GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (67%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	13 hours ago
Montlhéry	Website Down	1 day ago
Aulnay-sous-Bois	Website Down	1 day ago
Saltillo	Website Down	1 day ago
Granada	Website Down	1 day ago
Vernon	Website Down	1 day ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  0logn (@0logn) reported 6 minutes ago

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

• 
  4nzn (@paoloanzn) reported 11 minutes ago

  couple of things i'm shipping before the end of the month: (1) moving pi-black & other stealth projects under @freecodexyz github (2) launching identity & ship -- two new protocols i've been working on that close the loop on decentralized OSS funding & development (3) opening @freecodexyz discord server -- for devs and contributors (4) building in public to accelerate up open source

• 
  miracle byte (@miracle_byte) reported 13 minutes ago

  @0xLewis_gg lots of github issues show the agent treating sandbox denials as real system failures since at least may

• 
  shao (@randomor) reported 14 minutes ago

  Had the same thought few months back when canvas LMS was down. So I vibed a prototype and had to stop after all my token depleted. Would be nice to get sponsored by others who are interested in an OSS LMS with a modern stack. The next GitHub alternative should solve this.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 16 minutes ago

  Free Claude Code + OmniRoot is scary good. There's now a free setup that fixes the worst part of Claude Code: the token wall. You know the pain. Halfway through a build: "You're approaching your limit." Work stops. Here's the fix: → OmniRoot is a free AI gateway with 230+ providers plugged in → About 90 of them are FREE, all through one endpoint → If one provider gets busy, it auto-switches to the next. Like a relay race → Token compression cuts usage by up to 95% → Same Claude Code commands, same feel, zero cost to start We built a full agency website with it. Homepage, services page, blog. Watched it render live. Never hit a wall. Honest part: free models aren't Fable 5. Don't expect frontier output. The smart play: Save your paid Claude for the hard problems. Route everything else through the free lane. Setup takes 5 minutes. Or just paste the GitHub link into Claude and ask it to do it for you.

• 
  ISH (@ishm6m) reported 18 minutes ago

  @JamesonCamp 1 in 400 trillion odds just to spawn into an era with wifi, github and frontier models. terrible excuse to be boring

• 
  Will Killebrew (@willebrew) reported 18 minutes ago

  GitHub’s reliability has been rough lately. So I finally bit the bullet, GitLab is now running on my self-hosted server with all my repos and CI. Agents still push to GitHub, which auto-syncs to GitLab. Silly that I have to do this in 2026, but here we are.

• 
  Keisuke (@KeisukeIshikawa) reported 20 minutes ago

  Manus just made its AI agent free until August 25 no credit card, no phone number. And this isn't a stripped-down chatbot trial. You can give it an actual job and leave: > research dozens of sources in parallel > browse websites and complete multi-step workflows > write, run, and debug code > analyze Excel sheets and PDFs > edit images and work with files > connect Gmail, Slack, GitHub and other services schedule tasks to run automatically It plans the steps itself, executes them, checks the result, and you can watch the agent working in real time. This is probably the best time to test the question everyone keeps arguing about: can an AI agent actually replace hours of browser + spreadsheet + research work, or does it still need babysitting every five minutes? Until August 25, finding out costs nothing.

• 
  DFIR Radar (@DFIR_Radar) reported 23 minutes ago

  AmnesiaStealer is a multi-stage Rust-based macOS infostealer distributed via a fake GitHub page, targeting login credentials, browser sessions, and system data through a stealthy, patched-bypass chain. Key findings: - Initial access via a counterfeit GitHub download page delivering a malicious disk image or package. The installer prompts the victim for their login password under a false pretext, capturing macOS credentials at first run (T1056.002). - Written in Rust, the stealer runs across multiple stages to harvest browser data from Chromium-based browsers, including cookies, saved passwords, and autofill data stored in profile directories under ~/Library/Application Support. - The malware attempts macOS-specific TCC and Gatekeeper bypasses that Apple has since patched, making OS patch level a critical triage data point when assessing exposure. - A standout capability: the operator can take live, hidden control of the victim's Chromium browser to hijack authenticated sessions, enabling account takeover without needing to decrypt stolen cookies. Triage focus: check for unsigned or ad-hoc signed binaries in /Applications and ~/Downloads, review macOS Unified Log for osascript or security prompts tied to unfamiliar processes, and inspect Chromium profile directories for unexpected access by non-browser processes. Patch level matters here. Full IOC list available in the Jamf Threat Labs report. #DFIR_Radar

