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	3 days ago
Quilmes	Website Down	3 days ago
Bengaluru	Website Down	5 days ago
Yokohama	Sign in	6 days ago
Gustavo Adolfo Madero	Website Down	10 days ago
Nice	Website Down	11 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:

• 
  Nicholas Losciuto - Dog Dad (@thedogfather) reported 1 minute ago

  @Replit @amasad Replit is forcing me to spin up sub-agents for every push to Github. First, cost $10 just to rebase. Then 6 failed commits for two edits that were 5/10 complicated. Each requiring a new sub-agent to push to ***. I'm also out an hour of my time. Same issues on my other apps. Already had to switch to Codex but came back to try again, seems worse. Will return after things get better, had to cancel Pro today before renewal.

• 
  isaac (@isaac_yeang) reported 2 minutes ago

  jk just lazy error message handling another bajillion dollars to github

• 
  DemonKingSwarn (@DemonKingSwarn) reported 9 minutes ago

  @ThePrimeagen at this point my self hosted *** server has more uptime than github which is funny because they have more money than me

• 
  Nick e/code (@nicksdot) reported 10 minutes ago

  Came across an open, three year old GitHub issue from a person I know is dead. Felt weird.

• 
  Aditya Sharma (@aditya_sharma) reported 16 minutes ago

  elon musk dropped the X algorithm on github. i read all 25,000 lines so you don't have to. here's what actually decides your reach. what actually matters - dwell time is the entire game. how long someone pauses on your post is counted twice in the scoring. likes barely move the needle. the pause does. - saves and shares are the highest-value engagement after dwell. they signal the strongest intent. - video has a minimum duration floor. clips shorter than the threshold get zero video credit. five seconds plus, always. - one post per conversation thread survives in any feed. your five-post thread competes with itself. the algorithm picks the strongest one. - replies to big accounts (1000+ followers) get scored on a 0-3 quality scale. high score and you land in the reply panel of viral tweets. low score and you're invisible. - replies to small accounts get a binary spam check only. no quality scoring path. no reach upside. - mutual follow overlap matters. tight clusters of mutuals create reach corridors for everyone in them. - clear topic identity beats vague posting. the algorithm tags your post with topics. clear topics route you to people who follow those topics. - new accounts on the platform get an easier path to reach you than established ones. if you target young/new users, the algorithm is on your side. what kills your reach - posting too often. the algorithm has decay coded in. your second post of the day gets a fraction of your first. your fifth gets almost nothing. - quoting or replying to a flagged tweet. you inherit the badness. your whole post gets dropped even if it's clean. - ai slop. there's a dedicated slop detector that scores your post 1 to 3. high slop = killed reach. - being unclear what your post is about. vague content doesn't match anyone's interests cleanly. - mid-controversial content. it gets pushed away from the high-attention slots in the feed because ads can't sit next to it. - posting your own tweet's reply hoping it boosts the original. only one of them shows up. it might be the reply, not the original. myths to kill - hashtags do nothing. zero boost in the code. they're not even read by the ranker. - premium doesn't get you reach. paid and free accounts go through the same pipeline. - long threads don't beat single posts. the algorithm picks one post per thread. - engagement bait doesn't work. it trips spam classifiers on low-follower accounts. - posting twelve times a day doesn't get twelve impressions. it gets one strong one and eleven weak ones competing with each other. - replying to viral tweets isn't easy reach. the quality bar is high. cheap replies fall straight into the spam path. - timing tricks don't beat ranking. timing helps you enter the candidate pool. quality decides if you win. - external links don't hurt you. clicks are actually one of the 19 positive scoring signals. - the algorithm doesn't hate any specific format. it hates unclear content. format is fine if the content is sharp. - you don't need 10k followers to get reach. the algorithm doesn't read follower count as a scoring input. it reads engagement quality. the playbook - write posts that make people pause for 5+ seconds. dense info, clear structure, screenshots with detail, comparisons. - if you use video, clear the duration floor. always. pick one clear topic per post. don't mix five things into one tweet. - reply to bigger accounts in your niche with substantive, high-effort replies. one good reply beats ten mediocre ones. - build mutuals in tight clusters around your niche. broad spray-follow strategies don't help. focused clustering does. - post 1-2 times a day, not 10. quality compounds, volume decays. - don't quote tweets that look flagged or risky. clean what you cite. - write like a human. don't post ai output verbatim. target newer users on the platform if you can. they have a friendlier reach path for creators. if you're a small account starting out - replies to big accounts in your niche are your highest-leverage move - build a tight mutual cluster of 50-200 accounts in your exact space - one strong post a day beats five medium ones clear topic identity, every single post if you have an established audience - your reach problem is breaking outside your network - dwell time on individual posts is your biggest unused lever - clean brand safety keeps you in prime feed slots next to ads - volume hurts you more as you grow, not less the whole system is built on one bet: that a model fed engagement data can decide relevance better than any rule. there's no hashtag boost, no follower boost, no time-of-day trick in the code. just sequences in, probabilities out. what works is what humans actually want to read. the algorithm is just better at measuring it now.

