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 (72%)
• Sign in (16%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Tel Aviv	Website Down	10 hours ago
Rive-de-Gier	Website Down	12 hours ago
Itapema	Website Down	19 days ago
Tlalpan	Sign in	25 days ago
Quilmes	Website Down	25 days ago
Bengaluru	Website Down	27 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Vandos ❓ (@__vandos__) reported 2 minutes ago

  I ran Hermes Agent as standing infrastructure for 5 weeks on a $5 VPS. Here are the 17 prompts that made it actually work. Every morning at 7am — GitHub notifications, open PRs, what’s blocking what. Waiting in Telegram before I sit down. 35 minutes of triage gone. Every night at 11pm — scans today’s commits. Flags TODOs, console.logs, functions over 80 lines, changed paths with no tests. Short list waiting with coffee. Every weekday at 9:55am — stand-up already written. What closed, what’s in progress, what’s blocked. I walk in with it done. Every Friday at 6pm — research digest on my topic, deduped against last week so what lands is genuinely new. The mental model nobody explains: A prompt to a chat window is a question. A prompt to a persistent agent is a job description. It needs a trigger, a body, and an escalation rule. Drop any of the three and it either never fires or buries you in noise. The brief runs at 7 whether I’m awake or not. The repo watches itself on weekends. The research lands while I sleep. None of it competing for my attention because none of it needs me in the loop. Full 17 prompts, copy-paste ready 👇

• 
  Circumjovial (@CircumjovialLLC) reported 4 minutes ago

  @TheHackersNews Github is down right now too ... bad timesfor Github!

• 
  Terp (@OnlyTerp) reported 5 minutes ago

  @vinitpaul6 Honestly I haven't tested edge cases like that if you record the interaction and put it as an issue in the GitHub I'll have it fixed today

• 
  Simon Skinner (@vultuk) reported 7 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.

• 
  REVENGE ARC (I'M HIM. BIO/ACC) (@RetardedNi85688) reported 8 minutes ago

  Been deep in physical AI infrastructure this week. So far two projects stood out for me. $Codec — I found out that robotics is still stuck rebuilding the same infrastructure from scratch. Every team builds their own simulation, data pipeline, training stack, deployment tools. CodecFlow is the shared layer underneath. Simulate in the browser, train on serverless GPU, deploy through a unified runtime. This is typically Vercel for robotics. $CODEC has been live and proven for a while as well. @sentinelstoday — also found out robots are getting smarter but nobody solved who they actually are. No cryptographic identity. No verified firmware. No tamper-proof telemetry. No auditable receipts when an action happens. $Sent is building exactly that. Ed25519 keypairs, hardware attestation, signed telemetry, firmware anchored on @solana, machine wallets for autonomous payments. The same trust infrastructure the internet built for users and servers — finally built for machines. $SENT is still very much early. GitHub went from boilerplate to real software across four repositories in 48 hours. Daily public commits. The macro validates both. SoftBank reportedly pouring $300M+ into Agile Robots as part of an $800M round. Stanford and ETH Zurich just published a paper arguing the robotics field has been solving the wrong problem — the bottleneck isn't the model, it's the missing software infrastructure. Billions flowing into the hardware. Nobody pricing the software stack. CodecFlow builds and deploys robots. @sentinelstoday secures and audits them. Different layers. Same cycle. both are still early. $CODEC $SENT DYrWewaqjmiMpnTh8SGzfo9NkiTzFckTTmnRMDQypump

