explainer

An article simplifying the concept of LLM distillation for a political audience, explaining how smaller models learn from larger ones.

Berkeley 189 lecture provides a clear explanation of the attention mechanism, tracing the evolution from RNN+attention to Transformer and contrasting MLP/CNN parameter efficiency.

A simple explanation about AI benchmarks, what scores mean, and why 100% does not mean AI cannot improve further.

A tweet recommends a series of explainer articles on LLM fundamentals, particularly 'Vectors are all you need', adding to a list of top learning resources.

A Twitter thread explaining seven essential AI terms (LLM, tokens, etc.) for creators and founders, aiming to provide practical vocabulary for 2026.

A clear explanation of Standard RAG, Graph RAG, and Agentic RAG, covering their differences, use cases, and how they handle single-hop vs. multi-hop queries.

Explains attention in transformers as a lookup operation where each token builds a query, compares against keys, and retrieves weighted value vectors, with a video covering the full pipeline.

Hermes Agent by Nous Research introduces Mixture of Agents (MoA), allowing users to define presets that combine multiple models for consultation and a final answer model, improving performance by covering blind spots. The feature integrates seamlessly into the existing agent loop, maintaining tools, memory, and context.

A tweet explaining the core formula of the attention mechanism in transformer models: Q × Kᵀ computes relevance, Softmax converts to probabilities, and V delivers content, forming the foundation of modern AI.

Explores how large language models compress vast knowledge into finite space using feature superposition, explaining the distinction between dimensions and features with biological analogies.

This tweet shares a well-made explanation of the internal workings of LLMs, covering tokens, embeddings, positional encoding, attention, and feed-forward networks, via a blog post by 0xkato.

A visual breakdown of 8 major AI model architectures including LLMs, VLMs, MoE, SLMs, and more, plus a bonus mention of recursive language models from MIT.

A detailed personal survey of modern deep learning, focusing on foundation models, vision-language models, and their architectural decisions, written for those who want intuition rather than dense math.

This article provides a visual guide to the Transformer architecture in Large Language Models, covering self-attention, causal self-attention, masked multi-head attention, and the output layer with step-by-step explanations and examples.

A detailed walkthrough of how transformer-based LLMs work, covering tokenization, embeddings, attention, and next-token prediction without heavy math.

An in-depth walkthrough of how modern LLMs work, covering core mechanisms from tokenization to next-token prediction, without heavy math.

A technical exploration of ANSI escape codes, their origins in 1970s terminals, and their enduring relevance in modern CLI tools and terminal UIs.

Based on the SGLang Omni team's internal decision-making article, this post introduces the operating principles of LLM inference systems in an accessible way, starting from basic concepts such as autoregressive decoding, KV cache, and continuous batching.

A glossary from TechCrunch that defines common AI terms such as AGI, AI agents, API endpoints, and chain of thought, updated regularly as the field evolves.

KV cache stores previously computed key and value vectors during autoregressive generation, allowing models to avoid recomputing the entire sequence at each step, significantly speeding up inference at the cost of increased memory usage.