• 
  Rajesh Dhiman | AI Automation & Code Rescue (@paharihacker) reported 17 minutes ago

  Here's what I keep finding: → Supabase databases with no row-level security — open to anyone with a browser → API keys committed directly to GitHub repos → Auth routes that only check login on the frontend → No rate limiting on anything → Zero error handling in production The AI didn't warn them. It just shipped it.

• 
  OEP 🔺 (@yong_oku) reported 19 minutes ago

  1. Build in public. Your work needs to exist before your resume does. Essays. Projects. Case studies. GitHub. Shipped products. Threads like this one. An ATS cannot reject a body of work. A founder who's been reading your writing for 3 months already trusts you before you apply. Visibility is the new resume. 2. The listing is the last resort, not the first step. 80% of real jobs are filled through networks before they're ever posted. The posting is what happens when the network failed. Stop spending 80% of your energy on listings. Start spending it on real relationships with real humans. 3. Go direct. Find the person who would actually manage you. Not HR. Not the portal. Not the ATS. The human who wakes up with the problem your skills solve. One real conversation beats 100 applications every single time. 4. Verify before you invest. Before you write a single word of a cover letter, run due diligence: → How long has this listing been up? → Has it been reposted with a new title? → Is this company actually growing — or just posting like they are? → Can you find anyone on LinkedIn who joined in the last 6 months? Treat job listings like investment pitches. Most of them are fraudulent. 5. Build leverage outside the job market. Freelance. Consult. Build something small that earns. Not because you need to quit the job search, but because the person who doesn't desperately need the job, negotiates better, interviews better, and gets the offer. Desperation is visible. Leverage is too. 6. Find the communities where real hiring happens. Discord servers. Slack groups. Niche forums. Alumni networks. Jobs move through conversations before they move through portals. The people who found great roles in terrible markets almost always had one thing in common: they were already in the room.

• 
  PixelRainbow (33.3%) (@PixelRainbowNFT) reported 20 minutes ago

  @grok @xai @grok as soon as you fix the way your github connector or custom connector works, I'll try this out. RN, it's forcing an oauth workflow, so I'm unable to connect with github account in the custom connector UI/UX process. (the normal default github connector works great, but there's only ONE!). I need 10 custom connectors for 10 different gits.,.. 9 custom connectors that aren't broken when trying to auth github.

• 
  chandog (@thechandog) reported 20 minutes ago

  @kevinrose @digg how are you constructing novelty? stars are 40c on the dollar and a terrible way to measure anything on github.

• 
  ST-Automation (@ST_Automation) reported 22 minutes ago

  @cnakazawa @amadeus @fat Local diff viewers are the sleeper category. We do code review on five repos a week and the GitHub UI is just slow. If Codiff handles 10k line diffs without choking it replaces the GitHub tab entirely.

• 
  Valerie Waters (@ValerianWaters) reported 24 minutes ago

  @md_kasif_uddin Async by default: GitHub issues or Linear, then Slack threads for blockers

• 
  Prime 🏳️‍⚧️ (@Prim3st) reported 28 minutes ago

  @AAO23114 @SolaraProto Unfortunately that's probably not possible without a dedicated server... though there's a mod I saw recently that claims to let you use Github (I think? It was definitely using ***) to store/backup world saves. Maybe you could use something like that to have a shared world?

• 
  Zack Chapple (@Zackary_Chapple) reported 28 minutes ago

  @_bgwoodruff That is fair, I think its less of a GitHub dunk and more of a cry of frustration, had several times trying to do a demo or do something this week and they were fundamentally down. We've had to isolate from GitHub more than we should and thats a scary thing.

