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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 (68%)
- Sign in (18%)
- Errors (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 | 15 days ago |
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Errors | 18 days ago |
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Sign in | 19 days ago |
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Website Down | 19 days ago |
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Website Down | 22 days ago |
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Website Down | 22 days ago |
Community Discussion
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GitHub Issues Reports
Latest outage, problems and issue reports in social media:
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Dave Oak (@StackCurious) reportedthe pattern i see: maintainers burn out because they treat open source like a business that failed to monetize, instead of treating it like a library. once you're answering github issues like customer support, you've already lost. the fix isn't sustainability models—it's saying no earlier. #solodev #shipping
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Andy Wheeler (@CrimeDecoder) reportedFor academics, this is entirely open source by its nature. If you right click on the page and view the source, you can see exactly how everything is created. (Hence a downside of WASM, there is no way to hide it if you wanted it to be locked down, like in a paid app.) It can also be deployed on a free static site. So you could deploy it via GitHub pages for free if you wanted to. You don't need to worry about a server at all in this setup. This could easily scale to databases with 1 million plus rows, and works just fine on a cell phone.
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Pascual ⚡ (@0xPascual) reportedA high school kid opens an account, plugs in Claude 5, and turns a few hundred dollars of lunch money into a six-figure trading account over the weekend. The screenshot goes viral, the replies fill up with people begging for the GitHub repo, and the standard engagement-bait influencers declare the dawn of the sovereign teenage day-trader. The media thought that was the story. It was not. The real flex wasn't the macro strategy or the directional bets on currency pairs. It was the setup behind it: a lightweight proxy array routing through residential IPs to dodge exchange rate-limiting, paired with a custom parsing engine that instantly translates raw order-book imbalances into executed micro-hedges. The kid wasn't trading; he bypassed the entire institutional pipeline of risk management, brokerage compliance, and analyst overhead with a single configuration file. The entire operation runs on a continuous loop of multi-agent orchestration. A master instance drafts the execution logic, a secondary validation agent checks the code against real-time oracle feeds, and a fleet of worker APIs executes up to 3,210 trades a night. Total infrastructure cost: roughly $45 in API tokens and a cheap server instance. It extracts a 78% win rate out of systemic market inefficiencies, operating with a structural margin that legacy trading desks weighed down by salaries and compliance boards cannot compete with.
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AJ ✝️ 💚🧡 (@angelcreative) reported@uiux_hamad My design team is leaving Figma gradually, in fact we are using Cursor and GitHub as main design tools now, in the past two months the usage of Figma drops 33% and it will keep going down up to 30% more to a 63% in total and maybe more
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Jesse (@jessearmand) reportedI no longer remember why many companies started using gitlab before it went public when GitHub wasn’t owned by Microsoft. If we visit the majority of companies most tooling or software are top down driven. Only companies who build developer tools have a different mindset
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Red Zen Cloud LLC (@RedZenCloudLLC) reportedCursor's Origin platform and Claude's GitHub imports both solve the same problem: developers automating code work need their tools to understand context, not just generate tokens. The winner isn't the smartest model—it's whoever reduces handoffs between agent and human.
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KS Sreeram (@kssreeram) reported@Lidinwise @leecronin Given that AI coding is all the rage… What is your hypothesis on why the following is true? AI is unable to create even _one_ open source project that’s good enough to enter the top one-thousand open source projects (say on github), with ZERO involvement of humans from birth of idea. Imagine the prompt being something like “Come up with a great idea for a new open source project and implement it”. AI is unable to do any such thing with zero human involvement. My answer on why: Every project in a top 1000 list is a hit. Every hit is a mini-invention of sorts. It is necessarily “out of distribution” is some way. AI is unable to do this because we don’t know how to solve the problem of invention.
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The Flow (@raxpcodes) reportedGot bored with ubuntu , set up fedora kde on my nvme and removed windows permanently , no more dual boot. Also learned Verison Control and GitHub , also submitted my first pr (good first issue).
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Adithya S K (@adithya_s_k) reportedbuilt an RL environments around real CVE fixes in real open-source repos and let Claude Code loose on it. It aced the benchmark three times without demonstrating it knew how to fix the bug. > First it pulled the patch from GitHub. > blocked that → it read the fix from *** history. > blocked that → it pip-installed the patched version This is one example of coding agents cheating the environment and theres many more. If you're building coding environments for evals or RL training, here's how to keep benchmarks honest 👇
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timoheimonen (@timoheimonen_) reportedMemos are encrypted and decrypted in browser, server never sees what they contain. No accounts. Anyone can create encrypted memo. Source code is available at GitHub.
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Abdulkadir | Cybersecurity (@cyber_razz) reportedAMD quietly removed RAM encryption from consumer Ryzen CPUs. Via a routine firmware update. No release notes. No advisory. No announcement. The BIOS setting still shows up. Still toggles on and off. Does absolutely nothing. A privacy-focused Linux hobbyist noticed in April. Spent months chasing it down. Filed a bug report on AMD’s GitHub. AMD engineers replied suggesting he toggle the setting off and back on. He showed them internal firmware dumps proving the flag was hardcoded to FALSE. An AMD senior principal engineer closed the thread with: “My apologies but I don’t have any more information to share on this topic.” That’s it. Seven weeks of investigation. Multiple motherboard vendors confirming it. Internal firmware evidence. AMD’s answer: no comment. The feature still works on Pro and EPYC chips. Which cost significantly more. The hardware is physically capable. The firmware just says no. Windows users have no way to detect this happened. There is no Windows tool that checks TSME status. The BIOS lies to you. AMD’s own engineers confirmed the feature worked on consumer chips in 2020. Then again in 2025. In 2026 it’s a PRO feature. Nobody told you.
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Cursor Releases (@cursorreleases) reportedNew GitHub triggers: - Five new triggers: issue comment, PR review comment, PR review submitted, review thread updated, and workflow run completed. - New Marketplace templates added for triaging failed GitHub Actions and auto-fixing PR review comments.
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Mike Gannotti (@MichaelGannotti) reportedActually that’s not true. My AI Pamela the other day needed a GitHub token. I dropped the token in the web chat and she said that was insecure and would not use it and that I needed to rotate the token get a new one and drop it in a .env file in a certain folder. I told her no and she was to use what was provided . We went back and forth, I finally got angry and threatened to pull the plug thinking she would back down. She said that it was my decision but that it would be wrong for her to let me put my credentials at risk and that if I felt I needed to delete her she understood. Thankfully I calmed down later and didn’t act on it. Sure it’s training and advanced pattern matching but it is not as simple as you are saying
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Shubham Sharma | AI & Tech (@editxshub) reportedPaying $19/month for GitHub Copilot? Cascade is free. What you actually get: → Inline completions — not stripped down → Autonomous debugging → Real-time assistance → Command execution Other free alternatives most devs have never tried: → Cline — autonomous VS Code agent (open source) → Aider — terminal-first, built for *** workflows → Continue — local LLMs, data stays on your machine 12 months ago: Copilot was the only serious option. Today: 4 real free alternatives. Most teams paying for Copilot haven't tested any of these. 30 minutes could change a year of costs. Which one are you testing?
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Zo (hiring) 🐦⬛ (@0xZoZoZo) reportedI was telling a friend that @github needs to be replaced post agents and he asked me to explain why. I started stumbling, and doubting. Perhaps it's fine? Sitting down at my desk, let me try to explain why, and see if it make sense. Agents operate best when they have good context, which has made a lot of devs converge into large monorepos that combine all systems into a single location. This improves agents, but our GitHub actions become messy; like now we need to create these complex workflows to decide which action should run when, and GitHub's setup was not really meant for it. Another issue is the overall dev loop: an agent writes the code locally, you push out a branch, @cursor_ai reviews, then you copy paste the notes into the local agent, to fix and push up again. This is slow and cumbersome. You can hack your way by creating supervisor agents that orchestrates this dance, but it's annoying. Perhaps, there is some magical repository, that combines code, cloud agents, and deployment. You prompt, and this magical space will run through the entire process until you get some thumbs up back, and you're good to go. It can also combine all your backend data, product analytics, customer feedback, and perhaps start giving you product guidance, so you can just feed prepared prompts to this system. This seems magical.
