Project 2: File Explorer for android

Android

In this project I am building a file explorer library for android. As I was working on catcher it became obvious I would need a file picker and explorer solution. So I did a bit of looking on the web. I found three interesting projects that nearly did what I wanted. I put a few of them together to come up with a hybrid solution.

Project 1: Catcher

Android

This is an android application to transfer files from your phone to somewhere else. I will be built as a PC solution but can be used for a server solution also.

2.1 Agent Construction

That’s not let’s just just to push it on Construction defines the foundational capabilities of the agent. Essential aspects include:

  • Memory architecture: short term context memory, long term persistent storage, and retrieval-augmented generation (RAG).
  • Planning logic: approaches like chain of thought (CoT), hierarchical task decomposition, and dynamic planning models.
  • Tool integration: external APIs, file systems, and search engines.
  • Personality and behavior definition: controlled through prompt engineering and tone adjustments.

The central idea is modularity. A well-constructed agent should be easy to adapt, upgrade, and maintain—closer to a microservice architecture than a monolithic design.

Table of Contents

  1. Introduction to LLM Agents
  2. Methodologies and Core Patterns
  3. Construction - Building the Agent
  4. Collaboration - Multi-Agent Systems and Interaction
  5. Introspection, Memory, and Interpretability
  6. Applications in the Real World
  7. Agents That Enhance AI Itself
  8. Advanced Architectures and Coordination
  9. Challenges, Anti-Patterns, and the Future of Agent Design
  10. Conclusion and Final Thoughts

Chapter 1: Introduction to LLM Agents

What is an LLM Agent

An LLM agent is a software system built around a large language model (LLM) that can autonomously perform tasks by combining language generation with reasoning, memory, and external tools. Unlike traditional LLMs that simply respond to prompts, LLM agents maintain context, plan their actions, and interact dynamically with their environment. This allows them to handle more complex tasks and workflows independently.

AI Is the Interface: The Future of Human-Technology Interaction

Technology is the bridge that transforms data into knowledge.

In the coming years, artificial intelligence will evolve from being a tool that assists humans to becoming the primary interface through which we interact with technology and process information. The future of human-computer interaction will not be through keyboards, touchscreens, or even direct programming—it will be mediated by AI systems that understand, interpret, and execute our intentions seamlessly.