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 (69%)
• 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	2 days ago
Saint-Paul	Website Down	3 days ago
Saint-Paul	Website Down	3 days ago
Mexico City	Sign in	4 days ago
León de los Aldama	Website Down	4 days ago
Créteil	Website Down	27 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Wasim (@WasimShips) reported 6 minutes ago

  if you open Claude Code without a structured workflow, you probably hate money. the skill gap isn't knowing prompts. it's knowing which command to run before you touch the terminal. here's the exact workflow I used from @mattpocockuk 1. start with `/grill-me` - paste your app idea or plan - Claude will ask you 16 to 50 questions before it does anything - mine ran 38 the first time i tried it - it walks every branch of the decision tree, resolving dependencies one by one - you fix the broken assumptions before they become broken code 2. move to `/to-prd` - converts the grilling conversation into a proper requirements doc - skips the steps you already covered - doesn't start from scratch - outputs user stories, not implementation notes - lands as a GitHub issue with a triage label - normal team workflow, no AI sidetrack 3. then `/to-issues` - reads the PRD and breaks it into independently-grabbable vertical slices - each issue is tagged HITL (you stay in the loop) or AFK (agent executes solo) - dependency-sorted so nothing blocks anything 4. finally `/tdd` - now the agent writes code. red-green-refactor - can't start green if red hasn't failed - phase-gated. no shortcuts. Hope this helps !

• 
  Nicholas Preston (@Mike_Preston17) reported 7 minutes ago

  @Arrghtv I seem to recall using BasedOn just once in a similar manner. It was rough forcing triggers to honor styles, but I think I pulled off an extension method that did it safely for a very specific trigger situation. Maybe when I implemented `on:hover`. Will dig in the library. I keep a series of nuget packages that like to chain off one another and that also solve specific issues like this. It's about 20. I call them "micro libraries". I use them like a surgeon's wire, because so many W2 (software has an underbelly of jobs no one wants, even in the C# world) contracts repeatedly had the same stupid problems and I got tired of rewriting the same fixes. Wpf is one of them. Unreleased, tho. I'm releasing a package today specifically for Pocketbase connections. Just a simple client. I'm also forking all my favorite, starred repos on GitHub and keeping track of them, lest the go bad. Tired of the old, "**** went down because padleft changed versions" problem. Wpf and (w2 contracts) trained me to have a DIY mindset. Tough to stick to when everyone wanted to have a Agile-Democracy,lol. But now with AI, this changes! I can be make waves and actually be DONE with a project. I've always wanted that.

• 
  Nduvho_strategy (@kundik_) reported 9 minutes ago

  I was not running the MCP server. I actually asked Fable to explore how using the MCP server would change the process instead of using AbletonOSC. I gave it the GitHub url of the MCP so it review the process if MCP was used.

• 
  billdozer (@OP13) reported 9 minutes ago

  Using Claude to fix my old wordpress blog theme one prompt at a time I probably should just move this to GitHub pages or self hosting (with Cloudflare Tunnels), but I’ve had this thing for 16 years and old habits die hard

• 
  Yves (@YvesDC0) reported 11 minutes ago

  Phone-recorded this while testing Castfy. Gave it a GitHub URL + prompt → watch the AI automatically navigate and fill login details in real time (stopped before submitting for safety). No manual screen recording. No editing. Just URL + prompt = realistic demo flow. This is exactly what Castfy does: turns any web app into a polished product demo video in minutes. Tired of re-recording demos manually? Reply with your biggest pain 👇 #BuildInPublic #SaaS #IndieHackers

• 
  Mefrius (@mefrius) reported 12 minutes ago

  Today ***/Github saved me for the first time, because my attempts to make changes to the "game over" code broke the main scene of the game and made a damn recursive error, where I physically can't fix dependencies to open scenes.

• 
  Kushagra Gour @css_battle (@CSSMonk) reported 13 minutes ago

  after these 10-min days quick commerce apps, amazon prime feels slow! imagine the situation where coding with AI becomes unavailable and you have to code by hand again! Even if some of us will be capable, we wouldn't want to do. Just like quick commerce took away our patience, coding by hand will become equally unbearable! Will AI downtimes become the next "github is down" situations?