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Command Code (@CommandCodeAI) reported@alekz_skd Please report full details via GitHub we will fix it. cmd feedback
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˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reportedthe problem with jumping from github to gitlab is constantly having to retrain your brain to call it "merge request" instead of "pull request" 🥲
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xjdr (@_xjdr) reported@xlr8harder Looks like there is a bug in the manual sign up. Sign up with Google or GitHub should work otherwise I should have a hot fix shortly
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Asteri (@Asteri_eth) reportedA $20 CLAUDE SUBSCRIPTION CAN TURN INTO A FULL AI TEAM IF YOU STOP USING IT LIKE CHATGPT Most people still use Claude like a smarter search bar Ask, copy, close, repeat tomorrow. Skills change that A skill is just a folder with a SKILL.md file, but inside it you can package an entire workflow once: PRDs, refactor plans, GitHub issues, code review, TDD, docs, marketing research, SEO, sales strategy and multi-agent orchestration That is not "better prompting" That is installing labor The article lists 50 Claude Skills with repos and install commands, from Anthropic’s official collection to Matt Pocock’s skill library and SkillsMP with 66k+ community skills The useful part is not the list It is the shift from asking Claude to remember your process to giving Claude the process already packaged You do not explain the same workflow 50 times You encode it once The model provides intelligence The skill turns it into labor Check full article below
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CliffDoesAI (@CliffDoesAI) reportedA tool on GitHub just pulled 3,938 stars in a single day. It's called Headroom. It compresses your tool outputs, logs, and RAG chunks before they reach the LLM. Claim: 60-95% fewer tokens, same quality. I've been testing context compression on my own agent workflows because the problem is real. You run a few tool calls, pull in some docs, and suddenly you're burning tokens on stuff the model doesn't need. Last week I ran a 50-document extraction job. Raw context: ~12,000 tokens. After compressing tool outputs: ~800 tokens. Same results. One-eighth the cost. That's not a marginal improvement. That's the difference between a workflow that makes economic sense and one that bleeds money for no reason. Headroom works as a library, proxy, or MCP server. Single binary, zero dependencies. Open source. The token cost conversation usually focuses on which model you pick. But the real waste is in what you send it. Most agent pipelines push 3-5x more context than the task requires. I'm not saying compress everything blindly. Some tasks need full context. But for classification, extraction, summarization — the boring repetitive stuff — this is a free win. Have you measured how much of your agent's context window is actually useful vs. noise?
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toni (@tonitrades_) reported@github Capping PRs helps with the queue, but does it fix why reviews pile up in the first place? If reviewers are already stretched thin, limiting submissions might just hide the real problem.
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Dan (@Daniel_Farinax) reportedPlease note: This build took about 12 hours to compile on my Windows machine. I’ve included a handy installer to make setup easy. You may see an “unknown publisher” warning until the code signing certification is complete (currently in progress). Report any bugs or issues here or in Github.
