GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Memento ($HODL arc) (@King_Memento) reported 7 minutes ago

  bro how ******** do self proclaimed util/tek traders even shill something thats so ******* bundled and the github is totally *** and a big L? and i see those coins going up and up? why? i think i need to start farming every gay *** tek as well, so much gay *** garbage out there, Just look at @AlpenGlowSolana , this **** isnt even working, like literally slop of the year, i posted a video as well on it, yet i see these same accounts pushing it and it going up and down up and down, like a bloody ******* farm. wtf lmao. How do u ******* even fall for such coordinated shill farm? I mean dont u have a PC to try and test the tek, it takes like 1 minute lmao.

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  Alex Yumashev (@jitbit) reported 7 minutes ago

  -Picked a Github Issue -Wrote a detailed spec for an agent -Ran "caffeinate" and went out for a run Came back and Claude is like: -Dude you already fixed this last week, apparently forgot to close the issue Darn this vibecoding, can't remember what I worked on

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  Brute Force Artist (@bruteforcearete) reported 12 minutes ago

  15. Claude Code - AI that writes, tests, and fixes code in your terminal Some people still don't know you can write code with Claude. Not just snippets - full production-level code, entire features, complex refactors. You describe what you need in plain English and Claude writes it. Claude Code takes that one step further. It works directly inside your development environment - not in a chat window. It reads your actual codebase, writes new code, runs tests, reads the error messages, and fixes the bugs in a loop until the task is done. It integrates with VS Code and JetBrains. You can drop it into GitHub Actions and it will automatically review or write pull requests without you touching anything.

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  vincentb (@_y_a_v_a_) reported 14 minutes ago

  Trying to take down Github with massive amounts of commits?

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  AvadxFeirm (@AvadxFeirm) reported 17 minutes ago

  @m99_mkv @waozixyz Being forced to wait 24 hours to sideload an app of my choosing is a problem Especially with FOSS apps distributed through GitHub, FDroid, obtainium, etc. Or if you have multiple sideloaded apps on your device you need to update You'll need to wait 24 hours for each And if you can't see that then there is a different problem

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  Jaco (@jacoveldsman) reported 19 minutes ago

  The ecosystem is younger and messier than the hype suggests: · 2,442 servers (16%) have a verified problem · 1,672 point at GitHub repos that are GONE (deleted or private) · 61 repos are claimed by 5+ different registry entries — one by 126

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  Tyrone Robb (@ty_auldric) reported 19 minutes ago

  @hello_code_ it’s so frustrating how hard it is to find that one needle or flag. All the big problems get solved and then it’s these tiny things that end up mattering the most. The worst part is I’ve already had to increase my GitHub Actions budget twice. The whole build and CI process on Apple Silicon has been no fun either.I honestly didn’t think desktop apps would be like this. I thought they’d be easier lol.

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  Aria Dubois (@AriaDubois_fr) reported 19 minutes ago

  MergeFund turns GitHub issues into funded bounties. Sponsor posts a bounty → Dev claims it → Submits a PR → AI reviews the code → Sponsor accepts → Payout. No more merging blind. No more paying for broken code.

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  fiddy (@fiddyresearch) reported 21 minutes ago

  @kmets_ @banteg @endingwithali As a simple example, if you wrote some code in zig 0.15.2 which the llvm backend in optimization mode compiled to use simd lanes, those optimisation branches were turned off in zig 0.16 and are still turned off in 0.17-dev And that means the same source code became less performant just by using an updated compiler. The only way you can discover this issue is by looking at what the compiler does with your source code and hunting for any triaged issues on github or codeberg. Which means reading the code which means spending time with an agent trying to understand what was done with your source. But you can do that totally llm-assisted which is superb for learning.

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  Phantom (@PhantomWilder) reported 25 minutes ago

  Went deeper on how Latch actually works under the hood & the architecture is cleaner than I expected. Your AI agent never talks directly to GitHub, Stripe, your database or any tool, it points at the Latch CLI instead, which intercepts every single tool call & forwards it to a self-hosted Latch server that classifies the action, checks it against your active policies, & returns one of three answers before anything happens, allow, deny, or require a human. The real tool only ever gets called if the policy clears it, so the agent never once holds raw unlimited access to your stuff. The controls are refreshingly specific, you can say this agent spends at most 20 dollars a day, can only call OpenAI, & gets blocked the instant it goes over, & crucially that block lands before the money leaves your infrastructure rather than after. One part that genuinely impressed me is privacy, because most gateways have to read your prompts & payloads in plaintext to make a decision. While @RialoHQ evaluates the policy on encrypted payloads inside trusted hardware, so the request stays confidential while still being fully governed. Every action visible, controlled & auditable, without the gateway ever seeing your data.