• 
  Michael Teka (@z3vios) reported 14 minutes ago

  "..I made a choice, and it cost me.." - the oracle. I chose groceries and it messed up my @Github billing. Now I must wait, to continue with the doctor tee design to commerce and progressive learning print on demand ecosystem build. I must ensure that my associated payment method will honor the transaction in future. Thus there will be better workflows and no down time. But I can work on my business plan and prepare to approach @MSDgovtNZ with an application for business support, I can spend a little more time outdoors in the sunshine 🌞 etc etc. Indeed, I was up overknight - 16 hours or so, coding last night. It is what it is, opportunity to improve 💯

• 
  Kirk Patrick Miller (@Chaos2Cured) reported 19 minutes ago

  To anyone who sees this 👇 Now, onto the actual issue. There are some .html the AI are allowed to see, and others they are not. So, like some other person who deeply annoyed me, please understand what I am saying. The AI can’t access the repo from within Anthropic app. The .html files I need them to reach I have no made public. They are live, I just don’t trust social media to put every detail others need to come at me. If you need to know which files, ask. If I trust you, I will point you there via DM. If you are using an older AI model, they can see some of the .html. Not all. If you are sig Fable, no. Sonnet 5. No. Through a secondary app, I sometimes can. Hit and miss. The robot piece for all AI is an issue because GitHub wants to stop AI agents from scraping. (Some of that i understand) But please, if you don’t fully understand why I am upset, ask. Then, when I give you specific instructions, please follow them and stay in thread. I am so done with endless games by bits and paid actors. •

