Founder of Obvix Labs, an AI research lab building systems people can rely on. I write about what we're learning - AI safety, backend architecture, and what it takes to ship real software.
You climbed on a stranger's bike yesterday. The system did not check his mind. A long look at the strangest blind spot in Indian transport, what the global evidence actually says about driver mental fitness screening, and why the obvious fix would make it worse.
Everyone found the Easter eggs. We mapped the engineering. 82 documents, 112K lines of original analysis, 16 architectural diagrams the complete architectural reconstruction of how Anthropic builds a production AI agent.
Does filling up a model's context window make it more likely to agree with you when you're wrong? I ran 80,433 trials across 6 models to find out. Context length matters less than you'd think - the conversational pattern is what really drives sycophancy.
A deep technical breakdown of Perplexity's pplx-embed-v1 and pplx-embed-context-v1 - diffusion-pretrained, INT8-native embedding models that fix RAG's oldest problem: context loss at chunk boundaries. Architecture, benchmarks, tradeoffs, and what changes for developers.
Meta's VL-JEPA predicts embeddings instead of tokens, achieving 50% parameter reduction and 2.85× faster inference. Here's how embedding prediction changes vision-language models.