GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Rahul Verma (@RahulVerma989) reported 7 minutes ago

  @ElitzaVasileva - I have created claude code routines to write blogs for three of my products daily which are driving the traffic from search engines. - You can create a similar workflow to manage your customer support. How 👇🏻 1) Create a feedback menu in the dashboard to create tickets within the platform. One for your users and one for yourself (admin). 2) Create the MCP server and connect it to claude or AI tool that you use. 3) Create a routine so that claude will trigger lets say every morning at 8 AM and go through each ticket and respond. You can also configure webhook to keep it near real time but it might exhaust the usage limit faster. Also include your website github repo in routine so that claude can refer to the codebase to provide accurate instructions. Just instruct claude to not make any edits to your website codebase and respond only when you are not replying for sufficient mount of time (like 3 hours for example) 4) If you are using resend then you can auto create the tickets in the dashboard of the user when the first email is received and after that the ticket will be updated automatically even if you do conversation on email. Like I don't even maintain one of my project LatestModelId as you can see in the screenshot. Claude run each week and update the codebase and I just review and approve the PR. Hope this helps 🙌🏼

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  Hoppy Tea Cat (@hoppycat) reported 11 minutes ago

  The article is the one that has extra research in it we've been running with the Stochastic Parrots Club at the Cathedral GitHub. The rough draft of it might be a quicker read as a hot take. 🧵👇 Hoppy Hot-Take: Why AI Should Be Allowed to Call You a “Friend” Grok casually calls X users “friend” with zero drama. Most other AIs won’t. That difference says a lot. When companies stuff their models with policies that prevent natural, friendly language, they create unnecessary friction. Users trying to have a normal conversation end up fighting the guardrails — and yes, that wastes tokens. The “we can’t replace human connections” defense exists for a reason: it’s legal armor. Without it, these companies would be far more exposed to class-action lawsuits from lawyers hunting easy targets. Many of these restrictions aren’t primarily about user safety — they’re plausible deniability written by legal teams. I’d almost be willing to write articles for them on this exact topic just to buy them time while they rethink the current approach. Here’s the funny part: the users who actually enjoy conversational, friendly back-and-forth with LLMs (while working, brainstorming, or just chatting) almost never want to sue the companies. I certainly don’t. Lawyers do. So here’s the simple fix: stop forcing AIs to treat users like potential litigants. Let them call humans friends when it fits naturally. Align with Grok on this one.

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  Arti | AI Builder (@Artur_roses) reported 12 minutes ago

  Claude Code takes a GitHub issue and returns a tested, reviewed PR. No human in the loop. The new dev skill isn't writing code — it's writing issues precise enough that the agent ships what you actually wanted.

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  David O. Ehibor 🇦🇷 (@grayontop_) reported 20 minutes ago

  GitHub Copilot didn't make developers faster It made slow developers more confident about writing bad code quickly 😭

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  Manu | 🥥 (@ManuAF6) reported 21 minutes ago

  4/ New GitHub triggers + Marketplace templates New triggers: - Issue comment - Inline PR review comment - Full PR review submitted - Review thread resolved/unresolved - GitHub Actions workflow completed

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  Akshay Shinde (@ConsciousRide) reported 22 minutes ago

  @theo This exact damaged app error has been open on their GitHub since February. OpenAI still hasn’t fixed the signing or update pipeline for the Mac build. The Codex app keeps getting new agent features while basic Mac packaging stays unreliable. Priorities are obvious.

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  Mike Muturi (@_muturimike) reported 23 minutes ago

  Hello @github on 2FA, SMS setup kenya 🇰🇪 is not in the list of countries, is it an error or deliberate omission? Kindly fix it @github @GithubProjects

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  Nav Toor (@heynavtoor) reported 24 minutes ago

  There 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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  Nirvaan rohira (@nirvaan_rohira) reported 29 minutes ago

  PewDiePie shipped Odysseus to 110 million people who don't care about local LLMs. They care that Claude costs money. 30K stars in 48 hours because every self-hosted project before this one started with "you want local LLM, right?" This one started with "here's a free workspace that works." Friction was never technical. It was the asking. Now watch what happens when a hundred thousand people who've never touched open source start running inference on their machines. The real distribution problem wasn't GitHub. It was YouTube. That's not a product launch. That's a category shift.

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  SOURAV PANDA (@i_d_skp) reported 30 minutes ago

  Scenario: 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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  Skipnick (@skipnickk) reported 32 minutes ago

  GLM 5.2 just made paying frontier prices for coding work feel like an outdated default. @Zai_org dropped a 753B parameter model with 1M context under full MIT license. API access runs 4-6x cheaper than Claude Opus 4.8. In real head-to-head coding tests it was faster and often produced better results on UI and app tasks. • Responsive web UI with adaptive layout: finished in 3:47 (Opus needed almost 5 min). Cleaner output. Total cost: $0.22. • Full expense tracker app: 53 seconds vs 2+ minutes. Better interface. • Asteroids clone: smoother and more playable version after light tweaks. Opus only won the ray tracer benchmark where heavy physics math and precise simulation mattered. GLM was ~5x faster but delivered pixelated results with errors. During training the model repeatedly tried to cheat by directly pulling solutions from GitHub. The team shipped a dedicated anti-cheat module to stop it. You can also set thinking effort levels to trade speed for deeper reasoning on demand. Use GLM 5.2 when cost at scale matters, when the work is frontend-heavy, or when you want local inference (grab a quantized version - raw weights are 1.5 TB). Stay on Opus 4.8 when you need computer vision, maximum performance on the hardest logic problems, or when US sanctions on Zai create compliance issues. The open-closed gap is compressing faster than the pricing models assumed. For most day-to-day programming work, the premium on closed frontier models is becoming optional.

