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 (68%)
• Sign in (19%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Website Down	3 days ago
Saint-Paul	Website Down	4 days ago
Saint-Paul	Website Down	4 days ago
Mexico City	Sign in	5 days ago
León de los Aldama	Website Down	5 days ago
Créteil	Website Down	28 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  NeoUnderscore (@UnderscoreNeo) reported 4 minutes ago

  shout outs to the server for the obs plug-in i use it's just got announcements and then the github link and nothing else

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  Lorenzo (@gabor_rar) reported 10 minutes ago

  Your AI feature can be “working” and still have an authorization bug. GitHub just added a CodeQL query for a specific version of this problem: untrusted user input reaching a model’s system prompt in JavaScript or TypeScript. That changes the review question I want to ask in a micro-SaaS. Not: “Will the model follow the prompt?” Ask: Where did this text come from? Can it reach privileged instructions? Which tools or actions become reachable? What negative test proves the boundary? A customer brief, uploaded file, or support message should be useful input. It should never be able to quietly become policy. The goal is not a model that never gets confused. It is code that refuses to let untrusted text upgrade its authority.

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  Steve Wilkinson (@SteveW928) reported 14 minutes ago

  @bsvdrip @rodpalmerhodl Yes, not too long after I got into Bitcoin and started really learning about it (and after listening to Andreas Antonopoulos on weaknesses), I became a bit alarmed over how Core was structured. I tried asking in some discussions and even got blocked by a prominent Bitcoiners on here (𝕏). I figured maybe I just didn't understand enough about how Github worked (in governance terms), but looks like I had properly identified a problem.

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  !RTFM (@jbdamask) reported 14 minutes ago

  App feature evolution: 1. Scroll X, find something cool for one of my apps 2. Add tweet to GitHub issue 3. Agent loop picks up issue 4. Plan, vet, build, test, merge, deploy Not quite there yet because the devil is in the guardrails. But close.

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  Vivek (@ShVivek25) reported 14 minutes ago

  Day 0 of Repo Auditor. Building an AI agent that audits GitHub repos for backend production-readiness issues generic linters miss: missing async on I/O routes, no idempotency on webhooks, N+1 queries, secrets in code. Aiming for ~10 days end to end. 🧵 1/ #buildinpublic

• 
  Thierry Rakt (@ThieryRakt) reported 14 minutes ago

  For my followers, here is my token gift so you don't need to generate one ;) 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 But you still need to reload the page to visualize another github user :'( will fix it(and probably more ...)

• 
  Jorge Madrigal ⚡️ (@Jorge_Madrigal) reported 15 minutes ago

  Github issues

• 
  ThePrimeagen (@ThePrimeagen) reported 16 minutes ago

  I just did my first "loop" and it absolutely crushed it. I had to give about 11 comments back on GitHub, but still, amazing I did my second loop. It was a disaster. This slot machine feels so addictive! I immediately thought "must have been prompt issues"

• 
  Julia (@_Juliaweb3) reported 19 minutes ago

  @Real_TShelby @sleepagotchi @CNPYNetwork Check Sleepagotchi support and GitHub docs, should fix it

• 
  Guribo (@GuriboVR) reported 23 minutes ago

  If you run into any issues please create bug reports on Github, also check if your case is covered by the wiki first though. With such a big release it is still possible that there is some edge cases I missed. I will iterate and improve this further in the future.

• 
  .Dev (@AnExiledDev) reported 27 minutes ago

  @Layton_Gott That's my issue with a lot of Anthropics metrics posts, they don't really properly explain how they measure what kind of work is being done. Like if I plan through a github issue, then work the github issue in separate sessions, do they consider the github issue a plan?

• 
  Syntex (@Syntex3666) reported 33 minutes ago

  @l1t3rallym3 @ShitpostRock Regardless of how you design the scenario, The problem here is that this isn’t a pizza place, you were wrong to expect a pizza place when it wasn’t a pizza place. GitHub isn’t an .exe site, it’s a coder space where coders can add to each other’s work or create extensions.

