What Makes LLM Tokenization Slow?Exploring the performance of byte-pair encoding by optimizing a GPT-2 tokenizer.

Andrew Healey dissects GPT-2’s reference BPE tokenizer, measures what makes tokenization slow, and shows concrete optimizations on the hot path of LLM products.

Tutorial · LLMs · Programming · Performance · AI

How to Build a Reliable AI Assistant with the Claude API

A freeCodeCamp tutorial building ShopHelper with the Claude API: conversation history, tools, multi-block responses, workflow patterns, and evaluating whether prompt changes actually help.

Tutorial · AI · Tutorials · LLMs · Programming

Build an LLM Tokenizer and Attention from Scratch in TypeScript

SitePoint tutorial that implements BPE tokenization, cosine similarity vector search, and scaled dot-product attention in TypeScript—exposing LLM primitives as ordinary readable code.

Tutorial · LLMs · Tutorials · Programming · Machine Learning

Automating eval design and hillclimbing with Claude

Lance Martin (claude.dev) explains principles for production-like evals with held-out sets, then shows how the claude-api skill’s build-eval and hillclimb commands automate design and overfitting-aware improvement—including cost and performance case studies.

Tutorial · AI · LLMs · Tutorials · Benchmarks

Turn GLM-5.3-Flash into a Jev-like System One model

Johannes Hötter walks through turning GLM-5.3-Flash into a fast Jev-like “System One” decision model—typed options with probabilities in a single forward pass, matching Jev’s accuracy and speed.

Tutorial · AI · LLMs · Machine Learning · Open Source

A Jev-like wrapper for LLMs, including vision models

Allan shows a small single-function Jev-style wrapper for LLMs that also handles vision models, with practical code for local and API backends.

Tutorial · LLMs · AI Agents · Tutorial · Programming

Mixture of Experts (MoE) for Backend Engineers

A detailed visual guide to token routing, expert batching, weighted combination, and the memory and communication tradeoffs of MoE serving. Suppose a model has dozens of feed-forward subnetworks, but each token uses only two of them. The arithmetic per token can stay modest while the total weight set grows. Now place that model on eight GPUs. If every GPU stores all experts, memory can become the limit; if experts are split across GPUs, token activations must travel to whichever GPU owns their…

Tutorial · AI · LLMs · Machine Learning · Engineering

Local AI on a 12 GB GPU: what survived testing, and how to set it up

Hands-on notes testing local AI models on a 12 GB RTX 3060: which stacks fit in VRAM, how context length decides spills to CPU, and a practical setup that survived the author’s trials.

Tutorial · AI · LLMs · Hardware · Tutorials

Why updatedInput in a PreToolUse Hook Doesn’t Rewrite the Command

A deep dive into Claude Code PreToolUse hooks: why returning updatedInput does not rewrite the shell command, and how permission decisions actually work.

Tutorial · AI Agents · Developer Tools · LLMs · Programming

Heretic tutorial: automatic censorship removal for language models

A hands-on tutorial for Heretic, an open-source tool that automatically removes refusal/censorship behaviors from language models—setup, workflow, and what to watch for.

Tutorial · AI · Open Source · LLMs · Machine Learning

Transformer Explainer: LLM Transformer Model Visually Explained

Georgia Tech’s Polo Club walks through GPT-2’s Transformer stack—embeddings, multi-head attention, MLP, sampling—with an interactive in-browser model for learning how next-token prediction works.

Tutorial · AI · LLMs · Machine Learning · Education

Jev in 25 lines of Python

NobodyWho shows a local, 25-line Python sketch of Jev-style decision models: load a small GGUF, score labeled choices from logits, and print calibrated-looking probabilities without sending data to an API.

Tutorial · AI · LLMs · Programming · Open Source

aie_2.1: building it — a conversation engine where the LLM 'remembers'

Meraki builds a Python conversation engine that shows how LLMs fake memory: every turn resends the full history so the model appears to remember, with practical notes on context and design.

Tutorial · AI · LLMs · Programming · Education

Model Context Protocol with Spring AI, Building MCP Clients and Servers in Java

Ayush Shrivastava walks through building MCP clients and servers with Spring AI in Java: tool discovery, protocol basics, and wiring MCP into agentic Spring applications beyond a basic demo.

Tutorial · AI · AI Agents · Programming · Developer Tools

How LLMs Actually Work: A Practical Guide for Product Managers

Abhishek Jaiswal explains tokens, transformers, attention, RAG, inference, and agents in practical PM language—so product leaders can make better build-vs-buy and quality decisions without becoming ML researchers.

Tutorial · LLMs · AI · Product Management · Education

Design a Real-Time Voice AI Agent

# Design a Real-Time Voice AI Agent - Authors - Name - Amit Shekhar - Published on A Real-Time Voice AI Agent is a system that listens to a person speaking, understands what they said, thinks about it, takes actions if needed, and talks back in a natural human-like voice, all wit

Tutorial · AI · LLMs · Software Engineering

Build Your Own AI Agent Harness in C#, the MafClaw Live Series

Bruno Capuano’s four-part .NET / Microsoft Reactor series builds a finance-education agent on the Microsoft Agent Framework harness—tools, file boundaries, approvals, skills, shell, CodeAct, observability, and Foundry hosting.

Tutorial · AI Agents · Programming · Engineering · LLMs

llmman launch dsh: Run DeepSeek Harness on any local or hosted model

DeepSeek Harness treats the model as a plugin. llmman runs any model on your own hardware, in one command. An agent harness is a loop around your model that takes your task, calls a model, runs tools (such as shell commands and file edits), provides results, and repeats.

Tutorial · AI · LLMs · Open Source · AI Agents

Training Search Agents with GRPO

Hands-on introduction to reinforcement learning by training a search agent with group-relative policy optimization (GRPO), with open rollouts, code, and reward-design lessons for LLM search.

Tutorial · AI · LLMs · Machine Learning · Research