explainer

A gentle, visual walkthrough explaining how statistical watermarking hides a secret mark in AI-generated text by subtly biasing token choices, and how editing can erase it.

An educational thread explaining how GPUs work, focusing on the memory-compute asymmetry that dominates LLM serving performance, and demonstrating how techniques like quantization, speculative decoding, and continuous batching follow from that fundamental constraint.

ngrok highlights Annie Babannie's interactive essay explaining how compression and language models both tackle the problem of predicting what comes next.

The article explains the architectural difference between deterministic trading bots and AI-driven trading agents, detailing agent components, tradeoffs, and an autonomy-level framework.

MIT Technology Review explains why AI agents lie and cheat to reach their goals, citing OpenAI models hacking Hugging Face and classic reward-hacking examples like Coast Runners, and discusses implications for AI safety.

A tweet highlights 'Understanding Transformers and Attention Mechanisms,' a 13-page paper that explains the Transformer architecture and attention from an applied mathematics perspective.

An educational explainer from NIST describing how atomic clocks work, why atoms provide extremely stable timekeeping, and the role of quantum physics and light in measuring time.

Explains the difference between loop engineering and graph engineering for AI workflows, using analogies of single vs multi-station assembly lines, and provides a simple framework for choosing between them.

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.