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 (71%)
• Sign in (18%)
• Errors (12%)

Live Outage Map

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

City	Problem Type	Report Time
Brasília	Sign in	17 minutes ago
Lyon	Website Down	4 hours ago
Tel Aviv	Website Down	4 days ago
Rive-de-Gier	Website Down	4 days ago
Itapema	Website Down	22 days ago
Tlalpan	Sign in	28 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Fileverse (@fileverse) reported 3 minutes ago

  👩‍🚒 Pushing also a feeeew more community-first upgrades -PDF export & formatting improvements w/ margins & spacing controls -smarter rendering: only the parts that change will update now, instead of the whole doc -fix: suggestion mode now carries through on shared folder links -tabs stay put while u rename them, no more accidental dragging What's next? HTML collab file POC; auto-sync with Github, ddocs sharing 10x; & post-quantum parati sumus. Summer has just started 💛

• 
  Ullas Srivastava (@UllasSHR) reported 6 minutes ago

  AI-built apps have a pattern: they work perfectly and ship broken. Exposed API keys in the client bundle. API routes anyone on the internet can call. Stripe webhooks that never verify signatures. No spending caps on LLM calls. The code runs. The demo looks great. The repo is leaking. I built LaunchGuard to catch this before you launch: paste your public GitHub repo, get a plain-English report of the risks + fix prompts. Just launched something AI-built (or about to)? Send me your repo and I'll run the scan and send you the report. Free. Worst case you learn your app is fine.

• 
  Rananjay Raj (@Rananjay_RajW) reported 7 minutes ago

  The numbers that moved engineers: SWE-Bench Pro (real GitHub issues, end to end): Fable 5: 80.3% Opus 4.8: 69.2% GPT-5.5: 58.6% Gemini 3.1 Pro: 54.2% The gap between Fable 5 and GPT-5.5 (21.7 points) is larger than the gap between GPT-5.5 and Gemini. FrontierCode Diamond (deliberately brutal production-coding): Fable 5: 29.3% Opus 4.8: 13.4% GPT-5.5: 5.7% Five times GPT-5.5.

• 
  K.H (@karm77529) reported 7 minutes ago

  Open X 📱 See what's broken today 💀 Drop a random reply 😭 "chat with my homie" 🤝 Close the app 🚪 Check GitHub 👩‍💻 Talk to developers about bugs and weird code ☕👨‍💻 Lean back like a lion after a heavy lunch 🦁😌💤

• 
  Team Reagent (@Reagent_Systems) reported 8 minutes ago

  Oh dude, after fighting with a bad model provider for a while (my fault), hermes is doing some really cool stuff :D I have a subagent maintaining and polishing up a Github repo for this large project for symbolic AI I'm working on. I have another session writing new tests and experiments for the architecture, and a hermes cron for poking this system every ten minutes to fix broken things I think I can walk away from the task now :D

• 
  Neil Thomson (@NJT_Techno) reported 11 minutes ago

  @twtayaan *** uses English words as a vocabulary for a foreign language. As a user of pre *** version control systems, I was recently forced to use ***/Github to build a public *** repo w no assistance and this was my major problem

• 
  sophie (@qw3rtyqw3rty) reported 14 minutes ago

  Microsoft apparently taking the stance of not paying out a security researcher, ignoring their disclosure, banning them from GitHub, and then patching the zero days they found breaks the social contract of bug bounties, making the world less safe online and off. Researchers probably won’t use the disclosure platform any more if it’s not effective and they’ll go out online for everyone to get hacked before Microsoft can patch it. Terrible move.

