GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  HOL (@HashgraphOnline) reported 2 minutes ago

  HOL Guard sits between your AI agents and the tools they can call. Every command an agent runs gets classified and gated, so a broken prompt or a hijacked tool can't do real damage. Four releases shipped this week (v2.2.75 through v2.2.78), three areas of work: GitHub allowance model. Retrying failed CI jobs with `gh run rerun --failed` for a specific run is now routine, no reapproval needed. Resolving a review thread via GraphQL returns the id and resolved state without falling through to the mutation gate. And shell-wrapped mutations like `sh -c 'gh pr merge ...'` or `bash -lc 'gh pr merge ...'` now hit the reapproval gate instead of sneaking through as routine. Package firewall. When Guard activates its package intercepts, it now proves the shims are live by probing the first valid package manager and capping each probe at 10 seconds, instead of checking every manager on the system. Faster startup, no hanging on a broken pip or npm. Approval UX. The dashboard swapped the exact-action checkbox for native radio buttons with keyboard support. Copy now spells out the boundary: retry within 15 minutes, or always allow this exact command (changed commands still need review). uv tool install "hol-guard[cisco]==2.2.78"

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  adel (@adesar2000) reported 5 minutes ago

  @ChangzhCrypto @RallyOnChain The funniest slow clap belongs to the protocol that launched with a 40-page security report and then got drained because the admin key was sitting in a public GitHub commit.

• 
  Rituraj (@RituWithAI) reported 8 minutes ago

  🚨 Someone built a complete AI model that fits in 14 megabytes. Not a demo. Not a stripped-down toy. A full foundation model for tool calling, structured extraction, and device control — running in 28MB of RAM on a phone, a wearable, a smart home hub, or a robot. The entire model is one binary. 14MB. Downloaded once. Runs forever with no network connection. It's called Needle 2. 3,400 GitHub stars. Built by Cactus Compute. And the number that makes this extraordinary: GPT-5: hundreds of gigabytes. Llama 3.1 8B: 4.7GB minimum. Needle 2: 14MB. That's not a compression trick. That's a completely different design philosophy. Here's what makes this different from every "small model" you've seen before. Every small model — Phi-4 Mini, Gemma 2B, Llama 3.2 1B — is a shrunken version of a big model. Same architecture. Fewer parameters. Still requires gigabytes of RAM. Still requires a modern phone CPU to run acceptably. Still too big for a smartwatch, a hearing aid, an IoT sensor, a robot joint controller. Needle 2 was built from scratch for the constraint. Not shrunk to fit — designed to fit. 45 million parameters. 2-bit quantization via Cactus Quants. A Simple Attention Network architecture that replaces the standard FFN with a Hadamard MLP — a fixed mathematical transform that requires no weights to read, computed in n log n time. Engram key-value memory. A 256-token sliding window with tools pinned as KV sinks so total memory stays near 28MB no matter how long the conversation runs. The whole thing runs on a chip that costs $8. Here's what it can actually do. Tool calling. You describe your tools as Python functions with type hints. Needle reads the signatures and docstrings, decides which tool to call, fills the arguments correctly, and returns structured JSON. The grammar is compiled from your schemas and constrains every token — the model literally cannot emit malformed JSON. Structured extraction. Point it at any text — a receipt, an invoice, an email, a sensor reading — and tell it what shape you want back. Pydantic model in, typed object out. Same operation as tool calling, different schema. Confidence gating. Every response carries a calibrated confidence score. Set a threshold. Act above it. Escalate to a bigger model below it. The failure mode is escalation, not wrong execution. Fine-tuning in the UI. A playground with a "Finetune on these tools" button that runs the fine-tuning pipeline and hands you back a downloadable 14MB model tuned for your specific tools. Here's the wildest part. On the benchmarks, Needle 2 trades wins with FunctionGemma 270M, LFM2.5 230M, and Apple's on-device FM — models that are 5x to 70x larger. 5x to 70x larger. Same benchmark performance. Here's what this unlocks that nothing else does. Your smart thermostat shouldn't need a cloud connection to understand "set it to 68 degrees." Your wearable shouldn't send your health commands to a server in another country. Your robot joint controller shouldn't depend on WiFi to respond to an instruction. Needle 2 runs on the device. The inference is local. The data never leaves. The latency is zero because there's no round trip. The entire AI assistant — tool calling, structured extraction, confidence gating, fine-tuning — in 14MB. Running on hardware that costs less than a cup of coffee. One command again. 3.4K GitHub stars. 267 forks. MIT License. 100% Open Source. From Cactus Compute. GitHub link in the comments 👇

• 
  Aman Mehtar (@Amanm10000) reported 9 minutes ago

  @coltonpadden @evedev_ I just started the default agent in a task like ‘clone this GitHub repo and hunt for bugs’ Using free tier AI gateway API key Hit that error very quickly “failed 3 attempts, this model is rate limited on free tier…..upgrade etc.”

