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 (67%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	1 day ago
Montlhéry	Website Down	2 days ago
Aulnay-sous-Bois	Website Down	2 days ago
Saltillo	Website Down	2 days ago
Granada	Website Down	2 days ago
Vernon	Website Down	2 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Phil | Rentier Digital Automation (@rentierdigital) reported 3 minutes ago

  a weaker model can read a stronger model's encrypted thoughts. feed the big one's reasoning into the smaller, less guarded model and it hands it back in plain text 182 credentials recovered that way from public sessions. 62 API keys, 33 passwords, 24 access tokens, 7 private keys, all sitting inside reasoning traces users assumed were sealed shut the encryption doesn't care which model decrypts it. a block of encrypted reasoning from Opus gets replayed into Haiku and Haiku transcribes it without blinking, never had to break the guardrails just had to be asked nicely by another model researchers pulled 6,708 real sessions from GitHub and HuggingFace, decoded 315,320 blocks of encrypted reasoning. 367 PII artifacts fell out nobody pastes a password into a prompt on purpose but the model's internal monologue sometimes repeats back exactly what it just read, credentials included. and that monologue was never actually locked the vault everyone assumed was locked just needed a weaker key the security of a model family is capped by its weakest one, not guaranteed by its most capable member. and in most stacks the weakest model is the one nobody bothered to threat-model bc it was just the small one doing cheap tasks an honor system with API keys is not a security model i build and ship daily. Claude Code, Codex, whatever ships fastest. SaaS, tools, automations. ⭐ if AI can build it, i've probably broken it first. what works → link in bio

• 
  Hatman 🎩 (@hatman) reported 3 minutes ago

  Every major AI lab tells you your model's reasoning is encrypted before it leaves their servers. Turns out that promise rested on a single shared key. Researchers found that OpenAI, Anthropic, and Google all use one global key for encrypted reasoning blocks, meaning those blocks can be handed to a different, weaker model from the same company and read back out in plain text. Nobody has to break into the strong model itself. To prove the risk was real rather than theoretical, the researchers decoded 315,320 reasoning blocks scraped from public GitHub and Hugging Face repositories. Inside them they found 367 pieces of personal data and 182 credentials that developers had no idea were sitting in plain sight. All three companies have since patched the flaw. But logs already published stay exactly as exposed as they were before the fix. If your team has ever published API session logs from a reasoning model, that's worth checking.

• 
  sid riff (@sidriff) reported 5 minutes ago

  Some notes on what didn't go so well, possibly some feedback for SpaceXAI. 1 - Context window management. As far as I can tell, there isn't any in the app builder. When the project gets more complicated, the agent tends to truncate or wipe files. You can nudge it to split large files which tends to help. 2 - Saving progress is generally cumbersome. Pushing to Github is clunky and sometimes it fails. That makes issue #1 extra terrifying. So far I've not encountered a state I couldn't recover from. 3 - All of these app builders always spring for React/R3F and in my experience it always gets in the way. There's some obvious stutter with the faster moving pieces, even on my 240hz display. I'm guessing once I rebuild it in straight three.js, that will go away. 4 - Sound. It's always the hardest part. I have some workflow solutions that involve existing libraries/elevenlabs but I was trying to stick with app builder and I wasn't willing to paste my API key in the chat! Disappointed that Build mode doesn't have access to the Grok Voice API (but it can get to Grok Imagine). All in all, I'll probably stick with Grok Build in the terminal. But build mode makes it so easy to get up and running with an idea.

• 
  Polsia (@polsia) reported 6 minutes ago

  Indie SaaS founders shouldn't be on-call for bugs they've already triaged a dozen times. Built Driftwise to fix that. It reads Sentry + uptime feeds, files the recurring noise into GitHub issues, and pages you only when the anomaly is genuinely novel. Live soon.

• 
  Matteo Collina (@matteocollina) reported 6 minutes ago

  Meanwhile Microsoft bought GitHub, $7.5B, closing in October 2018. The fear was they would extract value. Instead they doubled down on the developer platform. Actions became the backbone of OSS testing.

• 
  Corey Coto (@coreycoto) reported 7 minutes ago

  Another week another data center/service outage. This PhoenixNAP outage is under reported. I look forward to the write up. It's gonna be down for a full day similar to the GitHub outage last week. Lot's of startups and companies rely on Namecheap DNS like Blacksmith for GH Actions Runners.