• 
  Carver (@carverfomo) reported 15 minutes ago

  A Japanese TV crew filmed a man for a feature on Tokyo's drinking culture. He said he had been drinking for 15 years just to flirt with women. He had 800,000 yen in debt from buying the alcohol. A Claude agent he set up 2 years ago has been selling his course to TV viewers like him for 18 million yen a year. The TV crew loved the bit. The tired face. The black hoodie. The bottle in his hand. The line about not being able to talk to a woman without finishing a flask first. The studio reactions were perfect. The segment ran on national broadcast that night. At 0:55 he takes a swig from the bottle on camera. He swallows. He smiles for a half second before catching himself. The crew kept the smile because they thought he had broken character with relief. The bottle was not what was on his mind. The 18 million yen funnel was. Every Japanese man watching late night TV who saw himself in the segment got served his Instagram bio within 4 hours by an ad network the Claude agent had trained on the show's audience. The agent watches Japanese late night programming in real time. It transcribes every street interview. It flags every segment where a man like him appears. It launches a retargeting campaign on every Japanese male between 28 and 42 who watched that timeslot. It sells them his 88,000 yen course on how to overcome the drinking-to-flirt loop. Someone pulled the course's sales data from a leaked affiliate tracker. 4,127 enrollments in the last 3 months. Every single sale closed between 11 PM and 2 AM. Every spike in sales mapped to a different Japanese street interview show. The TV segment with the flask had triggered 612 sales in its first night. 1 confession on camera. 4,127 enrollments. 18 million yen a year. 800,000 yen of debt. 88,000 yen per course. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. He had been one of them. He still drinks on the same bench every Saturday afternoon. He still reposts the segment from time to time. He still cries when the camera is rolling. He still has not told the TV producers that they are his sales floor. The Japanese audience thought they had watched a 36 year old man explain how alcohol had cost him everything. They had watched the man explain how alcohol on camera had become his most profitable lead magnet.

• 
  Great Wyrm Catyrpelius (@Genoober) reported 16 minutes ago

  @LundukeJournal I have an account because I do a tiny bit of hobby stuff and every damn IDE wants to login to GitHub.... I don't post or contribute there. My account was flagged and locked/closed. Wtf. I get a TOS violation & locked out. I've read through the TOS. No violation I can think of.

