Self-Improving AI

🔍 The Invisible AI Problem

How do you debug a system that generates thousands of database entries, hundreds of prompts, and dozens of knowledge artifacts for a single query?

SIS is our answer a visual dashboard that transforms Stephanie’s complex internal processes into something developers can actually understand and improve.

📰 In This Post

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• 🔎 See how Stephanie pipelines really work – from Arxiv search to cartridges, step by step.
• 📜 View logs and pipeline steps clearly – no more digging through raw DB entries.
• 📝 Generate dynamic reports from pipeline runs – structured outputs you can actually use.
• 🤖 Use pipelines to train the system – showing how runs feed back into learning.
• 🧩 Turn raw data into functional knowledge – cartridges, scores, and reasoning traces.
• 🔄 Move from fixed pipelines toward self-learning – what it takes to make the system teach itself.
• 🖥️ SIS isn’t just a pretty GUI - it’s the layer that makes Stephanie’s knowledge visible and usable.
• 🈸️ Configuring Stephanie – We will show you how to get up and running with Stephanie.
• 💡 What we learned – the big takeaway: knowledge without direction is just documentation.

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❓ Why We Built SIS

When you’re developing a self-improving AI like Stephanie, the real challenge isn’t just running pipelines it’s making sense of the flood of logs, evaluations, and scores the system generates.

🔍 Summary

Stephanie, a self-improving system, is built on a powerful belief:

If an AI can evaluate its own understanding, it can reshape itself.

This principle fuels every part of her design from embedding to scoring to tuning.

At the heart of this system is a layered reasoning pipeline:

• MRQ offers directional, reinforcement-style feedback.
• EBT provides uncertainty-aware judgments and convergence guidance.
• SVM delivers fast, efficient evaluations for grounded comparisons.

These models form Stephanie’s subconscious engine the part of her mind that runs beneath explicit thought, constantly shaping her understanding. But like any subconscious, its clarity depends on how raw experience is represented.

🔍 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.

🤖 The Static AI Trap

Today’s AI systems are frozen in time: trained once, deployed forever. Yet the real world never stops evolving. Goals shift overnight. New research upends old truths. Context transforms without warning.

What if your AI could wake up?

In this post, we engineer an intelligence that teaches itself a system that continuously learns from the web, audits its own judgments, and retrains itself when confidence wavers.

🧪 Summary

As AI developers, we often face the tradeoff between intelligence and accessibility. Powerful language models like Qwen3 run beautifully on servers but what about on the edge? On devices like Raspberry Pi or old Android phones, we’re limited to small models. The question we asked was simple:

Can we teach a small model to behave like a large one without retraining it from scratch using only its outputs and embeddings?

How to design a pipeline that turns vague goals into smart prompts

🧪 Summary

Why spend hours engineering prompts when AI can optimize its own instructions. This blog post introduces a novel approach toward creating a self-improving AI by treating prompts as programs. Traditional AI systems often rely on static instructions rigid and limited in adaptability. Here, we present a different perspective: viewing the Large Language Model (LLM) as a prompt compiler capable of dynamically transforming raw instructions into optimized prompts through iterative cycles of decomposition, evaluation, and intelligent reassembly.

📖 Summary

Imagine drowning in a sea of research papers, each holding a fragment of the knowledge you need for your next breakthrough. How does an AI system, striving for self-improvement, navigate this information overload to find precisely what it needs? This is the core challenge our Document Intelligence pipeline addresses, transforming chaotic documents into organized, searchable knowledge.

In this post we combine insights from Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers and Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training to build an AI document profiler that transforms unstructured papers into structured, searchable knowledge graphs.