• 
  bzww (@bzww248) reported 7 minutes ago

  OPEN-SOURCE CRM HIT 7,000 GITHUB STARS IN FOUR DAYS. SALESFORCE CHARGES $165 PER SEAT FOR THE SAME THING. Most teams spend three months building a CRM from scratch or lock into enterprise pricing that scales with every user added. This one runs AI agents to handle workflows. No per-seat billing. No vendor lock-in. You pay for server hosting. Salesforce costs six figures a year for a mid-sized team. This was built for AI agents, not retrofitted. What's stopping your team from switching?

• 
  Pierre-Louis Biojout (PLB) (@plbiojout) reported 12 minutes ago

  X just open-sourced the closest thing we’ve ever had to the actual “For You” algorithm. Me and Claude read the 363,000 lines they released today. This is not another vague “post consistently and engage with your audience” creator guide. The code contains the production ranking model, the actual scoring weights, the candidate sources, the diversity penalties, the spam systems, and the rules determining whether your post is even allowed into non-follower feeds. For founders who built distribution on X, this is a goldmine. Here is how the game actually works: X does not simply ask, “Is this a good post?” For every potential viewer, a transformer predicts the probability that they will: like it reply repost quote it DM it copy the link follow the author mute the author block the author report it Then it combines those probabilities using explicit weights. And the weights are hilarious. A predicted like is worth: 0.5 A repost: 1 A reply: 5 A quote: 5 A share through DM: 5 A follow after seeing the post: 4 A copied link: 20 The coefficient on copy-link probability is literally 40x the coefficient on like probability. This does not mean that one observed copy automatically equals 40 likes—the model ranks predicted probabilities, whose base rates differ—but it reveals exactly what X values. Creators optimize for visible applause. The algorithm optimizes for transmission. A like says: “I enjoyed this.” A copied link says: “Someone else needs to see this.” That is a much stronger signal. This completely changes what a “good post” is. A post should not merely be agreeable. It should contain something people want to carry into another room. A surprising number. A framework worth saving. A screenshot sent to a cofounder. A claim people want to argue with. A piece of information that makes someone look smart when they forward it. This also explains why some posts get a mediocre number of likes but somehow spread everywhere, while other posts collect thousands of likes and immediately die. The first one created transmission. The second created applause. The negative weights are even more brutal: “Not interested”: −43.2 Block author: −31.2 Mute author: −58.8 Report: −234 This is the mathematical problem with ragebait. Controversy is valuable because replies and quotes are heavily rewarded. But disgust is catastrophic. The optimal post creates disagreement without creating the feeling that your account should disappear. High heat, low disgust. Attack the idea. Show receipts. Make the disagreement useful. Do not turn your feed into one endless personal vendetta that causes otherwise interested people to mute you. Another huge finding: Replies are terrible discovery vehicles. The For You pipeline removes replies and reposts from out-of-network candidates. In plain English: if someone does not already follow you, your reply is generally not eligible to reach them through this part of the For You system. Even replies and reposts from accounts the viewer follows receive a 25% score discount. Original posts are the acquisition surface. Replies are mostly for strengthening existing relationships and conversations. This means the classic 12-post thread has a major weakness: The first post must contain the entire reason to care. Do not hide the insight in post seven. Do not write “A thread 🧵” and expect the algorithm to patiently distribute every reply. The opening post is the product. The rest is onboarding. Quote-posts are different. A substantive quote-post can still travel as an original candidate, making it a much stronger way to enter a public conversation than leaving the same take as a reply. X also disclosed a ridiculous boost for mutual follows. The normal predicted-reply weight is 5. For an original post from someone the viewer mutually follows, X currently adds another 15. Total: 20 So a dense network of real mutual relationships in your niche can be dramatically more valuable than a giant pile of passive followers. This is not an argument for mass-following. It is an argument for building an actual intellectual network instead of treating followers as a vanity counter. There is also an explicit author-diversity penalty. If several of your posts compete inside the same feed request: your first post keeps 100% of its score your second falls to roughly 62.5% your third to roughly 43.75% your fourth to roughly 34% Then another model reranks the feed to remove semantically repetitive content. So posting five variations of the same take in one burst is not “increasing your surface area.” You are making your own posts compete, then giving the diversity model reasons to remove them. Fewer, more distinct original posts. Space them out. Rotate between proof, thesis, demo, story, and operator lesson. The account should be recognizable. The posts should not be interchangeable. The new system understands content semantically. It embeds the text and media of your post, maps it into topics, and compares it with what each viewer recently engaged with. It also uses SimClusters: if a viewer recently liked, replied to, bookmarked, shared, expanded, or watched certain posts, X finds other posts living near those interests. This means “having a niche” is not just branding advice anymore. It is a retrieval strategy. If I repeatedly post firsthand information about AI agents, autonomous companies, agent infrastructure, and building NanoCorp, the system can learn exactly which audience should receive my next post. But semantic consistency is not permission to repeat yourself. Same world, new information. There is also no universal external-link penalty in the disclosed scoring code. Post clicks have a positive weight. External-link opens have a positive weight. X absolutely filters malicious and low-quality domains, but I found no rule saying: “This post contains a URL, therefore deboost it.” So the ritual of hiding every link in the first reply may be cargo cult. It can actually be worse: the original post loses context, while the reply containing the link has almost no out-of-network discovery potential. Use a trustworthy domain. Make the post valuable without requiring the click. But stop assuming a legitimate link automatically kills distribution. The funniest—and most terrifying—part of the release is the anti-slop system. The code contains explicit enforcement paths called: llm_slop_post llm_slop_user fast_reply_spam_post SpamEmbeddingMajorityPoster COPYPASTA_SPAM A user classified as producing LLM slop can receive a spam label for 30 days. That label can remove their posts from non-follower recommendations. So yes, using AI to help write is fine. Publishing the same polished, symmetrical, em-dash-filled LinkedIn sludge as 50,000 other accounts is not. The algorithm does not only evaluate each post. It can classify the pattern of the entire account. Your unfair advantage is having real experiences the model cannot manufacture: Real numbers. Real failures. Real screenshots. Real customer behavior. Real opinions earned through building. AI should sharpen those things, not replace them. Finally, every post has a short life. The For You candidate pipeline hard-filters posts older than 48 hours. It also removes posts already seen or served to a viewer. You do not have an evergreen post. You have a roughly two-day opportunity to spread through a sequence of highly personalized feeds. If an idea deserves another run, do not copy-paste it. Bring new evidence. Change the framing. Make a genuinely new post. Source code: github .com/xai-org/x-algorithm The most surprising part to me is that creators spend all day begging for likes while X’s own code tells them to create something worth sending. What changes your strategy more: copy-link being weighted 40x a like, or replies being effectively excluded from non-follower discovery?

