Articles about artificial intelligence, machine learning, and AI systems
ai
A practical implementation guide for securing file upload pipelines in LLM applications. Covers AWS services configuration, sandboxed parsing setup, content disarmament, and verification procedures with specific commands and code examples.
ai
A comprehensive advisory on securing file upload pipelines for LLM applications. Covers parser exploits, prompt injection vectors, and defense-in-depth architectures based on CVE-2025-66516 and OWASP Agentic Top 10 2026.
ai
The internet is full of stories about AI saying demonic things. But what would demonic possession of a language model actually look like, theologically speaking? I spent time with the research and found the answer is both more mundane and more interesting than you might expect.
ai
The pitch sounds reasonable on paper: fit more context, understand more, generate better. Context window size has become the primary marketing battlefield for frontier AI labs, with vendors announcing million-token contexts as if bigger is automatically better. But the numbers tell a different story, and the most telling data point
security
The economics of social engineering have fundamentally changed. Anyone with internet access can now launch sophisticated phishing attacks that were previously the domain of skilled criminals.
framework
After months using Claude Code with Anthropic models, I tried switching to OpenCode with MiniMax-M2.5. Here's the honest analysis of cost, speed, accuracy, and whether the tradeoffs are worth it.
ai
MCP tool descriptions are instructions, not metadata. Here's how attackers exploit that — and what the benchmark data actually shows about model safety alignment.
framework
A new ETH Zurich study finds that LLM-generated context files reduce task success rates and raise inference costs by 20%. Here's what the data actually shows.
culture
The clinical term 'AI psychosis' is now documented in peer-reviewed literature. Here's what's driving hospitalizations, cognitive decline, and a massive uncontrolled experiment.
ai
SOTA vision-language models fail at spatial tasks trivial to humans. Why perception gaps matter for reliable AI systems.