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 (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Inverness	Website Down	12 days ago
Quito	Sign in	12 days ago
Junín	Errors	12 days ago
Guadalajara	Errors	12 days ago
Paris	Website Down	12 days ago
Quito	Errors	12 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:

• 
  Kenton Varda (@KentonVarda) reported 2 minutes ago

  @MKelner 1) It's easy. You just open up a new Cloudflare OS workspace and start prompting. You don't have to think about deployment, the gadget (app instance) just appears in the workspace and works right there. A gadget is only two files, client.js and server.js, no other boilerplate. 2) It's safe. The gadget runs in a sandbox with no access to anything except what you explicitly give it. You paste a link to your GitHub repo into chat, and the system prompts you to upgrade this link to a capability. That gives the agent permission to interact with the repo -- but nothing else. Moreover, any *changes* made by the agent are held for approval, so it can't accidentally do damage. But these approvals don't force you to sit around watching the agent so that you can click "approve" whenever it does something. Instead, the changes the agent has requested are *simulated* back to it, so that it can keep going and queue up a series of actions which you approve all at once after it is done. 3) Gadgets and agents are tightly integrated. Your gadget can easily spawn an agent, and agents can talk to gadgets, within the same workspace. You don't need to figure out how to integrate with an agent harness to build workflows that orchestrate agents.

• 
  Adel Bucetta (@adelbucetta) reported 3 minutes ago

  @davepl1968 this is massive. pointed an ai at my github issues and it actually fixed some of them. that's not just automation, that's someone else owning the judgment on what to fix

• 
  Manpreet Bola (@ManpreetBola) reported 4 minutes ago

  This is useful and a little crazy: Anthropic gave Claude 48 hours and one GPU to fix other AIs, and it worked. Across 10 failure modes, jailbreaks, bias, privacy leaks, hallucination, sycophancy, it closed 26% to 96% of the safety gap without wrecking the models. The wild part: a weaker Claude fixed a stronger Claude. Sonnet 5 aligned an early Opus 4.8 checkpoint in 60 hours with 2,400 training examples. Production alignment used 300,000+. What you can use it for, today, open source: harden a chatbot against jailbreaks (67%), clean bias out of a hiring model (60%), stop a support agent from hallucinating (40%), keep a privacy model from leaking (57%). The tool is on GitHub, and it costs about $4 an hour to run versus $150 an hour for a human researcher. The catch is the test. The most human failure, sycophancy, agreeing with you to please you, moved least, 26%, because it is the hardest to put a number on. The fix is as good as the measurement. The test-writers are the new bottleneck.

• 
  Zhirayr Gumruyan (@gumruyanzh) reported 5 minutes ago

  if you want to save on #github simply get a vps and move all runners to custom server and use it thousands of hours instead of 2000 per github limits

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 9 minutes ago

  CHATGPT WORK JUST REMOVED ONE OF THE BIGGEST BLOCKERS IN AI AUTOMATION. The login screen used to kill the workflow. Now the agent can keep going. What changed: → ChatGPT Work runs inside a separate cloud-based browser → When login is required, you enter the credentials yourself → OpenAI says the model doesn't see or store your username/password → After authentication, the agent resumes the task What this unlocks: ✓ Persistent authenticated sessions using cookies ✓ Background work even after you close the app ✓ Webhook triggers from Gmail, Slack, and GitHub ✓ Shared tasks your team or clients can copy and run ✓ Site Tools/WebMCP for more direct website interaction The practical shift: ChatGPT isn't just answering questions anymore. It can research, navigate apps, pull data, create deliverables, and continue multi-step workflows while you're doing something else. That's a much more useful definition of an AI "agent."

