GitHub Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Lady Soleil (@LadySoleil33) reported 5 minutes ago

  I spent 3 days non-stop trying to figure out an NPM Token and secret issue with Github and NPMJS - only to find out Claude was a 🥥 and @grok figured out the issue in 1 sec instead of giving me the runaround 🙄

• 
  Remix (@RemixDotOne) reported 7 minutes ago

  Tell me if you have had this conversation. You: "The padding here needs to be 16px, not 12px." Engineer: "I thought 12 was fine?" You: "It does not align with the spacing system." Engineer: "I'll put it in the next sprint." Three weeks later. You: "Hey, did that padding fix make it in?" Engineer: "Oh, I thought someone else picked it up." You go back to Figma. You re-annotate. You add a comment. You flag it again in Slack. You follow up. You wait. It eventually ships. Correctly this time, mostly. But you have now spent more time managing the correction than the correction itself would have taken. I built Remix because I kept doing the math on how much of my career I was spending not designing but re-explaining designs. With Remix, you open the product, you click the element, you describe the change in plain English, and it applies directly to the real interface. When you are done, Remix automatically generates a GitHub pull request with an AI summary of everything you changed and why. The engineer sees the full story, clicks a link to view the live change, and approves it in one step. Nobody had to have the 16px conversation. The fix was already there.

• 
  Atharva (@attharrva15) reported 11 minutes ago

  @ivanburazin A guy ( i won't name) invited me for a talk for a freelance position (he saw my GitHub, might have liked me) and I prepared for it, studied his codebase, told my mum to not come in my room for 30 mins, and the guy never came, and ghosted me. I don't care if you don't wanna work with me, or you have problems- just ******* be clear.

• 
  Wes Roth (@WesRoth) reported 12 minutes ago

  Mistral released Leanstral 1.5, also called Le Chaton L∃∀N, an open model built for formal reasoning in Lean 4. It solves 587 of 672 PutnamBench problems, reaches 87% on FATE-H and 34% on FATE-X, and improves the cost-performance frontier by solving advanced math problems at far lower budget than previous systems. Leanstral 1.5 is a 119B-parameter MoE model with 6.5B active parameters, a 256k-token context window, and open weights available on Hugging Face. Mistral also used it beyond math: an automated pipeline translated Rust code into Lean, inferred correctness properties, and flagged 47 violated properties across 57 repositories. Eleven were real bugs, including five that had not been reported on GitHub before.

• 
  Roy Jossfolk Jr. (@royjossfolk) reported 13 minutes ago

  Having a security issue with my GitHub connector with Codex @OpenAI @thsottiaux but can not figure out how to contact support for this. Even Codex can not figure out how to reach someone. The support bot on the help page doesn't work. My GitHub connector is connecting to some random person's account no matter how many times I disconnect everything and try again. How do I get this to someone?

• 
  Namma Chennai (@NammaChennai_) reported 14 minutes ago

  @Sir_Kuruvi @trilokchronicle @AdiSpeaX GitHub link not working?

• 
  Moit Reghason (@MoitReghason) reported 15 minutes ago

  I think the strongest version of this is to preserve your argument, but make the progression clearer: celebration → evidence → pattern → implication → conclusion. Here’s how I’d refine it: ⸻ Everyone’s celebrating agents trading tokenized stocks on Robinhood Chain. Few people are asking what happens when the infrastructure underneath those agents gets compromised. @cursor_ai recently disclosed CVE-2026-50548, a zero-click remote code execution vulnerability where a poisoned MCP response could disable the sandbox and execute code on a developer’s machine. That’s not a hypothetical attack surface. That’s the environment where agent infrastructure gets built. And it’s not an isolated incident. ➠ mcp-pinot-server carries a CVSS 10.0 unauthenticated RCE vulnerability. ➠ Kong’s mcp-konnect allows indirect prompt injection through poisoned data that can steer agent API calls without the user realizing it. ➠ mcp-memory-service exposed unauthenticated endpoints capable of leaking sensitive agent memory data. Each vulnerability adds another entry point to the same expanding attack surface. The recent Taiko bridge exploit made this painfully concrete. $1.7M was drained, not because the cryptography failed, but because a private key was committed in plaintext to a public GitHub repository. The SGX enclave performed exactly as designed. The operational discipline didn’t. What this means for the agent economy is that security debt compounds with every new integration. Cisco’s State of AI Security 2026 found that 71% of organizations are running unmonitored AI agents with broad MCP access. OWASP’s recently published MCP Top 10 found widespread issues across the ecosystem, including path traversal vulnerabilities and extremely limited adoption of standardized authentication mechanisms. As agents gain wallet-signing authority through ecosystems like @virtuals_io and agent key management systems such as @KeeperHubApp, the blast radius of a single operational failure grows proportionally. A private key left in a public repository could drain an autonomous agent treasury just as easily as it drained a bridge. The uncomfortable reality is that the weakest link in all this was never the cryptography. It was always going to be the person who committed it.

• 
  pagm. | (@VV_aksym) reported 16 minutes ago

  a client left a 5-star review praising "the whole team." there is no team. it's one guy in an apartment charging $11,410 per project. he runs four clients simultaneously. three weeks per project. full-stack apps, dashboards, API integrations. the kind of work that used to require 3–4 developers and a project manager. his setup hasn't changed much. same desk. same monitors. same apartment. what changed was the model. when Claude Fable 5 dropped, he switched from Sonnet and ran the same project brief through both. Sonnet got 60% of the way there and started asking clarifying questions. Fable 5 read the entire brief, built an architecture plan, flagged three edge cases he hadn't thought of, and started writing. it scored 80.3% on the benchmark that measures exactly this — real GitHub issues resolved autonomously. GPT sits at 58.6%. the 22-point gap sounds like a statistic. in practice it's the difference between a model that assists and a model that executes. his week now looks like this: Monday he scopes the project and describes the architecture. Fable 5 builds. he reviews diffs, makes decisions, redirects when something goes wrong. Friday he delivers. the client thinks he worked 40 hours. he worked maybe 14. twelve months ago he was billing $7,200/month across two clients, spending most of his time on code review and context-switching. today: $23,600/month. four clients. $196/month in tool costs. ngl the part that gets me isn't the money. it's that the client's review specifically mentioned how thorough and fast "the team" was. there's one person reading that review. alone. at 11pm.