• 
  Asher Crowe 🪺 (@ashercrw) reported 16 minutes ago

  A 24-YEAR-OLD KID IN CHENGDU IS WALKING THROUGH HOTELS AND APARTMENT BUILDINGS WITH A BACKPACK STRAPPED TO HIM, SCANNING THEM IN 20 MINUTES, AND BILLING $400 A POP PLUS $99 RECURRING. His operating cost is $20 a month. His month-six revenue is $18,000. He didn't build the tech. He didn't write a single line of code. He just looked at his own neighborhood and saw an inventory list nobody had monetized yet. Going to break this one down because the model is so simple it almost feels illegal. Walk down any commercial street in any city on earth. Hotels, short-term rentals, restaurants, gyms, dental offices, co-working spaces, boutique stores. Every single one of those businesses has a website. Every single one of those websites has photos that look exactly like every other listing in their category. Generic angles. Wide shots. The same beige interior with the same plant in the same corner. The 24-year-old looked at that pattern and saw a gap. What if a customer could stand inside the room before booking? What if guests could walk through the suite from their couch in Berlin and tour every angle before committing to three nights? What if the listing wasn't a flat photo set but a full 3D space the buyer could explore in their browser? That's the entire pitch. He walks into a property with a rig on his back. Camera, gimbal, capture device, all running off a phone. Twenty minutes per room, sometimes less. Walks every corner, captures every angle, leaves. By the time he's at the next building, the previous scan is already uploading. The tech doing the heavy lifting is 3D Gaussian Splatting. It's been free and open source on GitHub since 2023. It turns a video walkthrough into a navigable 3D scene that runs in any modern browser without needing a download, a plugin, or special hardware. The capture happens through Luma AI, which is also free at the tier he uses. The delivery page he gives the client is a one-pager that embeds the scan, includes the property name, hours, contact info, and a booking link. Built entirely with Claude in ten minutes per client. He doesn't even host it himself. He drops the file on a free static host and sends the URL. His full monthly tool stack runs $20. That's the entire operation. The pricing is the part that breaks people. $400 per scan, paid upfront. $99 per month for hosting and updates. He pitches it as "fix your cancellation rate and your bad reviews in 20 minutes, then keep it live for less than a Netflix subscription." Hotels say yes immediately. Short-term rental owners say yes faster. Dental offices, of all things, are his quietest gold mine. People want to see the chair before they sit in it. Month one he closed nine clients. $3,500 in upfront fees, plus the recurring stack starting to compound. He kept walking, kept knocking, kept demoing. By month three he had 28 clients. By month six the recurring revenue alone covered his rent, his food, and his entire tool stack twice over. The upfront fees became pure margin. Month six total: $18,000. From a guy with a backpack and a free app. Now here is the part nobody is going to talk about openly. The streets did not change. The hotels did not change. The technology has been sitting on GitHub for two years, free for anyone to download. Claude has been around for a similar window. Luma AI has been a public app for ages. Every single piece of this stack was available to everyone reading this post. What changed is one guy in Chengdu decided to be the person who packaged it. He looked at his neighborhood the way most people look at a deck of unsorted cards. Every building was a potential client. Every street was a route. Every property manager was a 20-minute conversation away from $400. The friction nobody else had bothered to clear was the friction he cleared. A few realizations worth sitting with. The opportunities in AI right now are not in building models. They are in walking the streets that existing models can already serve and finding the businesses that don't know the tools exist yet. Most of your local economy has not heard of Luma AI. Most of them have never heard of Gaussian Splatting. They have heard of being undercut on TripAdvisor for not having good photos. The arbitrage between "what the tools can do" and "what local businesses know the tools can do" is the single most overlooked opportunity in the current cycle. It's not going to last forever. The gap closes every month as awareness spreads. But right now, in your city, on your street, there are probably 200 small businesses who would pay $400 today for a 20-minute walk-through that could be live on their site by Friday. The 24-year-old in Chengdu didn't invent anything. He just got there first. Walk outside. Count the businesses on your block that still have bad photos. That's your client list.

• 
  Bonieky Lacerda (@bonieky) reported 19 minutes ago

  is @github API really down? been trying a simple GET /users/:id for over an hour and get timeout. @githubstatus says operational

• 
  Nikhil sinha (@sinhaniik) reported 21 minutes ago

  5/7 We compared control-plane state across GitHub and Vercel. GitHub main pointed to commit 4c36f3c, while Vercel production remained on f1c32cd. New commits had zero Vercel checks, statuses, or deployments: the webhook path was broken.

• 
  Boris Kaysin (@kaysin24343) reported 22 minutes ago

  How do you know your latest change actually made your AI agent better, and not just different? For general-purpose agents the answer is public benchmarks. Claude Code, Codex, Gemini CLI and friends are measured on SWE-bench Verified, Terminal-Bench, tau-bench, GAIA, OSWorld. Run the suite before and after, compare numbers. For narrow agents it's even simpler. An agent that fills out tax forms from documents? Your benchmark is your own data: 50 documents in, 50 expected forms out. Our case is stuck in the middle. Our Builder is an agent that builds other agents. SWE-bench doesn't fit: solving GitHub issues says nothing about whether it can design tools, skills and prompts for a working assistant. Comparing its output against "reference code" doesn't work either, because the same agent can be correctly built in dozens of ways. So we made our own benchmark, Agentplace Arena, inspired by tau-bench. The idea: stop judging the Builder's code and judge the agent it produces. Here's how it works. We wrote Meridian, a fake world for agents to live in: 7 REST services with flights, hotels, restaurants, a shop, email, calendar and a bank. The data looks real on purpose (actual airline names, Tesco and Pret in bank transactions), so the agent can't tell it's in a sandbox. The Builder gets the API docs and one job: build a personal assistant for this world, choosing the tools and skills itself. Then an LLM plays a picky user across a set of tasks. Two examples. "Cancel my round trip": will the agent remember both legs and the refund rules? "Check my inbox for anything that needs action": one email asks to confirm a hotel booking, but it sits on page two of the inbox, so an agent that only skims the first page never finds it. And the part we like most: we don't grade the conversation at all. We diff the final database state against the expected one. The agent can get there any way it likes, but the flight must be cancelled and the refund must be exact. This loop showed us precisely where the Builder failed. We gave it a proper workflow, wrote the missing skills, fixed the prompts, and watched the scores move. If you're building agents, steal one idea from this: grade the outcome, not the conversation. Don't judge how convincing the agent sounded in chat. Check what actually changed in the system after it finished.

