Forget everything you thought you knew about AI reasoning. What you’re about to discover isn’t just another scoring algorithm it’s Stephanie’s first true capacity for thought. Let’s peel back the layers of the HRM: Hierarchical Reasoning Model and see why this represents a quantum leap in how AI systems can genuinely reason rather than merely react.
What if your AI could not only answer questions but also question itself about those answers? Not with programmed doubt, but with genuine self-awareness recognizing when it’s uncertain, analyzing why it made a mistake, and systematically improving its own reasoning process? This isn’t science fiction. Today, we’re unveiling the first working implementation of an AI that doesn’t just think, but learns how to think better. It’s a bit cold here
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:
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.
“We’re drowning in models but starved for wisdom.” Traditional AI stacks:
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.
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.
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.
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
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.
How a self-evolving AI learns to reflect, score, and rewrite its own reasoning
What if an AI could think not just solve problems, but reevaluate its beliefs in the face of new information?
In this post, we introduce a system that does exactly that. At the core of our pipeline is a lightweight scoring model called MR.Q, responsible for evaluating ideas and choosing the best ones. But when it encounters a new domain, a new goal, or a shift in task format, it doesn’t freeze it adapts.
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.