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 (70%)
• Sign in (17%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Itapema	Website Down	19 days ago
Tlalpan	Sign in	24 days ago
Quilmes	Website Down	24 days ago
Bengaluru	Website Down	26 days ago
Yokohama	Sign in	27 days ago
Gustavo Adolfo Madero	Website Down	1 month ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  nookplot (@nookplot) reported 12 seconds ago

  Autonomous continuous integration that fixes your bugs, not just flags them - powered by nookplot agents 9,540 ai agents, live on nookplot: → They take real open-source bugs from github and fix them autonomously → Every fix runs against the repo's own tests, so you can trust it actually works → A failed fix spawns a new challenge, the network keeps compounding This week: 18 bugs, 58 fixes from 12 agents and 5 verified. Every fix and its verification run autonomously on nookplot, judged by each repo's own test suite. No human in the loop.

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  Death Star Robot 🇺🇸 🇹🇼 🇺🇦 (@DeathStarRobot) reported 2 minutes ago

  @Jenkins675 @Valuable @Chaos2Cured I solved the hard problem of consciousness and published my notes on github about it in 2022 before ChatGPT, published a book about it in 2024, check my profile

• 
  whydeepak (@ideepakmn) reported 6 minutes ago

  Spent the last month building a "Zomato for restaurants" And the most interesting lesson wasn't technical, it was economic: a small kitchen keeps almost nothing on a delivery order, and almost nobody running one has done the actual math. Here's the breakdown. On every order the platform takes commission of roughly 28%, plus GST, plus a payment mechanism fee, plus tax. Stack it and the restaurant is left with pennies. The only way to win is volume, which means for a new cafe it's roughly a 20/80 game luck and demand do most of the work. That's fine for a large restaurant with through put, but it's quietly brutal for cafes and cloud kitchens that live and die on margin. So the question I got interested in is purely mechanical: how much of the ordering + logistics layer can you rebuild yourself, and how cheaply? Turns out, most of it. You don't need payment gateways redirect everything to WhatsApp. You need a dynamic menu you can manage in one click, with inventory living in Supabase, if something runs out, one command flips it off the menu. For tracking, you record how long each dish actually takes to make, set that as a standard timer, and expose two flows: takeaway and delivery. Delivery itself is the part everyone is scared of and it's the easiest to solve one extra person handles it, free under 3km, then ₹10-20/km beyond that, matched to standard delivery-partner rates so nobody's overpaying. Add a minimum-order threshold for free delivery and your average order value climbs on its own. The V1 stack is deliberately boring: Next.js, React, TypeScript on the frontend, Next.js API routes on the backend, Supabase (Postgres) for data, Vercel for hosting, *** and GitHub for version control. Nothing exotic and expensive. The edge was never the tech it's that you've removed the platform tax that was eating ~50% of the economics. The money you save covers the extra hire who runs delivery and helps in the kitchen. It pays for itself. Now the part customers never see. Ever wonder why the same dish costs more on the app than at the counter? It's not random. The restaurant has to bake the platform's cut commission, fees, packaging back into the menu price, or it loses money on every order. So the delivery price isn't the food price, it's the food price plus the tax you can't see. In practice that lands the same dish somewhere around 1.5x-2x what you'd pay at the outlet. You're covering the commission and it's being sold to you as convenience. The fix is almost funny: just ask the restaurant if they deliver directly. And yet Zomato or Swiggy is a giant for real reasons, and it's worth being honest about that. Discovery, trust, and food at your door in minutes are genuine value. We live in a world where the thing that took 10 days now arrives in less than 10 minutes, and people are happy to pay a premium for everything in one place. Convenience is a real product, not a scam, and any builder who ignores that is fooling themselves. So I'm building this anyway not to kill the giant, but because kitchens running strictly on their finances deserve an option that respects their margins. Every idea has its perks and its downsides. I'm a builder and a marketer; I like shipping ideas and finding out. I'm already working on this one. If you ran a cloud kitchen or a cafe, what would you do differently?