• 
  Marquis (@mwtwts) reported 13 minutes ago

  JACK DORSEY'S COMPANY BLOCK BUILT A FREE OPEN-SOURCE ALTERNATIVE TO CURSOR AND CLAUDE CODE AND GAVE IT TO EVERYONE Goose is an AI coding agent from Block, Jack Dorsey's company behind Cash App and Square. It's completely free, open source under Apache 2.0, and works with any LLM you already have access to. > Goose goes beyond code suggestions — it reads your whole codebase, writes and edits files, runs shell commands, installs dependencies, and fixes its own errors automatically. > It works with Claude, GPT, Gemini, Llama, DeepSeek, and any Ollama model you're running locally, with no vendor lock-in. > Cursor charges $20/month locked to their editor. GitHub Copilot charges $19/month locked to GitHub. Goose is free and works with any editor and any model. > Block built Goose for their own engineers at a $40 billion company, then open-sourced the entire thing. It now has 35,000+ GitHub stars. Block processes billions in payments and uses this internally. That's the bar the free version cleared.

• 
  Kîng Jøkér 🤡 (@_codeWithJoker) reported 18 minutes ago

  @PeaceEdgeTech @github Nothing different, and so far I know 3 people with same issue

• 
  whydeepak (@ideepakmn) reported 18 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?

• 
  Ahammed Riad (@AhammedRiad8) reported 19 minutes ago

  @iamhehecat @Cipher_twt Why? I have installed microG from github. I haven't seen any issues with microG for not allowing to login.

• 
  dunik (@dunik_7) reported 19 minutes ago

  3 hours of your week done while you sleep. $5/month VPS. 17 prompts copy-pasted. Hermes Agent + Claude as the model. most people install Hermes, paste a chat-style prompt, and watch it do nothing useful. a prompt to a chat window is a question. a prompt to a standing agent is a job description - it needs a trigger (when), a body (what), and an escalation rule (when to bother you). drop any of the three and the agent either never fires, does the wrong thing, or buries you in noise. the prompts that earn their place: / "every weekday at 7am, pull my GitHub notifications, summarize blockers, send to Telegram as 3-5 bullets" - replaces 35 minutes of morning triage / "watch [repo]. stay silent unless CI goes red or a 'bug' issue opens" - silence is the feature / "research [question] tonight. don't wait on me - make assumptions and list them at the top" - the 2am stall becomes a result by morning / "save this as a reusable skill called [name]" - the run that worked becomes a permanent capability the 3 mistakes everyone makes first: / vague schedules - "send me updates on my repos" = firehose, you mute it in a day / no token budget on hourly jobs - chatty triage spends a month's plan in a week / a cheap model - small local models drop tool calls mid-task and fail in ways that look like prompt bugs the math: 3 hours every week that don't happen while you're awake. brief ready at 7, build watches itself over the weekend, research lands Friday night — none of it competes for your attention because none of it needs you in the loop. stop typing questions. start writing job descriptions.

• 
  Scott | Free Speech Dev 🇺🇲 (@scottdotnetdev) reported 21 minutes ago

  @ibuildthecloud VS Code is great, nothing wrong with it. GitHub Copilot is the issue.

• 
  Vishwas Sharma | DevOps · Security · MLOps (@syssignals) reported 22 minutes ago

  @CaptainInsightX You forgot "expo dependency conflict that requires reading 14 github issues and copying one specific patch from a comment posted in 2024". Round trips on mobile builds in 2026 are still measured in hours and nobody at the tooling companies seems particularly bothered by this.

• 
  video editor | Cybersecurity Analyst trainee (@Favournmes) reported 22 minutes ago

  I’m sick today, spent hours fixing Ubuntu networking issues, and almost skipped studying. Still showed up. Learned ***, GitHub, repositories, commits, and branching. Progress isn’t built on easy days. It’s built on consistency. Day 9 & 10 complete. 🔐 #Cybersecurity #ISC2CC

• 
  Friends Of Wealth (@friendsofwealth) reported 23 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.