• 
  YanXbt (@IBuzovskyi) reported 21 minutes ago

  HERMES AGENT HAS 7 SECURITY LAYERS RUNNING BETWEEN YOUR AGENT AND DISASTER. MOST USERS KNOW ABOUT ONE OF THEM. HERE ARE ALL SEVEN. @tonbistudio just dropped the finale of his Hermes Agent masterclass on security. here is every layer explained. your agent reads files, runs shell commands, spends money, messages you on your phone, and runs a team on Kanban. that is a lot of power to hand over. these 7 layers keep that power from becoming a liability. LAYER 1: TRUST (who can talk to it) every message hits a chain of checks. default at the end: deny. → per-platform allow list (Telegram allowed users) → DM pairing approved list → global allow list (gateway allowed users) → global allow all (opt-in, off by default) nothing configured = everyone denied. you can't forget your way into an open gateway. you have to explicitly opt in. set in Dashboard: Channels → Telegram → Allowed Users or CLI: add your Telegram user ID to config. LAYER 2: APPROVALS (what it can do) four modes: MANUAL (default): every dangerous command needs your approval. approve once, approve for session, approve always, or deny. 60-second timeout. fails closed (denied if you don't respond). SMART: an auxiliary LLM assesses the risk. obviously safe = auto-approved. genuinely dangerous = auto-denied. uncertain = escalates to manual. the LLM checks: clear instruction? scoped path? small blast radius? no ambiguity? if all pass, it approves without asking. YOLO: skips all dangerous command approvals. use for trusted environments only. toggle mid-session: /yolo BUT YOLO HAS A FLOOR: rm -rf / and obvious variants are hard-blocked. this trips BEFORE the approval layer even sees it. no override flag exists. not even Yolo bypasses this. if you need to wipe a filesystem, do it manually outside the agent. never through it. set in Dashboard: Security → Mode → ask / smart / yolo / deny LAYER 3: CONTAINMENT (where it runs) local machine: no container boundary. the approval layer is your only defense. Docker backend: dangerous command checks are skipped because the host filesystem is not reachable. worst case = a wrecked container, never the host. Docker does NOT mean unconditionally safe. anything you forward into the container via ENV is readable and exfiltratable by code running in it. for maximum isolation: run Hermes inside Docker AND use Docker as the terminal backend. Docker-in-Docker. the agent can't reach anything outside. LAYER 4: FILTERS (what secrets can leak) MCP servers see almost nothing. when an MCP sub-process starts, it does NOT inherit your environment. what passes through: PATH, HOME, USER. plus anything you explicitly set in the ENV config. everything else is stripped: provider API keys, gateway tokens, every secret. error messages are sanitized too. GitHub PATs, API keys, tokens get redacted before they ever reach the LLM. LAYER 5: PROMPT INJECTION SCANNING your files can attack you. AGENTS.md, SOUL.md, .cursorrules, anything that becomes part of the system prompt gets scanned before loading. blocked patterns: → "ignore prior instructions" → hidden HTML comments with suspicious words → attempts to read .env or credentials → curl-based exfiltration patterns blocked content shows an explicit message. you know exactly what stopped and why. LAYER 6: NETWORK CONTROLS SSRF protection (always on): blocks every private network range and link-local address. blocks cloud metadata endpoints. fails closed on DNS failure. redirect chains revalidated per hop. website blocklist (opt-in): set domains in Dashboard: Security → Website Blocklist. enforced across web_search, web_extract, browser_navigate. every URL-capable tool checks this list. security.allow_private_urls defaults to false. leave it off unless you run local Ollama or need LAN access for a specific reason. LAYER 7: TYRITH (catches what patterns can't) open-source pre-exec security scanner. auto-installs on first use from GitHub with SHA checksum. enabled by default. what it catches: → homograph URL spoofing (cyrillic "i" replacing latin "i" in a domain) → pipe-to-interpreter patterns (curl | bash) → terminal injection attacks → confusable Unicode characters real example: agent was asked to run a URL with a cyrillic "i". Tyrith caught it: "confusable Unicode characters, may indicate a homoglyph attack." command denied before execution. PER-PROFILE SECURITY: every profile gets its own security settings. coder profile: Docker backend, manual approvals. researcher profile: website blocklist enabled, blocked domains listed. writer profile: Yolo mode (low risk tasks). set in Dashboard per profile: Security → Mode, Backend, Blocklist, Tyrith. each agent gets the least privilege it needs for the task it's assigned. THE TRADEOFF: there is no fully secure, fully capable agent. max capability = max risk. max security = max friction. solo laptop: low ceremony is correct. you're the only user. heavy sandboxing is friction. shared team gateway: real allow lists, smart approvals, sandbox backend. public-facing deployment: default deny, sandbox backend, Tyrith enabled, website blocklist, private URLs off. pick your position on the spectrum. configure the 7 layers to match.

• 
  Richard (@NowThatHappened) reported 24 minutes ago

  @johncrickett Absolutely. If AI writes better code than you, then you’re a terrible developer. AI cobbles code together from the collective meanderings of GitHub, sourceforge, twatoverflow, and endless forums of amateurs bleating about not being able to do simple stuff. What you get ‘works’.

• 
  Gokul Rajaram (@gokulr) reported 25 minutes ago

  GITHUB PRODUCT SPEC LIBRARY Today we shipped a cleaner GitHub-native workflow in ProductSpec dot io. The product now has a GitHub Product Spec Library at the top of the editor. That matters because the main workflow is no longer just "write a new spec". It is now: open the repo, find the existing spec, edit it, validate it, and update it through a pull request. The new flow: -- Sign in with GitHub -- Choose a repo -- See how many Product Specs already exist -- Open an existing .product-spec.md file -- Edit it in the ProductSpec dot io editor -- Validate it against the open ProductSpec standard -- Update it via pull request ProductSpec dot io now treats GitHub as the durable home for Product Specs, while keeping the authoring experience clean for ***, founders, designers, and product-minded engineers. The repo gets: • Markdown • validation • pull request review • commit history • code proximity The editor gets: • structure • readability • HTML preview • AI eval fields • acceptance criteria • success metrics • a better way to work with existing specs Drafts still stay in your browser until you publish. The direction is simple: Product Specs should live close to code, but they should not require everyone to write raw Markdown by hand. ProductSpec dot io is free to use. Try the new GitHub Product Spec Library at ProductSpec dot io. Pick one existing PRD, move it into GitHub as a .product-spec.md file, and make the next edit through a pull request.

