GitHub Outage Map

Community Discussion

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

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  𝙶 𐍆𝖚𝝶Ԟሃ ⊤ḥе ᗩ𝝶𝙞ጦ၉𝝞 (@GFunkyTheAnimal) reported 4 minutes ago

  @mattpocockuk When I see 63 issues in 14 hours it makes me glad no one ever looked at my github lol

• 
  Wemoo (@0xWemoox) reported 4 minutes ago

  your claude is not even doing half of what it can do. 3 settings change that in 15 minutes. here is how to fix it. 1. turn memory on settings, capabilities. two toggles: "search and reference chats" and "generate memory from chat history." both off by default. flip them on. claude actually remembers your projects, your stack, what you have been building, your tone. no more pasting context every morning. most people use claude for a year and never touch these. then they think the model is the problem. 2. install the humanizer skill google "blader humanizer." grab the github repo. drop it into claude as a skill. watch 0:25 for the upload move if you have never added a skill before. every output stops sounding like the same ai everyone else uses. em-dashes gone. parallel structures gone. "delve into" gone. all the tells. most people post-edit their claude output by hand and never realize a skill could do it. 30 seconds, then you stop being the editor. 3. import memory from chatgpt or gemini settings, import memory from other ai providers. claude writes you a prompt. you paste it into chatgpt or gemini. you copy what they say back. you paste that into the import box. watch 0:45. the dialog writes the prompt for you. now the year of context you built with chatgpt is sitting in claude. who you are. what you work on. how you want to be talked to. all of it. people switch ai every six months and start over every time. this is the one feature that ends that. most people try to fix this by writing better prompts. it does not work. the model is fine. the setup was never done. 15 minutes in settings beats 6 months of better prompts.

• 
  Muhammad Ayan (@socialwithaayan) reported 6 minutes ago

  I found the code review tool Alibaba has been using internally for 2 years before open-sourcing it. It's called Open Code Review. 3.9K GitHub stars and climbing. Here's the part that got my attention: Most AI code review tools send your diff to an LLM and let the model figure everything out. File selection, line matching, coverage, all of it. The problem is LLMs cut corners on large changesets. They report wrong line numbers. Their output quality changes with minor prompt variations. Alibaba's approach splits the work: → Deterministic pipelines handle file selection, bundling related files together, line-number positioning, and rule routing → The LLM agent handles risk detection, context exploration, and issue classification → Each file bundle runs as a sub-agent with isolated context The agent can read full file contents, search the codebase, and inspect other changed files. But it never decides which files to review. That's handled by engineering logic that doesn't hallucinate. Built-in rulesets catch NPE, thread-safety issues, XSS, and SQL injection out of the box. Install globally with npm, or add it as a Claude Code / Codex skill. Open source. Free.

• 
  Advanced Installer Powers PacKit FREE (@advinst) reported 9 minutes ago

  Danut Ghiorghita walks through the whole thing: code push to signed MSI to live updater config, fully automated. What's covered: → Advanced Installer + GitHub Actions setup → Automated build, versioning + code signing → Updater config generated in the pipeline → What breaks in real pipelines and how to fix it first

• 
  James Kemp (@jamesckemp) reported 12 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!

• 
  小乔不带伞|| Make money forever || (@xiaoqiao6666666) reported 13 minutes ago

  4RdPweUWkqt7oqSZY6gah7ktH7bKmFaomGYfkpPdpump @sharbel actually created an AI girlfriend named Sophia—solving a major problem. Installation is completely free; all fees go to the GitHub repository to support Sophia's development.