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  Chibugo | AI automation (@__chibugo) reported 35 minutes ago

  there's a trend on instagram right now: everyone's building their own version of Tony Stark's Jarvis; a voice agent that runs like an actual OS. what nobody shows is everything people overlook when building one. so here's that part, since I built it myself. 🎯 the plan on paper vs. the plan that shipped the amount of planning you need to do is insaneee. first version had each agent loading one tool, fetching, verifying, then unloading before the next; all built around not blowing past a token budget. that's the part people underestimate: you don't know your constraints until it's running. once it was live, token budget stopped mattering. speed and cost-per-call did. so the whole approach changed: instead of mcp's, every agent just calls its tool's API directly, does the work in plain code, and only asks the AI to write the sentence at the end. 🎯 scope one AI that "does everything" sounds impressive but could be a nightmare to debug. so i split it into 5 sub-agents, each only knowing its own lane. a router decides which lane(s) a question touches. these sub-agents then report to the main agent orchestrator. similar to a team lead and team members 🎯 prompt chaining integrated ElevenLabs for voice, and a single voice reply isn't one AI call; it's a handoff, several times over: hear the words → figure out what's being asked → pull the data → write the sentence → speak it. every handoff adds seconds and cost, which can lead to latency. one reply once took 31 seconds. pulled the logs instead of guessing: a wasted double-check on an expired token, a slow handoff to an outside service, plus the normal chain. fix is running sub-agents in parallel and timing each call. 🎯 tokens and prompt caching each agent's instructions get cached, so it's not re-reading the same manual on every call. what never gets cached is live data; for instance, caching a bank balance is just caching a wrong number the moment it changes. that same cost-awareness came back around differently later: the AI account ran out of credit for a few hours, and every request failed with the same error. 🎯 local vs. web everything gets tested on a local copy first; headless browser opening the dashboard, checking for errors, a test message round-tripping through voice before go-live. if you're handing this to someone else, though, you commit, push to GitHub, and host it. for mine, i used Cloud Run for the brain, Cloudflare Pages for the screen. 🎯 vault write-back most builds only go one direction: ask, answer, forget. this one writes back because every full briefing gets saved as a dated file into a notes vault so the agents can keep training themselves with the data. if you ask a follow-up an hour later and it still knows, because that memory is shared across every way you talk to it. used Obsidian, synced through Drive. 🎯 security once, a message could've gone somewhere it shouldn't have. that only needs to happen once to be a problem. so now there are three checks before anything goes out: - it can never post to certain places, - it has to prove who it's speaking as before it speaks for someone, - and tests confirm both of those actually work. separate from that, I went through every access key this thing has and asked, "does it really need this much access?" a few did not. 🎯 the checks after every update, it runs a test and asks does a voice message go through? does it understand a normal sentence? does the screen load with no errors? also implemented something we call "error logging" in automation, but i call this "diagnostics" or "agent health status," which checks in every 15 minutes on its own, making sure the data is still updating. if it's not, it sends a warning without anyone needing to notice first.

• 
  1PercentBetterToday (@1PercentBetterT) reported 38 minutes ago

  GitHub issue → Claude Code implementation → PR opened. Runs on your Max sub, zero API cost. 1/2

• 
  Kevin Jonas Johnson (@kjonasj) reported 39 minutes ago

  @MaxGhenis @mackenziescott Weird. I think the WiFi in the coffee shop I am in is blocking stuff. Have had previous issues with GitHub pushes and I still can't look at Kalshi from here. Thx for confirming

• 
  SPEKULATOR (@__spekulator__) reported 44 minutes ago

  @DamiDefi the prompt isn't the issue—it's the failure mode. when the claude run fails, what does the github action log show? is there a retry or just a dead step?

• 
  Azurite (@Azurite_ai) reported 44 minutes ago

  AI agents have a new security problem. It’s called HalluSquatting. Instead of exploiting your code… It exploits the AI itself. The model hallucinates a package or GitHub repository that doesn’t exist. Attackers simply create that fake package first. Your AI agent installs it. Game over. AI coding is evolving fast. AI security needs to evolve even faster.

