This is the first post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.
In this post, I’ll walk through how I implemented the ideas from
AI Co-Scientist: Towards an AI Co-Scientist
into a working system called co_ai.
“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.
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.
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
Prompt engineering has become the defining skill of the Large Language Model (LLM) era a delicate balance between science and art. Crafting the perfect prompt often feels like an exercise in intuition, trial, and error. But what if we could take the guesswork out of the process? What if prompts could optimize themselves?
Fin-R1 is a new model specifically fine-tuned for financial reasoning, with performance that beats much larger models like DeepSeek-R1.
This post will use this model and compare it with phi3 across various tasks.
Phi-3: a lightweight, general-purpose model known for its efficiency and strong reasoning performance at smaller parameter scales. It serves as a great baseline for assessing how domain-specific tuning in Fin-R1 improves financial understanding and response structure.
Most machine learning models need hundreds of examples, large GPUs, and hours of training to learn anything useful. But what if you could build a system that gets smarter with just a handful of preference examples, runs entirely on your CPU, and improves while you work?
That’s exactly what MR.Q offers.
🔍 It doesn’t require full retraining.
⚙️ It doesn’t modify the base model.
🧠It simply learns how to judge quality — and does it fast.
Machine learning operates on data. Essentially, it processes data to extract meaningful information, which can then be used to make intelligent decisions. This is the foundation of Artificial Intelligence. The more data you have the better your machine learning apps will be. There is a caveat though the data has to be high quality. The more data you have and the higher quality the better your apps will be.
Artificial Intelligence (AI) has revolutionized the way we generate and consume text. From chatbots crafting customer responses to AI-authored articles, artificial intelligence is reshaping how we create and consume content. As AI-generated text becomes indistinguishable from human writing, distinguishing between the two has never been more critical. Here are some of the reasons it is important to be able to verify the source of information:
In this blog post, we’ll explore the current most effective methods for detecting AI-generated text.
Could the greatest playwright of all time have secretly shaped one of the most influential religious texts in history? Some believe William Shakespeare left his mark on the King James Bible hidden in plain sight. With the power of AI, we’ll investigate whether there’s any truth to this conspiracy.
You can read about the conspiracy here: