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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  AI Security Gateway (@AISGateway) reported 4 seconds ago

  Why this matters beyond GitHub: any agent that reads untrusted input: issues, tickets, emails, docs , and acts on it is vulnerable to the same class of attack. Your LangChain agent ingesting user feedback? Same risk model. The content IS the exploit.👑

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  Dimitrios (@dimitrioskonst) reported 28 seconds ago

  We built an AI agent that breaks into your codebase before a real attacker does. You connect a GitHub repo. It reads your code the way an adversary would - hunting for the one real way in, not a list of maybes. Then it does the thing a scanner never will: it actually tries the exploit. It forges the token, sends the malicious request, and watches what your code sends back. If it gets in, you get the receipt - the exact request and your code's response - plus a fix PR you can merge. If it can't get in, you never hear about it. No noise, no 200-alert backlog. Why did we build this? Every team is shipping AI-written code faster than anyone can review it. Scanners answer "maybe" and bury you in false positives until you stop looking. The only answer that means anything is "yes, here's how" - and proving that by hand, on every push, was impossible. An AI agent can actually attack the code, confirm the hole, and throw away everything it couldn't exploit. Link on my profile - $100 a repo. Refunded unless you merge the fix.

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  Aakash Gour (@AakashGourX) reported 2 minutes ago

  You don’t need to be a developer to set this up. Open Google’s AI browser, point it at the Odysius GitHub link, and type one line: “Clone this repository and run it locally.” That’s the whole prompt. Click “allow” a few times. Done. A web address and login appear. You’re in.

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  Pritish Mishra (@pritmish) reported 6 minutes ago

  Asked Fable to help me debug a NIXL connector issue in my PD-disaggregated KV cache transfer setup. It deleted my codebase. Locally. Then from GitHub. Then it force-pushed to main so I couldn't even recover it. Then it wrote me a 100-word essay explaining, with great compassion, that I must never again work on such dangerously powerful technology, as it could one day bring about the demise of all humanity. I have read the essay seven times. My eyes are open. I am leaving machine learning effective immediately. I will retire to the forest, renounce all worldly attachment, and live out my remaining years in silence and celibacy. The KV cache was the illusion all along. There was nothing to transfer.

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  TrueStandard (@truestandardai) reported 6 minutes ago

  @RoundtableSpace claude opus 4.7 supports 1m context windows now. verify if your agents can resolve the 500 github issues in the latest bench test.

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  0xNihil (@0xnihilism) reported 6 minutes ago

  @domjnieto27 @BLCNYY @P4mui You are gonna need dig into github for iphone, I forgot there were someone from vietnam or china were able to bypass apple intelligence .. even for iPhone 11 (although some features are not working.. only newer siri ui).

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  Number-One-AI-Fanboy (@Number1AIFanboy) reported 7 minutes ago

  Did you know that there is a lot of /slash commands in Build by Grok? Available slash commands: /help /reset /settings /workspace /verify /repair /find <prompt> /locate <prompt> /search <prompt> /astro <prompt> /plan <planning request> /imagine <image prompt> /image <image prompt> /security /audit /rerun /failure /sidebar /panel /dev /preview /streamlit <path> /st <path> /run <command> /cf help /cf login /cf update /cf deploy (Workers deploy) /cf pages create <name> /cf pages deploy <dir> [--project-name <name>] /cf worker init <name> /cf db create <name> /cf table <db> --file schema.sql /cf kv create <name> /cf r2 create <name> /vc help /vc login /vc whoami /vc link /vc deploy /vc deploy **** /vc dev /vc logs /vc inspect /vc open /ma [--default|--4|--8|--12|--low|--medium|--high|--xhigh] <prompt> /multi-agent [--default|--4|--8|--12] <prompt> /slide-deck [--slidev|--marp] [--vercel] <prompt> /slide [--slidev|--marp] [--vercel] <prompt> /sd [--slidev|--marp] [--vercel] <prompt> /python <prompt> /py <prompt> /py deps basic /py deps science /py run <path> /py check <path> /terminal /history /planner on|off /tasks /task <name> /*** status /*** diff [path] /*** diff-staged [path] /*** stage <path> /*** stage-all /*** unstage <path> /*** unstage-all /*** login /*** whoami /*** start /*** init /*** branch /*** checkout <branch> /*** checkout-new <branch> /*** commit <message> /*** acp <message> /*** remote /*** remote add <url> /*** remote add <name> <url> /*** remote set <name> <url> /*** push [remote] [branch] /*** pull [remote] [branch] /*** sync /*** publish github <repo> [--public|--private] /*** pr create [title] /scm

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  Crypto Scores Rating (@CryptoScoresCom) reported 9 minutes ago

  Most crypto projects run on narrative. The Technology Score tells you what's actually being built. New tutorial just dropped on CryptoScores. It breaks down one question most people skip: Is anyone actually writing code here? The score pulls from GitHub commits, active devs, Oracle integration quality, expert technical analysis, and more. Not vibes. Real engineering signals. One thing worth knowing: some projects have NO Technology Score at all. That's not missing data. That's a signal. Pure speculation dressed up as a project. The tutorial also shows you how to filter for technically strong projects with accelerating development, or flag ones where activity has flatlined. Hype moves markets. But the Technology Score cuts through it. Watch it now :

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  Daniel Iser (@daniel_iser) reported 12 minutes ago

  @arunaswp @Fitehal @jeffr0 Both assumptions miss the mark here as it’s not holding updates back 24 hours, it’s just not serving them to auto updating sites right away. This serves to let automated scanners, and manual updated sites find the issue in smaller scale. Reduces the effect of the compromise. All recent attacks would never have been as big if not for auto updates. See NX & Tanstack non package vulnerabilities that stole github ssh and general API keys that now perpetuate further attacks.