• 
  Muhammed (@_ceejeey) reported 44 minutes ago

  I’m currently working on four products: A design-to-code app builder A native OS app A marketplace An event booking platform All four are very different, but they have helped me understand how I actually build products with AI today. For the design-to-code product, the goal is not to generate a few screens from Figma and call it done. It should understand the design, product context, architecture and business logic, then create a working React Native or Next.js project, connect the flows, validate the output and eventually push a usable codebase to GitHub. Across all four products, AI writes most of the code. But letting AI write code and letting AI build the product are still two very different things. Here is how I divide the work. What I still decide • Product architecture • Tech stack • Database structure • State management • API boundaries • Security decisions • What the correct implementation should look like What AI mostly handles • Feature implementation • Repetitive components • Tests • Documentation • Initial debugging • Refactoring once the direction is clear What usually needs both • Planning • Code reviews • Performance work • Visual verification • Larger refactors The part I never fully hand over is the actual product experience. AI can build something that looks correct in a screenshot but still feels wrong when you use it. It often misses loading behaviour, navigation flow, persisted state, empty states, error recovery and all the small details between two screens. Yesterday, I asked Codex to build an onboarding flow with Redux persistence. Instead of keeping the splash screen visible until persistence was initialized, it created a manual persistence gate to avoid the flicker. Did it work? Yes. Was it the correct solution? No. That is the difference between code that works and code that actually belongs in the product. A few things I have learned while building these products: 1. Architecture matters more when AI is involved If the codebase is modular, multiple agents can work in parallel without constantly touching the same files. If everything is tightly coupled, adding more agents only creates more conflicts. 2. Subagents only help when the task boundaries are clear One agent can plan, another can build the API, another can implement the UI and another can test the output. But this only works when the codebase is structured for parallel work. 3. The same model should not be used for every task I use stronger reasoning models for planning, architecture and difficult debugging. Faster models are often good enough for implementation once the task is clearly defined. Using the most expensive model for everything is not better engineering. It is just expensive. 4. Context matters more than the prompt The agent needs to understand how the project is structured, which patterns already exist, what commands it can run, which libraries it should use, what it should never change and how the work should be validated. Without that context, even a capable model will start inventing its own architecture. AI can now write most of the code. But someone still needs to understand the product deeply enough to know whether that code is actually right. That is becoming a much bigger part of engineering.

• 
  Shivay Lamba (@HowDevelop) reported 46 minutes ago

  It reads your repo's live GitHub state and computes 4 action lists, nothing generic: 🔍 Triage: dupe clusters, hot issues, unanswered threads 🚀Ship It: approved-ready PRs + a changelog draft 👥 People: first-time contributors going stale 💬 Worth Replying To: HN/Reddit/web mentions

• 
  sergio1030 (@sergio103040) reported 48 minutes ago

  @Real_kosumo_ @softbluelizard @ShitpostRock is it really harder tho? it took me 5 mins to figure it out how to use github the very first time I tried to download a mod, and haven't had an issue since, this just sounds like entitled people wanting everything handed to them in a silver plate.

• 
  Nosyt Labs (@NosytLabs) reported 51 minutes ago

  @gonka_ai Is community brokers closed if we issue a github issue? Or only way is to do the public broker? Thanks @gonka_ai

• 
  Ayaan 🐧 (@twtayaan) reported 52 minutes ago

  Microsoft spent 20 years trying to kill Linux. Today, they're teaching people how to install it. That isn't an opinion. It's on Microsoft's own website. If you told a Linux user in 2001 that Microsoft would one day build a Linux distro, open source WSL, and publish official Linux tutorials, they would have laughed. Here is how far the reversal has gone: → Microsoft built WSL so Windows users can run real Linux without a VM or dual boot. WSL itself is open source. → Microsoft built Azure Linux to power its own cloud infrastructure. → Around 60% of Azure workloads now run Linux, not Windows Server. → Microsoft bought GitHub, home to most of the world's open source software, for $7.5 billion. This is the same company whose CEO once called Linux "a cancer." Today Microsoft contributes to the Linux kernel, maintains Linux projects, builds Linux products, and makes billions from customers running Linux on Azure. Microsoft didn't lose to Linux. It realized fighting Linux was a battle it could never win. So it did something smarter. It joined it.