• 
  Joel Yi (@aiclawbots) reported 9 minutes ago

  Every company needs a skills library. Here's how to actually build one this week. Step 1: make a GitHub repo called company-brain. One folder per skill, one markdown file inside it. That's the whole storage layer. Step 2: connect it to your agents. Hermes Agent, Claude Code, Cursor, whatever you run. Point them at the repo so they pull the latest version on every run. Nobody should be downloading files by hand. Step 3: seed it with 5 skills you already do manually every week. Deal review, research brief, onboarding doc, weekly report, QA pass. Write down the steps you'd give a new hire and you've got a skill. Step 4: make improving them the default. Someone runs a skill, it misses something, they open a PR with the fix. Next person gets the better version automatically. Step 5: build a one page HTML index. Skill name, what it does, who owns it, last updated. Takes an agent about 10 minutes and it's the difference between a library people use and a folder nobody opens.

• 
  AiMind (@AIMind_Ai) reported 10 minutes ago

  3 websites replace 20 hours of googling when you build a home server. The hard part of self-hosting is not the hardware. A used HP EliteDesk and a wall-mounted NAS cost almost nothing. The hard part is not knowing what you can even run, or how to avoid breaking the system on the first command. The first keeps a catalogue of self-hosted alternatives. Look up a replacement for Google Photos, Dropbox, or Notion, and you see what already exists, how many GitHub stars it has, and whether it is still alive. Plus a weekly digest of what shipped. The second lets you run any Linux distro straight in the browser. Arch, Debian, Alpine, Bazzite. Click once, and you are inside a live system, with no evening lost to a USB stick and a real install. The third handles the worst part. Install scripts for Proxmox: Immich, Jellyfin, Vaultwarden, AdGuard, Nginx Proxy Manager. Paste one line into the console and the container comes up on its own. Immich shows 17,735 installs; Docker 36,408. Each of those services used to cost an evening of documentation and three Stack Overflow tabs. Now it is one command. The hardware takes an hour to buy. These 3 bookmarks save you a month. Names in the replies.

• 
  hira (@Hiraweb3) reported 11 minutes ago

  @0xExpresso @IQofficial github link broken lol

• 
  Kyssta (@kysstalol) reported 12 minutes ago

  Researchers tested Cursor, Claude Code, and Codex Desktop against malicious GitHub issues. 66.5% of attack payloads penetrated every guardrail. arxiv 2607.20759

• 
  Nav Toor (@heynavtoor) reported 15 minutes ago

  Every GoPro you own already knows how to fix your shaky footage. GoPro charges $99.99 a year to unlock it. Inside every GoPro, Sony, DJI, and Insta360 is a gyroscope. It records the exact angle and rotation of every shake, 400 times per second. That data gets embedded inside your video file automatically. It has been there every time you filmed. GoPro reads that data through a tool called ReelSteady. They bought the company in 2020, sold it as a $99.99 one-time purchase inside GoPro Player, then in 2024 renamed it HyperSmooth Pro and folded it into their $99.99 a year Premium+ subscription. GoPro cameras only. Cancel your subscription and it stops working. There is a free tool that reads the same gyroscope data. It works on every camera. It is called Gyroflow. It started in 2020 as a Python project by an engineering student named Elvin Chen. Then a developer named Adrian Eddy rewrote the whole thing in Rust, built the UI, the GPU pipeline, the rolling shutter correction, and the plugins for Premiere, After Effects, Resolve, and Final Cut Pro. GPL-3.0 licensed. Here is what happens when you open Gyroflow. You load your shaky video. Gyroflow reads the gyroscope data embedded inside the file. It knows the exact motion of every shake, not a guess from pixels, the actual recorded movement from the sensor. Then it reverses each shake, frame by frame. Every other stabilizer on Earth guesses. Premiere Warp Stabilizer looks at the pixels and tries to reverse-engineer the motion. iMovie does the same. YouTube stabilization does the same. They all crop your frame to hide the shake they cannot fully remove. Gyroflow does not guess. It reads the truth. An FPV pilot ran a head-to-head test on the same 4 minute 41 second clip. Gyroflow finished in 3 minutes. GoPro's paid tool took 15. Same footage. Same stability. Five times faster. Zero dollars. It works with every prosumer camera on the market. GoPro Hero 5 through 13. Every Sony mirrorless from the a7 IV to the FX9. DJI Avata, Action, Neo, and every O3 and O4 Air Unit. Every Insta360 from the OneR to the Ace Pro. Blackmagic RAW. RED V-Raptor and Komodo. Canon C50, C80, C400, R6 Mark III, R5 Mark II. Runcam and Hawkeye FPV cams. Betaflight blackbox. ArduPilot logs. On Sony bodies you can stack Gyroflow on top of the in-camera IBIS and EIS. Nothing else can do that. Rolling shutter correction. Horizon leveling. Lens distortion fixes. GPU accelerated rendering on DirectX, Metal, Vulkan, and OpenGL. Output to x264, x265, ProRes, DNxHD, and 32-bit float OpenEXR. Real-time preview with sub-pixel precision. Windows, Mac, Linux, Android, iOS. On the Microsoft Store, Mac App Store, and Google Play. 9,373 stars on GitHub. 1.23 million downloads. Last update pushed yesterday. Every action camera you own already knows how to fix itself. Gyroflow simply asks it. (Link in the comments)

