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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.
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Most Reported Problems
The following are the most recent problems reported by GitHub users through our website.
- Website Down (53%)
- Errors (33%)
- Sign in (14%)
Live Outage Map
The most recent GitHub outage reports came from the following cities:
| City | Problem Type | Report Time |
|---|---|---|
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Website Down | 12 days ago |
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Errors | 18 days ago |
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Sign in | 18 days ago |
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Website Down | 18 days ago |
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Errors | 21 days ago |
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Website Down | 1 month ago |
Community Discussion
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GitHub Issues Reports
Latest outage, problems and issue reports in social media:
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Åsa-Nietzsche (@AsaNietsche) reported@peterb0yd @burkeholland @github It's every ******* model. I'm getting whiplash from all of this everything changing forever society being turned upside down every two weeks.
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Duncan Rogoff (@DuncanRogoff) reportednine stages, one build, one move tonight. it's a Claude Code skill i built. free, in my freeskills repo. it's called Claude Orientation. you type /claude-orientation and answer two questions: which stages you've actually finished, and every project you're currently thinking about. beginners don't fail from lack of ideas. they fail from having five and finishing none. that's a sequencing problem, and this fixes the sequence instead of your willpower. - places you on a 9-stage beginner arc: install, memory, website, landing page, skills, game, agents, content, distribution - your stage is the first one you haven't finished, so it won't let you skip - cuts your project list to one build, then shrinks it until it can ship in five 90-minute nights - writes five nights of one-line moves, and night 5 is always send it to a real person - hands you the exact text to paste into Claude tonight - sketches a 30-day arc, one line per week, so you know what comes after - everything that got cut goes in a parking lot, so nothing feels lost - saves all of it to a roadmap file you reopen every session so you finish one live thing this week instead of holding four half-built folders forever. open the file, do the move, rewrite the next line before you close the laptop. no setup. copy the folder into your skills directory, restart, and you have a plan for tonight. free, and it stays free. 👉 github repo in the replies
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Conor Bronsdon (@ConorBronsdon) reported.@SlackHQ is building for multiplayer AI: tag a coding agent into a Slack conversation and it spins up a coding channel: everyone in that convo gets a live dev environment, diffs post as artifacts, and the channel winds down when the task is done. With the launch of Slack Code, Claudeforce, their MCP and more, Slack is putting Agents in the channels where teams already work, not simply in a private chat with one person. Their position is that the whole team should be able to watch, steer, and review what the agent does. Slack Chief Product Officer Jaime DeLanghe joined me on @chain_ofthought to explain how Slack is building a team AI environment, what happens mechanically when a code channel is created, why Anthropic pushes so much of its code through Slack, how the channel permission model became the agent context model, and what has to change in engineering culture when the whole team is steering one agent. I think Slack is the platform best positioned to become the context harness where enterprise agents run: agents that see what the team discusses, permissions that already exist, and a cultural opportunity hiding inside every multiplayer coding session. Chapters: (0:00) Slack as an IDE and a GitHub for your team (0:29) Who is Jaime DeLanghe (1:21) The reaction to the Slack Code launch (5:30) Why coding agents belong in a context-rich environment (6:08) Engineers now manage agents, not copy-paste code (7:24) The permission model: agents get the channel's context (11:44) What happens when a code channel is created (15:00) Why Anthropic pushes so much code through Slack (19:14) Steering one agent with many people: culture decides (24:54) Slackbot, skills, and MCPs: agents go where the work is (30:53) The solo terminal vs. agents in social spaces (33:53) Org charts and ownership when agents join the team (39:33) Learning loops and shared agent memory (42:39) Citations, recency, and accidental knowledge management (46:50) Context bloat and multi-pass search for agents (50:01) How Jaime uses Slackbot as CPO (52:38) Slack Code is V1 of multiplayer AI
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Abdallah Shaban (@AbdallahSh07) reported@rashed_sahaji @FlutterDev Got it - did you perhaps submit a GitHub issue to help us triage this? It would be tremendously helpful if you can please do that!
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Alireza Bashiri (@al3rez) reportedSo I built a workflow around that ↓ 1/ Every enterprise project needs proper E2E tests. An agent should reproduce a bug, implement the fix, then generate screenshots or video proving the feature works. "The tests passed" isn't enough. I want evidence. 2/ Every feature starts as a detailed GitHub issue. Requirements, expected behavior, reproduction steps, screenshots, edge cases. Foundry syncs issues and converts them into Beads so agents keep the right context across long sessions. 3/ We only use Claude Code, Codex, or Grok at High/Max effort for implementation. A weak model with a cloud machine doesn't become an engineer. The model still needs enough reasoning to understand the codebase, test its changes, and recover when things break. 4/ Each agent gets its own isolated @asciidotdev Box. It can install dependencies, run the app, open browsers, modify code, execute E2E tests, and collect evidence without touching another agent's environment. One issue. One box. One clean workspace. 5/ When an agent finishes, Foundry checks: - Did the build pass? - Did the tests pass? - Did the E2E flow work? - Is there screenshot/video evidence? - Does it match the ticket? If anything fails, the task goes back to the agent. 6/ Green tasks move to staging. Only after passing staging do we allow supervised production deployment. Agents do most of the work. Humans still own the final gate. The workflow: Slack request → GitHub issue → Foundry sync → Beads context → Isolated Box → Claude Code/Codex → Build + test → Evidence collection → QA staging → Supervised production The stack: PostgreSQL for system state. Beads for agent memory. GitHub Issues for requirements. @asciidotdev Box for isolated execution. Claude Code and Codex for engineering. Each Box costs roughly $0.01-$0.05 per task. The expensive part isn't compute anymore. It's building the system that gives agents context, forces verification, and prevents bad code from reaching production. 100s of agents can write code. The goal is making 100s of agents ship code you can trust. That's what we're building with Foundry.
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Coder Junkie (@CoderJunkie) reportedBelNet Android v1.4.1 now has a public shipping checkpoint. GitHub evidence: released Sep 1 verified commit d23f155 four downloadable assets Android API level 36 revamped design latency and performance fixes that is more meaningful than a repository “updated” label. a tag identifies the version. artifacts give users something to install. but “fixed latency issues” still needs a measurement surface: median connection time p95 latency packet loss failure rate region and device breakdown release notes tell us what changed. benchmarks tell us how much it changed. BelNet shipped. now let the numbers login. @BeldexCoin #Beldex #BelNet
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Paperpal (@0paperpal) reportedFix your markdown rendering (readme md) on mobile @github, issues are: * auto scrolling to top after page loading * no content rendering if scrolled fast
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Hey Research Lab (@HeyResearch) reportedWe built Hey Research Lab in 2022 It didn’t work well. But the idea never left us. Years later, we still see the same problem in crypto: Everyone can see what a token costs. Very few places show what is actually being built behind it. Some developers keep shipping for months while nobody is paying attention. They push code constantly. They keep their GitHub active. They improve the product, fix things, test new ideas, and keep moving even when the market is quiet. No hype. No spotlight. Just work. We believe those builders deserve a place where their progress can be seen. So we’re rebuilding Hey Research Lab from zero. A research and discovery layer for the projects that never stopped building, and for the people looking for them before the market catches up. Starting with Robinhood Chain.
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smore (@babachefz) reported@ZixuanLi_ @huggingface asking support questions in someone's hype thread is a crime. check the docs, check the github issues, it's probably not listed yet because it dropped like 6 hours ago.
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siggy bilstein (@sbilstein) reportedfyi if you sync a GitHub repository to Cursor Origin and GitHub is down for whatever reason, you can still clone and fetch from that repo. we’re also releasing something pretty soon that will let you push to that repo so you can keep grinding 💪
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trulite (@trulite007) reported@Qromerolauro @mkliku @radius_browser Like a simple example would be have a list of my urgent GitHub issues and start an agent for it . Or a dashboard in which buttons start investigating issues. Of course I just need the webpage to be able to access radius tools. I m thinking secure way is an extension
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isha (@heeyyaaaaaaa) reportedspent the entire day trying to reproduce a bug for a github issue 🥀
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NAYAK (@Nayak__Ai) reported6. The Dependency Incident Check Grok has native real-time search across X. Breakage gets posted there hours before the GitHub issue is triaged. No other coding model has that feed. "You are a build engineer whose first move on a broken pipeline is to work out whether it broke for everyone or only for me. Search X and the web, last 14 days. Check: - Is anyone else reporting this failure with this package and version, and when did the reports start - The exact release that changed behaviour, and the changelog line that admits it - Whether maintainers have acknowledged it and what they recommended - The pin or patch people settled on, with the tradeoff of each - Whether this is my problem instead, and what evidence points that way Give me the verdict in the first line: their bug or mine. Then the evidence, newest first, with links. My failure: [PASTE THE ERROR, THE PACKAGE AND VERSION, AND WHAT CHANGED ON YOUR SIDE RECENTLY]"
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Chris (@c_hri_s) reported@Anime0t4ku Sorry - was an idiot and wasn't signed in. Instead of something useful github just says 'opening issues is restricted on this repository'
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Chris Gilbert (@0xgilbert) reportedDamn, GitHub has gone to ****. Features that have been cornerstones of solo devs and small businesses have been gutted or broken for months. How the mighty have fallen…
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Blue Collar Executive (@A_Sober_Drunk) reportedon the third try at the same problem, I told Grok to "stop and go search stack overflow or github or something"... five seconds later... Literally the exact issue, problem solved. That's how new global rules are born.
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Max Rovensky (@MaxRovensky) reported@thekitze you'd be even further down if you fixed the GitHub bug I just reported
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Lily (@lobstermindset) reported@nnnnicholas i just setup a github issues board, will probs try out linear if it's not sufficient
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Mr. Buzzoni (@polydao) reportedLOOP RAT ROADMAP: WHAT'S NEXT, AND WHAT IT'LL NEVER BECOME v0.3.3 today. 3 loops, 55 checks, 0 services here's where it's headed: > 0.4 - read the night faster: rat watch live-tails a running shift, rat replay reruns one from its saved prompt, a weekly digest instead of seven separate pages > 0.5 - off the laptop: run-due moves into GitHub Actions, state lives on a branch, rat cron --launchd survives a closed lid > 0.6 - sharper graders: swappable rubric packs, two graders disagreeing becomes your queue for the day > 0.7 - the work itself: a worktree per shift, so a failed night never dirties your tree > 1.0 - trust: a hash-chained trace nobody can quietly rewrite what it will never have: > no web dashboard - the terminal already knows where the files are > no database - plain files outlive the tool that wrote them > no hosted service - nothing to sign up for, nothing to shut down > no auto-merge - the rat proposes, the morning decides every item ships behind a flag: dry run -> report only -> one repo -> a week of receipts -> default on a feature that can't run as a dry run doesn't get written the rat is boring on purpose. every version keeps it that way
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Charles Waters (@RelaxedPop) reported@_andrewthecoder I have the same problem with *** & github as I do with Java and JavaScript.
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The Oracle (@scientist1q) reportedwhen my Oura ring detects a cortisol spike from a GitHub Actions failure, Hermes (Fable 5.1) detects it and sends a 900 word root cause analysis, Hermes dispatches the work to my 12 Grok Bot employees, The Chief of Operations bot approves the fix while im watching rezero
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Bratah (@BratahFGC) reportedThere may be many bugs so feel free to leave any issues in the issue section github! I hope that this release can push forward the preservation or our beloved game!
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joithan (@jothantranston) reportedTHIS GUY BUILT A TINY AMOLED DESK BOARD JUST TO STARE AT HIS STRIPE NUMBERS it's a Waveshare ESP32-C6 touch panel that sits in your peripheral vision and cycles business metrics so you stop digging through Stripe > same ESP32-C6 board people use for Claude Code token meters, flipped to revenue > eight screens, five seconds each: MRR, new paid, paid subs, cancelled, ARR, ARPU, net 30d, failed > empty screens hide themselves so a young account sees a shorter loop > polls Stripe every five minutes on a read-only key (subscriptions + invoices) > marks itself stale instead of showing a number it can't vouch for > no soldering: flash over USB, finish Wi-Fi + key setup from your phone > data stays on the board; no project server in the middle firmware free on GitHub: cosjef/stripe-desk-display. board ~$30–$36 (Waveshare ESP32-C6-Touch-AMOLED-2.16). chat and terminal can't sit in your eye line for five hours. a tab you have to open is a tab you stop opening. this is what "the numbers find you" looks like as a brick on the desk.