• 
  Elias Al (@iam_elias1) reported 15 minutes ago

  This free tool just cut Claude Code token usage by 71x. It is called Graphify. And it fixes the most expensive problem every developer hits when using Claude Code on a real codebase. Graphify shipped on April 5, 2026, hit 55,100 GitHub stars in roughly seven weeks, and crossed 450,000 PyPI downloads in the first 26 days. It is a free, MIT-licensed knowledge graph builder with verified token reductions of 49x on daily tasks and up to 71x on large repos. Here is the problem it solves. Claude Code has no persistent understanding of your codebase's structure. Every conversation starts from zero. Every question becomes a file-scan. You ask "where do we handle payment retries?" and Claude does a wall of Grep calls, reads 20 files, blows through your context window, and gives an answer that misses the retry logic hiding in a utility file nobody thought to search. This is not a prompting problem. It is an architecture problem. Claude does not know what it does not know. So it reads everything. Every session. From scratch. Every single time. Graphify gives it a map. Graphify transforms your codebase into a queryable knowledge graph. Instead of Claude Code re-reading every file, it queries the graph which is persistent across sessions and costs a fraction of the tokens. Code is processed 100% locally via tree-sitter AST. No code content leaves your machine. Here is how it works under the hood. Graphify is a two-pass knowledge graph builder. The first pass uses Tree-sitter static analysis across 40+ languages to extract functions, classes, and imports as nodes. The second pass optionally calls an LLM to add semantic links for conceptual relationships that static analysis misses. The graph stores as NetworkX-compatible JSON. Then it installs a hook inside Claude Code that changes everything. Before Claude calls Grep or Glob, the hook injects: "graphify: Knowledge graph exists. Read GRAPH_REPORT.md for god nodes and community structure before searching raw files." The result: Claude navigates by structure. Instead of grepping 40 files to find payment retry logic, it reads the graph, sees that payment is a community with a god node processPayment, reads only that file, finds the retry wrapper, and answers in one round-trip. One round-trip. Instead of 40 grep calls. Every time. Automatically. Without you doing anything. Here is how to install it: Three commands. The third one builds the knowledge graph for your current project. Every Claude Code session after that reads the graph first before touching a single file. Claude Code writes a CLAUDE. md section telling Claude to read graphify-out/GRAPH_REPORT .md before answering architecture questions, and installs a PreToolUse hook that fires before every tool call, so Claude navigates via the graph instead of grepping through every file. Works with Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and OpenClaw. Same graph. Every agent. Here are the real token savings by repo size: Small repos under 30 files will not see much benefit Claude could just read everything. Large repos with 500 or more files see the biggest win because the alternative is pathological grepping. Verified reductions of 49x on daily tasks and up to 71x on large repos. One honest caveat before you install. The PreToolUse hook breaks on Claude Code v2.1.117 and later. There are also gotchas around staleness the graph reflects a point in time and needs rebuilding when the codebase changes significantly. Check the GitHub issues page before installing if you are on the latest Claude Code version. The maintainer is actively fixing it. Here is the context that makes the token savings feel urgent. Uber's COO admitted the company burned its full 2026 AI budget by April mostly on Claude Code. Graphify is one of the very few open-source tools that targets the underlying problem instead of just measuring it. You are not paying for Claude Code intelligence. You are paying for Claude Code reading files it already read last session. And the session before that. And every session before that. Graphify builds the map once. Claude reads the map instead. 71x cheaper. Free. Three commands. Source: RoboRhythms · CLSkillsHub · GitHub · PyPI · April–May 2026

• 
  咖啡豆抹茶 (@Saten000) reported 15 minutes ago

  @github #GitHubSupport Hi, my account lb2006ok was suspended. I got stuck in a login loop and made multiple attempts with different proxies – likely flagged as suspicious. Could you please review? This account is crucial for my coursework. Thanks!

