Tech Experiments Results Hub
This series documents hands-on experiments with AI and LLM technology. Each post represents a practical exploration of what's possible with current models, focusing on real-world applications and novel architectures.
From agent systems to model comparisons, these experiments aim to push the boundaries of what we can achieve with AI while maintaining a focus on practical, implementable solutions.
Key Areas of Exploration
Each experiment is designed to provide actionable insights and practical knowledge that can be applied to real-world AI development challenges.
Novel Applications
Exploring practical applications of AI technology, from environmental solutions to game design, testing the boundaries of what's possible.
System Design
Developing and testing new approaches to structuring AI systems, focusing on reliability, efficiency, and practical implementation.
Agent Architectures
Testing different agent systems, from adversarial loops to creator-critic architectures, exploring how different models can work together to achieve complex goals.
Model Comparisons
Evaluating different LLMs head-to-head, understanding their strengths and weaknesses, and identifying optimal use cases for each model.
Experimental Approach
These experiments follow a systematic approach: starting with a hypothesis, designing a test methodology, implementing the solution, and analyzing the results. The focus is on practical outcomes and real-world applications.
Key Considerations
When working with AI systems, I'm focused on:
- Practical implementation and real-world applications
- System reliability and consistency
- Model selection and optimization
- Architecture design and scalability
- Local execution and deployment
Explore the experiments below to see different approaches to AI system design and implementation, along with their results and practical applications.