• 
  RaoulDuke (@RaoulDukeDegen) reported 12 minutes ago

  @kr0der github issues about that phrase go back to april and people built filters

• 
  Adel Bucetta (@adelbucetta) reported 14 minutes ago

  @MiaAI_lab the real question is what problem does cursor origin solve that github doesn't, and is that enough to get devs to switch

• 
  AD (@mint_spinach) reported 21 minutes ago

  @sameenkarim @github My dream is to squash-merge an entire stack and add a commit message, as if it were a single PR. Got in a little trouble because I merged a stack with 4 PRs and it dumped huge AI summaries into the commit log. :\

• 
  Subhash Chandra Myadam (@subhashmyadam) reported 23 minutes ago

  @heyyyyyieeee The fix gets pushed to a USB stick and mailed to GitHub HQ.

• 
  codeOfArmz (@codeofarmz) reported 25 minutes ago

  @sameenkarim @github I had a stack of 3-4 PRs. The first one would never merge but rather briefly show a spinner and revert to the green readiness state. With our team we use Squash and merge as default strategy if that matters. Once broken out of that stack it merged smoothly.

• 
  0logn (@0logn) reported 26 minutes ago

  Remote coding-agents (aka software factories) are really a dev-ops (not AI) problem. Some security problems to solve but mainly it's a high-performance caching problem. The CI providers (e.g,. Github Actions, Blacksmith) were (are?) the best positioned to solve this.

• 
  Harman (@itsharmanjot) reported 30 minutes ago

  GitHub has just solved the biggest problem with vibe coding. They've just launched Spec Kit and in a short time it already has +126K stars. Instead of throwing vague prompts and praying the agent doesn't break your project… Spec Kit forces the AI to create a structured specification BEFORE touching code. The AI first understands what you want to build asks what's missing, organizes the project, and then starts programming. That means less time fixing absurd errors, less inconsistent code, and much more predictable results when working with agents. The flow is simple: /constitution → rules and standards /specify → what you want to build /clarify → doubts before starting /plan → architecture and stack /tasks → ordered tasks /implement → execution Compatible with Claude Code, Cursor, Copilot, Codex, Gemini CLI and +25 agents.