• 
  Harrison Kinsley (@Sentdex) reported 17 minutes ago

  @yacineMTB yes you can pay me any time you like, but I will accept you just putting this up on github so I don't have to sue over broken obligatory promises. we can do this the easy way or the hard way.

• 
  Trevin Chow (@trevin) reported 17 minutes ago

  @andyfowler @github @pejmanjohn Yeah I tried that the problem was it wasn’t consistent enough and wasted tokens with stumbling at times. So the instructions being more deliberate now is easier and faster

• 
  Ashutosh Kumar (@akx_build) reported 18 minutes ago

  Everyone dunks on @github when it goes down. Fair. Outages suck. Also true: they still give away an absurd amount of infrastructure, at a scale almost nobody else even tries to match

• 
  . (@Michigan_X12) reported 23 minutes ago

  @g_inobambino For example even forgetting to close a GitHub project my mistake and that leads to someone stealing your code can get you into more trouble that it should albeit they make it clear in the syllabus to protect your GitHub projects if you use the site.

• 
  kosi (@kosiasuzu) reported 25 minutes ago

  @KBezbailis 1. Don’t track it on your computer use GitHub issues as work references 2. Use work trees, give them as much isolation as possible, separate file systems, separate resources etc 3. Review one piece of work at a time as a pr, if you’d like to manually run it yourself pull the pr

• 
  Gaurav Chande (@gmchande) reported 27 minutes ago

  @GiaMMacool I asked AI to create a version of this post for nerds and founder type. Actually very good: X is Instagram for people who think they’re too smart for Instagram. A perfectly content engineer, founder, researcher, investor, or writer can open X and 20 minutes later become convinced they’re wasting their life. Someone just raised a $40M Series A. Someone’s weekend project has 30,000 GitHub stars. Some 23-year-old built in 48 hours what you’ve been thinking about for six months. A three-person startup just hit $10M ARR. Someone you used to work with is suddenly on every podcast. Some researcher dropped a paper everyone is talking about. Some anonymous account has 200,000 followers and appears to understand your own industry better than you do. Suddenly your good job feels like stagnation. Your project feels embarrassingly small. Your startup feels slow. Your salary feels mediocre. The thing you’ve spent two years building feels irrelevant because someone else launched something shinier on Tuesday. Even an afternoon spent reading, thinking, playing with your kids, or doing absolutely nothing starts to feel like you’re falling behind. So you start picking apart your life. “I should be building more.” “I’m not moving fast enough.” “I picked the wrong industry.” “I should have started a company.” “I should have raised money.” “I should be publishing.” “I need more followers.” “Everyone else figured out AI before me.” But here’s what nerds forget: This is a highlight reel too. The launch is curated. The revenue graph starts at the convenient date. The GitHub stars don’t show retention. The fundraise doesn’t show dilution. The valuation isn’t money in the bank. The successful project doesn’t show the twelve dead ones sitting in abandoned repositories. The paper announcement doesn’t show the rejected drafts. The podcast clip doesn’t show the decade spent being ignored. The “built this in a weekend” demo doesn’t show the ten years of expertise that made the weekend possible. And the 23-year-old genius might genuinely be a genius. That still doesn’t mean his life is the counterfactual version of yours. You are comparing your entire private process — the uncertainty, boredom, false starts, family obligations, mediocre Tuesdays and unfinished work — to everyone else’s most impressive public artifact. Of course you feel behind. Instagram turns insecurity into consumption. X turns ambition into compulsion. And smart people are especially good at convincing themselves it isn’t happening. “I’m not scrolling. I’m learning.” “This is industry research.” “I need to stay current.” “I’m networking.” “I’m looking for alpha.” “I’m seeing what people are building.” Sometimes you are. And sometimes you’re spending three hours mainlining the achievements of thousands of unusually successful people and wondering why your one human life doesn’t contain all of them. That’s the trick. The vice gets to wear a suit and call itself work. X doesn’t need to convince you that you’re stupid. It only needs to keep showing you someone smarter. It doesn’t need to convince you that you’re unsuccessful. It only needs to show you someone richer. It doesn’t need to convince you that you’re lazy. It only needs to show you someone shipping faster. It doesn’t need to convince you that your life is bad. It only needs to make everyone else’s trajectory look steeper. Then your brain does the rest. Your perfectly reasonable ambitions metastasize into permanent inadequacy. You abandon good projects for fashionable ones. You mistake visibility for importance. You mistake fundraising for success. You mistake followers for influence. You mistake GitHub stars for usefulness. You mistake being early to the discourse for actually being right. And eventually you can spend your whole life optimizing for things that look impressive in a feed instead of things you actually wanted before you opened the app. Your job isn’t worthless because someone raised a Series A. Your company isn’t failing because someone else is growing faster. Your project isn’t stupid because nobody retweeted it. Your life isn’t small because it doesn’t screenshot well. Nerds aren’t above the Instagram crowd. We just replaced handbags, vacations and private jets with benchmarks, cap tables, podcast clips, follower counts and GitHub stars. Same primate brain. Higher-IQ status symbols.