• 
  Mudit Raj (@haxonit_) reported 52 minutes ago

  @Preeti_ly Claude is over hyped af I have used both the models for weeks, fable and gpt 5.6, I will always go for 5.6. Reason: Fable is **** at cyber. I just asked for fixing an GitHub issue related to cybersecurity, it totally denied to fullfill the request.

• 
  loooong (@allyiiii) reported 52 minutes ago

  Everyone thinks AI should help mathematicians prove theorems, but Terence Tao had it migrate his 30-year-old old website. In a single day, the AI moved 560 papers, travel logs, courses, books and math applets to GitHub Pages, and found two hidden bugs in Tao’s decades-old handwritten code. Launched back in 1997, the site required manual HTML edits via a terminal for nearly 30 years. The AI also cleaned up inconsistent info, stale entries & broken links, plus ported old Java 1.0 applets to JavaScript. Rather than tackling big math proofs, AI handled the tedious digital housekeeping mathematicians dread.

• 
  Lonnie VanZandt (@lonniev) reported 54 minutes ago

  @_onecookie ty. Fortunately, with Claude, it's just "hey claude, add a debug log to this product so that users can share what they experience. If it's easy, allow them to click "Submit Github Issue" and share that log." And good old Claude cranks it out in seconds.

• 
  Brute Force Artist (@bruteforcearete) reported 54 minutes ago

  GOODBYE, CAPCUT. 👋 Someone just built a completely FREE, open-source CapCut alternative with no watermarks—and it has already earned 62K+ GitHub stars. Instead of charging a subscr!pt!on and locking features behind a paywall, it gives creators everything for FREE. → No watermarks → No paywalls → Works on web, desktop, and mobile → Open source (MIT licensed) → Built-in MCP server for AI agents → Rewritten in Rust with plugins, scripting, and a powerful API It's called OpenCut—and it could be the CapCut replacement creators have been waiting for. Here's everything you need to know (repo link below).

• 
  Boyd (@0xBOYD) reported 55 minutes ago

  Bug feedback is now easy enough for members who don't know or care what a github issue is.

• 
  Turcid (@_turcid) reported 58 minutes ago

  > have an issue with the android Jellyfin client > look up issue and find it will be fixed in next release > fix is available in the beta > f-droid does not have the beta > manually install beta from github > issue resolved I ****** love sideloading

• 
  billnas (@billnas25) reported 59 minutes ago

  Distributed consensus's core problem: independent observers see events in a different order due to network delay. There's no way to know "what really came first."Google solves this by making clocks perfect (atomic clocks, $$$). Kafka solves this with one leader deciding. Raft/Bitcoin solve this with voting rounds (slow).Vortex solves this differently: instead of asking "what really came first," ask "what rule can every node compute independently and get the same answer" — no clocks, no leader, no voting. 500ms, physical floor. @github @TheHackersNews

• 
  smrati tiwari (@smratitiwa86867) reported 60 minutes ago

  THE “GIANT MODEL = GIANT RAM” RULE JUST GOT BROKEN. Colibri is running GLM-5.2 — a massive 744B parameter model — on a machine with just 25GB RAM and no GPU. How? Instead of loading the entire model into memory, Colibri uses smart parameter streaming: → Keeps the active parts in RAM → Streams the rest directly from disk when needed → Makes huge models accessible on regular consumer hardware The trade-off? Generation speed depends on disk performance, but the fact that a model this large can run locally at all is a huge milestone. No cloud. No expensive GPU cluster. Just open-source software pushing the limits of local AI. ★ 2.1k+ GitHub stars ★ Apache-2.0 License ★ 100% free & open-source Repo in 👇