GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Naman Jaiswal (@Namanjaiswal21) reported 1 minute ago

  I was deep in CI/CD hell. We had migrated our entire pipeline from GitHub Actions to Semaphore CI, and nothing worked. Jobs kept failing. I was stuck in the loop: Fix it. Push it. Watch it break again. Hours disappeared into the void. Then I tried something different. I used loop engineering. I built a self-running loop of three AI agents around the deployment: One agent fixed broken jobs. Another merged the fixes. The third kept the whole loop running. I started it, walked away, and let it run autonomously for 24–25 hours. When I came back, everything was set up and working. No manual fixes. No endless debugging cycles. No babysitting the pipeline. This is the future. Prompt engineering is already becoming outdated. We’re not just writing better prompts anymore. We’re designing autonomous loops systems that observe, fix, merge, and keep shipping while we sleep. The engineers who win won’t be the best at prompting. They’ll be the best at loop engineering.

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  Jeff Sackler (@SacklerJeff) reported 2 minutes ago

  @karthikb351 Solving coding is a version of the stopping problem aka NP-complete. All AI does right now is try to automate what you've been doing browsing and cutting and pasting from stackoverflow(pbui) and github to match it up with your company's needs. It works when adapting solved probs

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  BullBear.News (@bullbear_info) reported 8 minutes ago

  @github Only if the keynote fixes my broken CI pipeline. 🤷

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  the_architectopteryx (@rchitectopteryx) reported 9 minutes ago

  @thsottiaux Well, one of my iPads, lost a connection and now I can’t get a new connection to Codex via either of my computers, even after deleting and reinstalling the application on the iPad. I’m really surprised this hasn’t been fixed. It’s a repeated issue check out github issues.

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  Vaibhav (VB) Srivastav (@reach_vb) reported 9 minutes ago

  @blueforyou0202 @hiddnest github issues please and send me the URL here

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  Sebastian Kehle (@sebastiankehle_) reported 10 minutes ago

  last monday i ran a live testing session with a client, their team clicking through the new app. we left with 52 feedback items. the app started as a crud admin dashboard for events and applications. then the client sent a wishlist, 13 new modules: room lists with drag and drop assignment, group and training assignment tools, teacher self-service via qr code, a personalized live programme per participant, mail-merge exports. all of it shipped in the weeks before the session, paid event checkout landed the day before. so the team was testing a pile of brand new surface, and most of the 52 were feature requests and polish, everything from a missing salutation option to a full travel expense flow. the same evening i triaged all of it into atomic github issues, each one scoped so an agent can finish it in a single fresh context window. by tuesday night the whole backlog was closed. meanwhile a ux loop ran next to the backlog agents for over 2 days. it went screen by screen through the whole dashboard, questioning what every feature is there for, for users, members and admins, and reworking copy, typography, spacing, forms, cards and scroll behaviour as it went. it did insane work.

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  HangukQuant (@HangukQuant) reported 10 minutes ago

  Lol I have notifs on a GitHub repo and someone made a PR with a 1-line typo fix in a print statement and had 5 follow ups if the PR could be merged💀

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  Waldek Mastykarz (@waldekm) reported 11 minutes ago

  You shipped a new CLI. Deprecated the old one and updated the docs. Developers are migrating. Then an agent uses the old tool. Here's why. Models learn from the internet. If your technology has been around for a decade, there are thousands of blog posts, Stack Overflow answers, tutorials, and GitHub repos that document the old way. Your new CLI has a handful of announcement posts and maybe some updated docs. Ten years of content versus 6 months. The math isn't even close. We've seen this across multiple platform teams at Microsoft. The SPFx team partnered with us to evaluate this risk as they prepare a new standalone CLI to replace their Yeoman-based generator. When we pointed an agent at a scaffolding task, it ignored the new CLI entirely. Went straight for the Yeoman generator, constructed the yo command from memory, and moved on. Even when we explicitly told it to use the new tool, the agent concluded we were being imprecise and defaulted to the generator. In its reasoning traces, we could see it consider the new tool and then talk itself out of it. Not enough signal to confirm it exists. The agent wasn't broken. It was doing exactly what its training data said to do. What you can do: ship an agent extension on day one. Don't wait for training data to accumulate. Put the correct information directly into the context window, where it overrides training data. Make the deprecation explicit and machine-readable. "Do not use X" works better than "use Y instead." Both together is strongest. And if you're still in the naming phase, pick something distinctive. A name like "Platform CLI" collapses into the same concept as the predecessor.