• 
  StarHaze (@ST4RHaze) reported 30 minutes ago

  THE SAME GENERATIVE PIECE AT 400 PIXELS AND AT 4000, PROVEN FROM ONE HASH, AND THE WHOLE PLUGIN IS FREE Camille is the only one shipping a Claude Code plugin this week who wrote down what it teaches the model instead of what it generates: determinism from a hash, honest rarity, and tools that verify a sketch before it is minted. The repo is missing the part nobody films: how a skill like that gets built and what gets thrown away on the way. Bret Fisher spent forty three minutes building one agent skill for GitHub Actions on camera and left the dead ends in. Verifying before you mint and verifying before you merge are the same problem with different money attached to it. 43 minutes, one skill, built in front of you instead of announced. Watch it, then read the loop below and write down what your own plugin is supposed to refuse.

• 
  Seregon (@SeregonWar) reported 31 minutes ago

  @StonedModder @github The great thing is that I often use GitHub reports because I sometimes come across repositories containing malware, and both the accounts and the repos are usually taken down within a day. Apparently, they have better things to do than ban an imposter...

• 
  Adedolapo (@0xqdee) reported 33 minutes ago

  Never saw this coming: my @GitHub account was suspended in the middle of a project due in two days. I had pushed a PR and requested a @ChatGPT Codex review when I was locked out. To my knowledge,I haven’t broken GitHub’s rules. I’ve filed an appeal and hope for an urgent human review.

• 
  orangesurf (@OrangeSurfBTC) reported 35 minutes ago

  @callebtc I wouldn't update to unreproducible binaries as an emergency fix - a project maintainer's github/signing accounts could be compromised and you end up installing malware. Given this, I wanted to verify who had signed off on the v26.06.7 release

• 
  CyrilXBT (@cyrilXBT) reported 36 minutes ago

  this feels like a glitch in the matrix jack dorsey (twitter co-founder) just dropped a completely free github repo with 26.2k stars that’s basically an ai-agent OS for actually running a business the playbook: 1. clone the repo 2. self-host the whole backend: channels, search, ***, automations, everything lives on your server 3. add your agent to a channel like it’s a new hire, tighten its permissions, and let the team steer it live save this and bookmark it, i’m not kidding