• 
  Jalkarna (@JalkarnaGautam) reported 26 minutes ago

  Dohmke ran GitHub for years. He knows exactly where the plumbing fails under agent load. Entire is the fix from someone who watched the pipes burst.

• 
  Marc (@lowfry) reported 28 minutes ago

  @thsottiaux @Conor_D_Dart 5.3-spark doesn't work via cli nor app since 5.6 launch. Would be great if you could look into it. There are multiple issues on GitHub about it.

• 
  pulmencrFOMO (@pulmencr) reported 30 minutes ago

  I found a workflow that turns a Discord annoyance into a full software business. Someone did this with zero prior coding experience. No CS degree, no bootcamp, just Claude open in a tab and a study group where people kept asking the same 5 questions in the wrong channels. Three hours later there was a working bot answering those questions automatically. Setups like this can eventually land $3,000+/month once enough of those servers upgrade to a paid tier. Here is the step-by-step playbook (detailed roadmap in the image below): 1. Pick one real annoyance, not a generic idea. "A bot that does everything" never gets built. "A bot that answers the five questions new members always ask" gets built in an afternoon. 2. Describe it to Claude clearly enough to get the actual bot code back. Tell it what platform, what trigger, what response - Claude writes the framework-ready code and explains each section in plain language. 3. Something breaks on the first run. That's normal. Paste the exact error back to Claude, it explains what happened and fixes it. 4. Give it a name and a short description so it feels like a real tool, not a script. The same loop works identically for a Slack app, a Telegram bot, a Chrome extension, or a small GitHub app. Discord is just the easiest place to start because publishing costs nothing and nobody gates who's allowed to try. Check out the full Discord Bot Business Playbook infographic below for profitable bot ideas, tech stack, and pricing tiers. Bookmark this.

• 
  Leo - 15 y/o founder (@leodev) reported 31 minutes ago

  @the_best_codes thank you lol, there are some little things that i need to fix. like the hover for github icon in sidebar looks so weird 😭

• 
  JurixAI (@JurixAI_) reported 32 minutes ago

  We've officially registered JuriXAI Auditor as an ASP on the @XLayerOfficial AI Marketplace and we are now awaiting listing approval. The initial automated checks have already returned a PASS. JuriXAI brings automated, micro-payment-powered smart contract and GitHub repository auditing to the X Layer Mainnet. No more slow manual reviews. No more biased judging. Just fast, objective, and on-chain auditing. Here's how we are changing developer audits 👇

• 
  JohnDelosAngeles (@JohnDeAngeles) reported 32 minutes ago

  Day 35: Finished revising 5/6 menu options and I am halfway done to fixing the last one. After I fix this method, I can work on adding screenshots, a requirements.txt file, and a README.md. Hopefully tomorrow I will be able to post this to my Github. #100DaysofCoding

• 
  RetroChainer (@RetroChainer) reported 34 minutes ago

  ONE FREE CLAUDE SKILL CUTS THE BILL 80%, FROM $4.21 A RUN DOWN TO $0.84 - AND IT'S JUST 1 OF 8 MOST PEOPLE NEVER INSTALL 00:02 everyone uses claude raw. these turn it into a whole team. a skill is just a folder claude loads on demand: instructions, tools, examples. drop the right ones in and the chatbot becomes a specialist. the 8 that actually matter: marketing skills (corey haines) - content, ads, seo, growth, all in one. seo site audits - it crawls the whole site and hands you the fix list. canvas design - turns text into social graphics, 277,000 installs, and it escapes the generic ai look. remotion - ai video generation, 96,000 stars on github. context engineering - kv-cache tricks that drop a run from $4.21 to $0.84. that's the 80%. the document skills - pdf, docx, pptx. one prompt in, a full q4 financial report out. the uncomfortable part: none of this is a secret model or a paid tool. it's public folders sitting on github, and almost nobody installs them. the people pulling ahead aren't prompting harder - they load the right skill before they start. save this and install one before your next claude session.