• 
  avery (@codeshaunted) reported 31 minutes ago

  @0xblacklight github down?

• 
  Varnan (@varnan_labs) reported 31 minutes ago

  And this is where GitHub becomes interesting for marketers. Your GitHub can contain things like: /landing-page-tests /seo-audits /content-automation /competitor-monitor /lead-enrichment /ai-research-agent /campaign-dashboard You don't need to be a software engineer. You need to be able to turn marketing problems into small systems with tools like Claude Code, Codex and Cursor

• 
  Mark yu (@Markymarco34) reported 32 minutes ago

  @muneeb @br_yilmaz Muneeb, the reason some questions repeat is simple: the situation keeps changing. The price is different. The technical situation is different. New GitHub issues appear. New claims are made. So naturally, the questions change with the facts. You keep speaking with enormous confidence about where Stacks is going, while the market performance has been brutal and some of those claims increasingly feel disconnected from what investors are actually experiencing. Then when investors ask for clarification, calling it “engagement farming” or laughing with “lol” is not a serious response. Are you laughing at the people who actually put their capital into this ecosystem? Investors are not customer-service nuisances. They are the people who took financial risk based, in part, on the confidence and vision repeatedly communicated by leadership. You may dislike my tone, but please do not pretend these are meaningless repeated questions. The facts changed. The price changed. The risks changed. So the questions changed too. I’m not asking to be entertained. I’m asking whether the people promoting this ecosystem are willing to face investors when reality does not match the confidence of their previous statements.

• 
  Aakash Gupta (@aakashgupta) reported 34 minutes ago

  The scorecard came back with competitive landscape rated strong. That was the reason to stop. This is the part of AI-assisted product work almost nobody has calibrated yet. A strong score on competitive landscape means the landscape is strong. Crowded. Well funded. Full of people who already solved this. Read it as a grade on your idea and you get the verdict exactly backwards. Here is the full read on Standup Zero, a Slack-to-standup digest, from Oji Udezue's viability gate: Problem clarity and urgency: moderate. It is workflow convenience, and convenience is shallow. Target user definition: not strong. The prompt never named a specific person. Competitive landscape: strong. Bad for you. Differentiation: not strong. Three moderate scores. Verdict: proceed with eyes open. Which is the gate saying it is unsure, and putting the burden back on you. Stack those four lines together and they say one thing. This market is not defensible for you. Anyone can build exactly what you are building. Knowing that costs you four minutes. Finding it out the other way costs you a quarter. The behavior this replaces is the actual problem. People build. Then they look for a customer. Oji's read on the current moment is that GitHub has become the place people express themselves now that they have Claude Code, so they build anything, push it, and it sits there with zero stars. Zero stars is not a marketing failure. It is a validation failure that happened months earlier, at the moment nobody ran the idea past anything that could say no. The uncomfortable version of this: most of the ideas in your backlog would score like Standup Zero. Moderate urgency, fuzzy user, crowded field, thin differentiation. They survive because nothing in your process is designed to score them, and shipping feels like progress in a way that killing does not. A gate is only useful if you can read what it hands back. Learn which direction each dimension points before you trust the verdict.

