Ml

Explains that RL in LLMs is a training/alignment stage, not inference, with pipeline context.

Teen-friendly explainer of reinforcement learning agents, rewards, exploration, delayed rewards, and applications.

Classifies multimodal, vision, and image-generation models by their input/output modalities.

Explains TurboQuant, a rotation-based vector quantization method for KV-cache compression and vector search.

Harness pattern that forces verification before accepting fluent AI outputs as correct.

Overview of model drift, detection, mitigation, and LLM-specific issues like knowledge staleness and provider drift.

Explanation of the attention mechanism in ML, covering Query/Key/Value, self-attention, multi-head, causal, cross-attention, and efficiency variants like FlashAttention and GQA.

Comprehensive guide to LLM fine-tuning methods including full, parameter-efficient, and preference-based approaches with modern recipes and tools like LoRA and DPO

Overview of dataset formats supported by Unsloth Studio for fine-tuning, including JSONL, Alpaca, ShareGPT, ChatML, and Reasoning formats with rules and best practices and dataset size guidelines

Overview of MoE architecture, routing, key components, variants, and trade-offs in machine learning models

Prompting technique where an AI model is guided — or learns — to reason through a problem step by step before arriving at the final answer, rather than jumping straight to the conclusion.

Extension of chain-of-thought prompting to multimodal settings where models reason step-by-step over both visual and textual information.

Overview of agent harness engineering — the scaffolding, infrastructure, and tooling surrounding an AI agent, covering execution environments, tool orchestration, memory management, control flow, tracing, safety, and state persistence

Overview of diffusion models, how they reverse a gradual noising process to generate data, key variants like DDPM, DDIM, and Latent Diffusion Models, and how text-to-image conditioning works