GitHub Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  HONESTEENDER (@honzeeeeee) reported 9 minutes ago

  @JamisonSlo55358 Hey, today I went to GitHub and saw that the 0.9.0 update was at 50% and went down to 40%. Does that mean it's progressing?

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  CatDog (@kdoggo1181074) reported 9 minutes ago

  Some important @FireCashX transparency questions before mainnet, especially for Kaspa users interested in new Kaspa forks. I first heard several of these concerns from community members, then reviewed the public GitHub repositories, wallet documentation, commits, and issue history myself. I am not identifying or attributing the original Discord participants here; the points below are based on publicly verifiable sources. Kaspa community members should be cautious when a new project presents a fork as necessary for functionality that may ultimately be implementable directly on Kaspa or through Kaspa-based applications and protocol extensions. A fork creates a new trust surface: new maintainers, modified consensus code, new wallets, new pools, new infrastructure, and new tokenomics. That does not mean FireCash is destined to fail, or that every Kaspa fork is illegitimate. FireCash may still improve substantially, and open testing can expose problems before mainnet. But the burden should be on the project to explain clearly why a separate chain is required and to demonstrate that its changes are secure.

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  NMB // v4 (@nmb_four) reported 9 minutes ago

  @pablostanley i wanna try it so bad, but i have too many github repos, and there seems to be a hard limit of 100... which i understand to some degree, but the newest repos are left out and not the oldest, would be sick if you can fix that.

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  Jason Fleagle (@jjfleagle) reported 10 minutes ago

  @pagerduty @github Putting incident context inside the PR is the kind of workflow detail that compounds. The fix is only half the work. The reviewer also needs incident state, likely blast radius, recent changes, and why this patch is the safest next move.

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  MarkBruns (@MarkBruns) reported 12 minutes ago

  The proprietary LLM PRODUCTS are defined and devalued by the gaurdrails ... but the REAL problem has always been quality of data going in to the system ... GIGO ... for example, the stolen-from codebases on GitHub were JUNK and irresponsibly coded Javascript/Python/Zig -- so the resulting vibe-coded crap replicated the systemic errors of the easy-to-code, anything will compile body of crapware. EVERYBODY using the frontier models ... except that maybe these people are idiots who'll pay for anything, so not everybody got it ... sensed this some time ago and it really drove the agentic harness excitement from at least last fall or well before that. It was clear from the start of the AIsplosion that the data going into the frontier models was lowest common denominator, ie Wikipedia-esque or GitHub-esque, bogus non-great pablum-for-the-masses dubious quality data. Anybody who actually wanted to use AI had to take much more control of their own specific data and training their small language models and not waste their time vibe-coding lowest common denominator nothingware. NOW it has become so glaringly obvious, that it's the kind of knack-for-the-obvious material that is being disseminated by even the last-to-know journos working for news orgs.

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  TheMarketMaker (@xTheMarketMaker) reported 13 minutes ago

  Companies are pulling models from Hugging Face at a rate that signals a structural break from rental contracts rather than any philosophical preference for openness. My read is that the move reflects operators locking in cost predictability after watching provider terms shift against them. Half the Fortune 500 now routes inference and fine-tuning through the platform instead of renewing with the original vendors. The mechanism is straightforward: when renewal clauses embed escalating usage fees or usage restrictions that outpace deployment cycles, teams treat the model as owned infrastructure instead. Clem Delangue has framed the pattern directly. Companies are done renting their AI once the economics no longer favor the hosted tier. Hugging Face functions as the distribution layer where builders share and download models and datasets in the same way code moved through GitHub. That infrastructure now sits inside production stacks at scale. The shift accelerates when providers alter pricing mid-contract or impose new compliance gates that were absent at signing. Apple’s lawsuit against OpenAI illustrates the control problem from the other side. The complaint names senior leadership involvement in alleged trade-secret misappropriation tied to a long-time former employee. The filing shows how dependence on a single external model owner creates legal and operational exposure that self-hosted alternatives avoid. At the same time Meta removed its controversial AI feature from Instagram after user backlash reached Dylan Byers at Puck News. Both cases reveal that model behavior and terms can change faster than internal roadmaps can adjust. The capital markets already price the hardware layer differently. SK Hynix completed a $26.5 billion foreign IPO, the largest in U.S. history, precisely because memory demand for training and inference continues to climb. The same announcement carried calls for the company and Samsung to site new fabs inside the United States. That capital commitment is possible only if end users expect sustained on-premise or private-cloud workloads rather than continued rental consumption. What this actually means is that predictability now outweighs the marginal performance edge some closed models still hold. Teams that once accepted variable per-token costs are converting those budgets into fixed GPU or inference-server line items. The open-source repositories supply the weights; the hardware build-out supplies the capacity. Once the model weights sit inside the perimeter, renewal risk disappears. The contrarian angle is that this is not a temporary cost-arbitrage play. The rental model worked while providers absorbed the early R&D risk and offered undifferentiated access. As differentiation moved downstream into fine-tuning and data, the same providers began protecting margins through tighter terms. Operators responded by moving the base model in-house and keeping only specialized layers on rented capacity where needed. Apple’s action and Meta’s quick reversal both underscore the governance layer that external providers retain. A single policy change or leadership decision can alter model availability or behavior overnight. Self-hosting removes that single point of control. The SK Hynix raise quantifies the downstream bet: memory and accelerator spend is rising because the workloads are now expected to run continuously under operator ownership. The number nobody is pricing yet is the cumulative option value lost each time a renewal clause is exercised under changed terms. Teams that moved early to Hugging Face-hosted open models have already converted that option value into fixed assets. Those still inside rental contracts face the same choice at the next renewal window. #OpenSourceModels #EnterpriseAI #ModelOwnership

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  ✨ (@portrays) reported 14 minutes ago

  @kyle_mccleary @theo yeah it can be resolved and already has been, oss is great. he can open up a github issue instead of being a ******* loser on x shitting on others with his superiority complex when he's never built anything remotely complicated

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  Sebastiano Mandalà (@sebify) reported 15 minutes ago

  @Colonthreee I had the same problem 20 years ago I am sure there are libraries to solve the problems on GitHub nowadays

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  Noreh AD (@akinoreh) reported 17 minutes ago

  @github This commit is the earliest I could find. The problem is across repos and accounts.