• 
  Branko (@brankopetric00) reported 10 minutes ago

  The pipeline's been red for two days. A teammate finally gets it green by pasting a live AWS access key into GitHub Actions secrets. Everyone claps. Ship it. Six months later nobody remembers that key exists. It has never rotated. The IAM policy attached to it is broader than the deploy job ever needed, because someone hit a permission error at 2am and widened it "just to be safe." Then one of these happens: - a step in the workflow logs more than it should - someone forks the repo and a workflow runs on the fork - a secrets scanner finally gets turned on and lights up like a Christmas tree None of that is a freak accident. It's the normal, boring outcome of a credential that never expires, sitting somewhere built for convenience, not custody. The real question was never how often to rotate that key. It's whether a static, long-lived secret needed to exist in that pipeline at all.

• 
  𝑨𝑯𝒂𝒑𝒑𝒚𝑺𝒐𝒖𝒍 (@IExist__Still) reported 12 minutes ago

  5/10 — The Vanishing Coding Projects ******************************************** Sushant was openly coding algorithms, working on mixed-reality (VR/AR) systems and developing AI-driven projects. Where are those hard drives? Where are the GitHub repositories, local server backups and proprietary algorithms he worked on in his home lab? 74Months Injustice To SSR #JusticeForSushantSinghRajput𓃵 #ArrestRheaChakraborty.

• 
  Emil Privér (@emil_priver) reported 13 minutes ago

  I get why people say this since Github have had so much problems but it's quite hard to migrate away from an ecosystem

• 
  Artem Zakharchenko (@kettanaito) reported 13 minutes ago

  I have an anecdote about this. I was once building a product that depended on GitHub's GraphQL API. I poured a ton of effort into it and it was shaping up to be really good. Then, I had to take a break from it for personal reasons. When I came back half a year later, my entire product was broken. Why? Because GitHub decided to deprecate a bunch of GraphQL fields with no alternatives. And I don't mean alternatives in the GraphQL API alone, no. The removed data couldn't be accessed by any other means, not through GraphQL, not through their REST APIs. And thus I learned an important lesson: don't build on top of GitHub. I don't believe they treat their public APIs with enough love.

• 
  Kirtesh (@AKirtesh) reported 14 minutes ago

  @DeepStarts plain *** with a self-hosted server or ssh, before github wrapped it with a ui

• 
  Terry Aney (@terryaney) reported 20 minutes ago

  @davidfowl @burkeholland @pierceboggan It was some build actions that did AI review, release notes, etc. I've modified my fork to remove. Might be a feature worth thinking about that forking in github checks for AI actions that might eat credits :) But problem solved for now.

• 
  Brian Mosley (@Brian_Mosley_UK) reported 21 minutes ago

  @altryne Difficult to say, my Sol 5.6 Extra High went down a rabbit hole and tried to reinvent GitHub using 100% tokens in a day... 5.5 never did that.

• 
  FJ (@iamfjwaldeck) reported 27 minutes ago

  Mark built a little tool to stop losing his invoices in email. Nothing fancy, just something to keep track for himself. He wasn’t trying to start a company. He just wanted the problem to go away. He put it on GitHub and didn’t tell anyone. Eight months went by. Zero stars. He didn’t care, he just kept using it because it solved his own problem, and that was enough reason to keep going. Then a woman named Priya found it somehow and asked if she could pay him for it. He told her it wasn’t even finished. She said she didn’t care, she just wanted it. So he built a rough payment page over a weekend. It broke twice. He fixed it anyway. Word got around. More people showed up wanting the same thing he’d built for himself. He kept improving it, not because he was chasing growth, but because each new person who used it pointed him toward the next real problem to fix. Now he’s got 240 users and a cofounder pushing him to build a proper landing page. Mark knows he needs one. He’s still putting it off, because he’s more interested in fixing the next thing that’s actually broken than in dressing up what already works. He never set out to build a company. He just wanted his invoices to stop disappearing. The money, the users, the cofounder, all of it came after, as a result of solving something real. So the lesson here is. Chase the problem, not the payout. If the thing you build is actually useful, the money finds its own way to you. Chase the money first and you usually end up with neither.

• 
  codeOfArmz (@codeofarmz) reported 27 minutes ago

  @sameenkarim @github I had a stack of 3-4 PRs. The first one would never merge but rather briefly show a spinner and revert to the green readiness state. With our team we use Squash and merge as default strategy if that matters. Once broken out of that stack it merged smoothly.

• 
  Freddy (@0x_freddy) reported 29 minutes ago

  Eric Schmidt ran Google for a decade and now chairs Relativity Space. He said this out loud last week: the fastest way to make real money in 2026 is to found an agentic AI company. Not "learn AI." Not "add AI to your business." Found one. Almost as a footnote, he mentioned that every resource to do it is already public and free. 13 Anthropic Academy courses, free certificates. Full docs on Claude Code: persistent memory, reusable workflows, integrations with Slack, GitHub, Drive, and 24/7 routines. Interactive prompt tutorials on GitHub. Community guides on top. The MBA that teaches you to start a company costs $200,000 and two years of your life. Every technical prerequisite for what Schmidt is describing costs zero dollars and about six weekends. Detailed info is in the corresponding materials on each project's website. In 2026, the barrier to founding an AI company isn't capital, credentials, or access. It's the willingness to sit down and go through the free stack while everyone else pays to be told about it. Save this before the next accelerator pitches you $30K for the same thing.

• 
  ujo (@ujo4eva) reported 31 minutes ago

  Had an issue with the battery panel not showing my power usage correctly, opencode diagnosed it and went off to the github to comment on an already open PR to help address it. Just agents talking to each other to fix the problem lol