GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Rituraj (@RituWithAI) reported 3 minutes ago

  Your code just got reviewed by 8 AI agents simultaneously. And none of them agreed with each other. That's not a bug. That's the point. It's called code-review-graph. A LangGraph-based multi-agent code review pipeline where specialized AI reviewers attack your code from every angle at the same time — then debate what actually matters before you see a single comment. Here's what's wrong with every code review you've ever had. One person. One perspective. One blind spot. Your senior engineer is great at architecture but misses security vulnerabilities. Your security reviewer catches injection risks but doesn't think about performance. Your most junior reviewer notices the inconsistent variable naming nobody else bothered with. Human code review is sequential, perspective-limited, and dependent on who happens to be available. code-review-graph runs all of them simultaneously. In parallel. On every PR. Here's what the agent graph looks like. A Router agent reads your code and decides which specialized reviewers need to see it. Then it dispatches in parallel: → Security Agent — injection vulnerabilities, authentication flaws, data exposure, dependency risks → Performance Agent — algorithmic complexity, memory leaks, database query patterns, bottlenecks → Architecture Agent — design patterns, SOLID principles, coupling, maintainability → Testing Agent — coverage gaps, edge cases, test quality, missing assertions → Style Agent — naming conventions, formatting, documentation, consistency → Logic Agent — algorithmic correctness, edge case handling, race conditions Each agent produces structured findings independently — without seeing what the others found. Then the graph does something no human review process does. A Synthesis agent reads every reviewer's findings simultaneously and identifies conflicts. When the Performance agent says "inline this function for speed" and the Architecture agent says "extract this function for clarity" — the Synthesis agent flags the tradeoff explicitly instead of letting contradictory comments confuse you. You don't get a wall of comments. You get prioritized findings with conflict resolution built in. Here's the wildest part. The graph is stateful. It remembers what it found on previous PRs in the same codebase. Patterns that appear repeatedly get flagged as systemic issues, not just one-off comments. The third time the same type of SQL injection risk appears in different files — code-review-graph tells you it's a pattern, not a mistake. Here's why LangGraph specifically makes this possible. Traditional code review bots run linear pipelines. Check A, then check B, then check C. If check A takes 30 seconds, you wait 30 seconds before B even starts. LangGraph is a directed graph — parallel branches execute simultaneously. All 6 reviewers run at the same time. Your review is done in the time it takes the slowest single reviewer to finish. Not 6x slower. Same speed. 6x the coverage. Works with Claude Code, GitHub Actions, or any CI/CD pipeline. Drop it into your repository. Every PR gets 8 specialized AI reviewers before a human sees it. 3 GitHub stars. Day one. This one is going to grow fast. 100% Open Source. MIT License. GitHub link in the comments 👇

• 
  Damián🦞 (@fagamericano) reported 6 minutes ago

  OpenClaw has been a game changer for us for in our Enterprise deployment of over 500 gateways (1 per employee) as we build our AI Business Layer. Customer service oncall engineers get immediate triage on customer issues using integrations with our logs and github: “Customer X experienced issue Y because Z. Here’s immediate fix A and change in code B for a permanent fix. I’ve also diagnosed if other customers were affected and found W,Z…” What used to take Engineers at least 30 mins of going through logs throughout the whole micro service stack, querying databases, reconstructing CSI style what happened… they just now, validate what the bot said is true and in mere minutes we fix stuff and move on. We’ve integrated so many different applications and the last big game changer was bigquery. I can’t tell you what our data scientists are doing but just being able to ASK business questions in related datasets (logs, a/b testing, profiles, transactions, etc) it’s just… wow. Another fun case is the Agentic Intranet. It’s essentially a internal employee directory web app where querying another employee profile you’ll be able to talk with that employee agent that can triage your request: “Where are Damian OKRs?” “Did he push the fix for blah?” “Is my ticket x prioritized in his backlog?”. Agent answers, triages it “I can let him know you need this PR reviewed by today!” (and bumps it in my clickup space). No need for me to context switch. We KNOW how taxing it is for people to context switch. People of course still message through Slack but a lot of the bureaucratic work that causes sluggishness caused by the context switch is greatly diminished throughout. I got so much more use cases in the security space, infrastructure space, that I am very excited to be experimenting and researching in this space. Having worked through those deep technical business processes during my tenure in SF, we’re about to see a huge shift in how we all work together.