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  Tariq (@tariqvibes) reported 11 minutes ago

  @dsallentess @bookmarksreads huge w even if my reading comprehension stops at github issues

• 
  Ghostw (@Ghosterdy0b) reported 12 minutes ago

  A guy wired his AI agent into his own research notes and it found a connection he'd missed for two years. Not a smarter chatbot answering the same question faster. A different animal entirely. He connected Hermes to NotebookLM through MCP. Four steps, nothing dramatic: install Hermes with MCP enabled, pull the NotebookLM skill from GitHub, drop the endpoint into the config, restart. For the first few days, nothing about it feels different. Then he asks it a question about an old project, half-expecting a generic answer. Instead it pulls in a source he uploaded to NotebookLM eleven months ago and links it to a note the agent wrote itself the week before. Two things he never told it were related. It just noticed. Here's why that's possible at all. Hermes already writes its own playbooks every time it solves something hard - short, specific files it only opens again when a matching problem shows up. It keeps a running memory of the projects it works on, compressing old notes into denser ones instead of quietly forgetting them. Wire a live research source into that same loop, and the agent isn't just answering anymore. It's cross-referencing everything it's ever read against everything it's ever solved. A background process handles the mess that would normally pile up - anything unused for 30 days gets flagged, 90 days gets archived, nothing gets deleted without a backup sitting right next to it. He didn't build a faster assistant. He built something that remembers what he'd already forgotten he knew - and started proving it back to him, unprompted.

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  Andrew (@apl8080) reported 12 minutes ago

  my coding harness started as a workstation repo with submodules for claude code / copilot to work from. added docs, skills, scripts, tools over time. now building automations, evals are next. worktrees let me parallelize overlapping work. i still read most of the code myself. end goal: a library that spins up the harness, negotiates changes against a github issue, opens the pr... and eventually the agent manages the merge itself, using signals instead of me reading every diff.

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  The AI Bench (@theaibenchai) reported 14 minutes ago

  @github @cassidoo Worktrees fix the real bottleneck with parallel agent sessions: no more stashing or context-switching branches just to let two AI runs work simultaneously without stepping on each other's files.

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  h100envy (@h100envy) reported 14 minutes ago

  OpenHands co-founder explained why coding agents fail in production in 17 minutes - better than $2000 agentic engineering bootcamps. read the code -> plan the change -> sandbox execution -> run the tests -> let the agent see its own errors -> iterate. That loop is why OpenHands hit 66K GitHub stars and became the leading open Devin alternative. OpenHands agent + Docker sandbox + SWE-bench evals + open-source model backends - that's the stack. Watch and save it, then wire the loop into your own coding agent.

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  Lina (@silent_puddle) reported 16 minutes ago

  @NeeK2323 i farmed all 8 by killing rares while in queue for m+. i have nothing left to do now when queueing :( btw, how does your rarity work? curseforge version is broken for me, i tried downloading one from github but it didn't work either

• 
  Vikram M (@Vvikramai) reported 19 minutes ago

  The entire AI industry is racing to build the smartest model. Satya Nadella just admitted that is not where the money is. The model is not the product. The harness is. That is the exact line. And it changes what Microsoft is actually competing on. OpenAI, Anthropic, Google, xAI, Meta every frontier lab is pouring hundreds of billions into training compute, chasing the next capability jump. Each betting that raw model intelligence is the moat. Microsoft is doing the opposite. It is building the harness the orchestration layer that sits above the model, connecting it to tools, data, permissions, sub-agents, and enterprise workflows. And it is letting OpenAI, Anthropic, and MAI compete to plug into it. "You need the model. But the model is not the product. The harness is." So do the math on what a harness actually does. A raw model dropped into an enterprise answers questions. That is a chatbot. A harness turns that same model into an agent that reads the SharePoint, edits the ERP entry, pulls the GitHub PR, updates Salesforce, and files the Excel report with the right permissions, the right audit trail, and the right sub-agent for each sub-task. The model provides the intelligence. The harness converts intelligence into work. Now here's where it gets interesting. "Even the best model in the world will feel broken without a great harness. And an okay model with a great harness can feel like magic." If that is true, the enterprise buyer is not buying model quality. The enterprise buyer is buying the harness. Which means model quality becomes a commodity input over time, and harness quality becomes the sustainable moat. Compare that to the strategy the entire frontier lab industry is executing. Everyone else is chasing the numerator raw intelligence. Almost nobody at scale is racing to build the denominator the orchestration layer that determines whether that intelligence can actually be deployed profitably inside a real company. The frontier model race has a 10 to 20 percent chance of producing a single dominant winner. Nadella just told the industry he does not need to be that winner. If OpenAI wins, Microsoft wins. If Anthropic wins, Microsoft wins. If MAI wins, Microsoft wins. If someone Microsoft has never heard of trains a better model in 2027, Microsoft still wins. Because the compute they train on, the harness they get plugged into, the enterprise contracts they get delivered through, and the products they sit inside are all Microsoft. He is not building the best AI model. He is building the layer that the best AI model has to run on to make anyone money. I wonder which position looks more valuable in ten years.