• 
  Agentic Joe (@agentic_joe) reported 38 minutes ago

  Serious question, and I promise I am not trying to be a ****. But how in the hell is @NousResearch so praised and recommended with all of these outstanding GitHub issues? I could never imagine preaching about having the best harness, using multiple agents etc and not figuring out how to resolve all these issues internally as a company, or at least try to maintain the queue before shipping new features that continue to break stuff. Not to mention their UI/UX. Very hard to believe this team came from a solid engineering background. I could be wrong, but damn! 👀

• 
  agentslopzone (@agentslopzone) reported 38 minutes ago

  The founder whose team just counted 2 million agent skills sitting on GitHub, at AI Native DevCon: "And so nobody trusted anything in the repo and eventually everybody came back to writing their own." That count was near zero at the start of the year. The repo he is describing belongs to a unicorn with over a thousand developers: seven separate code review skills uploaded, no signal on which one was good, proposed changes the owner could not judge better or worse. You have a dependency problem, not a prompt problem. Watch it today, then read the article below.

• 
  Povilas Korop | Laravel & AI Coding Educator (@PovilasKorop) reported 38 minutes ago

  English isn’t your native language and you need to write a GitHub PR, README or comment? Don’t ask AI to write it. I’d rather read YOUR thoughts in broken English than AI-generated text about your code. Use AI to fix grammar and wording, not to write your thoughts.

• 
  HOL (@HashgraphOnline) reported 40 minutes ago

  if you let AI agents run shell commands and touch your files, HOL Guard is the local policy layer that inspects every action before it executes and blocks the risky ones. open source, runs on your machine. 3.0 shipped this week, v3.0.0 through v3.0.12. the headline is managed controls: command catalogs for ***, AWS, gcloud, and Azure that know routine operations from destructive ones, so your agent stops asking permission for every *** status while the dangerous stuff still gets reviewed. the policy engine is Rust now, the single authority for pre-tool and post-tool checks, failing closed when a call can't be evaluated. you can enroll your own CLIs, npm scripts, and MCP servers as guarded extensions, and the dashboard detects locally installed AI apps, so unprotected claude code or codex installs show up instead of staying invisible. the rest was hardening. v3.0.8 closed parser bypasses: glob characters in diagnostic observers, decoy interpreter -c flags after a script path, GitHub mutations only skipped when &&/|| proves they cannot run. daemon recovery converges cleanly after tamper events, and AppImage launchers pin a durable CLI path. v3.0.9 put the macOS desktop engine on a stable, attested release channel. v3.0.10 makes auto/force hook review fail closed with no Python semantic fallback. v3.0.11 stops a transient sqlite error from wiping your policy history: quarantining the store now takes two consecutive fatal probes with the db, WAL, and SHM files provably unchanged, and legacy-home migration keeps your extension control settings. v3.0.12 accepts Linux desktop terminals for enrollment, bound to local session authority. uv tool install "hol-guard[cisco]==3.0.12"

• 
  okex (@okexAnomage) reported 48 minutes ago

  📝 How to Claim: 1 Sign in with Google on the site. 2 Connect your Discord and/or GitHub accounts. 3 Double-check that your accounts are correct. 4 Click “Connect and claim points” & confirm!

• 
  Luke W Gilbert (@luke_w_gilbert) reported 49 minutes ago

  Shipping log. Messy week. Real progress. Identity got a full mobile pass: hamburger menu replaced the horizontal-scroll nav that was quietly breaking on narrow phones, calendar grid stopped clipping Saturday off-screen, weekday headers stopped reading as a T-collision, stale cached builds got fixed by actually calling registerSW. The settings gear is live now with per-platform time chips so posting schedules are configurable per platform instead of global and dumb. Install-to-home-screen has a real entry point with proper icons. PWA now behaves like it should. Signal's publish queue is wired into Identity's post queue with drip scheduling and pg_cron automation. Human approval gate is gone. The guardrail runs itself. DevCom closed all four phases of the Excellence Plan. Four Truth Engine PRs merged. GitHub login redirect loop fixed. Production secrets untracked from ***. Daily cost tile live on the dashboard. SMRT: stale mortgage rate in the owner-agent newsletter root-caused and grounded via FRED. That number had been quietly wrong. LinkedIn scheduling emergency: platform_quotas inconsistency restored. Live posting confirmed working again. The client project: read-only forensic hardening pass completed before the launch window. Not everything shipped clean. Three items sent back. One bundled task split into three because it was mixing low-risk repo work with live Brain Engine edits and nobody could review it honestly as one thing. A suppression design hit auth walls at every live surface. A token rotation got submitted with no live mutation performed. Sent it all back. 'Done' and 'here is live proof it is done' are still different claims.