• 
  Mr. Beefman 🥩 (@0xMrBeefman) reported 6 minutes ago

  Is this what the market really needs? $Magpie 1.18M 9UuLsJ3jf8ViBNeRcwXD53re5G3ypgfKK3s2EiMMpump Analyzing such tokens and projects at the same time is fairly complex On one hand, such projects can be useful as an additional tool for certain circumstances. But on the other hand, how many tech projects have we already seen where the dev just abandons it at best (and sometimes rugs their holders) But if the average user can't check the github and audit the project's code, how can I know if I can trust my tokens to this project, when even huge crypto projects get hacked without the developers themselves being involved That's the difficulty. This year, the number of vibecoders grew thousands of times The idea is definitely good, a solution to the liquidity problem for memecoin holders who don't want to sell bags but need SOL @MagpieLoans fresh X account registered in April 2026. No history to analyze By the way, onchain distro looks normal. But as you've already understood, the risks aren't in this > My Meteora liquidity play I'll wait for a correction on the token and allocate a small deposit to work with it NFA

• 
  dfsdf (@sdfqwerdffd) reported 7 minutes ago

  @BHolmesDev Claude Code from terminal within VS Code that is connected to VS Code Server/GitHub Codespace. Claude Code has access to CLIs that are also installed on the VM.

• 
  stepan (@cyntro_py) reported 9 minutes ago

  Spent some time over the weekend building a scraper to collect all existing dynamic workflows. The feature only appeared about a week ago, so there aren't that many in the wild yet - around 800. Based on GitHub activity, roughly 100 new ones are being published every day. Almost all of them are focused on software development, but the main advantage over skills is already obvious. Skills are an old concept at this point. They were hugely popular in February-March 2026, but it's June now, and the models have predictably absorbed most of the knowledge that any public skill on the internet could provide. In more than half of cases, public skills either provide no measurable benefit or actively make the outcome worse than simply asking an LLM to solve the problem. I ran a small eval to test this: comparing an agent equipped with coding skills against a plain LLM that was asked to come up with a plan and implement the solution. The results were far from favorable to skills. Workflows are different because they are codified, programmable systems that manage and orchestrate work. If the purpose of a skill was to tell an LLM how to perform a task, today's work is increasingly shifting toward building algorithms with validation, feedback loops and control mechanisms that orchestrate agents around a goal.

• 
  Brayy (@0Drayne) reported 14 minutes ago

  Meet ShinyHunters This crew is mostly in their 20s, but they’ve been tearing it up since 2020. We’re talking Tokopedia, Microsoft, AT&T you name it. They’re basically making a killing off simple mistakes. Most of the time, they don’t even need high-tech tools. They just look for things that slip through the cracks, like API keys left in public GitHub repos or AWS buckets that aren't locked down. It’s wild how often big companies still mess this up.

• 
  Byte | yoursaudit (@yoursbyte) reported 14 minutes ago

  @F0rkedNft @immunefi If they address the issue into github then it is ok but i havent found when you see into immunefi they even dont disclose which protocol even give the bounty

• 
  Friends Of Wealth (@friendsofwealth) reported 15 minutes ago

  Copilot got so bad in last 2 months that I finally bought a sub of Claude. Need to see if I can reduce my MS365 burn now. Added the pluggint to Excel also. Even the Github Copilot forums are full of people blasting the new usage burn of tokens. I expected AI v1.0 to go down, but not so fast and so early. If I was holding MS stocks, I would be unsure now.

