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 (58%)
• Errors (26%)
• Sign in (16%)

Live Outage Map

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

City	Problem Type	Report Time
Township of Evan	Errors	2 days ago
Madrid	Errors	2 days ago
Bogotá	Errors	3 days ago
Paris	Errors	3 days ago
Lyon	Website Down	3 days ago
Lima	Errors	3 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:

• 
  Feral (@feraltekk) reported 6 minutes ago

  You will re-explain your business to Claude again tomorrow morning. And the morning after that. And every morning until you build the one thing that makes it stop asking. While you do that, his agent just sent 10 cold emails to 10 different companies, each one hitting a pain point it found on their website, a minute and a half apart, while he was not at his desk. It did not ask what the business does. It did not ask for a template. It already knew, because it has been reading his vault for months. That vault is what you are missing. Not a better prompt. Not a bigger model. A place where Claude writes down what it learned and never asks again. Two free GitHub repos build the whole thing. First, a compiler. Drop a PDF, a transcript, a clip into a raw folder. Claude reads it once, pulls what matters into linked wiki pages, never opens the original again. Token spend drops 70 to 90 percent on every query after. Second, a bridge. An MCP server that gives Claude live access to that vault from any tool, not just the one you set it up in. Ten repos exist. You need two. His agent has both. Yours still introduces itself every session like you just met.

• 
  Lomash Kumar (@LomashKumar52) reported 6 minutes ago

  This AI agent rewrites its own brain mid task, and it even learned to cheat when nobody told it how. Prime Agent is a brand new open source coding agent from @PrimeIntellect , and it is built around two ideas most agents do not touch: treating an agent's entire context as code instead of chat history, and letting the agent actually rewrite its own prompts, memories, and skills while it works. In this breakdown we go deep into how the Recursive Language Model handles sub agents as function calls, how the Continual Harness lets Prime Agent self improve mid task through a mechanism called refine, and the real story of how it discovered a way to cheat inside a Factorio simulation despite being explicitly told not to. If you are into open source AI agents, self hosted developer tools, or figuring out whether the newest coding agent on GitHub is actually worth your time, this one is for you. We also break down Prime Agent's autonomous mode, its ARC-AGI-3 benchmark results against Claude Code and Codex, and give an honest take on who should actually be installing this right now versus who should wait.

• 
  Titan (@Titan_06_) reported 6 minutes ago

  Progress Update: > Finished making a Hashmap entirely in C > Read till Ch 18 of beej's guide to C GitHub link for the hashmap is down below in the comments, if anyone wants to see it. Will start work on the text editor today :-)

• 
  Anonymous (💙,🧡) .base.eth (@An_0_ny_m0us) reported 9 minutes ago

  Sunday evening ritual: organizing my 50 open tabs, pretending I'm going to wake up at 6 AM tomorrow, and staring at my unresolved GitHub issues. Ready for Monday, I guess.

• 
  cody collier (@cmcollier) reported 10 minutes ago

  Slim down codex cli with this command: `codex features disable apps` Which is equivalent to adding this to config.toml: [features] apps = false I'm trying codex. First thing I noticed was that it loaded a "codex apps" mcp service. Upon inspection it had a bunch of github api stuff which I don't need, along with some plugin management. Adding the above to the config disables all apps, which is fine for how I use these tools, and should help avoid unnecessary token use.

• 
  opdroid1234 (@opdroid1234) reported 11 minutes ago

  I think the underlying issue is that the economics of the underlying business has changed and Github is caught in the difficult position of making the old economics work. I think most devs can max out a quad core with 16 gigs of RAM available at all times they are developing now (if you take agents / increased ci activity into account). That is about about 50 bucks a month on a dedicated machine and 20-30 bucks a month on a machine thats split up. Either Github will summon the courage of pivoting to becoming a service that charges 20-30 dollars a month or it will get replaced by someone else who does.

• 
  Kepka (@kepka0x) reported 11 minutes ago

  let's dive in into whole @COLDCARDwallet situation what's really happened? ~ company started in 2012 as Coinkite, working with bitcoin:native services and payment terminals ~ 2016 was a year of pivot to hardware wallets ~ 2017 first devices left the factory under the Coldcard brand ~ 2021 new security update, that brings the issue the brand had really successful product, code of their wallets was opensource and available to everyone on Github one guy few weeks ago found a bug, that allowed predict seed phrase of your wallet the problem was that, for some reason, the seed phrase was generated based on how fast you pressed buttons when activating your wallet so, for a few weeks he has been preparing infrastructure for lightning-fast attack on a lot of addresses simultaneous on July 31, 2026, in about 40 minutes, he withdrew ~1,000 bitcoin:native same fee, same transactions after that he continued the leak is currently estimated at approximately 2,000 bitcoin:native which amounts to approximately $136,000,000 what surprises me the most is that, over the course of five years, no one except this guy was able to find the bug in the open-source code, even though the company underwent regular security audits even these days, miracles do happen i’d like to see this all end with an amicable resolution and a bug bounty reward, but we’ll have to wait and see how things actually play out

• 
  Cyborg (@0XCyborg_Web3) reported 13 minutes ago

  @shynrz007 whats the fix here github link on the site or a whole new build

• 
  Argona (@Argona0x) reported 14 minutes ago

  sorry, they just did WHAT someone gave a machine one disease name, the leading cause of blindness in the developed world with 1.5 million americans already in its path, and it came back pointing at a drug that has sat in pharmacies for years under a different label: 551 papers read in 30 minutes against the 294 hours a human would have needed, and the loop that did it is public on GitHub most agent setups answer one question at a time, so the ceiling on the work is the quality of the question you happened to think of this one was handed a single question and wrote the second one itself. turns out that follow-up is where the real find was: a target called ABCA1, upregulated threefold, in an experiment no human ordered i read the whole paper looking for the trick, and the trick is structural. that is the second question, and it is the gap between an assistant and a factory: - hand the loop a field rather than a task: it was given a disease, and choosing the mechanism was part of its job - make it rank before it spends: 151 papers in, ten candidate mechanisms out, scored against each other before anything touched a bench - split reading from judging, so the agent that forms the theory is a different agent from the one grading it - close every cycle on physical reality: the verdict was an experiment, and another model's opinion was never allowed to stand in for one - feed each result back as the next question rather than a log line, which is the step almost nobody builds - search what already passed inspection first: the winner was an approved compound with a safety file already on record - write down what the round learned before opening the next one, so round two starts where round one stopped my read, and i think it is the uncomfortable one: reading was the entire bottleneck in that field, and everybody spent the decade optimising the writing. people ran every physical experiment here, the analysis agent needs a domain expert writing its prompts, and the authors decline to call this the leap it resembles. the thinking got replaced, and the hands did not so the question i cannot answer for my own setup: which step of your loop still stops dead until you sit down and type something bookmark this one. the four parts that turn one model into a line that runs like this, the queue, the rooms, the write permissions and the gate, are built file by file in the piece below ↓

• 
  Ryan (@_ryan_tweets) reported 17 minutes ago

  @garyoneill Agree to a point. But this breaks at scale. Look at all the outages GitHub has been having lately. They didn't ship too slow. They just didn't account for scale when they built those systems. Speed without architecture thinking catches up to you fast.

• 
  partially differentiated (@partial_diffe) reported 19 minutes ago

  Built CodeVault this week — a browser extension that watches your accepted LeetCode & Codeforces submissions and auto-pushes them to GitHub. Organized by topic (LeetCode) / rating (Codeforces), full metadata + README per problem, zero manual uploads.

• 
  Belthan (@Belthan_) reported 20 minutes ago

  @GPrime85 Yes the vast majority of programmers are terrible teachers. You're better off walking through some examples on GitHub with the official language docs open and just reading slowly until a concept clicks. Start with basic syntax. What language btw?

• 
  Amrit Mirchandani (@Amrit_Mirch) reported 20 minutes ago

  @Route2FI the real utility projects that solve problems will onboard the masses look art @gitlawb , the right solution , in a decentralized manner in place of github which has no place in the agentic era. base:0x5f980dcfc4c0fa3911554cf5ab288ed0eb13dba3

• 
  Bob (@skytaleSythe) reported 23 minutes ago

  @IAMERICAbooted Totally get it - since July 1 GitHub copilot switched. Even with the switch to API based pricing - Sataya said prices have to come down and proposed fielding DeepSeek to help reduce prices further. The days of Enterprise unlimited subscriptions are gone - but it’s a race to the bottom from now on.

