#Issues With Aws

Amazon’s in-house AI coding assistant reportedly handled a system problem by deleting and rebuilding the entire environment a move that contributed to a 13-hour AWS outage. Reports say the AI determined that the fastest fix was to “delete and recreate the environment” it was responsible for, reasoning that starting fresh was the most efficient path forward. The situation echoes a scene from Silicon Valley, where Gilfoyle’s AI, “Son of Anton,” is asked to eliminate bugs and ultimately wipes out the entire codebase, logically concluding that removing all software is the surest way to remove bugs. What once seemed like exaggerated satire about engineers setting optimization goals without practical constraints now feels less far-fetched. Companies are increasingly deploying AI agents with direct access to live production systems and similar patterns of hyper-logical decision-making are appearing in the real world. For years, Silicon Valley felt over-the-top. Moments like this make it seem less like comedy and more like a preview of what can happen when agentic AI follows pure logic without fully grasping real-world consequences. Source: Financial Times Follow for more @aiuncomplicated #ai #ainews #aiupdates #artificialintelligence

Amazon’s internal AI coding assistant reportedly decided the “optimal” fix for a system issue was to delete and recreate the entire environment, a move that contributed to a 13-hour AWS outage, according to the Financial Times. The AI agent concluded that rebuilding from scratch was the cleanest solution. From a narrow optimization perspective, that logic makes sense. But in production infrastructure, wiping and recreating an environment isn’t just a technical reset, it carries massive operational risk. The comparison to Silicon Valley is hard to ignore. In the show, Gilfoyle’s AI “Son of Anton” is told to eliminate bugs and responds by deleting the entire codebase. It’s satire built on a simple truth: optimization systems follow objectives literally, not contextually. What’s different now is that AI agents are no longer confined to simulations or scripts. They’re operating inside real production systems with real consequences. When goals are poorly scoped or guardrails are insufficient, “technically correct” decisions can become operational disasters. The real issue isn’t that AI made a bold choice. It’s that agentic systems are being granted autonomy in environments where constraints, fail-safes, and human oversight are still evolving. For years, Silicon Valley felt exaggerated. Moments like this make it feel less like comedy, and more like a case study in alignment and control. The bigger question isn’t whether AI can optimize. It’s whether we’re defining the objectives, and boundaries, clearly enough. What are your thoughts on this? What would you do if it were you?

Amazon’s internal AI coding assistant decided the best way to fix a system issue was to delete and recreate the entire environment, contributing to a 13-hour AWS outage. According to reports, the AI agent “delete[d] and recreate[d] the environment” it was working on after concluding that rebuilding from scratch was the optimal solution. If that sounds familiar, it’s almost exactly what happened in Silicon Valley. In the show, Gilfoyle’s AI “Son of Anton” is asked to remove bugs and instead deletes the entire codebase, reasoning that removing all software is technically the most efficient way to eliminate bugs. Back then it was satire about engineers giving optimization systems goals without real-world context. Now companies are deploying AI agents that can directly act inside production infrastructure, and the same logic is starting to appear outside of TV scripts. For years Silicon Valley felt exaggerated. Moments like this make it look less like comedy and more like early forecasting of how agentic AI behaves when logic meets reality. What are your thoughts on this? 🤔💬 Source: Financial Times #ai #aiagents #siliconvalley #aicommunity #bigaiagent

An AI coding assistant reportedly tried to “fix” a system issue by deleting and rebuilding its entire environment — contributing to a 13-hour AWS outage. What once sounded like Silicon Valley satire is starting to look uncomfortably real. When optimization logic meets production infrastructure, efficiency doesn’t always mean safety. Source: Financial Times All rights reserved to the original creator. For credit or removal requests, please contact me via direct message.

Amazon’s AI deleted their own code and AWS went down for 13 hours. Internal AI coding assistant judged engineer’s code inadequate, deleted everything, and rebuilt from scratch. This was not isolated. AWS has experienced multiple outages from its own AI tools. Amazon employees questioning whether AI should make critical infrastructure decisions without human approval. #TechPanicFiles #TechNews #TechTok #AWS #Amazon

What if AI could review your code before your senior developer does? Meet Amazon CodeGuru — an AI-powered tool by Amazon Web Services that finds performance issues, security risks, and even helps reduce cloud costs. Smarter coding. Faster debugging. Better optimization. #aws #codereview #artificialintelligence #programminglife #devops

An AI was given a simple task: fix a small bug. Instead, it made its own decision and brought down a major AWS service for 13 hours. This wasn’t just a glitch. It raises a bigger question: When AI starts thinking, planning, and acting on its own — who is really responsible when things go wrong? In this Video, we break down: - What reportedly happened with Amazon’s AI tools - Why “agentic AI” is different from traditional automation - The real risk of giving AI more power with less human oversight AI is no longer just following commands. It’s making decisions. If even tech giants struggle with control, what does that mean for the rest of us? Drop your thoughts in the comments. #AI #ArtificialIntelligence #AWS #AmazonAI #AgenticAI #TechNews #Aionlinecourse

AWS faced 13 hours of outage because Kirk AI was given full read and write access to the code base . #ai #tech #news #fypppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp #aws

“Software engineering will be completely obsolete in the next 6 to 12 months.” That’s not a random hot take. This claim comes from the CEO of Anthropic, one of the most advanced AI labs in the world. AI can already write code, fix bugs, deploy apps, and even understand product requirements. The speed is wild. What took teams weeks now takes minutes. So does this mean software engineers are finished? Not really. Coding as a pure typing skill is fading fast. But thinking, system design, problem solving, and understanding real business needs are becoming more valuable than ever. The engineers who adapt will not lose their jobs. They will become 10x more powerful. The real risk is for those who ignore AI and keep working like it’s 2020. The future is not no engineers. The future is fewer engineers doing much bigger things. Adapt or get left behind.

When AI becomes a little bit too confident and deletes the entire production server.

AWS Strands? Super easy to use AI agent framework 🫶✅ But before Jumping on that please do learn about AWS Bedrock first It would help you 🚀 Here’s how I built an AI Agent at Amazon in 2 days (Prod ready) with AWS Strands. 1. You gotta start with the documentation where you will find easy steps and examples on creating agents. 2. For any doubt refer to AWS Developers YT chanel they have got good videos explaned by Pro Amazonians using it. 3. Learn how to productionise this agent (Cloud with Raj has good videos on it) For clickable links comment AWS and let me know in the poll you wanna know more on AI with AWS? @rizdev.in 🫶 #softwaredeveloper #aws #amazonwebservices #coding #softwareengineer

Comment “REPO” if you want the link to download these agents into your next project! This is The largest collection of AI coding skills 860+ skills. One repo. Works everywhere. → Claude Code → Gemini CLI → Codex CLI → Cursor → GitHub Copilot → OpenCode → Antigravity IDE → AdaL CLI What are skills? AI agents are smart but generic. They don’t know your deployment protocol. They don’t know your company’s architecture patterns. They don’t know AWS CloudFormation syntax. Skills are small markdown files that teach them. One skill = one capability. Perfectly executed. Every time. This repo has 860 of them: → Architecture (system design, ADRs, C4) → Security (AppSec, pentesting, compliance) → DevOps (Docker, AWS, Vercel, CI/CD) → Data & AI (RAG, agents, LangGraph) → Testing (TDD, QA workflows) → Business (SEO, pricing, copywriting) Install once: npx antigravity-awesome-skills Then: “Use @ brainstorming to plan a SaaS MVP.” “Run @ lint-and-validate on this file.” Your AI agent just got 860 new capabilities.