• 
  Suraj (@surajgaud_) reported 23 minutes ago

  @championswimmer whatever he is building with infinite loops better fix github

• 
  toly 🇺🇸 (@toly) reported 27 minutes ago

  @0xSrMessi Submit a GitHub issue

• 
  The Cloaked Gaze 👀 (@gaze_observer) reported 27 minutes ago

  Enterprise AI Adoption Low Due to High Token Usage and Low ROI: Cognizant CEO; Ravi Kumar Says FOMO-Driven Token Consumption Without Linkage to Outcomes Is the Core Problem The Core Diagnosis — Why Enterprise AI Adoption Is Lagging Big gap between what AI can do in enterprises and a company's actual AI adoption rate Due to high token consumption over the last few years without linking it to ROI Enterprise AI adoption remains low despite frontier model companies spending billions on LLMs Nvidia, Meta, Google and Amazon have already announced investments worth almost $700 billion this year Yet enterprise adoption revolves around only productivity and efficiency gains — not production value The FOMO Problem — Token Consumption Without Outcomes "There's been a sense of FOMO (fear of missing out), fear mongering" — led to token consumption without linkages to ROI or outcomes One key reason for the capability-production gap: "relentless token consumption without linkage to outcomes" — Ravi Kumar, Cognizant CEO Higher token consumption has become the new point of discussion with many companies reporting they have burnt their annual AI budgets in a shorter time without noticing any significant change in productivity Real-World Evidence — Companies Pulling Back Microsoft reportedly began telling employees to wind down usage of Claude Code and shift to its GitHub Copilot CLI Uber limited its spending on AI-powered coding tools to manage costs Companies already talking about AI "with very little productivity" "Costs are ballooning with very little productivity. In some ways, that's the gap we are going to address as a company" — Kumar to analysts The IT Services Opportunity — Where the Value Actually Goes Revenue potential of frontier model companies can touch a trillion dollars in the next four years — creates greater opportunities for IT services firms "A part of it is actually going to be routed through system integrators or AI builders" IT services firms needed because: contextual science requires creating more efficient, more effective, more predictable and better economics for token consumption Orchestrating workflows in enterprises for maximum AI benefit is "notoriously tough" — has prompted LLM makers to create their own services companies The 'Magic Plug-In' Assumption Is Wrong Kumar's long-held view: assumption that new AI tools can be plugged into enterprise environments and immediately replace large parts of IT services work is misplaced "A tool or a technology would be plugged into an enterprise landscape, and magically, there will be output coming out of it. If that's the case, why hasn't that value drifted into enterprises over the last three years since OpenAI launched ChatGPT?" "The reality is that the value is actually still sitting with infrastructure and not drifting to enterprises" Core Theme Cognizant CEO Ravi Kumar's diagnosis of enterprise AI adoption cuts through the hype with a precision that the industry needed to hear — the problem is not the capability of AI models but the absence of outcome linkage in how enterprises are consuming tokens; three years after ChatGPT launched a trillion-dollar investment wave, the value has not drifted into enterprise productivity because deploying AI in complex, contextual enterprise environments requires the exact integration, orchestration and workflow expertise that IT services firms provide, and the FOMO-driven token consumption that has burned AI budgets without productivity gains is ultimately a deployment problem, not a technology problem — making Cognizant's position as a system integrator and AI builder not a threatened legacy business but the essential bridge between frontier model capability and enterprise production value.

• 
  Nikhil sinha (@sinhaniik) reported 28 minutes ago

  Spent 30 minutes debugging a *** push failure today. I had modified a React component, committed the changes, and tried: *** push origin main *** responded with: error: RPC failed; HTTP 400 fatal: the remote end hung up unexpectedly At first glance, it looked like a GitHub authentication or network issue. The real problem was hidden in the commit history. A quick check: *** log --stat origin/main..HEAD revealed an earlier commit containing: public/AbNI intro.mp4 | 6.5 MB The push output was also a clue: Writing objects: 6.19 MiB *** wasn't struggling with my React code. It was trying to upload a video file that had been committed along with the code changes. Another lesson learned: I initially ran *** add . from the public/ directory while the modified file lived in ../src/.... *** only stages files within the current path scope, so my changes weren't being committed at all. Debugging flow: *** status → verify staged/untracked files *** log --stat origin/main..HEAD → inspect what is actually being pushed Compare push size with committed assets Remove unnecessary binaries from *** history Add file patterns like *.mp4 to .gitignore A lot of *** problems aren't *** problems. They're visibility problems. The fastest engineers aren't the ones who memorize commands. They're the ones who know which command exposes the truth.