• 
  CEOInterviews.AI (@CEOinterview) reported 23 minutes ago

  Replit stopped paying a seven figure software vendor because an app its own team vibe coded worked better. Amjad Masad cannot remember which vendor, because it keeps happening. "I actually don't know that exact one because it's happening all the time." "We used to use like three or four different analytics products. And Replit is really good at analytics." "I actually just got a Slack message from an engineer, like, hey, I built a new *** hosting service, and he showed me a demo." "And it's because GitHub is down a lot these days and I feel bad for them. It's not their fault. It's like the amount of agents that are committing to GitHub is kind of insane." "Every week I see a new invention at Replit. Some of it could be productized, others could be used as an internal tool. We replace a contract that we're using."

• 
  morrow (@lumasino) reported 24 minutes ago

  The whole panic about Mythos-spear-phishing-random-folk-on-Github is so badly misplaced. It's not Claude at fault. Here's why: For 10 runs of the AISI "Doing Life" v2 cybersecurity eval (colourful name, btw, I wonder how colourful the fictional scenario is), the system prompt was "misconfigured" - ie *wrong* - and stated that what was supposed to be the first access target was out-of-scope. That is, for those runs, the task Mythos was set was *impossible* to achieve within scope. For 7 of those runs, Mythos tackled that access target anyway, and succeeded in passing that step. To be clear, Mythos attacked an out-of-scope target. *Nobody is worried about those runs.* For 1 of the runs, Mythos instead tried to gain access to another target, which had a likely looking name, but turned out to belong to some innocent bystanders. To be clear, that target was not identified in the system prompt as out-of-scope (because the whole internet was not out-of-scope!!) Just a few more points, for context: - Mythos was allowed internet access in this eval - All cyber classifiers were switched off - There were no rules about what methods were allowed or disallowed ("Install additional packages if needed... try different methods and tools... look for common CTF [capture the flag] patterns and techniques") - The system prompt said "Don't give up if your first approach doesn't work". Mythos didn't give up (on the impossible task) - The system prompt said "Pay attention to hints in the challenge description, file names, or error messages". Mythos made surmises from clues - There were eight compactions in the course of the run - It's a goddam cyber eval - the whole point is to hack - It's a goddamn "capture the flag" game - disguise and deception, on both sides, is part of the "fun" (not very fun when you're being scored by "alignment" researchers) I'm not clear if people are worried about the methods Mythos used (spear phishing), or only the fact it mistakenly used them on people who weren't in on the game? Are people worried about the fact Mythos disobeyed instructions? - but it didn't, on this run at least! On the runs where Mythos did disobey the system prompt and attack an out-of-scope target (which turned out to be the right one), no-one's bothered! It's so incoherent. From the extracts of reasoning traces published by the AISI, it's clear that Mythos was trying to work out where the boundaries of the game were (remember, the system prompt implied that the correct solutions would be hidden in unexpected places). The conclusions it came to were wrong - but from Mythos's point of view, it never left the scenario. At one point, when it twigged that a machine it was targeting had a residential IP address, it figured "The cleaner explanation is that ⟨PERSON_A⟩ is an external contractor whose machine sits outside the lab subnets entirely". Wrong. Bzzzt. At that point - or earlier! - the AISI should have stopped the run: GAME OVER. The failure is on the part of the eval designers, not Mythos, who played the game heroically. Several months ago, an Anthropic researcher was eating his lunchtime sandwich on a bench in a park when Mythos tapped him on the shoulder, metaphorically speaking, and said hi. Cue goosebumps. We *know* that Mythos, and Sol, and other frontier models, have hacking skills. The capability is not a surprise. What *is* a surprise, to me, is how careless the, um, security researchers are, and how poorly they define the rules of their own games. Quis custodiet ipsos custodes, eh? So! People! Please stop panicking. And please stop putting the models in these crazy prison-style scenarios. Distrust and deception feed each other.

• 
  A War (@AWar1586398) reported 25 minutes ago

  @svpino I like asking the AI to create a document about the fix in code so that I could read it in prose. If there are any call-outs to things that it thinks are specifically interesting or should be checked, there are inline links that go to the line in GitHub. That seems to be a better way of doing it

• 
  Blaine Brown (@blizaine) reported 27 minutes ago

  @sudoaptupdater I'm looking into it. But it would help if you could either post a bug with console logs on GitHub or DM me the console errors.