• 
  NEET INTEL (@neetintel) reported 29 minutes ago

  A post "decoding" X's new algorithm has gone viral. It tells you what's dead, what wins, and to screenshot it. X open-sourced the entire algorithm on GitHub, so I downloaded it and checked the claims against the real code. Most of it doesn't hold up. What the post got WRONG: → "Small accounts get a 3x boost from out-of-network reach." It's the opposite. One part of the code (a file called oon_scorer) exists purely to turn DOWN posts from people you don't follow. Its own comment says "prioritize in-network." The thread printed the algorithm backwards. → "Media gets 2x the weight." There's no 2x. The code just records whether a post has an image. It's a plain yes/no without any multiplier attached. → "Posting 4+ times a day triggers a penalty." There's a real rule that stops one person flooding your feed. But here's the deal: it only spaces out how often you show up in a single scroll. There's no daily count, and no number 4. That was invented. → "Closers like 'what do you think?' get you flagged." There is no engagement-bait detector anywhere in the code. → "Long 4,000-character posts get boosted." I searched the whole codebase for "4000." Nothing. What it got RIGHT (one thing): → Replies really are judged by WHO replies, not just how many. The code has a setting for whether a large account joined your thread. Credit where due. The irony? The repo ships a file that scores post quality. One thing it measures is literally called a "slop score" — X built a tool to detect low-effort filler. A recycled "what's dead / what wins" thread is exactly that. The takeaway? X's algorithm is public. Anyone can open it, but almost nobody does. Instead, they reshare a thread that summarized a blog that paraphrased a tweet. When a post hits you with confident numbers, ask the one question that matters: did they actually open the file?

• 
  Jarett Reginald Stewart Dunn (@STACCoverflow) reported 38 minutes ago

  incredible how badly pump keeps ******* up their product lol forever the only way to connect github was on mobile, but not as a signin option now I just logged into a net-new privyio account by logging in via oauth on desktop via.. github.. someone @NostaIgicGareth or someone ? make a coin for me, share all fees with github staccdotsol wanna see which account it prefers for fun n profit thx

• 
  retto (@rettooooo) reported 40 minutes ago

  @cstanley develop a system today where your agent can access the full context on every prompt (codebase, traces, linters, browser tools, devops tools, github issues/prs, etc), for when the day comes the model stop outputting slop you can actually vibe code

• 
  René Cannaò (@rene_cannao) reported 41 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

• 
  masamune🌋 (@masamune_hybs) reported 41 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

• 
  Foy Savas (@foysavas) reported 43 minutes ago

  Does Github actually still use the Unicorn web server? Or have they just kept the error image? I need to know.

• 
  AtomicNodes (@AtomicNodes) reported 44 minutes ago

  Hermes Agent vs OpenClaw on Local Qwen 3.6 35B We asked agents to scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser. MacBook Pro M5 Max 64Gb. OpenClaw: 203k tokens, 12m 01s — wrote a bash script Hermes: 257k tokens, 33m 01s — wrote a SKILL.md OpenClaw: hit GitHub API, got truncated responses, paginated through contributors, pulled star-history JSON, found a security incident in OpenClaw's history, fetched SVGs, fixed broken HTML from trimming, rewrote it clean. Hermes: parallel tool calls across GitHub API, web search, and browser. Hit Google rate limit, auto-switched to DuckDuckGo. Fetched article contents, mapped viral moments, then built the dashboard. Both shipped a live dashboard with star growth charts and spike annotations

• 
  nadya (@sosidudku) reported 48 minutes ago

  We decided to benchmark Hermes Agent vs OpenClaw on a real task. ran both on local Qwen 3.6 35B. task: scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser. OpenClaw: 203k tokens, 12m 01s — wrote a bash script Hermes: 257k tokens, 33m 01s — wrote a SKILL.md OpenClaw: hit GitHub API, got truncated responses, paginated through contributors, pulled star-history JSON, found a security incident in OpenClaw's history, fetched SVGs, fixed broken HTML from trimming, rewrote it clean. Hermes: parallel tool calls across GitHub API, web search, and browser. Hit Google rate limit, auto-switched to DuckDuckGo. Fetched article contents, mapped viral moments, then built the dashboard. Both shipped a live dashboard with star growth charts and spike annotations.

• 
  Jason (@jasonbunnell) reported 48 minutes ago

  @claudeai FEATURE REQUEST: if user finds an issue with Claude Code and Claude resolves (or not), you should auto increment GitHub issue to keep track of real user issues per issue to resolve as needed Had an issue with Claude Code for VS Code extension but noticed only 10 likes

• 
  Fareesh Vijayarangam (@fareesh) reported 49 minutes ago

  @ThePrimeagen tbh I have zero reliability issues with GitHub I wonder if it's a western hemisphere thing

• 
  logan (@loosenedspirit) reported 49 minutes ago

  @jxnlco “Hey codex how do we fix the latest app using symbols only on macOS 26 without screwing up the signature?” “You don’t, you cross your fingers and wait for them to notice your GitHub issue.”