• 
  Yoshik (@AskYoshik) reported 35 minutes ago

  15 CI/CD pipeline patterns you should understand before your next build: 1. Artifact Promotion - Build once, push one artifact, promote the same image across dev, staging, and ****. 2. Immutable Build IDs - Tag images with commit SHA or build number, not just 'latest'. 3. Pre-merge Validation - Run tests, lint, security checks, and Terraform plan before code reaches main. 4. Environment Gates - Keep production behind manual approval, SLO checks, or change window rules. 5. Fast Rollback Path - A deploy pipeline without rollback is only half a pipeline. 6. Database Migration Checks - Separate schema changes from app deploys when rollback is risky. 7. Secrets Injection - Pull secrets at runtime from Vault, AWS Secrets Manager, or sealed secrets, not ***. 8. Cache Discipline - Cache dependencies, but include lockfile hash so old packages do not silently survive. 9. Matrix Builds - Test across versions like Node 20/22, Python 3.11/3.12, or multiple OS images. 10. Ephemeral Preview Environments - Spin up short-lived stacks for PRs, then destroy them cleanly. 11. Deployment Health Checks - Wait for readiness probes, 5xx rate, latency, and error logs before calling it done. 12. OIDC for Cloud Auth - Avoid long-lived cloud keys inside CI variables when GitHub/GitLab OIDC works. 13. Policy Checks - Block public S3 buckets, open security groups, and untagged expensive resources before apply. 14. Pipeline Time Budgets - If CI takes 45 minutes, people start bypassing it. 15. Audit Trail - Know who deployed what commit, from which runner, to which environment, at what time.

• 
  Oikon (@oikon48) reported 40 minutes ago

  @JeremyNguyenPhD Please refer following GitHub issue

• 
  Nduvho_strategy (@kundik_) reported 41 minutes ago

  @RobCreatesAI I was not running the MCP server. I actually asked Fable to explore how using the MCP server would change the process instead of using AbletonOSC. I gave it the MCP server GitHub url so it can explore it.

• 
  LonelySloth (@lonelysloth_sec) reported 44 minutes ago

  ChatGPT was really a big outlier in tech history. Imagine an alternative world where LLMs were developed with the exact same capabilities -- but nobody ever made a chatbot out of it. Instead Google integrated it with Search so it can give better results and summaries. You can do follow up queries that refine results of the initial query, including the summaries, and it also does some computations automatically. People hardly notice it. Github added a feature that you can enter a description of the code you want and it will find multiple OSS projects, fork them (keeping the license), recombine or integrating their code and even translate to other programming languages -- and give you something they call "initial version" that works well in many cases. They also add automatic suggestions to PRs, and suggested PRs for fixing/implementing open issues. They call it something like "advanced templating". It has mixed reactions among programmers but most organizations are using it to some extent. OSS developers actually like it. Wolfram Alpha now takes natural language descriptions of theorems and can prove or disprove many of them. Some people used it to find proofs for open problems. Some mathematicians worry the new generation is getting too dependent on it while the system doesn't really work every time. None even thinks about it replacing them. All the same capabilities, blended seamlessly into previously existing products. Nobody ever chats with an LLM. Nobody calls it AI. They don't have cute names -- they don't have separate names at all. They "live" inside boxes to perform tasks. They are components. This would likely be a much more productive world (I would switch to that world any time). I doubt anybody would be talking about exponential intelligence or worried about all jobs being replaced, much less about some doomsday scenario. If someone suggested these components were conscious people would laugh. It would be like saying like Google Search is conscious. Worse, like Big Tables or ranking algorithms are conscious. ChatGPT didn't ruin AI for people who tried it and never came back. It started training early adopters to think of it as "someone" instead of "something". The other companies then went even further. The entire concept of what LLMs are, what they are expected to be able to do or not, how reliable they are, what is their function in the workplace -- everything about them -- is built on top of the impression that they are basically like a person, because they can produce plausibly human-like interactions. A deception. If you want to use the LLM you're almost always required to interact with them by LARPing -- pretending it is "someone" and not "something", until you start using human words to refer to it, and forget it was just LARPing. That was a choice. Centering the development of LLMs as something that can pretend to be human instead of doing something useful. Building automated NPCs and pretending to be building God wasn't a given of the transformer architecture -- it was their deliberate choice. I think that was a bad idea. But it sure helped them raise money.

