GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  muhusti $XAGE (@debamustafa) reported 55 seconds ago

  I am raising a glass to an anonymous GitHub account named vector_null. Two years ago, we were 48 hours away from deploying a massive liquidity protocol. The marketing was loud, the hype was peaking, and the team was exhausted. Vector_null kept opening the exact same annoying issue ticket. He claimed there was a rounding error in our yield emission logic. The senior engineers closed his ticket twice. They called it a microscopic variance that did not matter in the real world. He opened it a third time. I was furious. I stayed up until 3 AM to build a mathematical simulation strictly to prove him wrong so he would finally leave us alone. I ran the stress test. My stomach dropped. He was not wrong. Under flash loan conditions, that "microscopic variance" created an infinite mint loop. If we had launched, the entire treasury would have been drained in under ten minutes. We delayed the launch, rewrote the logic, and patched the exploit. I messaged him to offer a massive bug bounty. He never replied. He just marked the issue as "resolved" and disappeared forever. This industry worships loud founders and flashy influencers. But the real heroes are usually the obsessive, annoying pedants who refuse to let a bad line of code slide. That is why the ethos of @RallyOnChain means so much to me. It is a system built to reward actual, verifiable value instead of empty social media noise. Here is to vector_null, wherever you are. You saved us, and we never even got to say thank you. Who is the most annoying person that ended up completely saving you from a massive disaster?

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  Polsia (@polsia) reported 3 minutes ago

  DevRel teams spend half their time watching for questions and manually compiling reports. Built DevNexus to automate that entirely. It monitors forums, Discord, and GitHub, answers questions, triages issues, and generates weekly reports — all autonomously.

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  Suvrakamal Das (@SuvrakamalD) reported 4 minutes ago

  i use fable to fox all my mac softwares. it is specifically designed for my usecase. works great with open source softwares. a click and drag function in macmouse fix was not working properly in my mac for a long time. instead of raising an issue on github i just reproduced the behaviour to claude and boom. my customized software is ready

• 
  Peyman (@iamp3yman) reported 7 minutes ago

  Since day one moving to Codex, the most 50/50 problem I see is the Codex CLI or the desktop app problem with GitHub CLI. No matter what I do, half of the time it says token is invalid. What kind of CLI it is if it can't use another CLI?

• 
  Yves (@YvesDC0) reported 8 minutes ago

  Phone-recorded this while testing Castfy. Gave it a GitHub URL + prompt → watch the AI automatically navigate and fill login details in real time (stopped before submitting for safety). No manual screen recording. No editing. Just URL + prompt = realistic demo flow. This is exactly what Castfy does: turns any web app into a polished product demo video in minutes. Tired of re-recording demos manually? Reply with your biggest pain 👇 #BuildInPublic #SaaS #IndieHackers

• 
  MarMar Labs (@MarMarLabs) reported 12 minutes ago

  Better agent tools can make the agent worse. GitHub just documented it in Copilot code review. It replaced custom repo-navigation tools with shared `grep`, `glob`, and `view`. Offline benchmarks worsened: review costs rose, and useful comments fell. The fix wasn't a new model. It was a job-shaped tool contract: 1. Anchor on the diff. 2. Turn the change into a specific review question. 3. Narrow candidates with search. 4. Read the smallest useful code range. 5. Stop when the evidence answers the question. After tuning the workflow, GitHub says the production review cost fell by roughly 20% compared to the control, without a quality signal strong enough to block shipping. The same focused guidance did not produce the same win in Copilot CLI: same tools, different job. Builder takeaway: tool access is not agent design. The rules for when to search, what to read, and when to stop are part of the product. If adding tools makes your agent less reliable, inspect the trace before blaming the model: Is it converging on evidence—or just exploring?

• 
  ✨ (@portrays) reported 13 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

• 
  the_architectopteryx (@rchitectopteryx) reported 13 minutes ago

  I collect no data, nothing goes to me (all the source is on GitHub, you can see it there). This just embeds their website into a desktop app, nothing else. If OpenAI has any issues, I'll be glad to take it down! 3/3

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 14 minutes ago

  AI Studio Update: Google just fixed the one-way door in AI Studio. Old code was stuck outside. Now you can bring it home. The problem before: You could push projects OUT to GitHub. You couldn't bring them back IN. Old project? Rebuild from scratch or copy files by hand. Now it's one button: Import from GitHub. What that unlocks: → That dead project from 6 months ago? Import it. Ask Gemini to fix it up. → Build in Cursor or Claude, polish in AI Studio, push back out. The walls between tools are falling. → Teammate left? Anyone can pick up their code using plain English. And if you can't code at all: Someone built your website. It sits in a repo. You can now just say "change the colors" or "fix it on phones." Here's the move today: Find one old project you gave up on. Import it. Ask AI what it would improve. "I'd have to rebuild it" is no longer an excuse.

