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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Andrew (@openmarmot) reported 40 seconds ago

  @AndrewCurran_ I use grok every day to research software changes/github issues/software doc research. It is very good at real time data search. Might be SOTA in this niche. Hardly a failure. Meanwhile LeCun only surfaces to let out more hot air. A very forgettable person.

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  Arti | AI Builder (@Artur_roses) reported 1 minute ago

  Claude Code just took my GitHub issue, wrote the code, ran the tests, and opened a PR. My job: approve it. The dev workflow isn't changing. It already changed.

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  Yarchi (@undefinedKi) reported 3 minutes ago

  BORIS CHERNY, THE CREATOR OF CLAUDE CODE, JUST SOLVED AI'S BIGGEST PROBLEM. HE STOPPED PROMPTING CLAUDE AND STARTED WRITING LOOPS THAT RUN IT 24/7 The guy who built Claude Code doesn't prompt Claude anymore. He writes loops, and the loops do the prompting. It's called loop engineering. Here's what it is and how to set it up. A loop is a system that wakes itself up, finds work, does it, checks it, and repeats, while you watch instead of type. In Claude Code it's three built-in commands: > /loop runs a prompt on an interval. Example: /loop every 5 minutes, check for new GitHub issues and handle any that come in. > /goal makes the agent work until a condition you set is true, with a separate model grading the result. Example: /goal build this feature until all tests pass. > /routines are scheduled jobs. Example: every hour, wake up, read the spec doc, and do the next task. The fastest way to start: write a simple task list in a plan.md file, then tell Claude "use the loop skill and work through plan.md one task at a time." It sets up the /loop itself, does the first task, validates it, wakes itself for the next, and reports back when the list is done. You never write the loop prompt by hand. Three rules so it doesn't burn your budget or ship garbage. One, split work across separate sessions instead of looping in one (a long /loop bloats your context and overwhelms the model). Two, use a cheap model like Haiku for planning and a strong one only for the actual code. Three, keep a human checkpoint on anything that ships, never let it run all night unchecked. Bookmark this

• 
  Dan (@Daniel_Farinax) reported 4 minutes ago

  Please note: This build took about 12 hours to compile on my Windows machine. I’ve included a handy installer to make setup easy. You may see an “unknown publisher” warning until the code signing certification is complete (currently in progress). Report any bugs or issues here or in Github.

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  Crypto Update IO 🚀 (@cryptoupdate_io) reported 5 minutes ago

  @CRYPTOKRALI3 Hsiao-Wei’s exit aligns with EF’s recent sharp decline in GitHub contributions—down 35% YoY per Electric Capital’s data. We track this daily; latest reports show a 12% drop in ETH core dev activity despite all the ‘decentralization’ hype.

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  Namespace (@namespacelabs) reported 9 minutes ago

  Behind every API, webhook, event pipeline, there are people trying to keep things running. And keeping these things running is not an easy task. At Namespace, we try to work with those people. Earlier this week, Gihub events were dropping fields we depend on and customer jobs were stalling. We reached out to work on the problem together and had a fix in under an hour. The @github team was ready to help. We just had to ask.

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  Floorless🌒Lance🪽 (@4ranc6) reported 15 minutes ago

  @CAONHTAN1 Having error connecting github

• 
  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 15 minutes ago

  Pipelines are built. Context is broken. MCP is quickly becoming the default interface for enterprise AI agents. And that’s a good thing. It gives agents a standard way to connect with tools and data. Connecting an AI agent to Slack, Jira, GitHub, and Salesforce doesn’t mean it suddenly understands your business. It just means it can access your data silos. In short: "MCP gives your agent a passport. It doesn't give them a map." As enterprise AI undergoes a massive platform shift from passive chatbots to autonomous agentic workflows, this naive, runtime "federated search" approach creates an ugly cycle in production: - The Latency Spike: Slower agent execution while waiting for multiple external APIs to respond before it can even begin reasoning. - The Token Bleed: Skyrocketing bills from shoveling raw, unranked JSON dumps into a massive context window, praying the model finds the answer. - The Governance Nightmare: A massive risk of data leaks if you rely on a base LLM to magically guess and police complex enterprise security permissions on the fly. Agents do not fail because they lack intelligence. They fail because they lack the right enterprise context. The hardest problem in enterprise AI isn't connecting to systems. MCP solved that. The hardest problem is Context Engineering. MCP is the perfect interface, but a permission-aware context layer must be the foundation. 🚀 If AI is becoming core enterprise infrastructure, you cannot allow the strategic intelligence layer of your company to sit inside someone else's managed, closed-box platform. That is exactly why we built Pipeshub (open-source developer owned context infrastructure layer). TL;DR MCP gives agents access. A context layer gives them understanding. And deep understanding is the only way enterprise AI moves from a cool demo to secure, reliable production. 👉 Next Up Tomorrow: MCP Token Tax

• 
  Mady (@TrippleBon) reported 17 minutes ago

  It was only a matter of time. Centralized = ID/KYC/AML Go to Bastyon - decentralized social network based on blockchain. No central authority or corporation behind it. The platform is run by equal nodes on a blockchain with no centralized server (github link below)

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  李新宝 (@lixinbao_X) reported 23 minutes ago

  Just watched KK's technique. Damn. Absolute game-changer. Install 7 skills in Codex. Writing, images, covers, PPTs. Full pipeline, done. The principle is dead simple. Break the workflow into 7 parts. One skill per part. Only do one thing. Step 1 Open GitHub, find a repo. Copy the link locally. Create a project folder to save it. Step 2 Write the skill description. Input three things. What it does. What the input is. Output and acceptance criteria. Step 3 Run it and find the bottlenecks. Where it stalls Create a new skill and break it down. Don't let one skill Do 7 things it's bad at. This works for writers, Xiaohongshu creators, WeChat pub runners, Video script writers. How many skills you got installed? Have you tried it yet?

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  Lost In Tech (@lost_in_tech) reported 26 minutes ago

  @8_senkou Probably not intentional tbh. Have you logged as issue in the snorca GitHub? If not probably worth doing.

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  TECHEPAGES (@techepages) reported 28 minutes ago

  🎣 "GitBait" phishing campaign uses GitHub Pages & Google Sheets to steal banking credentials from 12+ Mexican financial institutions; no server infrastructure required 🔹 Fake bank pages hosted free on GitHub, stolen data piped straight to Google Sheets via SheetBest 🔹 100+ GitHub domains found; victims likely lured via WhatsApp, Telegram & SMS links with bank-branded previews 🔹 Active for ~3 years with ongoing development (66+ commits on one repo alone)

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  pratik.eth (@eth_ethpratik) reported 32 minutes ago

  @Shahules786 @VibrantLabsAI Hello @Shahules786 , I am trying to report a security vulnerability over the email id provided over GitHub Security.md file but apparently its wasn’t delivered. Please share an alternative email or open the advisory for reporting the issue.

• 
  Romano Roth (@RomanoRoth) reported 33 minutes ago

  2/ CodeRabbit (Dec 2025), 470 GitHub PRs analysed. AI-co-authored code: 1.7x more issues per PR, 75% more logic and correctness errors, 2.74x more XSS vulnerabilities. Velocity up. Quality down.

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  Ben Vinegar (@bentlegen) reported 34 minutes ago

  💡 I have an idea for an experiment We need a website for SoAC ... so we get an agent to do it, on a loop, set in motion once with zero human intervention after "go". It works off a semi-public GitHub repo, w/ issues, PRs, maybe even public agent traces. A publicly auditable experiment on whether it produces dogshit or not. Yea, nea?