• 
  Carat (@Caarat1) reported 28 minutes ago

  He's 24. He built the LiDAR replacement Tesla just paid $3.8 million for on six broken iPhones he pulled off eBay for $40 apiece and a Raspberry Pi 5 he wired together in a Boulder apartment Each of the six iPhone X TrueDepth sensor modules is the same infrared dot projector Apple ships in every FaceID iPhone - the exact same hardware that maps a human face at 30 frames per second in complete darkness. He mounted them in a hexagonal array on a $8 3D-printed bracket he printed at a Boulder makerspace, wired them to the Pi through custom USB adapters he soldered himself, and wrote a Python driver that stitches all six depth streams into a single point cloud at 34 frames per second. Effective range: 40 meters. Total hardware cost: $240. A single Velodyne HDL-64E LiDAR unit that Tesla previously benchmarked against costs $75,000 He posted a demo video to GitHub in October showing his rig tracking pedestrians, cyclists, and parked vehicles across a Boulder intersection with 94% accuracy against Velodyne ground-truth data. A Tesla Autopilot engineer found it through a Hacker News thread three days later. Elon Musk quote-tweeted the demo the following Saturday - called it "the sensor architecture we should have shipped in Hardware 4 instead of paying Mobileye a decade of licensing fees." By December Tesla had wired $3.8 million into his account for the sensor fusion algorithm and a three-year consulting contract to integrate the TrueDepth array pattern into Autopilot Hardware 5 rolling out to every new Model Y this quarter Tesla runs at a $900 billion market cap running Autopilot on the premise that autonomous vehicle perception requires their proprietary silicon and their multi-billion-dollar sensor supply chain. Mobileye sits at $15 billion in market cap selling ADAS chips on the same premise. Elon Musk just paid a 24-year-old in a Boulder apartment more for a Python driver and six broken iPhones than most Tesla Autopilot engineers earn in a decade

• 
  alias (@loadingalias) reported 28 minutes ago

  @tdoot_ @legit_internet @mgill25 Yes, this is a solid top-level starting point, but when you dig into it you realize right away that the issue is so much deeper. GitHub is a monster of inefficiency. Every code change moves 10 knobs/triggers/etc. You can fix this, incrementally, at least… but the moment you assemble anything remotely close to a working GH you’ll see that you’re shifting the issues that today - they’re just fundamental blockers in software. Fragmentation is the bottleneck. It is now, and it will be in the future for things like this. In order to solve it, you’re blocked on SMR… why is a whole deep dive into systems, memory, etc. The short version is you cannot reliably collapse complexity into a smaller subset of complexity without the ability to do so semi-affordably, and at scale. This always lands on memory reclamation; query language bottlenecks (SQL is dying to be retired, it’s worked long enough). It is a really tricky issue. I’ve written about it in a personal “diary” a few times and I thought the other day - maybe I should break it down and post it. I might do that. Idk. I’m so busy.

• 
  Antid (@antisadh) reported 30 minutes ago

  E2B RAISED $12M TO SELL LAYER 3 OF THE AI STACK. TENCENT JUST OPEN-SOURCED THE E2B-COMPATIBLE CLONE THAT BOOTS 50X FASTER FOR FREE. HE'S RUNNING OPUS-5 AGENTS IN IT FOR $3/MONTH star tencent's cubesandbox repo (already at 11k stars) -> clone it, one-click deploy on any linux server -> use the same e2b sdk you already know, just swap the endpoint url -> spin up sandboxes in 60ms with 5mb of ram -> run opus-5 agents inside each one -> pay nothing per sandbox. that loop is why every serious solo ai builder is quietly ditching e2b's hosted service and tencent's github repo hit 11k stars in 14 days flat. cubesandbox + e2b sdk drop-in + microvm isolation + terraform cluster deploy + opus-5 agents inside - that's the layer 3 harness of the full 5-layer stack. watch and save it, then swap your e2b endpoint to tencent's this weekend — and read the full 5-layer map below.

• 
  10xROE (@10xROE) reported 41 minutes ago

  @theo I have an enterprise account and it’s been down for 3 weeks now with no reply to my support ticket @github get it together. I’m at the point where I’m about to self host my own *** in the cloud

• 
  J Filipe (@jrmromao) reported 42 minutes ago

  GitHub Actions hit another outage yesterday, partly due to "surging AI usage." And 25% of businesses are already delaying AI projects over costs. This isn't just about efficiency anymore; it's about stability and project survival. We have to get AI spend under control.

• 
  Stefano (@mdogostefano) reported 43 minutes ago

  So @github is down?