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  Shaun Smith (@evalstate) reported 17 minutes ago

  @DanielLockyer It's a GitHub CLI error message isn't it?

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  Dan (@thedansho) reported 18 minutes ago

  @TFTC21 @ODELLXYZ @MartyBent Just switched to radar from Molly last night. Unfortunately there's a bug at the moment and I can't use the payments feature, so I've temporarily shifted back to Molly, but will be keeping an eye on the issue in github to migrate again! Very cool stuff.

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  alias (@loadingalias) reported 20 minutes ago

  @_dylanga Yeah, GitHub has a serious issue with provenance and authenticity. Aside from the *** graph being kind of useless… repo stars are totally ******, too. It seems like an important problem, but GitHub is on autopilot or something. IDK. They move with zero intention nowadays.

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  Rulya (@Rulyaxd) reported 21 minutes ago

  3 hours with Claude. Two months later, servers he'd never visited had his bot installed. Zero coding classes. The article beside this is the exact four prompts he used. The story: a small study-group Discord server, one recurring annoyance (new members asking the same five questions in the wrong channels), Claude open in another tab. Three hours later he had a bot answering those questions. Sixty days after that, dozens of servers he'd never been in were installing it, because someone had shared it in a room he wasn't a member of. The build was four prompts. Prompt 1: describe the annoyance clearly enough that Claude writes the whole bot in a standard framework, with plain-English explanations of every section. Prompt 2: paste the exact error message when it breaks, ask Claude to explain what it means, what caused it, and the exact fix while explaining what changed so you understand it instead of copy-pasting. Prompt 3: turn the working script into a real product name, short description, add-to-server instructions. Prompt 4: after the first outside install, edge cases start showing up. Same loop. Paste the error, get the fix, ship it more stable than before. The growth curve is boring in a good way. First install: your own server. Second stage: someone shares it in a room you don't belong to. That's when the interesting bugs appear. From there it compounds handful → dozens → hundreds → thousands, once bot-listing directories start indexing it. The monetization shape follows. Free is what spreads. Premium at $19/month unlocks advanced automation for the server owners already depending on it. At 100 servers with a 50% paid conversion on Premium alone, that's $950 a month from a project that started as a study-group irritation. The illustrative math in the article 10 servers/4 paying/$76, 25/10/$190, 50/20/$380, 100/50/$950 isn't a promise. It's the shape of a low conversion rate compounding on top of a free version that already spread on its own. The article's kicker lands harder than the numbers do. The hardest part isn't building the bot anymore. It's believing you're allowed to. The kid in the video already believes. The article beside it hands you his four prompts. Most people will read this and think it's about Discord. A small number will notice that the same loop works for Slack, Telegram, Notion, Chrome extensions, and internal GitHub apps. He wrote no code. What he learned was how to describe a problem clearly enough that the software would build itself.

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  Yoshik (@AskYoshik) reported 21 minutes ago

  15 CI/CD pipeline patterns you should understand before your next build: 1. Artifact Promotion - Build once, push one artifact, promote the same image across dev, staging, and ****. 2. Immutable Build IDs - Tag images with commit SHA or build number, not just 'latest'. 3. Pre-merge Validation - Run tests, lint, security checks, and Terraform plan before code reaches main. 4. Environment Gates - Keep production behind manual approval, SLO checks, or change window rules. 5. Fast Rollback Path - A deploy pipeline without rollback is only half a pipeline. 6. Database Migration Checks - Separate schema changes from app deploys when rollback is risky. 7. Secrets Injection - Pull secrets at runtime from Vault, AWS Secrets Manager, or sealed secrets, not ***. 8. Cache Discipline - Cache dependencies, but include lockfile hash so old packages do not silently survive. 9. Matrix Builds - Test across versions like Node 20/22, Python 3.11/3.12, or multiple OS images. 10. Ephemeral Preview Environments - Spin up short-lived stacks for PRs, then destroy them cleanly. 11. Deployment Health Checks - Wait for readiness probes, 5xx rate, latency, and error logs before calling it done. 12. OIDC for Cloud Auth - Avoid long-lived cloud keys inside CI variables when GitHub/GitLab OIDC works. 13. Policy Checks - Block public S3 buckets, open security groups, and untagged expensive resources before apply. 14. Pipeline Time Budgets - If CI takes 45 minutes, people start bypassing it. 15. Audit Trail - Know who deployed what commit, from which runner, to which environment, at what time.

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  Mo Ayob (@Mossiah) reported 23 minutes ago

  Your company has AMNESIA. Every single day. It’s not one brain, it’s hundreds of people, each holding a tiny piece of what’s ever happened. Why a decision was made. What didn’t work last time. What the client actually asked for in March. Nobody has the full picture. So the same mistakes repeat. The same questions get asked again. And when someone leaves, their piece of the memory disappears with them. Funny thing is , it isn’t a “hire smarter people” problem. It’s actually a massive Organizational Intelligence issue. @OCTAMEM gives your company a memory of its own, one that sits underneath your files, docs, and code and actually remembers what the company knows. GitHub ingestion is landing next. The desktop app ships Wednesday and pulls straight from your machine, OneDrive and Google Drive. Still in beta. The price won’t stay this low.