A

artificial intelligence

Wally is a GitLab assistant powered by AI language models (OpenAI, Anthropic, or Ollama). With Wally, you can interact with your GitLab project using natural language and receive helpful suggestions and feedback from the AI.

Instance Hardness analysis in Machine Learning

The project that actually runs Wally (https://gitlab.com/WallyTheWobot/wally) using GitLab CI.

@WallyTheWobot

Taller "Utilizando los servicios de IA de AWS con Python y Boto3" durante AWS Community Day México 2025. https://agenda.awscommunity.mx/921930

This package provides a modular framework for the semi-automated processing of entomological specimen labels. It uses artificial intelligence to perform label detection, classification, rotation correction, OCR, and clustering laying the groundwork for comprehensive information extraction. It is designed to work in conjunction with the python-mfnb package for downstream clustering tasks.

UPMC Master 2 ANDROIDE: AI for robotics project

NeuroVortex Accelerator is a cutting-edge, open source AI optimization engine, finely tuned to deliver maximum performance from your hardware—you can even maximize performance from less powerful cards such as the GT 740 by exceeding their standard requirements

Imagine the excitement of turning a mere GT 740 into a phenomenal training beast. The NeuroVortex Accelerator thoroughly analyzes your system configuration, utilizing aggressive optimization methods including multi-GPU scaling where possible, remarkable quantization capable of dropping as low as 4-bit precision, and intelligent memory management with dynamic memory optimization supported by clever RAM and SSD caching. It leverages a combination of cutting-edge acceleration platforms: CUDA for NVIDIA architectures, OpenCL for Intel integrated GPUs, Vulkan/WebGPU to achieve unprecedented degrees of parallelization, and oneAPI for Intel accelerators, reaching your hardware's full potential.

Take, for instance, the GT 740. The NeuroVortex Accelerator holds an incredible amount of sway, augmenting data streams and computations and slashing training times by huge margins. With this innovation, a high-end level of performance becomes accessible to a far more affordable GPU. With each layer of your AI model—even a transformer-based natural language processing system, a convolutional neural network used for image processing, or something else with more general applicability—meticulously tuned by this tool, a common GPU is turned into an AI behemoth, removing performance bottlenecks and enabling rapid innovation.

The NeuroVortex Accelerator unifies all aspects of your system into a vital component of an integral, hyper-optimal computing system. With NeuroVortex, scientists, coders, and hobbyists worldwide can reach cutting-edge performance, even with hardware on the cusp of obsolescence by today's high levels of expectation.

Must fill remote forms using AI Workflow

SUSTAIN app for Android, built using Kotlin

SUSTAIN app for iOS, built using SwiftUI & TypeScript

SUSTAIN is an environmentally-friendly, token-optimized AI wrapper designed to reduce compute costs and increase productivity.

SUSTAIN is an environmentally-friendly, token-optimized AI wrapper designed to reduce compute costs and increase productivity. By filtering out irrelevant words and phrases from prompts, SUSTAIN minimizes the number of tokens sent to and received from the AI, saving energy and boosting performance.

My own introspection

Welcome to the "AI_Course_Template" - the basic repository template for students enrolled in the Artificial Intelligence course in the Department of Computer Science. This repository is designed to provide a standardized structure and set of guidelines for student projects, facilitating organized development, easy navigation, and consistent documentation.

Key Features:

Structured directory: Includes predefined folders for data (both raw and processed), source code, tests, and documentation, ensuring a clean and organized project workspace.

Comprehensive README Template: A guide to help students effectively document their project overview, goals, installation process, data descriptions, usage instructions, and contribution guidelines.

Resource Hub: Serves as a central location for essential resources, reference materials, and project-specific instructions.

Collaboration Ready: Configured to support collaborative projects, encouraging students to work together efficiently and share their progress.

Purpose:

This template is designed to streamline the project setup process, allowing students to focus more on the innovative aspects of AI and machine learning, rather than the initial setup and organization. By following this template, students will learn the importance of project organization, clear documentation, and consistent coding practices that are essential for any aspiring AI professional.

Package for generating and inverse-designing 2D lattice materials. Represents lattices as heterogeneous graphs and utilizes message passing, automatic differentiation and surrogate gradients for the inverse design.

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.