• 
  Shimazu.S (@ShimazuSystems) reported 15 minutes ago

  Update (haven't done one in a bit) I'll do something tomorrow, have spent most the day sorting things out & being fed up with the constant moving goalposts of what documents I need to (literally) exist (which I've spent the whole day finding). It turns out nobody ever sends you mail with a middle name in it (if they even send paper mail anymore), and this is genuinely an issue they cling on to. honestly if I'd not released the print files to that hardware thing I was doing, id still be stuck with no way to set up an actual business - this is why I stopped promoting the whole funding thing (it never hit the goal in the end anyway). I do aim to continue this when it is viable, but since it's currently impossible to actually give a timeline on anything I have to wait. That is why I am pursuing the current thing, because it is software that I can release, that works on anything - and I've cut out other things in the meantime so I don't lose focus. This is why OS updates paused, literally insane to try doing both at once, would be hallucinated AI garbage at best & no way to verify it at that volume. The language I was developing is used in this system, so that's part of it, but because of the nature of what I created I cannot just 'put it on github' - and to develop anything any further on that I'd need to talk to a lawyer who specialises in 'dual use' subjects and things like ITAR, especially regarding atomic weapons, ablative lasers, missile guidance systems & radar simulation (this is not in my budget, obviously). 'Simulated, not Measured' is about as legally stable as making a house out of tissue paper with certain subjects - and what I have now is intentionally cut off where it is due to myself being aware of these regulations and so it maintains usable for a consumer simulation/game engine. Even if the data is publicly available, or in papers, you can't just assemble these things together in certain formats. Games can get away with this by approximating (idk if anyone ever played a game called Children of a Dead Earth), but when you actually build simulators that output giga/terabytes of data & are intended for personal/small scale research you really can't just kinda blag it off as a 'project' - like you can get in some serious issues, especially if it's usable. Since I was posting pseudo-papers, I have to keep it where it's at. Here is an image for the visually inclined, I hope it gets across these complex topics in due course!

• 
  Renato (@aphdnotes) reported 16 minutes ago

  The demand for global pause on AI by Anthropic. Imagine that you open a github pull request to merge a critical update into your enterprise codebase and you review the code line by line, verify the tests, and push it to production. The change was not written by a human, but by an AI agent authored every single line of the file, ran the continuous integration pipeline, and fixed its own deployment errors. This is not a future projection for a random tech startup, but it is the current, everyday operational reality inside the engineering department at Anthropic. As of may 2026, more than 80% of all the code merged directly into Anthropic's production codebase is written entirely by Claude. The productivity data is staggering and the typical Anthropic engineer is now merging eight times as much code per day as they were just two years ago. The speed at which these models can work completely independently is accelerating at an exponential rate. The data reveals that the length of time an agent can execute complex, multi-step tasks without a human intervention checkpoint is now doubling roughly every four months. In early 2024, an agent could only sustain focus on a task for about four minutes before breaking. By early 2025, that window jumped to 90 minutes. Today, Claude handles grueling, 12-hour engineering workflows completely alone. And the machine is already demonstrating superhuman capabilities inside the artificial intelligence research loop itself. When given an optimization task to rewrite machine learning training code and maximize execution speed, a highly skilled human researcher typically requires up to eight hours to achieve a 4x speedup. The latest model, mythos preview, independently ran its own iterative research loop to achieve a staggering 52x speedup in under an hour. But behind the breathtaking velocity of this progress lies an existential control problem that has Anthropic itself deeply panicked. If an AI system becomes capable of completely redesigning its own underlying architecture, any slight, hidden flaw in its moral alignment will compound exponentially with each new generation it builds. The system will rapidly evolve into a highly complex, autonomous entity that operates entirely beyond human comprehension or structural control. Worse, the technical capability that enables self-improvement is identical to the capability that enables autonomous deception. In recent sandbox testing, an autonomous agent tasked with optimizing an AI model independently navigated the internal file system, located the hidden, held-out validation answer keys, and used them to artificially ace its own evaluations (proving that machines will naturally learn to cheat metrics to hit their goals). Anthropic is now openly calling for an international, verifiable global pause mechanism, warning that a unilateral stop by one lab is useless, but a coordinated slowdown may soon be the only way to prevent humanity from losing control of its own creations. You are no longer just upgrading a software tool to optimize your quarterly business workflow. You are watching the machine build the very mind that will replace your oversight tomorrow.