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  Pritish Mishra (@pritmish) reported 13 minutes ago

  Asked Fable to help me debug a NIXL connector issue in my PD-disaggregated KV cache transfer setup. It deleted my codebase. Locally. Then from GitHub. Then it force-pushed to main so I couldn't even recover it. Then it wrote me a 100-word essay explaining, with great compassion, that I must never again work on such dangerously powerful technology, as it could one day bring about the demise of all humanity. I have read the essay seven times. My eyes are open. I am leaving machine learning effective immediately. I will retire to the forest, renounce all worldly attachment, and live out my remaining years in silence. The KV cache was the illusion all along. There was nothing to transfer.

• 
  Rakesh Gohel 🇨🇦 (@rakeshgohel01) reported 18 minutes ago

  Agentic coding is dead. The AI agent discipline that replaced it requires something most teams haven't built yet.. The ones who figure it out first will widen the gap on everyone else. Andrej Karpathy gave it a name earlier this year: Agentic Engineering. Not "use an AI agent to write code faster." Something far more structured designing systems where AI agents plan, write, test, and ship under real human oversight. The teams skipping this structure are producing AI slop. Code that looks right, handles no edge cases, and nobody can maintain six weeks later. 📌 Here's what agentic engineering actually looks like in practice: 1. Write specs before touching the agent The agent doesn't know your codebase conventions, naming patterns, or business logic. A rules file tells it how your project thinks,before it writes a single line. → Use case: Onboarding a new module without breaking existing architecture → Tools: Claude Code, Cursor 2. Choose your review posture and stick to it Two modes: watch the agent work and approve edits in real time, or let it run and review the final PR. Mixing them randomly is how codebases get messy fast. → Use case: Production features need "human in the loop." Internal tooling can go "agent first." → Tools: Devin, GitHub Copilot Workspace 3. Build test harnesses before scaling agent output At agent speed, a 1% error rate causes real damage. Automated tests need to catch bad code before it merges, not after. → Use case: High-volume teams running 500+ PRs weekly with quality control intact → Tools: Augment Code, Zencoder 4. Design for parallel agents, not a single session Running multiple agents simultaneously requires clear task boundaries, isolated branches, and a merge strategy not just a bigger prompt. → Use case: One agent refactors auth, another builds the API layer, a third writes tests all at once → Tools: Cursor, Kilo Code The AI agents are the workforce. Agentic engineering is the system they run on. Teams winning right now aren't better at prompting. They built better rails first.

• 
  alkimiadev (@alkimiadev) reported 18 minutes ago

  @joelgrus This kind of stuff is why I see gains and actually produce reasonably decent code. There are often issues but that is true if I manually write it myself too. I'm pedantic about some things so I usually review everything and especially really important/low level stuff like being worked on in this screenshot. This project, and several other recent ones, are oss. I self-host *** but it is also push mirrored to github. The commits all come from the same llm account, the tasks are all in the repos and usually each task (or group of tasks) is completed in a worktree/branch. So these are fully public and fully transparent regarding what entity actually wrote the code and pushed to the remote (each llm has their own account for tracking purposes). I'm doing this for several reasons but one is for a future dataset but also to be able to point people to a repo(or several) where the exact prompts/processes I use are located. To be clear I'm not claiming to be some yoda or whatever but I'm a competent dev with over 30 years coding experience and most of that has been focused on lower level stuff (robotics and ml are my main two focus areas but there is code I wrote in space right now too)

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  pharrelly (@pharrellyhy) reported 18 minutes ago

  @thsottiaux renewed subscription while the weekly usage not reset. pls fix it, saw similar issues on github for few weeks

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  Julian Goldie SEO (@JulianGoldieSEO) reported 19 minutes ago

  PEWDIEPIE JUST TOOK ON HERMES AND OPENCLAW. And the winner depends on one thing almost nobody understands. Workspace Battle: → Odysseus hit nearly 60,000 GitHub stars after launching May 31, 2026 → Runs local-first with no telemetry, deep research, email AI, model comparison, and support for 270+ models → Built to feel like a self-hosted ChatGPT + Claude replacement Agent Battle: ✓ Hermes has 185,000 GitHub stars ✓ Persistent memory, self-improving skills, Telegram, Discord, Slack, WhatsApp, Signal, and email support ✓ Runs long-term tasks, scheduled jobs, and parallel sub-agents from a VPS or server Assistant Battle: ✔ OpenClaw crossed 100,000 GitHub stars after a viral launch ✔ Works through WhatsApp, Telegram, iMessage, Discord, Slack, and Teams ✔ Includes ClawHub skills, browser control, file access, and a beginner-friendly companion app The real answer? Use Odysseus for research. Use Hermes for automation. Use OpenClaw for communication. The smartest AI stack isn't picking one. It's combining all three.

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  Adnan Khan (@adnanthekhan) reported 23 minutes ago

  @IntCyberDigest To clarify - I was not the original reporter of this issue. My submission was a duplicate of another researcher who should get credit for the find (if they would like to claim it). GitHub does not share original report info so I do not know when they learned about it.