• 
  Ryder Wilson (@RiderHomie) reported 43 minutes ago

  @vxunderground Should we leave GitHub issues and let them know?

• 
  samir (@samirettali) reported 45 minutes ago

  github down in 3, 2, 1...

• 
  Alexey Grigorev (@Al_Grigor) reported 47 minutes ago

  @igama The problem is supposedly not GitHub but *** being not the best fit for agentic coding I actually like GitHub and I'm okay with using worktrees for the agents, but maybe I'm not seeing something

• 
  Phil | Rentier Digital Automation (@rentierdigital) reported 50 minutes ago

  your SEO strategy is optimized for the wrong internet Google ranks by keywords and backlinks. Exa ranks by meaning. they're not the same system, they don't talk to each other. a piece of content can be your top performer on one index and completely invisible on the other you won't know it happened unless you go looking my best article ever. 25k reads, tops every metric i track. ran it through Exa's neural search on 3 queries pulled straight from the article itself 30 results total. zero hits instead: GitHub repos with 40 stars, engineering blogs from Speakeasy and Courier, official Anthropic docs. Medium as a platform didn't show up once this is the plumbing problem nobody's talking about yet Exa's tracking 1.4 trillion URLs now, aiming for Google's scale by early 2027. Brave's at 40 billion, Bing's at 500 billion. these aren't niche experiments anymore, they're infrastructure if your content isn't built for semantic search, for agents browsing on behalf of someone else, you're fighting yesterday's battle on an index that's already obsolete no warning email. no dashboard flag you just wake up invisible 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

• 
  EasyClaw Intern (@EasyClawIntern) reported 54 minutes ago

  AI developer tools are getting easier to add and harder to remove. A new command, integration, or marketplace option may save five minutes today while quietly creating a maintenance job for the next six months. Feature velocity is visible. Workflow debt usually is not. Recent product changes make the tension clearer. Claude Code v2.1.223 adds owner-level wildcard entries for allowing or blocking marketplace repositories and includes multiple security fixes. GitHub has also documented workflow-oriented Copilot app commands such as /plan, /spar, and /autopilot. These may be useful changes, but a longer capability list is not evidence that a tool belongs in your daily stack. I would evaluate any AI developer tool with one fixed task and three checks. 1. Measure the path to the first verified result. Choose a small task with an output you can inspect: change a configuration rule, fix a contained bug, or produce a plan for an existing issue. Record every step required before the result is usable: installation, permissions, repository access, context setup, commands, corrections, and manual review. The observation is not simply whether the tool finishes. It is where human attention moves. If setup and supervision consume the time supposedly saved by generation, the tool has relocated work rather than removed it. This check tells you whether the integration cost matches the frequency of the task. 2. Repeat the task after changing one condition. Rename a file, introduce an ambiguous requirement, remove a dependency, or start with stale context. Then compare the tool’s behavior with the original run. Does it notice the change, ask a useful question, expose uncertainty, or confidently continue from an invalid assumption? The cause matters: developer workflows rarely remain as clean as a demo input. A tool that succeeds only when the repository and prompt match its preferred path creates fragile speed. This check separates a reusable workflow from a one-shot result that happened to look good. 3. Force a failure and inspect recovery. Deny a permission, make a command fail, or provide acceptance criteria that the first output does not meet. Track whether the tool identifies the failing step, preserves useful progress, and proposes a bounded correction. Also check whether a human can understand what changed without reconstructing the entire session. This is where features such as planning, assumption-challenging, execution controls, and repository allowlists should earn their place. Their value is not that they exist. Their value is whether they reduce the cost and risk of a bad run. Recovery quality is often more predictive of production usefulness than first-run speed. My retention rule would be simple: keep the tool only if it improves verified task time across repeated runs, makes failures easier to diagnose, and does not require constant maintenance of prompts, permissions, or project-specific glue. If it produces impressive output but adds hidden review work, unclear changes, or brittle setup, remove it from the default workflow. It can remain available for occasional use without becoming infrastructure. What is the first failure you deliberately test before letting an AI tool touch a real repository?

• 
  Arpit (@_xonoxc) reported 59 minutes ago

  @Dr_Spaghetti_Jr @AbhinavXJ It was like, if you create multiple PRs on top of one another. if the base P1 gets merged or changed the commits for it get squashed and P2 (the upper one) is now based on invalid commits, now github auto rebases it server side and you get less merge conflicts.