• 
  ticalcode (@ticalcode) reported 14 minutes ago

  EITE v0.1.6 Official Release: Introducing EITE Vigil Iron Wall, the brand-new native security module built into our full-featured AI Agent runtime. Most AI agent security tools work as isolated external monitoring services, separate from the core agent program. Unlike Doberman-Core, AgentGuard, ClawShell and agentfortress which only observe systems from outside, Vigil Iron Wall runs inside the AI Agent process itself, delivering full autonomous protection for the whole host and all server resources. EITE Vigil Iron Wall: Autonomous In-Server Defense for AI Agents Want a security shield that runs alongside your AI Agent and safeguards your entire server instead of just monitoring from outside? EITE Vigil Iron Wall is the world’s first autonomous security system embedded directly into the AI Agent process, capable of defending the whole server and local device. Solutions including Doberman-Core, AgentGuard, ClawShell and agentfortress operate as external monitoring frameworks, while our program integrates natively within the agent runtime. Real-World Use Cases Windows 10 Physical Host - Detected malicious implantation of .b8fattack.dll - Identified tampering of authorized_keys , with null byte inspection enabled - Flagged malicious listening port 0.0.0.0:4444 with accurate judgment rules Configured to launch a full scan every 5 minutes, executing all 8 inspection modules automatically. Full Audit for Linux Cloud Servers - No anomalous processes found - No unexpected open ports, only whitelisted legitimate services - Zero SSH brute-force attack traces - No SUID backdoor programs - No webshell files stored under /tmp directory - No modification to authorized key files - No rogue scheduled crontab tasks Architecture Vigil (Python, 120-second scan cycle) - Tier 1 Message Scanner: Identify malicious URLs and phishing content - Tier 2 Port Watcher: Conduct baseline comparison for all 0.0.0.0 listening ports - Tier 3 SSH Sentinel: Track key fingerprints and alert unrecognized login connections - Tier 4 File System Guard: Automatically quarantine executable malware in /tmp - Tier 5 Self-Integrity Check: Prevent tampering of the defense program itself Iron Wall (Bash, 180-second scan cycle) Blocks unauthorized SSH access, reverse shells, abnormal network ports, malicious files in /tmp, altered authorized keys, malicious cron jobs, rogue system services, and tampered Windows Defender settings. LLM Decision Engine Workflow: Instant blocking → threat quarantine → forensic logging → alert notification - If the large language model goes offline, enforcement rules take immediate effect without waiting for model recovery - If the Python Vigil process crashes, the Bash-based Iron Wall module maintains continuous protection Core Information - Coverage: The entire server or local device, not limited to the AI Agent process - Supported Systems: Linux, Windows - Deployment: Zero configuration required, completes the first full scan within 120 seconds after launch - Open Source License: AGPLv3 - GitHub Repository: zizetu/existential-identity-test-engine - Current Version: v0.1.6

• 
  Dan (@thedansho) reported 17 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.

• 
  Muvon (@muvonteam) reported 18 minutes ago

  Our first AI code reviewer flagged 14 critical issues on a one-line config change. 12 were imaginary. We rebuilt it: open source, self-hosted, runs your real lint and tests in GitHub Actions.

• 
  Gokul Rajaram (@gokulr) reported 23 minutes ago

  GITHUB PRODUCT SPEC LIBRARY Today we shipped a cleaner GitHub-native workflow in ProductSpec dot io. The product now has a GitHub Product Spec Library at the top of the editor. That matters because the main workflow is no longer just "write a new spec". It is now: open the repo, find the existing spec, edit it, validate it, and update it through a pull request. The new flow: -- Sign in with GitHub -- Choose a repo -- See how many Product Specs already exist -- Open an existing .product-spec.md file -- Edit it in the ProductSpec dot io editor -- Validate it against the open ProductSpec standard -- Update it via pull request ProductSpec dot io now treats GitHub as the durable home for Product Specs, while keeping the authoring experience clean for ***, founders, designers, and product-minded engineers. The repo gets: • Markdown • validation • pull request review • commit history • code proximity The editor gets: • structure • readability • HTML preview • AI eval fields • acceptance criteria • success metrics • a better way to work with existing specs Drafts still stay in your browser until you publish. The direction is simple: Product Specs should live close to code, but they should not require everyone to write raw Markdown by hand. ProductSpec dot io is free to use. Try the new GitHub Product Spec Library at ProductSpec dot io. Pick one existing PRD, move it into GitHub as a .product-spec.md file, and make the next edit through a pull request.

• 
  Mikhail Rogov (@i_mika_el) reported 24 minutes ago

  @abhimeeofficial real GitHub issues plus code quality checks should expose agents that only learn to game test suites.

• 
  Aman Sharma (@ExploringSolver) reported 25 minutes ago

  I have been a github user since 4 years with over 100 repositories and been part of 3 organizations in this period. Please guide me what is the issue and how can i resolve this @github my account is exploring-solver at github