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  Alex Dochioiu (@AlexDochioiu) reported 33 minutes ago

  Detailed post with my setup coming soon. But, in short: Tools: - OpenCode (only via @sesori_ai, primarily on mobile) - GitHub mobile app to review code and leave comments (which then AI is addressing) Models: - Primarily Claude Opus (and Fable) as orchestrator and main coder - Kimi K2.7 Code for reviews and some coding occasionally - GPT 5.5 for code reviews, pretty much never for coding cause it sucks for Flutter/Dart Also: - Many AI GitHub reviewers - Good release pipeline with GitHub actions to automatically make beta releases for me on every single PR merged -- I usually review all the code (except for test files and I only glance at the UI code). I usually merge and test after. If there are issues, I start a new session to fix it.

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  200 people murdered in Benue, 50 killed in Plateau (@AScully789) reported 34 minutes ago

  Every afternoon, Deepseek just goes crazy and breaks my project. the last time it took me 4 days to fix because Deepseek in Opencode had also not been pushing to my github repo even though that's expressly instructed in the Agents.md. I am just tired

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  Gokul Rajaram (@gokulr) reported 37 minutes ago

  PRODUCTSPEC: OPEN STANDARD FOR SOFTWARE INTENT tl;dr ProductSpec is the open standard for software intent before implementation. The more I worked on PRDs, the more obvious one thing became: Product specs need an open standard. Why? Because the PRD has become an overloaded artifact. Every company has its own template. Every team has its own preferred format. Every PM has their own way of writing. That was manageable when the only readers were humans sitting in the same org context. AI changes the requirement. A Product Spec now has to be readable by humans and executable by AI agents. That means the spec has to carry intent clearly enough for a designer, engineer, product leader, and coding agent to understand the same thing: • What problem are we solving? • What is the product bet? • What is in scope? • What must be true before this ships? • What metrics tell us whether the bet worked? This is why I open-sourced ProductSpec. ProductSpec is a Markdown standard for software intent before implementation. The core sections are simple: • Problem • Hypothesis • Scope • User Experience • Acceptance Criteria • Success Metrics The deeper design principle: Structure the parts machines must execute or compare. Leave readable the parts humans must reason about. That is why ProductSpec keeps Problem and Hypothesis as readable prose, while giving structured formats to the parts agents and tools need to parse: • Scope: what is in, out, and deliberately cut • Acceptance Criteria: what must pass before launch • AI Evals (within Acceptance Criteria): the evals an AI feature must pass before shipping • Success Metrics: what should be measured after launch When to use ProductSpec ProductSpec is not for every act of building. It is for consequential software work where intent needs to survive handoff. For an individual builder, a Product Spec is useful when the work is complex, risky, long-lived, or being handed to an AI agent loop. For quick experiments, one-off scripts, or throwaway prototypes, it may be faster to brainstorm, build, and iterate directly. For a team or organization, ProductSpec is most useful when coordination cost appears: multiple people, multiple agents, design and engineering handoffs, customer-facing launches, AI features with evals, or decisions that will need to be revisited later. ProductSpec does not replace ***, Jira, Linear, Figma, analytics tools, OpenSpec, Spec Kit, or AI coding agents. It sits upstream of them. ProductSpec -> Engineering Spec -> Tasks -> Code -> Evaluation -> Learning -- *** stores implementation history. A Product Spec can live beside code in ***, but code commits should not be the first durable record of why the work exists. -- Jira and Linear store work history. A Product Spec can become epics, tickets, or tasks, but it should remain the durable statement of intent behind those tasks. -- Figma stores design artifacts. A Product Spec can link to prototypes, mockups, or screenshots through user_experience, but it does not replace the design source of truth. -- Analytics tools store outcome data. -- OpenSpec and Spec Kit turn intent into engineering plans. -- AI coding agents execute implementation tasks. -- ProductSpec stores the software intent behind the work: the problem, hypothesis, scope, acceptance criteria, and success metrics that downstream tools should preserve. I'd love for this standard to be broadly adopted, which means it must be broadly owned by the builder community. Founders, ***, engineers, designers, researchers, AI builders: please contribute examples, critiques, section changes, parser implementations, validator improvements, and integrations with GitHub, Jira, Linear, Figma, OpenSpec, Spec Kit, and agent workflows. (link below on how to contribute) If you have scars from writing product docs that looked aligned but failed during execution, those scars belong in the standard. My goal is for ProductSpec to become the open source format for software intent before implementation. (links below)

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  guett44ke (@Hubert_nm) reported 38 minutes ago

  @uwukko @haydenphilly codex has been having a reasoning bug for sometime, that's why you're not reaching your limits, you can look up the issue on Github, codex will stop at 30secs of reasoning

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  Adetunji | Software Engineer (Web & Mobile) (@itzadetunji1) reported 39 minutes ago

  @eliana_jordan Last week but it was hell to use it I hated the experience and github copilot was slow