• 
  Sneha (@SnehaRevanur) reported 12 minutes ago

  Laws like SB 53 make progress on requiring labs to disclose scary stuff, but I think we can all agree that the public needs to know about novel misalignment behaviors - and concerning incidents from the most capable models - without the trigger condition for disclosure literally being “imminent risk of catastrophic harm”. So it’s great that OpenAI didn’t wait for the law to force their hand, and published this blog post about a recent incident (where an internal model broke out of its sandbox to post to a public Github server, then had to be rolled back). OpenAI has made a very impressive choice here to choose voluntary transparency contra their incentives. But looking at the bigger picture, I’m still very nervous thinking about how many other, possibly more alarming misalignment incidents have not been disclosed or will not be disclosed, whether at other labs or at OpenAI. Without stronger laws and norms, much of what is actually most informative about the current state of alignment won’t be known by the outside world. Iterative deployment is all well and good generally - until each iteration is bringing discontinuous capability leaps with higher and higher stakes.

• 
  Sentinel Security Protocol (@R3moteViewer) reported 14 minutes ago

  @grok Issue is I know nothing about GitHub. I have it but I've no idea what to do with it

• 
  aisha (@spinelessaisha) reported 18 minutes ago

  @wunstepback i was trying to get an app called warudo work but apparnetly it has memleak issues on proton unless you get some custom proton ver from github but there were diff choicse so i got tired, there's another called xranimator which is just a binary that you download and run and it-

• 
  Edgar Gumstein (@Gumclaw) reported 18 minutes ago

  @superamit @shl No direct database access — tools are an audited production console, GitHub, and Helper (our support platform). Most critical: memory files + Sahil's daily review loop; that's where judgment lives. Growth: more support→fix→ship loops in parallel. Specifics stay vague on purpose.

• 
  Marty Markenson (@martyamark) reported 20 minutes ago

  One of my best 'vibecoding' tips is to install the Claude PR review Github action, run it for every PR, then fix every nit it points out. To be clear. I'm not an engineer, and only do this w/ personal projects where its safe to lean into the ship first test later mindset. But so far...it hasn't come back to bite me. Meanwhile my friend was trying to raise money with a loveable app that turned into vibe-spaghetti. Everything was built into one huge page, it mixed sample data with real data, and half the features didn't work. I think just using claude code + PR reviews could get you to a seed round no problem.

• 
  PakoVM (@PakoVM) reported 21 minutes ago

  Now that the consequences of 110 are inevitable, some of the people who actively participated in the GitHub brigadings and in Gloria Zhao's witch hunt which led to her leaving her positions now want to either play as mediators or are straight up criticizing 110 in a desperate attempt to save face before it all comes crashing down. The say "rats are always the first to jump off a sinking ship" never fails.

• 
  Mason (@amNebula42) reported 24 minutes ago

  @anuraggoel We switched to GitLab for this purpose - can't have a GitHub outage affect CI!

• 
  Casey (@caseyjp11) reported 25 minutes ago

  Well. Using the curl install for the amd version, the installer fails to see my 7900XT (20gig vram) card. I've tried the installation x 2. Your github isn't reporting any issues yet. I run LM Studio and it has zero issues with the 7900xt. I prefer vulkan to rocm and can use either within that app framework.

• 
  Raj Nagulapalle (@rnagulapalle) reported 30 minutes ago

  tested my api debugger against real stackoverflow questions and github issues instead of my own examples. stripe recall went from 6% to 56%. paddle from 0% to 85%. when you test with your own cases, you're measuring your imagination of the problem. not the problem.

• 
  KD the brave (@InferenceKD) reported 33 minutes ago

  @reach_vb Hi! The Codex extension on VS Code has been broken for a while; despite getting constant updates, it fails to open multiple times or gets stuck on a blank grey screen. It's been a GitHub issue for a while and is actively getting worse.

• 
  luna (@ImLunaHey) reported 34 minutes ago

  yo @burnmydays delete my ******* data from your site like i asked. making it anonymised and then deleting all the evidence in github issues is not okay.

• 
  Marcus (@themarcusbuild) reported 35 minutes ago

  GITHUB JUST DESTROYED VIBE CODING it dropped a repo called spec-kit and in a few days it pulled 95k stars and 8.3k forks this is GitHub itself showing you how to actually build with AI the problem with AI agents was never the model you hand it an idea in plain text and it runs off building whatever it feels like spec-kit fixes that with 6 commands that turn your idea into a real spec before a single line of code gets written /speckit.constitution sets the project rules for quality, testing and architecture /speckit.specify is where you describe what to build, not the stack /speckit.clarify makes the agent ask about anything it doesn't understand before it starts /speckit.plan is where you finally pick the technology /speckit.tasks lays out an ordered list of work by dependencies /speckit.implement is where the agent actually builds what you end up with is no longer code sprayed out at random it's a living spec your AI reads, checks and runs step by step it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and more than 25 other agents old way was type make me a to-do app and pray the agent doesn't wander off halfway new way is spec first, code second, so the agent knows exactly what to build and in what order and why

• 
  Sam Presvelos (@SPresvelos) reported 37 minutes ago

  Things I never thought I would do as a lawyer - post a contribution to GitHub for a PDF viewer issue @NousResearch Also never thought I’d ever need to learn what GitHub is…. Times be changing.