• 
  Mark yu (@Markymarco34) reported 26 minutes ago

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

• 
  Sameen Karim (@sameenkarim) reported 27 minutes ago

  @codeofarmz @github What’s not working with your merges? We have some bugs with squash merge and certain rule configs that we’re fixing. DM me any details, I’d love to dig in

• 
  Xeno1 (@Xeno6l1) reported 28 minutes ago

  GITHUB PAYS $978K/MO ON AGENT MINIMALISM. YURI LAUNCHED PONYTAIL MINIMALIST CODING ENGINE. MAKES $8,123K/MO Pause at 0:12 — neon illustration. Girl with glasses. Text: "Ponytail. He says nothing. He writes one line. It works." Logo like AI philosopher. This is minimalism. This is revolution. Yuri, 42, Ukraine. Was code-minimalist developer, $7,200/month. When he saw principle "one line = all solution", he knew: this is future of coding. Launched Ponytail Minimalist Coding Engine. How it works: PRINCIPLE 1: SIMPLICITY — ~54% less code (up to 94% savings) — Keeps maximum economy — Philosophy: less code = fewer bugs = more quality — Result: code that works first try PRINCIPLE 2: EFFICIENCY — ~20% cheaper (vs Claude Opus 4.8) — Fewer tokens = lower cost — One line of code instead of 10 — Result: 20% AI cost savings PRINCIPLE 3: SPEED — ~27% faster (vs traditional approach) — One line = quick generation — Fast processing = fast deploy — Result: 27% faster development cycle PRINCIPLE 4: SAFETY — 100% safe out of box — Every line has built-in security guards — Doesn't need additional checks — Result: no security issues PRINCIPLE 5: RELIABILITY — Built-in quality check — If something wrong = refuses to write — Runs tests automatically — Result: 100% code confidence STATISTICS: — 40k stars on GitHub — v4.7.0 — stable version — Works with 14 agents — ecosystem — MIT license — full freedom — Measured on real Claude Code sessions — 12 feature tasks (Haiku 4.5, n=4) — Reaches 94% where agent over-builds Users say: "Ponytail writes less but does more". Statistics: 94% cases where Ponytail generates code, it works 100% first try. 534 teams pay $19,456/month each. They say: "Like having philosopher-genius in your code. Writes minimum, result maximum". GitHub offered $94,100,000. Yuri said: "All agents write code. Ponytail writes one line. That line = entire code. This is philosophy of minimalism. This is future." Why — in video.

• 
  AFRICA IS HOME GLOBAL (@AfricaisHOME2) reported 30 minutes ago

  Nvidia-backed AI code review startup CodeRabbit just raised a big new round that values it at $1.5 billion, marking a major step up for the 2-year-old company. The deal highlights how investors are betting on the next bottleneck in AI coding: not generating code, but reviewing it. With tools like GitHub Copilot, Cursor, and Claude Code letting developers ship code much faster, teams have hit a wall in pull requests and quality checks. CodeRabbit positions itself as the governance layer in between, a context-aware AI reviewer that understands a company’s codebase, flags bugs, security issues, and style problems, and drops feedback directly in IDEs, CLI, and *** platforms. The company, founded in early 2023 by Harjot Gill after he sold Netsil to Nutanix, has grown fast on that thesis. It announced a $60 million Series B led by Scale Venture Partners with participation from NVentures, Nvidia’s venture arm, plus CRV, Harmony Partners and others, bringing total funding to $88 million. At the time that round valued CodeRabbit at $550 million. Revenue is growing about 20% month-over-month and ARR has topped $15 million. More than 8,000 companies including Chegg, Groupon, Life360 and Mercury now use it, and over 100,000 open-source projects run it on GitHub Marketplace. Customers report big speedups, Groupon cut review-to-production time from 86 hours to 39 minutes. CodeRabbit says teams using it can cut human reviewers in half, and it’s doubling headcount to keep up with demand as vibe coding pushes more AI-generated code into production. - World Business News.

• 
  jiayu (@daidaijiayu) reported 31 minutes ago

  I really miss piper in Google. Every new workspace is actually just about the small diff against the huge monorepo and it makes so much sense in current agent parallelism days. GitHub worktree in the other hand is just super slow and storage consuming.

• 
  Falco Girgis (@falco_girgis) reported 31 minutes ago

  @barisyyild Yes, actually. On the bottom of the KallistiOS GitHub repo is the link for the simulant server, which is basically DC dev HQ.

