Taught myself to code 5 months ago and built a live 7-layer AI defense system
A person who learned to code five months ago claims to have built a live seven-layer AI defense system, showcasing a rapid self-taught programming achievement in cybersecurity.
Background
- The author of the linked page (testyourllm.com) claims to have learned to code only five months ago and then built a "live 7-layer AI defense system" — presumably a security or moderation system for AI models.
- "7-layer" likely borrows from the OSI model in networking, implying a multi-layered security approach, though applied to AI (e.g., prompt injection filters, content moderation, rate limiting, etc.).
- The key context a reader needs: there is currently a boom in AI safety and "red-teaming" tools, as companies rush to protect LLM-powered apps from attacks like prompt injection, jailbreaking, and data leakage. Building any production-grade defense system in 5 months of self-taught coding would be extremely ambitious; the claim invites scrutiny of what the system actually does and how well it works.
- No prior context on the author or company is provided — the piece appears to be a personal portfolio/showcase post.
Max Weinbach says he had early access to OpenAI's new model GPT-5.6 Sol, calling it his favorite model by far. He highlights that it never gives up and will keep reasoning until it's done. OpenAI announced that GPT-5.6 Sol, along with Terra and Luna, will launch publicly on Thursday, with preview access expanding globally now.
The US government ordered Anthropic to suspend access to its Fable 5 and Mythos 5 models for all customers, citing a potential jailbreak technique that involved asking the model to review a codebase for vulnerabilities—a capability Anthropic says is available in other public models. Access was abruptly cut off on June 12.
Andrej Karpathy announces the release of Claude Fable 5, the same underlying model as Mythos but with added safeguards. He calls it a major step forward, particularly for long problem-solving sessions on difficult tasks, and describes it as state-of-the-art on nearly all benchmarks with exceptional performance in software engineering, research, and vision.
Roman Storm warns that the legal theory in his case could set a precedent making open-source developers liable for how others use their code, potentially criminalizing the mere publication of privacy, messaging, or crypto tools. He notes that developer Michael Lewellen cannot publish lawful code due to prosecution fears, and argues this chilling effect extends beyond any single case.
Meta's engineering culture is deteriorating under Mark Zuckerberg and Scale AI CEO Alexandr Wang, who have introduced keyboard tracking, reassignments to data labeling, and AI-centric performance metrics. Critics argue this incentivizes performative AI use, drives away experienced engineers, and contributed to a major Instagram hijacking incident caused by AI-written and AI-reviewed code.