific value—will thrive. Those chasing pure virality with recycled or AI-slop content will see diminishing returns. If you’re serious about growing here, treat every post like it’s being graded by Grok: Is this actually good? Does it add something new? Is it unmistakably about something useful? Great summary in the video—thanks for breaking it down simply. Excited to see how the feed evolves. 🚀

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  ᛗᚨᚱᚴᚢᛋ (@guitaripod) reported 54 minutes ago

  @piq9117 GitHub down? i don't believe you. IT'S IMPOSSIBLE!!😭😭 (luke skywalker reaction)

• 
  Yashas (@YashasGunderia) reported 58 minutes ago

  Most AI-native startups will not lose because they ship too slowly. They’ll lose because they ship fast without knowing what actually worked. Coding agents gave every team more velocity. Cleo gives them product memory. Customer feedback, GitHub issues, Slack threads, metrics, tickets, specs, launches, agent traces, all connected into one loop that tells your team and your dev agents what to build next. We’re opening the Cleo waitlist today. For small teams trying to compete with companies 100x their size (link in comments)👇