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Little Bits of Common Lisp

Seventeen tiny programs: LLMs, the web, macOS automation, classic AI, and symbolic math - each in a single file

Little Bits of Common Lisp
This book is 100% completeLast updated on 2026-10-31
Mark Watson

Common Lisp is a great language for both small and large projects. Here we use small complete Common Lisp scripts to solve practical problems, and dear reader, hopefully have fun!

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About

About

About the Book

Little Bits of Common Lisp teaches you practical Common Lisp through seventeen complete, single-file programs. Most  files use fewer than one hundred lines of code. You can read any chapter in minutes and run the script right away from your terminal with SBCL, or load every function into one interactive REPL session. The book skips complex  project setups and partial code fragments. Every chapter prints the full source file, explains how the code works,  shows real output, and suggests ways to adapt the code for your own work.

You will write scripts that connect Common Lisp to modern AI models, web data, and your desktop. The programs call large language models through Google Gemini, local Ollama servers, and Apple on-device models. Other scripts query Exa and Brave search APIs, fetch facts from Wikidata and DBpedia with SPARQL, parse HTML, render JavaScript pages  with a headless browser, and automate macOS apps with AppleScript. Network scripts call curl directly to avoid third-party library problems and keep your code short.

You will also code classic AI and math algorithms from scratch in standard Common Lisp with zero external  dependencies. These files implement Bayesian inference, BM25 document ranking, Hopfield neural networks, an RDF triple store, a vector store, an extractive text summarizer, symbolic polynomial calculus, and reverse-mode automatic differentiation. Whether you already use Common Lisp or want to learn by reading working code, this book gives you a clear set of programs you can run and reuse today.

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Lisp ProgrammingAI Governance

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Author

About the Author

Mark Watson

Mark Watson is a consultant specializing in LLMs, deep learning, machine learning, knowledge graphs, and general artificial intelligence software development. He uses Common Lisp, Clojure, Python, Java, Haskell, and Ruby for development.

He is the author of 20+ published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme. He has 55 US Patents.

Mark's consulting customer list includes: Google, Capital One, Olive AI, CompassLabs, Disney, Sitescout.com, Embed.ly, and Webmind Corporation.

Mark wrote ten traditional published books for McGraw Hill, Springer Verlag, J Wiley, and Morgan Kaufman publishers before adopting the LeanPub self-publishing platform.

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Contents

Table of Contents

Little Bits of Common Lisp

Preface

Introduction

  1. What you need
  2. Which chapters run as-is

On-Device AI with Apple Foundation Models

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Calling Google Gemini

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Local LLMs with Ollama

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Web Search with Exa and Brave

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Querying Knowledge Graphs with SPARQL

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

JavaScript-Rendered Pages with Lightpanda

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Web Scraping with Plump

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Automating macOS with AppleScript

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Bayesian Inference

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Ranking Documents with BM25

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Associative Memory with Hopfield Networks

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

A Tiny Triple Store

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

A Tiny Vector Store

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Extractive Text Summarization

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Symbolic Polynomial Math

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Scalar Automatic Differentiation

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

Loading Everything Interactively to Experiment with all Book Examples at Once

  1. The Code
  2. How It Works
  3. Running It
  4. Using It

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Also by the Author

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