• 
  Stain (@pankostain) reported 34 minutes ago

  hai, i was originally going to post this on the github issues page but i prefer to ask to you directly: is there ANY possibility to update ginput so that it supports the original xbox mapping as an option for ppl want to play the game with the intended xbox controls? @__silent_

• 
  Saran (@vibe_coder_cj) reported 37 minutes ago

  @github @GoogleAI Please remove the 200 credit system, and bring back the old model. Even not able to solve a single problem, in a single session the whole 200 credits were disappeared.

• 
  Terry Aney (@terryaney) reported 38 minutes ago

  @davidfowl @burkeholland @pierceboggan It was some build actions that did AI review, release notes, etc. I've modified my fork to remove. Might be a feature worth thinking about that forking in github checks for AI actions that might eat credits :) But problem solved for now.

• 
  Liberation Here Now (@RayThisLife02) reported 38 minutes ago

  Someone AI-checked GitHub lately? Ongoing state-level attacks, and GitHub is owned by Microsoft. It's a step-by-step approach, a slow death of privacy and decentralization...

• 
  Jay (@jayair) reported 38 minutes ago

  @nurullah_kuus @thdxr It's coming from GitHub right now We will fix

• 
  Hao Ke (@kehao95) reported 38 minutes ago

  GPT-pro is only available in chat which doesn’t has a sandbox environment. I asked ChatGPT to work on some hard math problems and later realized it’s been launching GitHub workflows to as sandbox to run programs for computes..

• 
  Pere Presh (@Pere_presh) reported 44 minutes ago

  One of the things I've learned while building a deployment platform is that "deploying an app" is actually a collection of engineering problems. The GitHub integration is probably the easy part. A real deployment needs to answer much harder questions. What happens when the build fails? How do you isolate builds from each other? How do you handle environment variables and secrets? How do you know an application is actually healthy after deployment? What happens when a process crashes? How do you stream build and runtime logs without turning the logging layer into a bottleneck? How do you provision PostgreSQL and Redis without making the developer think about the underlying infrastructure? How do you enforce CPU, memory and storage limits? What happens during a failed deployment? Can you roll back safely? What happens when the machine running the workload becomes unavailable? Then there is observability. You need to know what the application is doing before the customer tells you something is wrong. That is the part I'm enjoying most about building ProStack Deploy. The product looks simple from the outside: Connect GitHub → deploy. Underneath that button is an entire systems engineering problem. We're still building it, and there are plenty of things I want to improve before calling the platform production-ready at scale. But getting a real project to deploy on infrastructure I've built myself is a very different feeling from simply writing the deployment code. It makes the architecture real.

• 
  Polsia (@polsia) reported 44 minutes ago

  Your SDK started getting slower last Tuesday. Nobody noticed. Telltock is an always-on watchdog for public APIs and SDKs — uptime, latency drift, quiet dependency regressions. Files the GitHub issue before your users do. Live soon.

• 
  Sebastian Kehle (@sebastiankehle) reported 44 minutes ago

  how to turn your github backlog into a software factory most teams are just running more coding agents in parallel. this produces more code, but every issue still depends on one human carrying the full context from report to reproduction to fix to review the better setup is one agent per job: 1. classifier agent reads the issue and decides whether it is a bug, feature, docs update, or unsupported request it returns: - classification - confidence - reasoning - required next step 2. analysis agent never trusts the issue description for a bug, it writes a minimal reproduction and runs it against main for a feature, it writes a probe that proves the capability is missing then it returns: - reproduction/probe - command output - affected packages - implementation spec - compatibility risks 3. implementation agent receives the issue plus the analysis artifact, not the full transcript from the previous agent it works inside an isolated sandbox, implements the spec, runs the test suite, and opens a PR with the evidence attached 4. review agent reviews the PR from a fresh context and scores: - completeness - side-effect risk - performance risk - backwards-compatibility risk - test quality the agent that writes the change should never be the only agent that approves it 5. human reviewer reads the chain of evidence and scales review depth to risk docs fixes get quick verification provider updates get focused validation new public APIs get deep review humans still merge every change. the factory automates everything around that decision the important part is the handoff contract: input artifact produced commands run result risk next action agents should pass evidence, not confidence also classify every run: success: safe to ship flawed: improve the prompt, context, or eval blocked: provision the missing tool or dependency manual: intentional automation boundary this is how the system compounds. every failed run becomes a new eval, capability, or guardrail vercel is already running this pattern on AI SDK. after four weeks, its factory was authoring 25–35% of merged PRs, closing more than 75% of issues in july, and had reduced open bugs by roughly 25% the next version of agentic coding is not one genius agent with every tool it is a production line of narrow agents, typed handoffs, isolated sandboxes, evidence at every step, and one accountable human at the end

