Model Landscape
Text, vision, audio, multimodal, robotics — and embeddings / vector DBs
Text Models Landscape: From GPT to the Open-Source Wave
LLM capabilities, mainstream model comparison, closed vs open source—the 2026 text model map in one article.
Vision Models: From Understanding Images to Generating Worlds
CLIP, GPT-4V, Stable Diffusion, Sora—two vision AI paths (understanding vs generation) and engineering use cases.
Music and Audio Models: From Whisper to Suno
Speech recognition, synthesis, and music generation—three audio AI product lines with tech basics and real deployments.
Native Multimodal: Unified Model Architecture Today and Tomorrow
From pipeline glue to any-to-any—GPT-4o, Gemini, and Chameleon dissected at the architecture level.
Robotics and AI: From LLMs to VLA Models
Embodied AI, vision-language-action models, sim-to-real—how AI learns to act in the physical world in 2026.
Embeddings and Vector Databases: The Memory Layer for AI
How embeddings work, how vector DBs store and search them, and where they power RAG, semantic search, and recommendations—with Pinecone, pgvector, and Chroma.