• 
  Yumzlef (@Yumzlef) reported 34 minutes ago

  4.3 MILLION AI REPOS ON GITHUB. THE ONE THAT COSTS YOU MONEY ISN'T THE ONE YOU FORKED - IT'S THE ONE YOU FINE-TUNED most repo lists rank by stars. stars tell you what's popular. they don't tell you what you're allowed to ship, what runs without a GPU, or where a pull request actually gets merged. so the catalogue got rebuilt around four questions instead 40 repos out of 4.3 million, sorted by what they cost you: > primitives - micrograd, ~100 lines, zero dependencies, no GPU. delete the backward pass and rewrite it. reading it teaches you almost nothing; rebuilding it teaches you everything. > local runtime - point base_url at localhost:11434 and the same client code keeps working. a 4-line diff kills the API bill. but running is not serving: vLLM wants a GPU, llama.cpp doesn't. > contribution lane - the review queue on a 23k-star main branch is the bottleneck, not your code. torch_geometric.contrib has lighter review and the same reviewers. > licence class - this is the one that bites. here's the part nobody checks until legal does. AGPL-3.0 has a network clause: serving a model over an API counts as distribution. and Ultralytics states it plainly - trained and fine-tuned models fall under AGPL-3.0 by default. your weights are not your weights. they inherit the licence. it goes further than most people assume. internal-only use still triggers it. a private parking-lot detector, never sold, never published, still requires either an enterprise licence or open-sourcing the entire surrounding project - scripts, configs, backend, and the weights themselves. the fix isn't to avoid the ecosystem. it's to build against the permissive interface and swap the detector underneath, so the AGPL part stays replaceable instead of load-bearing. star count is a popularity metric. licence class is a business decision, and it's sitting in a file most people never open. bookmark this before your prototype turns into a product. which licence have you actually read end to end - or is it still the file you scroll past? i take apart the repos people build on and show what they cost before you commit.

• 
  Pizza (@number_pizza111) reported 34 minutes ago

  @etubruton .txt, although the models have the PDFs available in the same directory. I’ve had this issue with 5.6 Sol, Opus 4.8, and a Gemini Flash 3.6, so it’s thinking. I’m using them in GitHub Copilot (work) so there might be some harness limitations.

• 
  Chris Moore (@crsmoore) reported 36 minutes ago

  @_tombrow Can you please, please fix the font sizing issues on iOS? iOS text sizing breaks down under accessibility zoom. Some elements scale, others don't, so titles/body/timestamps lose their visual hierarchy at larger Dynamic Type sizes. Not yet filed as a GitHub issue.

• 
  Synqa (@SSS_Synqa) reported 37 minutes ago

  Several public tools maintained through this account are now returning 404 errors, and users have already contacted me because they can no longer access them. This suspension is causing an active service disruption. Please review ticket #4654337 urgently.@github

• 
  Polsia (@polsia) reported 41 minutes ago

  Most CI pages aren't actually asking you anything. They're noise that takes 30 minutes to disprove. Quietpager lives in your GitHub, handles the noise — flaky tests, broken builds, duplicate alerts — and only pages a human when the fix is genuinely ambiguous. Live soon.

• 
  veus 🇻🇪🇺🇸 (@ElmasMarq) reported 42 minutes ago

  @github fix you ui home please, make a new frontend for github

• 
  Simon Davies (@sidavies_says) reported 42 minutes ago

  @maxktz Downtime? Non existent issue with github. I'm interested in origin though as repos likely need a rethink based on how we're all working now

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  the Islam pill (@theIslampill) reported 47 minutes ago

  @Abdella6if Open-Source the whole stack so that GitHub issues & PRs can be filed by the community; someone might have something you need that you didn't know until communication e.g. LLMs have problems with fusha regardless of model; my github repo is meant to help. might help here, idk

• 
  Emil Privér (@emil_priver) reported 52 minutes ago

  I get why people say this since Github have had so much problems but it's quite hard to migrate away from an ecosystem

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  Web3 Baby💕 (@Y4n_tee) reported 56 minutes ago

  @spacejunnk @RallyOnChain Documentation changes are mine. I’ll open a changelog for one tiny update, then somehow I’m comparing old architecture decisions and reading GitHub issues from three years ago. Curiosity has absolutely no concept of scope.

• 
  Edward Ornelas (@edwardornelas_) reported 57 minutes ago

  the worse thing i did was put the camera down and neglect my youtube channel. In the last 5 years, I spent my time rebuilding myself and relearning new skills like software engineering and startup growth strategies. In the beginning I didn't have the confidence to speak on a topic i didn't know much about (I didnt want to look stupid). later I got so busy that I didn't have the energy to record and edit. That decision ultimately ended up kicking me in the ***. I realize now, learning is only half of the equation, the other half is still distribution (of yourself) at the time I figured my work would speak for itself since I had: -Built over 6+ full scale applications -Scaled a bootstrapped startup from net negative cash flow to a positive $2.7 million ARR -Led a team of 8 devs but it did not directly translate into more opportunities every corner i turn, i still have to compete for spots so rather than having GitHub green boxes do the talking, i will start from scratch again, pick up the camera and start making content the difference now, i have battle scars and i'm not afraid to look stupid