MRQ Scoring

Getting Smarter at Getting Smarter: A Practical Guide to Self-Tuning AI

Getting Smarter at Getting Smarter: A Practical Guide to Self-Tuning AI

Self-Improving Systems, Adaptive AI Architectures, Energy-Based Reasoning, LLM Evaluation Techniques, Scoring Frameworks, Reinforcement Learning in NLP, Cognitive AI Pipelines, Agent-Based Collaboration, Automated AI Judgment Systems

🔥 Summary: The Self-Tuning Imperative

“We’re drowning in models but starved for wisdom.” Traditional AI stacks:

  • Require constant manual tuning
  • Suffer from version lock-in
  • Can’t explain their confidence

What if your AI system could learn which models to trust and when without your help?

In this post, we’ll show you a practical, working strategy for building self-tuning AI not theoretical, not hand-wavy, but a real system you can build today using modular components and a few powerful insights.

Epistemic Engines: Building Reflective Minds with Belief Cartridges and In-Context Learning

Epistemic Engines: Building Reflective Minds with Belief Cartridges and In-Context Learning

Self-Improving Systems, In-Context Learning, AI Architectures, Knowledge Representation, Agent-Based AI, Machine Reasoning, Adaptive Learning, LLM Evaluation Strategies, Autonomous Knowledge Systems

🔍 Summary: Building the Engine of Understanding

This is not a finished story. It’s the beginning of one and likely the most ambitious post we’ve written yet.

We’re venturing into new ground: designing epistemic engines modular, evolving AI systems that don’t just respond to prompts, but build understanding, accumulate beliefs, and refine themselves through In-Context Learning.

In this series, we’ll construct a self-contained system separate from our core framework Stephanie that runs its own pipelines, evaluates its own beliefs, and continuously improves through repeated encounters with new data. Its core memory will be made of cartridges: scored, structured markdown artifacts distilled from documents, papers, and the web. These cartridges form a kind of belief substrate that guides the system’s judgments.