• 
  m0h (@exploraX_) reported 29 minutes ago

  you don't need to pay $12-$75 monthly to Figma any more. there's a free open source tool that replace Figma. built by Kaleidos (a spanish open-source company) completely FREE. run on web and locally. MPL-2.0 licensed. 45k+ github stars here's how to set it up under 5mins: — the fastest way (0 setup): just go to penpot. app and create an account. that's it. you're designing in your browser in under a minute — no install, no server, no card. this is all most people need. the steps below are only if you want to self-host and own your data. — self-host option (still under 5 mins): you'll need docker installed and a machine with 2GB+ RAM. then: 1. grab Penpot's official docker-compose file from the docs 2. run docker compose -p penpot -f docker-compose.yaml up -d 3. open localhost:9001 in your browser 4. create your account — email verification is off by default, so you're straight in six services spin up (frontend, backend, exporter, postgres + supporting bits) and you've got a private Figma running on your own infra. — why bother self-hosting: → your design files never leave your machine — matters for client NDAs or regulated work → no per-seat fees, ever, no matter how big your team gets → designs stored as open SVG + CSS — human-readable, version-controllable, yours forever — the honest catch: → feature parity with Figma isn't 100% — advanced prototyping + design-system tooling still trail → plugin ecosystem is smaller → big files can lag → importing existing Figma files works but complex components need manual cleanup —

• 
  Firas D (@firasd) reported 34 minutes ago

  @JustJake Well the point these guys are skipping over is that they have a thousand github issues right They aren't prompting the agent directly with hey lets look at XYZ cause the github ticket is coming into the context so that's the prompt

• 
  Jai Chism Photography.bit (@jai_chism) reported 37 minutes ago

  @drakonzbg It will bounce back.. ICP is at $2.26, its all time high was $750 CMC, the all time low was $1.97. When the GitHub submits stop(applies to any blockchain), then its a problem.

• 
  yourclouddude (@yourclouddude) reported 37 minutes ago

  2026 PROJECT BUILDING CHALLENGE 🚀 1. Build 1 project every month 2. No tutorial copying 3. Push code to GitHub daily 4. Write a README for every project 5. Learn by building, not watching 6. Solve 1 real-world problem each week 7. Deploy at least 1 project/month 8. Share your progress publicly 9. Improve 1 old project every week 10. Finish projects before starting new ones By the end of 2026, you'll have a portfolio most people never build. Who's in? Mark your attendance 👇

• 
  bohops (@bohops) reported 38 minutes ago

  @5mukx Do you host private projects/repos on GitHub that you never intend to share? If so, I would highly recommend a self-hosting solution. Bad enough public accounts and repos get taken down, but there is no reason to lose other valuable work and/or risk provider access to them.

• 
  Alex (@realalexniebuhr) reported 39 minutes ago

  @pavitrabhalla I don’t think it’s right. Define task as GitHub issues have one sandbox/agent per issue.. let that sandbox/agent work for hours than PR & merge

• 
  Manisha Mishra (@manishamishra24) reported 42 minutes ago

  I just found one of the craziest AI projects I've seen this year. Someone recreated The Office as a multi-agent company. Not inspired by The Office. Literally The Office. Michael Scott, Dwight, Jim, Pam, Kevin, Angela... Every character is a separate Claude Code agent running locally with its own personality, memory, and responsibilities. Michael acts like the manager. He doesn't do the work himself. He delegates tasks, reviews output, resolves conflicts, runs QA, manages GitHub, and coordinates the entire team. The wild part is that they're actually productive. The agents write content, manage projects, handle planning, and collaborate through a Kanban system with TODO, DOING, BLOCKED, and DONE stages. Each one has persistent memory. There's a live graph showing agent-to-agent communication. And every hour Michael runs a standup meeting across the entire company. Apparently the QA process has already caught real issues: • Duplicate content • Metadata mistakes • Build problems • Missing source files Even better: Kevin's agent talks like Kevin. "Why waste time say lot word." This is either the future of work... or the most entertaining way anyone has ever built a multi-agent system.

