Freestyle Cognition is a hands-on system for using AI as a creative partner. This site will guide you—step by step—through everything you need to get started.
Begin the JourneyWatch the slides to understand what Freestyle Cognition is and how it works.
▶️ ViewSee how prompts shape the conversation and guide the AI’s response.
💬 TryExplore frameworks like GROW and ALIGN that guide better interaction.
🔧 LearnUse the builder to chain techniques together for powerful results.
🧰 BuildSee how the system works in practice—real prompts, real outcomes.
📘 ViewVisual slideshows of each template, technique, and system loop.
🖼 Slides“If you’ve ever pasted 50 prompts into an image generator one-by-one, this is for you. I hit my limit and built Clipper to solve it.”
In the previous blog post I wrote a research paper: Cross-Modal Cognitive Mapping. This paper is about turning your conversations into images to gradually map your thought patterns. The implementation of this paper is an application called Prism.
A component of this app is image generation from prompts or your conversations. All of the Foundation models support this but it’s a pretty janky process where you have to generate the prompt paste it into a text box and download the image. I just went through a week of doing this while building a prompt toolkit. While I was doing this I kept wishing I built the app which I’m going to share with you now.
Read MorePosted by Ernan Hughes Author of Freestyle Cognition
“You’ve probably already felt it. You gave the AI a messy thought… and it came back with something better. That little magic moment? That’s Freestyle Cognition.”
Freestyle Cognition is not just another AI prompt strategy. It’s a process — a new way to think with AI, not just ask it for help.
Read More“The best way to predict the future is to create it.” A. Lincoln
When you first hear about Freestyle Cognition, it might sound like just another buzzword:
“Talk to the AI a bit differently. Reflect. Iterate.”
But is there actually a real method underneath?
Or is it just a vibe a way of feeling like you’re doing something smarter?
We put that question to the ultimate test:
We asked a dedicated Reasoning Model to rigorously evaluate Freestyle Cognition, using structured thinking loops (ROW, CRITIC, GROW).
A Technical Overview of System Design and Implementation
Author: Ernan Hughes
Published: April 2025
Cross-Modal Cognitive Mapping is a new framework designed to extend traditional text-based cognition modeling into multimodal representations.
This system combines text prompts, visual generation, human selection behavior, and semantic memory retrieval to better understand and track human conceptual architectures.
This post presents a technical overview of the core architecture, database design, embedding workflows, search functionality, and resonance mapping built during the initial research phase.
Read More“If you want something new, you have to stop doing something old.” Peter Drucker
Today, most people interact with AI like it’s a vending machine:
Type request.
Wait for result.
Repeat.
It’s effective but deeply limited.
It’s like watching someone dig through concrete with their bare hands when a bulldozer sits nearby.
Freestyle Cognition changes that.
You’ve seen it in every great detective movie:
The suspect sits in a bare interrogation room, silent.
But as the conversation unfolds question after question, misdirection after misdirection the suspect begins to talk.
Soon, they reveal truths even they didn’t realize they knew.
Large Language Models (LLMs) like DeepSeek-R1 show remarkable reasoning abilities, but how these abilities are internally represented has remained a mystery. This paper explores the mechanistic interpretability of reasoning in LLMs using Sparse Autoencoders (SAEs) — a tool that decomposes LLM activations into human-interpretable features. In this post, we’ll: • Explain the SAE architecture used • Compute and visualize ReasonScore • Explore feature steering with sample completions • Provide live visualizations using Python + Streamlit
Read MoreErnan Hughes writes at the frontier of human-machine collaboration, building frameworks that help people think, create, and live more powerfully with AI.