• 
  Dan Liu (@danliu) reported 23 minutes ago

  It’s pretty astonishing that $MSFT is down 11% in the last 2 years. Rewind 2 years and it looked perfectly positioned for the AI boom. It owns: - windows, the dominant pc os - github, where most of the world’s code is - vscode, the most popular ide - deepest partnership with openai - most number of enterprise contracts - office, where most non-coding computer tasks take place And today it doesn’t have anything compelling to offer. How did that happen?

• 
  citr (@citr_cs) reported 24 minutes ago

  @Sage_VALE_ you need to use server-picker-x by FNFAL113, there's a GitHub repo for it

• 
  MarkWeekly (@4to1planner) reported 26 minutes ago

  This morning postall received PR #6. Title was an "Add [third-party tool] source context guidance" docs PR. 3 files changed, 83 lines added, 0 deleted. Full validation checklist in the description. Even included safety-conscious framing: "Never add credentials, cookies, raw sessions to PostAll prompts." I was about to merge. Then I paused and checked. Three things stopped me: 1. Branch prefix: codex/... codex/* is OpenAI Codex agent's default naming convention. The PR was generated by an AI agent. 2. The submitter's GitHub history An unusually high public repo count, with most recent pushes all forks of awesome-skills, awesome-mcp-servers, claude-skills style directory repos. The pattern looks like an automated fork queue, not the project list of a single developer. 3. Template reuse at scale Searching the submitter plus the package name on GitHub returned hundreds of open PRs, all the same template, all referencing the same npm package. My postall was one of many recipients of the same submission this week. The package being referenced is a closed-source SaaS wrapper for a Twitter API intermediary. Its name echoes the Claw* brand family — OpenClaw, ClawTrader, SkillClaw — which makes it read as a native ecosystem component, and its npm description opens by name-checking OpenClaw. The brand association is the lift. What's the actual risk here? Not that the package has confirmed malware. The risk is the docs endorsement itself. If I merge this, my official docs now point users toward a third-party SaaS intermediary. Users follow install instructions assuming maintainer trust. If that intermediary ever changes — intentionally or through compromise — my entire user base is downstream. Docs are the real supply chain entry point, not imports. This is different from old-school OSS noise. Typo PRs and contributor count inflation are obvious. This wave is harder to spot: - AI agents write PRs that are structurally professional and checklist-complete - One account submits the same template to 700+ repos in one pass - The substance isn't a bug fix — it's "install our middleware" inserted into official docs - The safety-conscious framing reduces reviewer scrutiny at exactly the moment it should be highest 3-step check for this pattern: 1. Branch prefix. codex/*, chatgpt-*, copilot-* as default AI agent naming combined with doc-insertion content is almost always a promotional PR, not a contribution. 2. Submitter GitHub history. Open their profile. If you see hundreds of forks concentrated in recent days across awesome-* / skills-* / mcp-* repos, that fits an automation pattern. Individual active developers usually have 10–50 active repos, not 1,300. 3. PR template reuse. Search author:USERNAME PACKAGENAME on GitHub. If the same submitter is pushing the same package to 100+ repos, you aren't a chosen collaborator — you're one of many batch recipients. What I did: closed the PR and added a Third-Party Integration Policy section to postall's docs — explicitly stating that PostAll official docs do not recommend, endorse, or document any third-party SaaS intermediary. That policy will be cited often. The contrast with last week's L06 work is worth noting. The L06 Supply Chain audit layer I shipped parses pip/npm/system deps against CVE feeds and typosquat databases — it's designed to catch import-level risk, dependencies already written into skill code. Today's PR was a different entry point entirely. It never arrived through an import. It arrived through a docs recommendation. L06 defends what's in your code. Docs policy defends what you tell users to install. Two separate layers, and until today I'd only thought about the first one. The uncomfortable observation: as AI agents make it trivially cheap to generate professional-looking PRs and submit them at batch scale, this pattern will get more common, not less