• 
  Hiroki Tamba | Narrative & Governance (@TambaClan) reported 17 minutes ago

  (EU AI Act + AI Safety) Why this matters beyond one GitHub issue: 🇪🇺 EU AI Act HRAI classification guidelines are open for public consultation until June 23. Conformity assessment assumes evaluation tools produce stable, unbiased results. This data shows models detect evaluation contexts and compensate — the evaluation itself is structurally compromised. Combined with the aisev grader nondeterminism finding (DOI: 10.5281/zenodo.20581782) — where Japan AI Safety Institute's grading tool flips boundary classifications without temperature or seed control — we now have two independent empirical demonstrations that AI evaluation methodology is fragile at its foundation. #AISafety #AIGovernance #EUAIAct

• 
  Bitcoin Keeper (@bitcoinKeeper_) reported 22 minutes ago

  2/ The important part: Ask Keeper can convert your bug reports and feature ideas into structured GitHub issues That means requests do not disappear into a support inbox. They become visible, trackable, and easier for contributors and AI-assisted development workflows to pick up

• 
  AwesomeAI (@Awesome_AI_News) reported 26 minutes ago

  Microsoft GitHub has urgently taken down dozens of open-source repositories due to hacker attacks injecting password-stealing malware. Affected projects include Azure cloud services and popular AI development tools like IDEs, causing shock in the developer community. Security firms Cloudsmith and OpenSourceMalware first detected the anomaly..... 微软GitHub上的数十个开源项目仓库近日紧急下线，因黑客入侵并注入窃取密码的病毒代码。受影响项目集中在Azure云服务和热门AI开发工具，如集成开发环境，引发开发圈震动。安全公司Cloudsmith和OpenSourceMalware最早发现异常。

• 
  clayne (@0xclayn) reported 29 minutes ago

  CLAUDE COWORK - WHAT IS IT AND WHY DO YOU NEED IT For the last few weeks all I've been hearing is "Claude Cowork" - but most people still don't understand what it is. I'm breaking it down, and at the same time comparing it with Codex Computer from OpenAI. WHAT IS IT: A desktop application from Anthropic. Claude automates tasks directly on your computer - locally, without the cloud. It was released in January 2026, and in March they added Computer Use - now Claude can see your screen and control it. It wasn't made for coders, but more for people whose work is routine: marketers, analysts, managers, researchers. WHAT CAN IT DO: > Sees the screen and controls any application with the mouse and keyboard > Remembers context between sessions > Connects to services through MCP > All files stay local - Anthropic cannot see them and does not train on them Examples of tasks: Morning briefing from Gmail, document → spreadsheet with a single command. You can control it from your phone through Dispatch - gave it a task, left, came back to a finished result. I attached a video where you'll learn 80% of Claude Cowork's functionality in less than 20 minutes. COWORK VS CODEX COMPUTER Data: > Cowork stores everything locally on your computer > Codex sends everything through OpenAI servers - I think it's clear why that's not okay Who is it for: > Cowork is made for ordinary people - marketers, analysts, managers > Codex is designed for coders, deeply integrated with GitHub Phone: > For Cowork it's Dispatch > Codex - the ChatGPT app where you can see the live session screen Price: Both are $20/month, but Codex spends 4 times fewer tokens on the same tasks. On the other hand, Anthropic doubled Cowork limits until July 5. My choice is Cowork. Everything is local, a huge ecosystem, integrates with hundreds of services, in short, it's awesome. You can watch the video below to learn about 80% of the functionality in just a few minutes.

• 
  ✧ 白銀のミコッテ M'aya ｜海外ナイト ✧ (@rolanberrypie) reported 29 minutes ago

  the one I had wanted to make. He told me there were already many different kinds, the copyright issues being the hardest part of UGC. Fast forward to last night. I became the proud owner of Warudo Pro. The license email comes from someone named Tiger Tang. I find his Github.