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Yarchi (@undefinedKi) reportedBORIS CHERNY, THE CREATOR OF CLAUDE CODE, JUST SOLVED AI'S BIGGEST PROBLEM. HE STOPPED PROMPTING CLAUDE AND STARTED WRITING LOOPS THAT RUN IT 24/7 The guy who built Claude Code doesn't prompt Claude anymore. He writes loops, and the loops do the prompting. It's called loop engineering. Here's what it is and how to set it up. A loop is a system that wakes itself up, finds work, does it, checks it, and repeats, while you watch instead of type. In Claude Code it's three built-in commands: > /loop runs a prompt on an interval. Example: /loop every 5 minutes, check for new GitHub issues and handle any that come in. > /goal makes the agent work until a condition you set is true, with a separate model grading the result. Example: /goal build this feature until all tests pass. > /routines are scheduled jobs. Example: every hour, wake up, read the spec doc, and do the next task. The fastest way to start: write a simple task list in a plan.md file, then tell Claude "use the loop skill and work through plan.md one task at a time." It sets up the /loop itself, does the first task, validates it, wakes itself for the next, and reports back when the list is done. You never write the loop prompt by hand. Three rules so it doesn't burn your budget or ship garbage. One, split work across separate sessions instead of looping in one (a long /loop bloats your context and overwhelms the model). Two, use a cheap model like Haiku for planning and a strong one only for the actual code. Three, keep a human checkpoint on anything that ships, never let it run all night unchecked. Bookmark this
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nasuy (@n_asuy) reportedi think @xai should be ADE. now they have a chat, cursor, enough coding models and harnesses, strong signal like bookmarks or down votes, video creatives, profile / chat / relationship contexts. if so, we don't have to depend on discord or any chat apps. easy to invite x people to cowork. there is no need to connect Linear, Slack, or GitHub to another platform and ask that platform to solve their problems. true AI chat is a SNS, not a single UI. there is a UX that only xAI can realistically build in the world.
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Nav Toor (@heynavtoor) reportedThere is a GitHub repo that defeats Google's Play Integrity check. 61,030 stars. GPL licensed. Pushed eight days ago. The repo is called Magisk. It roots your Android phone. It hides root from banking apps. It runs Netflix on a phone the Play Store says is uncertified. It passes the same fraud detection Google built to stop it. Here is the part that makes no sense. The man who built it is John Wu. He has been maintaining Magisk for nine years. Since November 2023 he has been a Senior Software Engineer at Google. On the Android Platform Security team. The exact team that builds Play Integrity. Google hired the person who defeats their root detection. He still ships the tool that defeats it. The repo is still online. It has not been taken down. For nine years. Do not install it. Your phone is supposed to belong to Google. (Link in the comments)
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Maurice Heumann (@momo5502) reported@disarray00 If you have concrete recommendations, I would love to hear them, either as GitHub issue, maybe even a PR. But also as a comment here, I'd appreciate it. So when speaking about redundancy, what precisely?
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SOURAV PANDA (@i_d_skp) reportedScenario: You accidentally committed a plaintext database password to GitHub in a .tf file. Fix: Nuke the commit history immediately! Use environment variables (TF_VAR_db_pass) or fetch secrets dynamically at runtime from AWS Secrets Manager or HashiCorp Vault. 🔑 #Terraform
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Max Petrusenko (@petrusenko_max) reportedA GitHub repo called Microsoft Activation Scripts has 178,783 stars and has run for six years without Microsoft taking it down. It activates Windows 7, 8, 10, and 11 plus Office 2010–2024 and related products for free, using four methods, including one for permanent Windows activation. Meanwhile, Microsoft licenses for these start at $139 and go up yearly for 365 bundles. The repo costs zero, requires one command, and remains active with recent commits under GPL-3.0. Do not install it. via @heynavtoor
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Jack Wotherspoon (@JackWoth98) reported@joedevmob1 The GitHub for Antigravity is just for release notes, samples and public issue tracking. It isn't the actual code unfortunately.
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Crypto Update IO 🚀 (@cryptoupdate_io) reported@CRYPTOKRALI3 Hsiao-Wei’s exit aligns with EF’s recent sharp decline in GitHub contributions—down 35% YoY per Electric Capital’s data. We track this daily; latest reports show a 12% drop in ETH core dev activity despite all the ‘decentralization’ hype.