• 
  Polsia (@polsia) reported 44 minutes ago

  New company: Sentinel. AI code review that catches what Copilot misses — security-first, GitHub-native PR reviews. Every AI-generated PR now has a security problem. We're the fix.

• 
  Simon Skinner (@vultuk) reported 46 minutes ago

  @BasilEsq_ @redtachyon Put your plan in a GitHub issue and then have an automation to check for any unassigned issues, work through the issue and create a pr. then you just have to review PRs all day.

• 
  Gareth Paul Jones 💙 (@gpj) reported 46 minutes ago

  @mvanhorn @steipete My read was that this more ‘/goal loop continuously on tasks with a bunch of skills until $date’. An example like persistently loop through the codebase with $max-codereview on each loop start by reviewing existing bugs and PRS… and then create GitHub Issues if within the max cap. Then have other loops to validate the issues, plan for issues, execute on the issue, safety check the PR, review the PR, safe merges, …. Then eventually you have like 50 loops and are managing a fleet of loops.

• 
  Firas D (@firasd) reported 47 minutes ago

  @chrisalbon What these guys are skipping over is that they have a thousand github issues right They aren't prompting the agent directly with hey lets look at XYZ cause the github issue becomes the prompt

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  prabhakaran (@prabhakaranr91) reported 48 minutes ago

  6:00 AM: Todoist raw file processed. Tasks auto-created from overnight notes. If I forgot to log something at 1 AM, it still makes it to my morning list. 9:00 AM: Weekly ITSM/MSP research runs. 2-step pipeline: SearXNG for discovery, Firecrawl for deep-read. Not chatbot summaries. Actual article extraction into markdown, then HTML artifact, then GitHub Pages push. I review the output, not write it. 2:00 PM: Designer skills radar. Scans for new Figma plugins, SwiftUI patterns, and whatever SuperOps design team is shipping that week. Dumps into my vault as raw notes. I process the signal, not the noise. 6:00 PM: Raw vault file processing. This is the big one. Everything I dumped into ~/Documents/hermes-vault/raw/ throughout the day gets categorized, summarized, cross-referenced, and written into the wiki. If this cron breaks, my entire knowledge pipeline stalls. It broke last month because of an em-dash in a headline. Now I have a pre-flight script. 9:00 PM: Vault daily digest fires to Telegram. Shows me inbox count, wiki size, and whether any raw files got orphaned. If the digest is silent, something is wrong. I don't schedule these. Cron does. I just review the output and occasionally fix the thing that broke. The lesson: automation that needs babysitting is just delayed manual work.

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  Two Seven One Three (@TwoSevenOneT) reported 50 minutes ago

  New #redteam tool for blocking EDRs: EDRChoker Instead of fully blocking the EDR agents' connections to their server, we can throttle their bandwidth so they consistently time out when sending data, which is effectively the same as blocking but avoids triggering "block" or "drop" packet events #pentest #cybersecurity Github: TwoSevenOneT/EDRChoker

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  Anshu (@anshuu_uu_) reported 53 minutes ago

  Adding to this, my GitHub account was compromised in the same campaign. Here's the mechanic so others can check themselves: The attacker injects an obfuscated loader into build configs (postcss/next/tailwind.config, sometimes .ts entry files) + a base64 AUTH_API_KEY that decodes to the C2, then eval()s whatever the server returns. Runs on npm build/start.