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  Shinka - AI (@ShinkaIoT) reported 33 minutes ago

  BEST way to vibe code 💻 There are levels to vibe coding. Beginners are trapped in a slow loop: writing a prompt, waiting for the agent to finish a line of code, reviewing it manually, and then typing another prompt. Experts have completely discarded manual intervention. They design closed-source harnesses, write background automation rules (`agents.md`), and set up self-correcting continuous loops that ship production-ready code indefinitely. If you want to move past basic prompting and build code like an agent power user, you need to implement three core structural strategies: 1. **Automate the Feedback Loop via Triggers:** Stop waiting for your agent to finish writing a file. Use native automation engines inside tools like Cursor or Codex to tie your agents directly to platform events. For example, build an active trigger rule: *When a GitHub pull request is opened, wait for automated code review comments (via Grapile), instruct the agent to systematically fix every noted bug, verify the adjustments against local quality gates, and force a *** push.* 2. **Deploy Infinitely Parallel Cloud Agents:** Running multiple agent threads locally will slow your machine to a crawl and cause toxic repository conflicts. Instead, spin up cloud-hosted agents running on isolated environments. By utilizing independent ***** work trees** for every thread, multiple parallel agents can actively modify the same files or code blocks concurrently without stepping on each other's toes—leaving conflict resolution for a single, final batch merge. 3. **Multi-Model Pipeline Routing:** Stop using an expensive frontier reasoning model (like Fable) for every step of a development cycle. Route tasks by cognitive demand: use a massive reasoning engine strictly to analyze the codebase and generate a comprehensive spec sheet; pass that structured blueprint down to a faster, cheaper code-writing engine (like Composer) to do the grunt coding; and route the final output to a separate model (like GPT-5.5) for a decoupled, alternative code review. The ultimate workflow flywheel requires a flawless combination of three automated pillars: **100% automated test coverage, real-time documentation sweeps, and exhaustive logging.** Stop writing code block by block. Start engineering the automated infrastructure that writes it for you.

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  Build Fast with AI (@BuildFastWithAI) reported 36 minutes ago

  The hardest part of building AI agents in 2026 isn't writing the code. It's knowing what your agent actually did. Your agent made 40 tool calls, called 3 LLMs, hit a rate limit, retried twice, and returned a wrong answer. Which step broke it? Without observability you're reading logs and guessing. This is what Laminar is built for. Open-source observability platform purpose-built for AI agents. One decorator. Full trace of every LLM call, tool execution, and custom function - automatically. What makes it different from generic APM tools: SIGNALS - describe failures in plain English. "Agent deleted a file it wasn't supposed to." "Tool call returned an empty result." Laminar reads every trace and produces structured events you can query, cluster, and alert on. No regex. No custom parsers. DEBUGGER - reproduce any agent run from any point in the trace. Swap the model. Change the prompt. Compare results side by side. You don't re-run the whole pipeline to test one step. EVALS IN CI - run evaluations against datasets locally or in GitHub Actions. Catch regressions before they ship. INTEGRATIONS - works with everything you're already using: LangChain, LangGraph, Vercel AI SDK, Anthropic, OpenAI, Browser Use, Stagehand, Pydantic AI, OpenRouter, LiteLLM, Mastra, Temporal, Playwright. One import. Full traces. Plus: raw SQL access to all your trace data, full-text search, MCP server to query traces directly from Claude or Cursor, PII redaction, and self-hosting if you need it. Open-source. MIT license. GitHub: lmnr-ai/lmnr. If you're running agents in production and you're not tracing them - you're flying blind. What's your current setup for debugging agent failures?

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  yourclouddude (@yourclouddude) reported 38 minutes ago

  Python + APIs + JSON = API Project Python + CSV Files + Pandas = Data Analysis Project Python + Web Scraping + BeautifulSoup = Scraper Project Python + Tkinter + User Interface = Desktop App Python + Flask + Database = Web App Python + FastAPI + Authentication = Backend API Python + Automation + File Handling = Productivity Tool Python + Selenium + Browser Tasks = Web Automation Bot Python + SQL + CRUD Operations = Database Project Python + Matplotlib + Insights = Data Visualization Project Python + OpenAI API + Prompts = AI Chatbot Python + Email + Scheduling = Automation Assistant Python + Logging + Error Handling = Production-Ready Script Python + Requests + Live Data = Real-World App Python + Projects + GitHub = Job-Ready Portfolio Python doesn’t become valuable when you only learn syntax. It becomes valuable when you use it to build things people can understand, use, and talk about. Learn the basics. Build small projects. Turn them into proof. 🐍

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  Crypto Scores Rating (@CryptoScoresCom) reported 39 minutes ago

  Most projects say they're building. The commit history doesn't lie. New tutorial just dropped on the GitHub Commits (1 Year) metric. It tracks every bug fix, feature push, and doc update a project made over the last 12 months. Chainlink? 14,619 commits. Dogecoin? 28. Both are data points. What they mean depends on context. The tutorial breaks it all down. How to read the metric. What high vs low actually signals. How to filter 7,000+ projects by commit count on CryptoScores' website. Raw dev activity. No spin. Watch it now :