PHP Image Processing

Stanford NLP Python library for understanding and improving PyTorch models via interventions

WordPress plugin to configure wp-admin and application state using a single config file.

Generate avatars with initials from user names.

Intervention layer with audit logs for OpenClaw agents. Browser-aware. Trajectory-aware. Human-routable.

Stanford NLP Python library for benchmarking the utility of LLM interpretability methods

Laravel Integration for Intervention Image

🌄 📐 A Laravel Nova advanced image field with cropping and resizing using Vue Advanced Cropper and Intervention Image

Upload image using Laravel's build in function and resize it automatically.

A maintained non-steam version of Tactical Intervention. Includes mod manager/support, loadouts, server browser and other features!

Evaluate interpretability methods on localizing and disentangling concepts in LLMs.

Explainability of Deep Learning Models

VIPS driver for Intervention Image.

On-demand image manipulation for WordPress via the Intervention Library.

Causal Inference with Invariant Prediction

Wrapper for PHP's GD Library for easy image manipulation. Support for scaling multi-line text, shapes, filters and smart resize.

Episimmer is an Epidemic Simulation Framework for Decision Support. It is a highly flexible system that can be easily configured to help take decisions during an epidemic in closed communities like university campuses and gated communities.

An annotated corpus of discussion forum threads from Massive Open Online Courses.

A mechanistic interpretability study invvestigating a sequential model trained to play the board game Othello

Symfony Integration for Intervention Image

Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

Image manipulation library

Create colored avatars with letters in PHP

Implementation for the NeurIPS 2025 paper: An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation

Laravel Framework Important Files

No description provided.

iReporter app enables users (citizen) to bring any form of corruption to the notice of appropriate authorities and the general public

Projet refait entièrement dans la v2 web

Heart Disease Prediction Using Machine Learning is a logistic regression model that predicts heart disease based on medical data. It analyzes features like age and cholesterol, achieving 85.24% training accuracy and 80.49% testing accuracy, facilitating early detection for timely intervention.

Ressources norme NF X08-070