The most talked-about development methodology in AI coding right now is named after Springfield Elementary’s lovably dim student. Anthropic’s official Ralph Wiggum plugin for Claude Code implements an autonomous development loop that keeps working on your code until completion—embodying the philosophy that persistent iteration, not perfection, drives results. Developer Geoffrey Huntley’s technique has enabled developers to ship six repositories overnight and complete $50,000 contracts for $297 in API costs. The technique’s elegance lies in its simplicity: a feedback loop that intercepts Claude’s exit attempts and re-feeds the same prompt, letting the AI iteratively refine its work until success criteria are met.

TL;DR Three Pwn2Own competitions in 2025 (Tokyo, Berlin, Cork) resulted in 150 zero-day vulnerability disclosures across automotive, enterprise, and consumer IoT systems, with $2,989,750 awarded to researchers. Key findings: Memory corruption (45%): 68 instances of buffer overflows (CWE-787, CWE-121/122), use-after-free (CWE-416), integer overflows (CWE-190), and type confusion (CWE-843) Injection attacks (30%): 45 instances of command injection (CWE-78) and format string vulnerabilities (CWE-134) Authentication failures (13%): 20 instances including hard-coded credentials (CWE-798), missing authentication (CWE-306), and authentication bypasses (CWE-287) Notable exploits:

Hot take: Most “secure” cloud deployments are just expensive theater. You’ve got firewalls, access controls, endpoint protection - but what if someone compromises your bootloader before any of that even starts? This isn’t theoretical. Real attackers are targeting the boot process, hypervisors, and kernel-level compromises that happen before your security stack loads. Your fancy SIEM won’t help if the system reporting to it has been compromised from day one. The Trust Problem No One Talks About Here’s what keeps me up at night: How do you trust a system you can’t physically touch?