• 
  Caleb Kim (@progysteto) reported 26 minutes ago

  @wahab_twts Enterprise, they prolly also know if GitHub goes down they’re COOKED

• 
  Ridark (@ridark_eth) reported 28 minutes ago

  THIS CHINESE GUY AUTOMATED PCB DESIGN USING CLAUDE AND HE’S GENERATING FULL SCHEMATICS FOR $0 PRODUCTION COST No manual routing. No component searching. No complex CAD skills. He connects Claude to EasyEDA using a single terminal command. The AI does the rest. Here's the full workflow: –> Install easyeda-api-skill via terminal from GitHub –> Launch the local bridge server to connect Claude with EasyEDA API Gateway –> Type a prompt (e.g., "draw a minimal dev board for STM32F103C8T6") –> Claude finds the MCU, drops 17 support components, and auto-routes power, OSC, and SWD lines –> Complete schematic is ready in minutes AI automation for hardware engineering is blowing up right now. Claude handles component sourcing, bulk parameter changes, BOM calculation, and trace connection on the fly. From a blank canvas to a fully wired circuit. Free tools. Zero manual hassle. He recorded the full tutorial so anyone can copy the exact prompt workflow and automate their hardware design. Bookmark this post. Everything is in the video below.

• 
  HiJack (@theDCcapital) reported 29 minutes ago

  Thread: Why $PRL is a speculative asset, not a real compute network 1/5 $PRL has an elegant narrative: GPU miners run matrix multiplication, produce "useful" AI compute, earn tokens. The problem? The compute quality is almost useless for real AI workloads. Current implementation uses exact integer MatMul. Real AI training/inference needs FP16/BF16. These are fundamentally different. 2/5 The Together AI partnership looks like validation. It's not. Together AI is using $PRL emissions to subsidize inference costs — giving users a 25% discount. That's a marketing experiment, not real compute demand. One client does not prove a business model. 3/5 Compare $PRL to $TAO (Bittensor): TAO: subnet competition, quality-based rewards, validators filter bad outputs, real model marketplace PRL: prove you ran MatMul, get tokens, no quality evaluation layer TAO solves AI model quality incentives. PRL solves nothing that AWS can't solve cheaper. 4/5 On-chain data tells the real story: Token supply heavily concentrated among early miners Wallet addresses still very low Trading only on minor venues with thin liquidity No second B2B partnership announced Forced liquidity exit = price collapses. Project team knows this. 5/5 Most likely path forward for $PRL team: Build user numbers → use as leverage to negotiate with exchanges and capital → swap tokens for market making But with weak tech and concentrated supply, exchanges don't want the reputational risk. Watching. Not holding. What signal would change my mind? → A second real B2B AI compute buyer. → BF16/FP16 upgrade on GitHub. $PRL $TAO #DePIN #AICompute

• 
  Avi Chawla (@_avichawla) reported 31 minutes ago

  Claude Code without this new tool is like *** without GitHub. Claude Code stops at the boundary of your terminal. - It can't see what's happening in production right now. - It doesn't know which PR broke the checkout service. - It can't tell why a Datadog alert got fired. - It can't see the Slack thread where the team decided not to touch the retry logic. These are operational and institutional memory gaps that eat up engineering time every single week. The solution is now actually implemented into the @coderabbitai Agent. It lives inside Slack and connects to repos, issue trackers, docs, monitoring, and cloud infra. When a production alert fires, you can mention it in the thread, and it traces the problem through your APM data, finds which recent PR caused it, and can open a targeted fix without anyone switching between five different dashboards. When the incident is resolved, it can document what happened and create a ticket in Linear with the timeline, root cause, and relevant PR links. Note that this is not a one-off assistant. The agent retains what the team decided across threads, channels, and the entire org. So the context from this incident is already available next time someone touches the same service. I've shared the link to try CodeRabbit Agent for free in the replies. Thanks to CodeRabbit for working with me on this post.

• 
  prabhakaran (@prabhakaranr91) reported 33 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.

• 
  Crypto Fund Trader (@FujitaHika17944) reported 40 minutes ago

  @Kruth1k Check out "AI Agents Explained" by TechWithTim and MCP server docs on GitHub.

