This is a summary of the second chapter of a book I wrote:

Agent Architectures: Advanced Strategies for Intelligent LLM Systems

🤖 Chapter 2 : How to Think With AI Agents

Agents aren’t just tools they’re thinking partners. This post explores the core mindset shifts, methodologies, and feedback loops that define how to work with intelligent systems.

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🌊 Five Core Shifts in the AI–Human Paradigm

Before diving into methods, we need to understand the big changes redefining how we work with AI:

This is a summary of the first chapter of a book I wrote:

Agent Architectures: Advanced Strategies for Intelligent LLM Systems

🚀 Introduction to LLM Agents

🤖 What is an LLM Agent?

An LLM agent is an intelligent software system built around a large language model (LLM). Unlike traditional LLMs, these agents don’t merely respond to prompts they actively reason, maintain context, and interact dynamically with external tools and environments. This autonomy enables them to manage complex workflows independently.

Summary

Searching through massive datasets efficiently is a challenge, whether in image retrieval, recommendation systems, or semantic search. Faiss (Facebook AI Similarity Search) is a powerful open-source library developed by Meta to handle high-dimensional similarity search at scale.

It’s well-suited for tasks like:

• Image search: Finding visually similar images in a large database.
• Recommendation systems: Recommending items (products, movies, etc.) to users based on their preferences.
• Semantic search: Finding documents or text passages that are semantically similar to a given query.
• Clustering: Grouping similar vectors together.

In many of the upcoming projects in this blog I will be using it. It is a good local developer solution.

Summary

Imagine you have a dataset of customer profiles. How can you group similar customers together to tailor marketing campaigns? This is where K-Means clustering comes into play.

K-Means is a popular unsupervised learning algorithm used for clustering data points into distinct groups based on their similarities. It is widely used in various domains such as customer segmentation, image compression, and anomaly detection.

In this blog post, we’ll cover how K-Means works and demonstrate its implementation in Python using scikit-learn.

Summary

Large Language Models (LLMs) are powerful, but their size can lead to slow inference speeds and high memory consumption, hindering real-world deployment. Quantization, a technique that reduces the precision of model weights, offers a powerful solution. This post will explore how to use quantization techniques like bitsandbytes, AutoGPTQ, and AutoRound to dramatically improve LLM inference performance.

What is Quantization?

Quantization reduces the computational and storage demands of a model by representing its weights with lower-precision data types. Lets imagine data is water and we hold that water in buckets, most of the time we don’t need massive floating point buckets to hold data that can be represented by integers. Quantization is using smaller buckets to hold the same amount of water – you save space and can move the containers more quickly. Quantization trades a tiny amount of precision for significant gains in speed and memory efficiency.

Summary

This post provides a practical guide to building common neural network architectures using PyTorch. We’ll explore feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, transformers, autoencoders, and GANs, along with code examples and explanations.

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1️⃣ Understanding PyTorch’s Neural Network Module

PyTorch provides the torch.nn module to build neural networks. It provides classes for defining layers, activation functions, and loss functions, making it easy to create and manage complex network architectures in a structured way.

Summary

This post provides a comprehensive guide to prompt engineering, the art of crafting effective inputs for Large Language Models (LLMs). Mastering prompt engineering is crucial for maximizing the potential of LLMs and achieving desired results.

Effective prompting is the easiest way to enhance your experience with Large Language Models (LLMs).

The prompts we make are our interface to LLMs. This is how we communicate with them. This is why it is important to understand how to do it well.

Summary

In this blog I aim to try building using open source tools where possible. The benefits are price, control, knowledge and eventually quality. In the shorter term though the quality will trail the paid versions. My belief is we can construct AI applications to be self correcting sort of like how your camera auto focuses for you. This process will involve a lot of computation so using a paid service could be costly. This for me is the key reason to choose solutions using free tools.

Introduction

Activation functions are a component of neural networks they introduce non-linearity into the model, enabling it to learn complex patterns. Without activation functions, a neural network would essentially act as a linear model, regardless of its depth.

Key Properties of Activation Functions

• Non-linearity: Enables the model to learn complex relationships.
• Differentiability: Allows backpropagation to optimize weights.
• Range: Defines the output range, impacting gradient flow.

In this post I will outline each of the most common activation functions how they are calculated and when they should be used.

Summary

In this post I will implement a Support Vector Machine (SVM) in python. Then describe what it does how it does it and some applications of the instrument.

What Are Support Vector Machines (SVM)?

Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression tasks. Their strength lies in handling both linear and non-linear problems effectively. By finding the optimal hyperplane that separates classes, SVMs maximize the margin between data points of different classes, making them highly effective in high-dimensional spaces.