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A Coding Implementation on Microsoft’s OpenMementos with Trace Structure Analysis, Context Compression, and Fine-Tuning Data Preparation

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Machine Learning, Software engineering, Staff, Technology, Tutorials

In this tutorial, we work with Microsoft’s OpenMementos dataset and explore how reasoning traces are structured through blocks and mementos in a practical, Colab-ready workflow. We stream the dataset efficiently, parse its special-token format, inspect how reasoning and summaries are organized, and measure the compression provided by the memento representation across different domains. As we […]

A Coding Implementation on Microsoft’s OpenMementos with Trace Structure Analysis, Context Compression, and Fine-Tuning Data Preparation Read More »

A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing

agentic ai, ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Software engineering, Staff, Technology, Tutorials

In this tutorial, we explore the implementation of OpenMythos, a theoretical reconstruction of the Claude Mythos architecture that enables deeper reasoning through iterative computation rather than increased parameter size. We build and analyze models using both GQA and MLA attention mechanisms, examine memory efficiency through KV-cache comparisons, and validate stability via the spectral properties of

A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing Read More »

How to Design a Production-Grade CAMEL Multi-Agent System with Planning, Tool Use, Self-Consistency, and Critique-Driven Refinement

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we implement an advanced agentic AI system using the CAMEL framework, orchestrating multiple specialized agents to collaboratively solve a complex task. We design a structured multi-agent pipeline consisting of a planner, researcher, writer, critic, and rewriter, each with clearly defined responsibilities and schema-constrained outputs. We integrate tool usage, self-consistency sampling, structured validation

How to Design a Production-Grade CAMEL Multi-Agent System with Planning, Tool Use, Self-Consistency, and Critique-Driven Refinement Read More »

A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows

ai, AI (Artificial Intelligence), Artificial Intelligence, deep learning, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we explore Equinox, a lightweight and elegant neural network library built on JAX, and show how to use it. We begin by understanding how eqx.Module treats models as PyTrees, which makes parameter handling, transformation, and serialization feel simple and explicit. As we move forward, we work through static fields, filtered transformations such

A Detailed Implementation on Equinox with JAX Native Modules, Filtered Transforms, Stateful Layers, and End-to-End Training Workflows Read More »

A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, deep learning, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search space that dynamically switches between different model families, demonstrating how Hyperopt handles hierarchical and structured parameter graphs. We build a production-grade objective function using cross-validation inside a scikit-learn pipeline, enabling

A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping Read More »

A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence

agentic ai, ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we build an end-to-end implementation around Qwen 3.6-35B-A3B and explore how a modern multimodal MoE model can be used in practical workflows. We begin by setting up the environment, loading the model adaptively based on available GPU memory, and creating a reusable chat framework that supports both standard responses and explicit thinking

A Coding Implementation on Qwen 3.6-35B-A3B Covering Multimodal Inference, Thinking Control, Tool Calling, MoE Routing, RAG, and Session Persistence Read More »

A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning

agentic ai, ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, deep learning, Editors Pick, Machine Learning, RAG, Staff, Technology, Tutorials

In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by setting up a stable environment, loading Microsoft’s Phi-4-mini-instruct in efficient 4-bit quantization, and then move step by step through streaming

A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning Read More »

How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Data Science, Editors Pick, Knowledge Graphs, Machine Learning, Staff, Technology, Tutorials

Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models based on decision trees, such as Random Forest, XGBoost, and CatBoost, have become the default choice for these tasks. Their strength lies in handling mixed data types, capturing

How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost Read More »

A Coding Implementation to Build an AI-Powered File Type Detection and Security Analysis Pipeline with Magika and OpenAI

ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine Learning, Security, Software engineering, Staff, Technology, Tutorials

In this tutorial, we build a workflow that combines Magika’s deep-learning-based file type detection with OpenAI’s language intelligence to create a practical and insightful analysis pipeline. We begin by setting up the required libraries, securely connecting to the OpenAI API, and initializing Magika to classify files directly from raw bytes rather than relying on filenames

A Coding Implementation to Build an AI-Powered File Type Detection and Security Analysis Pipeline with Magika and OpenAI Read More »

A Coding Tutorial for Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF, Benchmarking, Chat, JSON, and RAG

ai, AI (Artificial Intelligence), AI Shorts, Artificial Intelligence, Editors Pick, Language Model, Staff, Technology, Tutorials

In this tutorial, we implement how to run the Bonsai 1-bit large language model efficiently using GPU acceleration and PrismML’s optimized GGUF deployment stack. We set up the environment, install the required dependencies, and download the prebuilt llama.cpp binaries, and load the Bonsai-1.7B model for fast inference on CUDA. As we progress, we examine how

A Coding Tutorial for Running PrismML Bonsai 1-Bit LLM on CUDA with GGUF, Benchmarking, Chat, JSON, and RAG Read More »