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

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Yaseen Shaik (@YaseenTech4) reported 8 minutes ago

  Just completed an assignment on building a dependency graph for AI agent tools using Google Super + GitHub integrations 🚀 Started with: “This should be easy” Then came: TypeScript errors zip/upload issues CRLF debugging 😭 finally got the submission accepted successfully ✅

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  Kea (@alpinoWolf) reported 9 minutes ago

  " we literally cannot programmatically trade from this account until Polymarket's engineering team patches the V2 library and resolves GitHub Issue #65. " How does you evpoly bot do ? Please help me ? Is python coding problem here ? 3/3

• 
  BigShark🦈 (@King_Shark02) reported 9 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. 🚀

• 
  𝒹ℯ𝓁𝓁𝓎_𝓉𝒽ℯ_𝒹ℯ𝓈𝒾𝑔𝓃ℯ𝓇 (@dellyricch2) reported 10 minutes ago

  Elon says the latest 𝕏 algorithm has been published to GitHub Can someone please break it down for us

• 
  Drini (@drini_kasmot) reported 11 minutes ago

  @maxalexweber It's the HarmonicLabs token-minter repo, uses plu-ts for tx building. Getting a PPViewHashesDontMatch error on Preprod when submitting. Already raised it on their GitHub!

• 
  atomicbot.ai (@atomicbot_ai) reported 12 minutes ago

  Hermes Agent vs OpenClaw using Qwen 35B Local Model 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

• 
  Lazi (@algoritmii) reported 12 minutes ago

  @github bro ffs fix your ******* issues stop pushing features

• 
  John Evans Okyere | TheAISolutionist (@Ananselab) reported 12 minutes ago

  Deployment failed with: dial tcp :22: i/o timeout The app was fine. SSH was fine. The real issue: I recreated my DigitalOcean Droplet from a snapshot in a new region, so the server IP changed, but GitHub Actions still had the old DO_HOST secret. Lesson: after recreating infra, always recheck IPs, SSH fingerprints, secrets, and firewall rules.

• 
  nadya (@sosidudku) reported 16 minutes ago

  Ran Hermes Agent and OpenClaw on the same task: scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser. Local model: Qwen 3.6 35B OpenClaw: 203k tokens, 12m 01s — wrote a bash script Hermes: 257k tokens, 33m 01s — wrote a SKILL.md 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.

• 
  NEET INTEL (@neetintel) reported 18 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?

• 
  John Iosifov ✨💥 Ender Turing | AiCMO (@johniosifov) reported 19 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.

• 
  Benjamins (@The__Benjamins) reported 20 minutes ago

  @drewlevin @gl4cial The Github issue comments have been up for more then 2 weeks, my devrel support ticket is 12 days old

• 
  Darnisha Patel (@Darnisha_patel) reported 24 minutes ago

  • Claude for coding. • GitHub for version control. • Vercel for deploying. • Clerk for auth. • Supabase for backend. • Stripe for payments. • Cloudflare for DNS. • Resend for emails. • Upstash for Redis. • Pinecone for vector DB. • Namecheap for domain. • Sentry for error tracking. • PostHog for analytics. You can literally ship a startup from your bedroom now. It’s not that deep bro.

• 
  Yashas (@YashasGunderia) reported 27 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)👇

• 
  ST-Automation (@ST_Automation) reported 27 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.

• 
  Chris Dunne (@_chrisdunne) reported 28 minutes ago

  @AishwaryaDevv GitHub audit logs are terrible, you have to work around a lot of crap to get visibility, if no one’s reached out by now they’re not even looking.

• 
  AtomicNodes (@AtomicNodes) reported 29 minutes ago

  Hermes Agent vs OpenClaw on Qwen 3.6 35B Local Model 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

• 
  Omri Ariav (@omriariav) reported 29 minutes ago

  @avivsinai workstream briefs seed deterministically from a file or GitHub issue: `up --seed-from file:./brief.md` `up --seed-from issue:31` `up --seed-from gh:owner/repo#31` `--dry-run` previews, the live form writes the brief and brings the team up in one call.

• 
  ATXA (@AtxaTrades) reported 32 minutes ago

  This is the ONE problem the X Algorithm has: It contradicts itself. Here is why: X has shared their algorithm update in Github today. Everyone is going crazy about it. So i decided to go take a look at it. I asked Grok to analyze it and explain it to me. Once it did, i took my last post and shared it with grok. I asked him to analyze the post (based on the Algorithm shared in Github) and rank it based on the metrics and steps the algorithm takes. This is the crazy part. It gave it a score of 72-82/100!! Not so bad right? I am a small account, i am not expecting a 100 score. But wait, there is more. It said it would likely rank in the top 20-40% of candidates in the mixed batch for the right users, and strong enough to appear HIGH in the "For You" tab. Reality Result: 22 views. So my question is: If Grok is a big part of the algorithm dictating what´s good and what is not, and technically Grok just told me my post was suppose to do good in the "For You" tab... Why only 22 views?

• 
  Rinnegatamante (@Rinnegatamante) reported 36 minutes ago

  @dgosiq Did you grab the update from GitHub? I think it might be a broken version (updating it right now there as well). If you have a psp_apps.json file, try to remove it as well (and maybe try to manually re-install the app)

• 
  Hiren Thakkar (@thehirenthakkar) reported 38 minutes ago

  Microsoft's GitHub got a 400% increase in organic traffic. No new content. No link building. No redesign. They fixed cannibalization. Blog posts competing with marketing pages. Multiple pages fighting for the same keyword. None winning. Removed the mess. 400% increase. Check it today for free: → Screaming Frog: crawl your site, export titles + H1s + canonicals. Two pages targeting same keyword? One is killing the other. Missing canonicals? Even worse. → Detailed SEO Extension: free Chrome plugin. One click shows canonical, title, H1, H2s. → Google Search Console: filter by keyword → Pages tab. Multiple URLs? That's cannibalization. Fix: pick strongest page. Redirect or no-index the rest. Add missing canonicals. Sometimes you don't need more content. You need the basics done right. Try it this weekend and tell me what you find. (GitHub case study via Brain Labs.)

• 
  Aditya Sharma (@aditya_sharma) reported 39 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.

• 
  AtomicNodes (@AtomicNodes) reported 41 minutes ago

  Hermes Agent vs OpenClaw on Qwen 3.6 35B Local Model 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

• 
  Crystalwizard (@crystalwizard) reported 42 minutes ago

  @omnivaughn @ClaudeDevs you are that's not an issue with github itself? github has copilot and is microsoft - and might be restricting other AI

• 
  AtomicNodes (@AtomicNodes) reported 44 minutes ago

  Hermes Agent vs OpenClaw on Qwen 3.6 Local Model 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 52 minutes ago

  We decided to benchmark Hermes Agent vs OpenClaw: scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser. Local model: Qwen 3.6 35B OpenClaw: 203k tokens, 12m 01s — wrote a bash script Hermes: 257k tokens, 33m 01s — wrote a SKILL.md 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.

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  David Cramer (@zeeg) reported 53 minutes ago

  @eternalmagi dont have context, is there a github issue by chance

• 
  Franz Hemmer (@franzhemmer) reported 53 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.

• 
  Mitch Fultz (@fitchmultz) reported 57 minutes ago

  @badlogicgames @matteocollina Ah! figured it out. It is broken with Node 26. If I launch pi via `mise exec node@24 -- pi` and then /login it works. I can create a GitHub issue.

• 
  Gopinho (@gopiinho) reported 59 minutes ago

  @apoorveth @walletchan_ will do for sure, also open issues on github if there is some backlog