Data Science

Moonshot's Kimi K3 report reveals the training, architecture, and RL decisions most frontier labs keep private. Here's what it says.

MiniMax's Agent Teams architecture splits tasks across Leader, Worker, and Verifier roles. Here's what that means in practice, tested against the real API.

Learn how stateless and stateful agent designs differ, and how each approach shapes memory management, scaling, and deployment architecture.

A curated guide to 10 AI newsletters organized by purpose: daily news, technical research, policy analysis, and builder ecosystem coverage.

This week's top picks cover LLM optimization, SQL portfolios, Git worktrees, agentic AI, and Python patterns for production-ready data workflows.

Five free resources covering agentic AI from hands-on courses to foundational theory. Build, understand, and evaluate agents that actually work.

Install Ollama, download a small language model, and run private AI inference on your own hardware in three straightforward steps.

Build a local video summarization pipeline using SmolVLM2-2.2B. Runs on consumer GPUs with 5.2 GB VRAM, no cloud API required.

LAMs and agentic LLMs both take actions, but differ fundamentally in how. Learn which to use and when.

GraphRAG and Vector RAG serve different retrieval needs. This guide breaks down their architectures, query handling, and when to use each.

A reproducible benchmark shows classical ML owns the synchronous payment hot path, while LLM agents belong on the asynchronous cold path.

A large context window is not the same as agent memory. Learn how retrieval, compression, and summarization fit into an agent's cognitive stack.