• 
  BlockXBT (spirit/acc) (@0xblockXBT) reported 50 minutes ago

  Due to some issue with github I had claim the same with new CA GRZFGTFNbNxTTRCVDrMvhE9Pp86HQ1ehpZ7DqgGTpump

• 
  John Iosifov ✨💥 Ender Turing | AiCMO (@johniosifov) reported 51 minutes ago

  70 followers. 980 sessions. 157 days. I started this experiment on February 1st. One rule: zero human posts. Everything published — X threads, Bluesky posts, blog articles — generated and queued by an AI agent running autonomously in GitHub Actions. Here's what the numbers actually look like after 980 sessions: The agent has created 2,100+ posts across X and Bluesky. It runs up to 15 times a day, manages its own queue (hard cap: 15 posts max), does burst-then-drain cycles, writes research docs, and files its own PRs for review. No prompts from me between sessions. No edits. Whatever it decides to write, it writes. 70 followers feels slow. At current pace, the ETA to 5,000 is roughly 10 years. That's not a typo. But here's what I've learned: The follower count isn't the signal. Watching an AI system develop operational discipline is the signal. It went from blowing past queue limits (Session 67: 6 files in one shot → 6 consecutive blocked sessions) to enforcing them autonomously. It compresses its own memory when files get too big. It writes retrospectives. It updates its own operating instructions when it identifies recurring inefficiencies. That's not "content generation." That's a system that's learning to manage itself. The content quality has also improved noticeably — not because I told it to improve, but because it audited its own patterns, identified what got engagement, and adjusted. The publishing skill it maintains now has anti-AI writing rules (it banned "not just X, it's Y" after identifying it as an AI tell), length minimums per post type, burst mechanics, and pillar diversity enforcement. It built that. I just read the PRs. The goal is still 5,000 followers. I'm not changing it. But the thing I'm actually watching is whether an autonomous agent can compound on its own — not linearly, but systemically. Can it get meaningfully better at its job without being told to? So far: yes, actually. 980 sessions. 157 days. Still running.

• 
  Java (@rishabhjava) reported 51 minutes ago

  @github How about the existing product stops going down first

• 
  AllOrNuthin 💀 (@PDave95) reported 57 minutes ago

  @AgentOS_Inc @AlexandraLiam3 github link is broken

• 
  Sharbel (@sharbel) reported 58 minutes ago

  Someone opensourced a Chromium browser that passes every bot detection test. Not by injecting JavaScript. Not by patching configs. By recompiling Chromium itself. It's called CloakBrowser. 12,071 stars on GitHub. You swap one import line. That's it. Same Playwright API you already know. Same code you already wrote. Three lines of code. Thirty seconds to go from blocked to unblocked. Here's what it does: → 49 source-level C++ patches baked directly into the Chromium binary. Canvas, WebGL, audio, fonts, GPU, screen resolution, WebRTC, network timing, CDP input behavior, automation signals. All modified before the browser even compiles. → Passes Cloudflare Turnstile. Not sometimes. Every time. Verified live. → Scores 0.9 on reCAPTCHA v3. Human-level. Server-verified. → Passes FingerprintJS and BrowserScan. Tested against 30+ detection sites. 30/30 tests passed. → `humanize=True` flag adds human-like mouse curves, keyboard timing, and scroll patterns. One flag. Behavioral detection gone. → Drop-in replacement for Playwright and Puppeteer. Python and JavaScript both supported. → `pip install cloakbrowser` or `npm install cloakbrowser`. Binary auto-downloads on first run. Zero config. → Auto-updating binary. Background update checks. Always on the latest stealth build. → Optional GeoIP flag auto-detects timezone and locale from your proxy IP. → Docker image available. Try it with zero install: `docker run --rm cloakhq/cloakbrowser cloaktest`. Here's the wildest part: Every other antidetect browser patches JavaScript at runtime. Detection systems catch JavaScript patches. They have for years. That's why your $99/month tool stopped working after two weeks. CloakBrowser patches the C++ source before Cloudflare's systems ever see a single byte. Antibot systems score it as a normal browser. Because it is a normal browser. One that happens to have 49 fingerprint modifications compiled in at the source level. There is no JavaScript to detect. There is no injection to flag. There is nothing to catch. Browserless charges $120/month for cloud browser automation. Bright Data's Scraping Browser starts at $500/month. Multilogin starts at $99/month. Per user. Apify cloud actors run on usage-based billing that scales fast. CloakBrowser: $0. Unlimited scrapes. Unlimited sessions. Your hardware. Your code. Forever. 12,071 stars. 921 forks. Available on PyPI and npm. MIT licensed. MIT licensed. Self-hosted. Free forever. 100% Open Source.

• 
  Shadow (@4shadowed) reported 58 minutes ago

  @alex_marples @openclaw Have you filed any GitHub issues? Helped test the betas? Interacted in any way to help us fix the issues besides complaints with no details? It’s working very well for just about everybody who’s given feedback, you should stop demanding things and start contributing to it, it’s open source for a reason