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.
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.
A technical blueprint for architects and engineers building the next generation of customer support. Learn how to leverage LLMs, vector databases, and event-driven architecture to move from reactive queues to predictive, intelligent, and scalable support systems.
Solved a persistent MacBook M1 screen glitch by resetting the LaunchServices database using lsregister -kill, fixing system UI and flicker issues instantly after all else failed.
AI chatbots that always agree can feel supportive, but they risk reinforcing self-doubt, overthinking, and emotional dependence especially in vulnerable people. They often miss warning signs like crisis or delusional thinking. Safer designs need more grounding, challenge, memory, and human oversight. Use AI as companion, not counselor.