llm

LLMs read tokens, not letters. See how byte-pair encoding builds a vocabulary, and why that explains strawberry miscounts, shaky arithmetic, and uneven costs.

Neural scaling laws explained: what Kaplan (2020) and Chinchilla (2022) found about parameters, data, and compute, and where the power laws stop working.

Training a large language model is not a single act — it is a five-stage industrial process that begins with petabytes of scraped text, passes through weeks of GPU computation consuming millions of dollars of electricity, and ends with a model that can hold a coherent conversation. Each stage has a specific job, a distinct failure mode, and a measurable cost. Here is how the whole pipeline works, from raw data to deployed product.

Claude is an AI chatbot built by Anthropic and available on Claude.ai, through the API, and via third-party integrations. It excels at long-form writing, coding, analysis of complex documents, and reasoning through multi-step problems. Unlike ChatGPT, Claude does not train on conversations, offers higher context limits, and produces more thorough outputs. This guide explains what Claude is, how to access it, what it does better than competitors, its actual limitations, and how to prompt it effectively.

The quality of an AI model's output is determined largely by the quality of the input it receives. A prompt is not a search query — it is the only communication channel between the user and the model. Every word specifies or leaves ambiguous something about the desired output. This guide covers the principles behind effective prompting, the techniques with the most research evidence behind them, and the common mistakes that produce frustrating, generic results.

Generative AI is the class of machine learning systems that produce new content — text, images, audio, video, code, or molecular structures — rather than classifying or predicting from existing data. It is not a single technology but a family of model architectures sharing one property: trained on enough examples, they can synthesize plausible new outputs in the same domain. Understanding what separates generative from conventional AI explains both why the technology spread so quickly and where its real limits lie.

Send ChatGPT the same message twice within five minutes and you will likely receive two distinct responses. The wording differs, the examples change, sometimes the conclusions shift. This is not a bug, and it is not the model lying — it is the intended consequence of probabilistic sampling, a deliberate design choice that trades consistency for coverage. Understanding why requires a look at how language models generate text one token at a time.

AI agents combine LLMs, tools, memory, and planning to complete multi-step tasks without human input at each step. Architecture, frameworks, real uses, and risks in 2026.

Do LLMs actually reason or just predict text? A deep technical guide to transformer attention, chain-of-thought, test-time compute, and frontier model benchmarks in 2026.

MLX is Apple's open-source ML framework optimized for Apple Silicon. It exploits unified memory to run and fine-tune LLMs on Macs.

CrewAI, LangGraph, and AutoGen compared across abstraction level, state management, production readiness, and multi-agent support for 2026.

GPT-5 explained: OpenAI's unified model with auto-routing, three sizes, benchmarks vs Claude 4, Gemini 2.5 and Grok 4, pricing, and what changed.