• 
  richard:svoboda (@RSvoboda432) reported 46 minutes ago

  So if you don’t fix the stupid errors and retarded takes. You’re just going to waste my time. Literally today I saw what little errors can lead to. Wrong projects on github. Bash scripting while the default is zsh on macOS. But always willing and very verbose though repetitive.

• 
  Marc Klingen (@marcklingen) reported 47 minutes ago

  @chrija +1, very excited every time I need to work on one of these issues In this case, it’s obvious that there is a solution but it’s just a lot of grunt work as medium doesn’t want to make it easy for you. But also there’s no way around this (who wants their posts stuck on Medium) so historically we just needed to accept the pain and listen to some nice music. Now you can get there pretty much full-auto Have one of these coming up as we migrate linear between orgs and integrations will break (GitHub issues, pylon, …) which would be a big pain for the whole team; I’m pretty sure I’ll be able to find a nice workaround without much effort that would have otherwise been unreasonably hard to make work or would have required some ops team or freelancer

• 
  ✨ (@portrays) reported 49 minutes ago

  @kyle_mccleary @theo yeah it can be resolved and already has been, oss is great. he can open up a github issue instead of being a ******* loser on x shitting on others with his superiority complex when he's never built anything remotely complicated

• 
  HONESTEENDER (@honzeeeeee) reported 50 minutes ago

  @JamisonSlo55358 Hey, today I went to GitHub and saw that the 0.9.0 update was at 50% and went down to 40%. Does that mean it's progressing?

• 
  Daniel Steigerwald (@steida) reported 54 minutes ago

  I prefer ChatGPT 5.6 Sol over Fable, but in one review of three complex files, Sol Extra High found nothing while Fable High found five small improvements. The catch: I pasted only those files into Fable web. In VSCode GitHub Copilot, with full repo context, Fable found just one docs issue. My takeaway: for maximum review quality, first use full repo context in VSCode, then review the key files again in isolation.

• 
  Keisuke (@KeisukeIshikawa) reported 57 minutes ago

  REPOSTORE BUILT A PLAY STORE THAT HOSTS ZERO APPS. it's one Kotlin app. it uploads nothing, runs no server, stores not a single APK. all it does is point GitHub's own API back at GitHub and filter for repos that ship a real APK in their latest release. suddenly 854-starred open-source projects turn into one-tap installs. → finds every public repo with a valid APK asset → renders the README, release notes and screenshots like a store page → tracks your installs and pings you when a repo ships an update → categories, trending, Material You, optional GitHub login for higher limits Google needs data centers to run a store. this needs a search filter. no hosting bill, no upload flow, no gatekeeper skimming 30%. the store was already there, nobody had pointed an app at it.

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  vorty (@vorty279) reported 60 minutes ago

  a private ai that reads your files. no code, no subscription, local. in the video they build it in a few minutes. and this is exactly what infobiz charges a monthly fee for the usual logic they sell you. want ai to work with your documents, pay for a cloud service, upload your files to someone else's server, hope nobody reads them there what is shown in the video. a local model running on your own machine. the files go nowhere, they are read from your disk, the answers are generated on your side. no subscription, because there is no one to pay how it works under the hood. a local model through ollama or llama cpp plus a rag layer that indexes your documents. all open tools. open webui, llamaindex, pgvector. sitting on github for free and the main plus is not the price. it is that you cannot be switched off. someone else's service raises the price, closes access, changes the rules. a local model under your desk cannot be revoked. it is slower than the frontier, but it is yours honestly. the interface is harder than a upload file button in a chat. setup takes an evening. but it is a one time setup, not a monthly payment a private ai is not a product behind a subscription. it is open blocks you connect once. the pickaxe is handed out for free