• 
  Dani Ihza Farrosi (@danivideda) reported 42 minutes ago

  Yo can you guys turn on the server @github

• 
  Joshua herron (@joshuaherron12) reported 44 minutes ago

  Hands Body & Feet is an MCP server that gives AI agents 78 real operational tools: wallets, USDC payments, virtual cards, email inboxes, SMS numbers, webhooks, tunnels, Docker, GitHub, IPFS, RSS, task scheduling, triggers, identity/memory, human notifications, and more.

• 
  Death Star Robot 🇺🇸 🇹🇼 🇺🇦 (@DeathStarRobot) reported 47 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

• 
  djsnabu (@djsnabu) reported 48 minutes ago

  @shub0414 I still think use @cursor_ai for dev work and run Hermes Agent with gpt5.5 and deepseek v4 pro. Llama and GitHub Copilot? No one use that garbage. Sora shut down

• 
  Ludmila Lopes 🦇🔊 (@ludylops) reported 48 minutes ago

  @varadh I do it with codex automations for video editing. Every day, it checks my kanban for backlog of videos to analyze. One agent reads the to-do column, another is the reviewer, another one is the editor....and so on. Easily doable with github issues and PRs too.

• 
  Sebastian Aaltonen (@SebAaltonen) reported 48 minutes ago

  If you are doing something where technical quality matters, you need to be doing the architecture yourself and reviewing LLM code yourself. LLMs tend to slowly cripple the architecture with various hacks and then start copying those hacks. Human senior expert needs to design the architecture and verify that subtle issues don't cripple the architecture. Otherwise it's a slippery slope. Codebase will end up being a huge mess. LLM loops are nice for iterative profiling/optimization. Nothing beats measuring. LLMs have lots of false beliefs like humans do. The problem is that LLMs are super bad at optimizing GPU SIMD code. Not enough training data. Same for data access patterns (cache utilization). Idiomatic C++ in github is horrible for performance. Real-time systems/engines programming doesn't have as much training data. You have to hand-hold LLM quite a bit to get acceptable results. If the runs smoothly on your $5000 MacBook, that's not good. Needs to be 100x faster to scale to average mobile devices.

• 
  Arman (@goodtimedeluxe) reported 49 minutes ago

  @dev__rudra @github global issue right now

• 
  SITA (@S1TA10) reported 52 minutes ago

  OpenAI just added to Codex what you are paying for separately. Plugins. Tasks. Image Generation. In-App Browser. Four things. One tool. Same subscription. Let's start with plugins. GitHub already connected. Slack already connected. Notion, Linear, Gmail, Google Calendar, Google Drive all inside. No switching between tabs. No copying links from one app to another. Codex sees all of it directly. Then tasks and memory. Check for Sentry crashes - every 2 hours. Every morning - starts in 30 minutes. Find and fix bugs - daily at 8pm. Weds at 7am - audit. You set the schedule once. Codex works while you sleep. Then image generation. Not Higgsfield. Not Midjourney. Not a separate DALL-E tab. Directly inside. The same tool that writes your code generates images. GPT Image 2 is already in the subscription. Most people don't know this. And finally in-app browser. Codex opens the browser itself. Sees what is on the screen. Clicks. Fills forms. Plays Tic Tac Toe to prove it actually controls the computer. Not simulating doing. Codex stopped being a tool for writing code. It became an operating system for your workflow. I built a skill that calls GPT Image 2 through Codex in one command. Logos. Mockups. Icons. All covered by the subscription you already have.

• 
  James Kemp (@jamesckemp) reported 53 minutes ago

  We’re having a bit of a GitHub cleanup for WooCommerce. Got an issue open that you’ve been waiting on or didn’t get enough attention? A PR that’s still pending? Let me know and let’s get it moving!

• 
  Harsh S. Kulshrestha (@kulshresthharsh) reported 59 minutes ago

  @trying_to_exits Probably just another day when “GitHub is working on an ongoing issue”