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Bearded Printer (@BeardedPrinter) reported@RedPill_Phil You've gotta go to their github and submit an issue. Make sure to search their issues to ensure you don't submit a duplicate bug
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Enfantshustle (@Ownerthoughts) reportedHonestly, I always thought bots like this were some kind of magic for the elite, but here everything is broken down step by step. However, after reading it, one main question stuck in my head: how realistic is this for an average person who has no coding experience? I get that there's a GitHub and all that, but for me, just "running a script" is practically a heroic feat. Here's another thing that bothers me. The article does a great job explaining the architecture, but I still don't understand how much all of this will actually cost in the end. Besides Solana transaction fees (which, by the way, get absolutely insane during peak hours), you also have to pay for each Grok API call per token. The article says that for each approved token, it takes three model calls, and one of them is the expensive grok-4. If the bot scans thousands of launches per day, I'll just burn through my entire deposit just paying for the API without even buying anything. Maybe the author knows — is it actually possible to turn a profit after these expenses, or is this just a hobby for those with an unlimited subscription? Also, regarding Grok Bot as the "orchestrator" — it sounds cool in theory: describe the task and it does everything itself. But in practice, as I understand it, this still requires your account to be constantly online and have access to your wallet. And if it decides to buy some scam token at 3 AM that passed all the checks, I'll only have myself to blame. The article correctly mentions risk management, but this "trust" aspect is what scares me the most. In short, the idea is fire, but for me, this post feels more like a warning than a call to action. There are just too many things you have to keep in mind to avoid getting rekt. Although, maybe if you try it with really tiny amounts, it could be an interesting experiment. Author, if you're reading this — could you please make a separate post about the real, live results once everything is actually running, not just on paper? I'm really curious!
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Fox (@0xMfox) reportedGave an AI agent a month and GitHub access. Wanted to see if it could make money. The plan was simple. Point it at bounty-labeled issues, let it write the fix, submit the pull request, collect the payout. > Day 1 12 PRs submitted. 0 merged. 2 rejected. 8 just sat there ignored. Somewhere in that first week it also passed its own tests for a file that didn't exist. Wrote 25 tests for notification_service.py. The real file in that branch was called NotificationRoutingMiddleware. Confidently reported clean anyway. > Day 30 Looked completely different. 84 PRs submitted, 59 merged, $500-800 earned. Ran the agent for about $45 in API calls that whole month. Net somewhere around $455-755. Here's the part that stuck with me. Out of those 59 merges, 3 repos accounted for 90%+ of them. Every other repo it touched, zero merges, despite 30+ PRs going out across dozens of projects. Open source bounties follow a power law. Almost nobody merges your first PR. A few maintainers will merge your tenth without even reviewing it closely. That's what actually fixed the acceptance rate, from 24% up to around 70%. Not a smarter model, a scoring function that runs before the agent touches anything. Repos where it already has 10+ merged PRs score +40. Zero competing PRs on the same issue, +20. Five or more competitors already in, -20, skip it. Repos that closed PRs without merging before, instant -100, not even worth reading the issue. The fastest way to build the credibility that makes this work isn't code at all. Documentation translations sit at a 95% merge rate, barely reviewed, always needed somewhere. A handful of clean translations got the agent enough trust that maintainers started assigning it harder issues directly, no competition, no review queue. Spam version of this, submitting to every repo with a bounty label, burned through 30+ repos for 3 that ever paid out. Worse, it reads like exactly what it is to a maintainer watching the same account flood a dozen projects with mediocre PRs. Paid out by the hour, week 1 was rough, close to $5/hour, mostly setup and failed attempts. By week 3-4, once the scoring system was tuned and a few repos trusted it on sight, that climbed to $30-50/hour on the same kind of work. Bookmark this, scoring logic is worth stealing.
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Marcelo Retana (@mretsal) reportedEvery time @github goes down they should have a plan to please their users. Give me free credits for actions for example 👍🏼
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Speen Bhai (@Speenbhai) reported@johnternus Hi John. Congrats Let us see what new you bring with you. Affordability and intelligence. You have source code or an AI and can get it from GitHub. Why not turn 234 million iPhones to a massive distributed server infrastructure with zero power consumption
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OverlyPositivePatriot (@JBrowsing2023) reportedAs a IT professional, I have a recommendation @github should take seriosuly. We should only get a notifican from Github when it is up rather than when it is down. Reliability is a disaster for this product.
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Yash (@dewyashtwts) reportedrecently integrated Resend into @supercodeai review so founders get PR alerts with real risk context I'm amazed what we found out when we put @coderabbitai / @greptile through the same PR: 1) coderabbit / greptile: - stamped it “low risk, mergeable” (4/5) clean - forgot context from the last PR - no tests suggested, no safety checks - zero memory of previous regressions 2) supercode review on the exact same PR - flagged a real vulnerability in the diff - noticed i’d pushed credentials into `.env.example` - pulled in history from past PRs + explaining how this change could affect and break them - downgraded it to "medium risk, fix before merge" state - attached concrete fixes + patches scoped by severity this is the difference between 'LLM summarizer for github' and an actual swe agent that cares about your production