Feedback Loops on AIBussin — AI applications, systems and books

Methodologies and Core Patterns

Mon, 01 Jan 0001 00:00:00 +0000

Five Core Shifts in the AI-Human Paradigm

Chapter 1 introduced agents as systems built around language models, not as models alone. This chapter looks at the working patterns that make those systems useful.

The patterns are not complicated. They are easy to miss because they do not look like traditional programming. You converse, inspect, revise, compare, preserve, and try again. The loop is simple, but its consequences are large.

Five shifts sit underneath that loop.

Living Against Parkinson’s: A Practical Guide to Fighting Back

Tue, 21 Apr 2026 11:52:11 +0100

🧩 SECTION 1: Introduction

How would you actually live if you were trying to fight this properly?

Not cure it. Not pretend it’s easy. But fight it intelligently, consistently, and over time.

That’s what this is.

Parkinson’s isn’t the largest disease in the world but it is one of the most demanding long-term neurological conditions a person can face. It reshapes movement, energy, thinking, and daily life and it does so unevenly, changing from day to day.

Learning to Learn: A LATS-Based Framework for Self-Aware AI Pipelines

Thu, 12 Jun 2025 09:23:46 +0100

📖 Summary

In this post, we introduce the LATSAgent, an implementation of LATS: Language Agent Tree Search Unifies Reasoning.. within the stephanie framework. Unlike prior agents that followed a single reasoning chain, this agent explores multiple reasoning paths in parallel, evaluates them using multidimensional scoring, and learns symbolic refinements over time. This is our most complete integration yet of search, simulation, scoring, and symbolic tuning bringing together all of our previous work on sharpening, pipeline reflection, and symbolic rules into a unified, intelligent reasoning loop.