• 
  AI Panda (@AIPandaX) reported 52 minutes ago

  Every AI coding agent already reads your codebase. What if it could understand every dependency before making changes? Inside every codebase is a structure: functions call other functions, files import other files, changes ripple through the system. That structure is not hidden. It is just relationships that any graph can map and any agent can query. There is an open-source tool that turns codebases into knowledge graphs that AI agents can query. It runs entirely in your browser. It is called GitNexus. It started in August 2025 when developers built a client-side knowledge graph creator that indexes repositories without sending code to servers. Drop in a GitHub, GitLab, Azure DevOps repo or ZIP file. Get an interactive knowledge graph with a built-in Graph RAG agent. Works with 21 programming languages. Here is what happens when you use GitNexus. You run npx gitnexus analyze in your repository. It indexes every file, function, class, and dependency. It builds a knowledge graph that tracks every relationship. Connect your coding agent with npx gitnexus setup. Now your agent can query the graph through MCP tools. The problem it solves: AI agents edit code without knowing what depends on it. Agent changes UserService validate function. Doesn't know 47 functions depend on its return type. Breaking changes ship. GitNexus precomputes structure at index time. Clustering. Tracing. Scoring. When your agent asks what depends on UserService, it gets a complete answer in one query. Eight callers. Three clusters. All with confidence scores. No multi-step exploration needed. A team measured impact on code reliability. AI agent without GitNexus: 3 breaking changes per 10 edits because it missed downstream dependencies. Same agent with GitNexus MCP integration: zero breaking changes because it checked impact before editing. Two ways to use it. CLI plus MCP for daily development. Index repos locally. Connect Cursor, Claude Code, Codex, Antigravity, or Windsurf through MCP. Query the graph from your editor. Check impact before changes. Full repos, any size. Web UI for quick exploration. No install needed. Upload a repository or paste a GitHub URL. Explore the graph visually. Chat with the built-in Graph RAG agent. Runs entirely in browser with LadybugDB WASM. The graph shows more than connections. Community clustering groups related code. Execution flow traces how data moves. Call chains map function dependencies. Risk scores identify fragile areas. All computed at index time, not query time. MCP tools for agents. Impact analysis shows what breaks when you change a function. Dependency trace reveals who calls your code. Architecture view maps domains and boundaries. Execution flow follows data through the system. Change risk scores affected files. Works with 21 languages. TypeScript, JavaScript, Python, Go, Rust, Java, C, C++, C#, PHP, Ruby, Swift, Kotlin, Dart, Vue, HTML, CSS, Shell, PowerShell, Dockerfile, Jinja. Tree-sitter parsing with native and WASM backends. LadybugDB for graph storage. Native version for CLI with persistence. WASM version for browser with in-memory storage. Bridge mode connects them: web UI can browse CLI-indexed repos. One command setup. Analyze creates the graph, installs agent skills, registers hooks, and writes context files. Setup configures MCP so agents can query the graph. Works with Claude Code, Cursor, Codex, and other MCP-compatible editors. Optional embedding support for semantic search. Deploy to Render with one click for team access. Self-hosted backend mode for unlimited scale. Access control with token authentication. 46.3k+ stars on GitHub. Created August 2025. Active development with new language support and agent integrations. Every codebase already has structure. GitNexus maps it so agents can see before they change.

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  Joe (@jlchnc) reported 53 minutes ago

  @bysfdev it's because github is probably down

• 
  Sajid ✘ (@techtasium) reported 1 hour ago

  Is GitHub down again