96% Correct Next Token Prediction, with No DNN, no Training, auto-distilled model
Over the last 12 months, I’ve built a model to predict the next token and to suggest synonyms or related queries to a user prompt, with 100% correct predictions on the training set in one shot, without training or deep neural networks (DNNs). The same model is now integrated in some of the most recent […]
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Invitation to Crack Codes Using AI
Could you use AI to crack a cypher? For instance, predict the next bits in bitstreams produced by a high-quality PRNG (pseudo-random generator). Or correctly guessing the next bit in sub-sequences of 100,000 consecutive binary digits of π starting at arbitrary positions, with a success rate above 55%. Without knowing that the digits come from […]
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NPG: New Random Generator, 3x Faster & Stronger than PCG64
The NumPy library in Python, and many other systems, relied on the Mersenne Twister PRNG (pseudo-random number generator) for a long time. It was slow and did not mimic randomness well enough, failing some statistical tests. In addition, it could be cracked, raising security issues. It was replaced recently by PCG64 which addresses some of […]
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Mesmerizing Video that will Make you Love Math, Engineering, and Probability
Featuring the roots of various polynomials whose coefficients are the binary digits of numbers like π. Complex roots cluster around the circle, and the few real ones are on the horizontal X-axis (positive on the right, negative on the left). Download the free paper to see how to interpret the colors and get the […]
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xLLM Version 2.0: GitHub Repository with Innovative AI Agents
I am putting all the new code and documentation about xLLM v 2.0 on GitHub, starting with various AI agents. At least, what is open-source and public (there is a lot more behind the public material). All home-made from scratch with radically different technology. You can check the new repository, here. Start with the README […]
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Checking for Randomness: Replacing Test Batteries with a Single Test
In cybersecurity applications where replicability is critical, or when building pseudo-random number generators, it is typical to perform a large number of various tests to check if a sequence of bits is random enough for practical purposes. This is also true in scientific research, to assess whether or not the digits of π or other […]
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Cybersecurity Use Case: AI Agent for Anomaly Detection – Part 2
In the first part of this series, here, I feature a click fraud case that we are working on, litigated by one of the largest law firms in the US. The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation […]
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xLLM: 30 Articles Shaping the Future of Enterprise AI in 2026
Over several decades, I unlearned everything that I learned in college classes, and built a new discipline from scratch, as much different from traditional AI than it is from standard machine learning, statistics and computer science. Outside academia with a focus on practical applications. Item #2 in the list below is the culmination of this […]
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How to Build and Optimize High-Performance Deep Neural Networks from Scratch
With explainable AI, intuitive parameters easy to fine-tune, versatile, robust, fast to train, without any library other than Numpy. In short, you have full control over all components, allowing for deep customization, and much fewer parameters than in standard architectures. Introduction I explore deep neural networks (DNNs) starting from the foundations, introducing a new type […]
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xLLM by BondingAI: 5–min Demo
This document features screenshots from a quick live demo intended to potential investors and clients interested in seeing how our production platform works. Contact the author for a live presentation and discussion. Here I run a typical short session on the Nvidia corpus, a repository of public financial documents (PDFs). Overview Figures 1–3 show the […]
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