Educational Implementation · Rust

4cast-livive-net

Jarad Peckenpaugh@jpeckenpaugh · Atlas 69.9 · #4= builder

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What it is

This repository implements four machine learning classifiers (centroid, k-NN, perceptron, and MLP) from scratch in Rust, with a fixed input/output contract for comparison. It includes educational tooling for data preparation, model training, evaluation, and visualization in a browser-based demo.

Scored from the code at its last scan, 20 Oct 2026. How merit is scored

Where it counts

Machine Learning90%
Data Science30%
Computer Vision20%
MLOps10%

How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.

Around it on the Machine Learning board

Whole board

RankProjectBuilderFieldMerit

13mamba3-atlas-imageAI Developer Tool · PythonThis repository provides a toolkit for analyzing the internal activations and properties of the Mamba-3 language model, including scripts for capturing activations, performing changepoint analysis, and probing GPU performance.61.5/100AdvancedRickMachine Learning
14kernel-workArchitecture Modification / Research · PythonThis repository implements a gather-matmul-scatter overlay for the `deep_chaos_scheduler` to optimize training by computing only on active channels. It includes the overlay implementation, tests for correctness, and benchmarks to measure performance impact.60.5/100AdvancedRickMachine Learning
154cast-livive-netEducational Implementation · Rust60.3/100AdvancedJarad PeckenpaughMachine Learning
16, shared rankAurora-SDG-Publication-ClassifierML Application · PythonThe Aurora-SDG-Publication-Classifier is a Streamlit-based machine learning application that classifies academic publications according to the United Nations Sustainable Development Goals (SDGs). It integrates data from multiple sources (OpenAlex, DSpace, OAI-PMH), enriches…58.9/100AdvancedJens MittelbachMachine Learning
16, shared rankrag-pocEducational Implementation · TypeScriptThis repository is a client-side, zero-backend Retrieval-Augmented Generation (RAG) proof-of-concept that runs entirely in the browser, using PDF.js for text extraction, Transformers.js for embeddings, and web-llm for local LLM inference. It is designed as an educational tool…58.9/100AdvancedJarad PeckenpaughMachine Learning

What it is

Where it counts

Around it on the Machine Learning board

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