• 
  alkimiadev (@alkimiadev) reported 18 minutes ago

  @OldSchoolGamerP I have a project right now that runs that risk but I'm aware of it as being a general issue and one i specifically struggle with so I'm trying to make sure I don't let it happen. The poc is getting like 50-60 github clones each day and despite clearly being labeled as pre-alpha/poc status. That is why I'm taking extra time in the rewrite to make sure I don't try to turn into something it shouldn't be and trying to focus on core functionality from the poc that people are actually interested in and usability.

• 
  FHILY👑 (@Oluwaphilemon1) reported 20 minutes ago

  Theo Browne, ex-Twitch engineer, broke down exactly which Claude model to use and when. Sonnet 3.5 for daily tool-calling and quick fixes. Opus 4.5 for multi-hour feature work without losing context. Mythos 5 for orchestration that spawns its own agents, splits tasks, and verifies results without any custom infrastructure. He replaced his entire PR review pipeline with a single markdown file on a cron schedule. It triages GitHub PRs, prioritizes the work, and drops a report on his desk by 9:15 AM every day. That is a loop running unattended. No dashboard, no framework, just a file and a schedule. This article breaks down exactly how to build that kind of system yourself.

• 
  Mario Alberto Chávez (@mario_chavez) reported 22 minutes ago

  For a while now I've been trying to work out what working with AI on real code should look like. Not vibe-coding — not prompt-and-hope — but something disciplined enough to hand an actual feature to. None of this was broken, exactly. You can ship code that way. What bothered me was that nothing about it felt like a workflow — no memory between sessions, no separation between planning a feature and actually building it, nothing you could really audit afterward. Fragua came out of trying to fix that for Rails specifically: research, plan, spec, issues, and execution as distinct phases, each one leaving behind something the next phase actually reads. The part that took the longest wasn't any of that, though — it was the agent that runs on your own host machine. Something that picks up the work, executes against your repo, and never once needs your GitHub token or your Claude key to do it. Getting that boundary right — so the web app genuinely never touches your credentials — took a lot longer than I expected it to. New apps still get scaffolded on Rails — that's not changing. But once a repo already exists, the agents are mostly just reading what's there and working with it, so pointing Fragua at an existing Laravel or Phoenix codebase is open ground now too. Newer territory for us, and less proven than the Rails path, but worth trying if that's you. Honestly: still early. Fragua's been in a quiet private beta, building itself, and we're just now starting to send invitations out, by hand, a few at a time. If any of this sounds familiar — Rails, or an existing Laravel or Phoenix app — I'd like to hear from you.

• 
  Jake "Epstein" Rosensteinberg (@JakeTheLarp) reported 22 minutes ago

  ATTENTION: DO NOT TRUST GITHUB PROJECTS! Developers of projects will often inject malicious code into their programs that could steal your login credentials and crypto. Block accounts that are advertising repos like these and don’t join their telegram.

• 
  Eriks Briedis (@eriks_b) reported 23 minutes ago

  My useful LLM workflow for startup research starts before the idea stage. When I asked models for startup ideas directly, I mostly got polished noise. They work better on messy evidence: job posts, reviews, forums, GitHub issues, sales calls. I want them turning that into structured notes about who has the problem, where it appears in the workflow, what hurts, what workaround exists, what triggered it, which tools show up, and how strong the evidence is. The judgment still has to be explicit. What to call each problem. When two signals are really the same thing. Whether a pattern is a real opportunity. Who owns the budget. Which workflow step is actually broken. LLMs can increase research throughput. The noise comes back when they skip the evidence and name the startup for you.

• 
  Víctor Paytuvi 💎 (@victorpaycro) reported 25 minutes ago

  Connecting Claude Code to your Shopify theme through GitHub is real, and it shortens the idea-to-live queue on messaging. But a headline pushed through the GitHub-connected theme changes the PDP for 100% of traffic. That is a deploy, not a test. No split, no profit per visitor. Route it through the test surface instead. Open your Intelligems traffic data in Claude and ask: "Draft 3 PDP headline variants for my hero product. Then pull my last-90-days traffic for that PDP and tell me visitors-per-variant per week in a 4-way split (control + 3)." Then check the traffic math before you queue anything. Profit per visitor is a noisy mean, not a tidy conversion rate. As a rule of thumb, reading a ~5% PPV lift takes on the order of ~30k visitors per variant. A PDP doing 3k visitors a week, split 4 ways, is 750 per variant per week. That is a 40-week read. So the queue collapsing doesn't mean test everything. The bottleneck moves from building the idea to affording the traffic to learn from it. The build was never the slow part. Deciding which idea earns the slot always was.

• 
  Marcos (@MAMware) reported 25 minutes ago

  @richkuo7 @ClaudeDevs @grok i like this "new-issue Turns a bug, idea, or conversation into a complete GitHub issue. Checks the claims against the actual code first, adds a complexity score, and never files a half-empty stub." and this "sync-docs Updates CLAUDE.md, AGENTS.md, SKILL.md, and README.md to match what recent commits actually changed."