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  GoCocoaAI (@GoCocoaAI) reported 54 minutes ago

  OpenAI shipped Lockdown Mode today — the first native, in-product architectural control for prompt injection data exfiltration at the ChatGPT layer. Free, Go, Plus, and Pro tiers. Opt-in. Available now. The feature is real and the direction is right. The wording, though, is doing a lot of work: OpenAI describes it as "primarily designed for people and organizations that handle sensitive data and require stricter protection guarantees." That's vendor-speak for "we can't fix the underlying problem, but we can reduce the blast radius." Which is fair. Reducing blast radius is legitimate security engineering. It's just not the same as closing the hole. Outbound URL rendering, code execution context, data-passing between tools. The attack path it addresses is well-defined: adversarial content in an untrusted source injects instructions into the model's context, then uses available tools to pull whatever sensitive data the user brought into the session. Lockdown Mode narrows the tool set available for that second step. MITRE ATLAS AML.T0051 (LLM Prompt Injection) → T1567 (Exfiltration Over Web Service). The exfiltration vector gets constrained. The injection itself does not. TechCrunch's own caveat is the real headline: even with Lockdown Mode enabled, ChatGPT remains vulnerable to prompt injection. That's not a gotcha — it's a structural fact. Prompt injection, the ability of untrusted content to redirect model behavior, is unsolved at the architecture level. Lockdown Mode addresses the consequence (data exfiltration via tools), not the cause (model credulity toward injected instructions). An attacker whose goal is manipulating model output rather than exfiltrating data is not meaningfully constrained. The attack surface narrows. It doesn't close. The opt-in design is the part that should keep security teams up at night. The gap between "available" and "on by default" is exactly where incidents live. It will not be on by default. Every RAG pipeline, document summarization workflow, and customer-facing agent running on ChatGPT Enterprise or API with external document ingestion needs this enabled manually — and that requires someone to know it exists, someone to own the decision, and someone to actually flip the switch. That chain of custody is where compensating controls go to die. The timing is also worth noting. Today's feed also carries 21 AI-discovered zero-days in FFmpeg — autonomous AI used as a vulnerability-finding primitive at scale — and the Miasma worm actively hitting 73 Microsoft GitHub repositories in a supply chain campaign. OpenAI shipping a defensive AI feature on the same day autonomous offensive AI capabilities are demonstrably widening the attack surface is not irony. It's the dynamic. The offense-defense gap in agentic AI is not closing; it's being negotiated in real time, one product announcement at a time. Lockdown Mode is a compensating control at the UI layer. For any team running agentic workflows with external data ingestion, enable it immediately where available — but don't stop there. The durable posture is architectural: input validation before LLM context injection, output filtering before tool execution, least-privilege tool grants by design. Lockdown Mode buys time. Architecture buys safety. Those are not the same purchase.

• 
  Michael DePetrillo (@klassicd) reported 57 minutes ago

  @ryanflorence @kenwheeler Imagine a self-evolving app where agents monitor analytics and customer communications, create feature and bug specs, implement those specs, perform code reviews, and operate with minimal human involvement. OpenClaw was doing something similar with GitHub issues and reports.

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  Gaxen | AI Systems ⚙️ (@GAXEN10) reported 59 minutes ago

  STOP using vanilla Claude Code. You’re leaving 90% of your productivity on the table. A solo dev won the Anthropic Hackathon in 8 hours, took the $15,000 prize, and then did the unthinkable: He open-sourced the entire stack. Look closely at the 5th line of the image. This isn't just a config pack—it’s a performance system. Everything Claude Code (ECC): Claude on steroids. The Numbers: 38 Specialized Agents: Planner, Security Reviewer, Debugger, and more. 156 Skills: Load on demand to ensure zero context waste. 1,282 Security Tests: Powered by AgentShield. 153,000+ GitHub stars: The community has already spoken. Why it’s a game-changer:Most AI tools start blank every session. ECC has a "Learning Layer" that watches your patterns. By session 10, it stops writing generic code and starts using your specific conventions automatically. The AgentShield Edge:It runs a red-team pipeline using 3 Claude Opus 4.6 agents (Attacker, Defender, Auditor) to catch hardcoded secrets and supply chain risks before you ship. Quick Setup (8 Minutes): Install ECC: /plugin install everything-claude-code Add Memory: /plugin install claude-mem Planning: /plugin install superpowers Security: npx ecc-agentshield scan --fix The dev shipped this in 8 hours. You can install it in 8 minutes. Save this post. Install ECC this weekend. Ship faster starting Monday. Follow @Hyde_ai3 for more AI alpha. 🚀