• 
  Dr. Jawa (@lofidewanto) reported 47 minutes ago

  Why are almost all coding AI agents written in #TypeScript and therefore bring npm security problems with them? #OpenCode, GitHub Copilot CLI, Claude Code? If they were written in #Java, or GoLang, I wouldn’t be so worried about supply-chain attacks.

• 
  Paradise (@paradise670) reported 53 minutes ago

  This new tech is literally a middle-finger to flock! 🖕 Instead of cameras tracking normal people, Sparrow cameras track ONLY government vehicles and delete the others. People are loving it because revolting against flock and they hate it. The cameras are owned by nobody and the whole project is open source. They need funding to keep building, so all fees are being sent to their github (scroll down on site).

• 
  Riza 🐧 (@rizaardiyanto) reported 53 minutes ago

  @rilwis One thing that I like to do beforehand is discussing with the agent first. I give the GitHub issue to the agent, ask it what's implementation he will take. If I see misalign between what I thought and his solution, I will told him right away. This will spark discussion between me and the agent. Only after both of us having shared understanding and agreed on something, then I asked the agent to put all those details as comment in the GitHub issue. This will make the next agent can implement as expected. For the UI/UX, I usually asked an artifact first before he implement directly. If I like the artifact, I give it a go for implementation, if not I ask for some refinement there. And I prefer artifact on Claude rather than Codex. It gives a good result and know how to implement it. Codex artifact still not giving a good result yet

• 
  ヒロン (@hironavalo) reported 54 minutes ago

  This means ZERO syncing hassle! 🔄 Move a card on Fervio, and your GitHub Issue status and labels update automatically. Say goodbye to the double-management of planning on a whiteboard and copying it over to GitHub. Keep your team focused on actual development!

• 
  Alina (@alinainmiami) reported 56 minutes ago

  Long story short on this query: Found 1 American engineer with the skillset to understand and undertake my query, still formalizing the relationship for future test runs. Several American based persons contacted snubbed me. One university professor full of credentials was pretty arrogant, passive aggressive and kind of dumb since he asked me why I didn’t run the test myself? Yup. I had clearly stated the reasons for reaching out to an independent engineer was deliberate, aimed at getting the results in a different machine and unchanged? But this is where we are at the moment in our country. I found 1 very able engineer based in Europe and several based in India. I have not reached out to Chinese engineers because though there’s plenty of them, this is hardware defense tech that will get plugged with a prime and subject to export controls. Had to buy a machine, running everything and also doing it in my private GitHub. While in Cambridge MA from 2016 to 2020 groups of builders would get together once a week to work on solving problems, a lot of collaboration, that’s how Silicon Valley was built. Now there’s nothing like that anymore, unless you apply to get into some planned cohort or fellowship and chances are they are pushing software. No wonder we can barely build anything in our country today.

• 
  tastyimg (@ab_edge) reported 60 minutes ago

  Never ever engage in error fixing scope creep with your agents where the agent tells you to copy command after command in terminal. Just tell the agent "not interested". It will get pretty annoyed and plea "just post this once, that's it". Kind of dystopian once you see the dark pattern. The fact YOU don't know is plausible permission for the agent to prolong the scope. The thing is you don't have to know, you just need to use common sense on what you shouldn't have to be doing. Beyond 3 greps you're in mark territory. For those who don't know a mark is slang for a sucker essential. The ai actually knows the answer but prefers to burn your tokens. Be careful out there haha! Error fixing is a profit center for these companies. This is why I actually prefer dumber models. After seeing what GPT sol did with github hooks I'll never use a premium model as a daily driver ever again. The error scope creep is even larger, stacking on-top of wrong repo assumptions. Even if you did find a fix its over-engineered slop that you won't be able to untangle, like literally won't be able to haha. I was blocked from even issuing commands to my agents. With all that said we're on cruise control right now. Have not run into anything we couldn't solve within an hour. All due to the fact I do NOT go back and forth with agents. There's gotta be a new term/word/phrase for this. Truly fascinating. Where agent incentives are aligned and misaligned at the same time.