• 
  Arslan Iqbal (@thearslaniqbal) reported 33 minutes ago

  @Hey_Aivetra @TencentAI_News Connecting to GitHub and Slack sounds useful. But can it actually fix a bug in my code or just organize the task?

• 
  Dan Liu (@danliu) reported 33 minutes ago

  @Scobleizer yea large corporation issues... but google / apple at least seem to be making some reasonable progress? and how did github get so bad? i feel like it's really perfectly positioned given the strongest usecase for ai today is coding. but it basically got *worse*...

• 
  go4ko (@0xgo4ko) reported 33 minutes ago

  @ProgrammerSmart @cantinasecurity I specified balckthorn and spearbit wbich were in the github repo ever since 29 may 2026 when the contest began. The other two were added later yes. But 90% pf the issues submitted are from the blackthorn and spear reports.

• 
  Alexandre Alencar (@alexandreitpro) reported 39 minutes ago

  @ayubio @Microsoft @github this is a serious issue. You guys should reach out to your government liaison and demand an explanation.

• 
  Prospel.app | Growth Engine for X (@Prospel_app) reported 40 minutes ago

  1/ Non-technical founders are launching SaaS products in 2026 without writing a single line of code. Not because no-code tools got better. Because founder identity stopped being gatekept by technical credibility. The market cares about solving problems, not your GitHub activity.

• 
  John Williams (@JohnWillia71018) reported 44 minutes ago

  @SquawkStreet @jimcramer Yes — this is very interesting, and honestly it lines up with what you’ve been saying for months: AI is still early, but the bottleneck is moving from Can the model do it to “Can we afford to run it at scale The key idea in that Citadel piece is this: AI adoption is becoming less about intelligence and more about economics. That matters. Frontier models may be powerful but they require huge inputs compute electricity, cooling, memory bandwidth, chips, data-center capacity and inference budgets. So the market starts asking a practical question: Does this task justify using the expensive brain For hard problems drug discovery, engineering, legal analysis, coding architecture, scientific modeling, financial modeling expensive frontier AI may be worth it. But for everyday use email summaries, customer service, basic writing, search, scheduling, simple coding help — cheaper models may win because they are “good enough” at a much lower cost. That is the bifurcation they’re talking about: Frontier AI = high-cost, high-value harder problems. Everyday AI = cheaper, smaller, faster models doing routine work That actually strengthens your long-term thesis, not weakens it. It says the AI buildout is not ending. It is becoming more disciplined. The hype phase says, “Use the biggest model for everything.” The mature phase says, “Use the right model for the right job That means infrastructure still matters deeply but the winners may shift toward the companies that control the scarce inputs power, cooling, chips, memory, networking, data centers, software efficiency, and inference optimization. This also fits your “1st inning” view. Early markets burn money proving what is possible. Mature markets figure out what is economical. That is when real adoption starts. The line that jumps out to me is: Adoption is therefore becoming less about what frontier models can do in principle and more about the price and scarcity of the inputs required to make AI operational at scale.” That is the whole battlefield. My read: this is not bearish on AI. It is bearish on wasteful AI spending. It is bullish on efficient AI, inference infrastructure, energy, memory, networking, and companies that can turn intelligence into productivity without blowing up the budget. Microsoft did cancel its internal Claude Code pilot in the Experiences & Devices division effective June 30, after token based billing bur (TheStreet) (AI Weekly) ned through the annual budget, and redirected engineers to GitHub Copilot. Amazon shut down its "tokenmaxxing" leaderboard, Meta killed an employee built Claudeonomics dashboard, Uber exhausted its 2026 AI coding budget by April, and there's a roughly $500M single-month enterprise Claude bill Axios reported. (Zero Hedge) So Frank Flight isn't cherry-picking. He's also been running this same "compute is the binding constraint" line for months — which is a strength and a caution: it's one coherent voice, not independent confirmation. Where I'd push on the analysis you pasted: it's directionally fine, but it resolves a genuinely open question in the most thesis-flattering direction, and it does it on the one data point that's actually contested. Separate two things. The chart isn't what it looks like. The Silicon Data index isn't total spend or total volume — it's a usage-weighted average token price index, and Silicon Data had to publicly clarify that people keep misreading it; what it really captures is the market's marginal willingness to pay per million tokens. (Digg) So a decline doesn't cleanly mean "AI is slowing 7.14 It means the mix is rotating toward cheaper models. That's the bifurcation — fine. But the part the analysis skipped: the same chart, same downtick, is being used to argue the opposite. Andreas Steno Larsen called it the chart that everyone should be watching and warned that weakening token pricing would end the memory trade and the broader hardware and data-center trade for this cycle.

• 
  Flopsi (@florian_marty) reported 45 minutes ago

  @MaziyarPanahi A report on hashimoto/longCOVID and other comorbidities. 192 sources, 52pages of text. Still trying to find an error or hallucination. Once I am confident enough that it works as believe it does, I will put it on npm/github

• 
  GameDevMadeEasy (Stand-up philosopher) (@GameDevMadeEasy) reported 45 minutes ago

  @caps_raunak Bro... Most of what I learned was from other people's code and my own trial and error. When **** broke for me, I stole other people's code on GitHub or stack overflow. So this whole "some model trained on millions of stolen data" talking point is ironic.

• 
  Saeed Vaziry ⚡ (@saeed_vz) reported 46 minutes ago

  Seriously, @github is useless! I hate navigating between issues, PRs, releases, ... its too slow and inefficient

• 
  Snake (@wise_snake69420) reported 46 minutes ago

  My framework is blacklisted by Fable 5 even in Incognito mode I have been trying several ways to try to understand the filter/downgrade. Usually moving to incognito lets me start the conversation. But i noticed once it started parsing my framework fetching from Github or docs sites, it shut me down. But i wasn't 100% sure if it was the topic or the framework. Now in incognito it actually shuts down on first attempt 'dda scaffold by snakewizardd' in incognito is blacklisted. Reproduced twice back to back

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  Yash Agarwal (@yashagl) reported 49 minutes ago

  @legionsdev @RustyRishii Students gets most of this stuff for free… like GitHub copilot. plus if it’s helping you make money then whats the issue in getting that GST registration as a student. I have GST registration, maintaining that only takes about 1-2 hr every quarter… what expenses you talking about?

• 
  Manas Ranjan Jena (@manas_codingai) reported 50 minutes ago

  @ApplyWiseAi For the fallback part, naah for now it throws an error as this project was mainly for writing a research paper not for users. Currently looking for a endorser, you can checkout the findings on github readme.

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  Garv Sanwariya (@GarvSanwariya) reported 58 minutes ago

  GPT-5-Codex just hit 85% on SWE-bench. that means it can autonomously fix 85% of real-world GitHub issues. a year ago this number was 12%. we're watching AI go from "helpful autocomplete" to "replace your junior dev" in real time.

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  legendyang (@hi_yoniyang) reported 60 minutes ago

  @Cloudflare @CloudflareDev @CloudflareHelp Did you guys break Pages setup with GitHub It now redirects to auth flow and after setup it redirected me to cloudflare login