all of the workflows of n8n i could find (also from the site itself)

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In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

Basic RAG prototype for PDFs

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Code for "Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds"

PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Segmentation [CVPR 2024]

DN-Splatter + AGS-Mesh: Depth and Normal Priors for Gaussian Splatting

Pointcept: Perceive the world with sparse points, a codebase for point cloud perception research. Latest works: Utonia (ICML'26), Concerto (NeurIPS'25), Sonata (CVPR'25 Highlight), PTv3 (CVPR'24 Oral)

NLP

Mask3D predicts accurate 3D semantic instances achieving state-of-the-art on ScanNet, ScanNet200, S3DIS and STPLS3D.

Custom build of stable-diffusion-docker-ui with various plugins, ad-hoc compiled (SYCL, CUDA 7.5+PTX, Skylake Server Arch) tensorflow and optimized (RTX 7.5, CUDA+cuDD 11.2, Arch Skylake) OneDNN bi…

Point cloud diffusion for 3D model synthesis

AI on the way. An auto deep learning pipe dream. An RDBMS approach to deep learning. Declarative, explainable, scalable, optimizable, easy to deploy, all that good stuff.

Production-ready platform for agentic workflow development.

Mask3D predicts accurate 3D semantic instances achieving state-of-the-art on ScanNet, ScanNet200, S3DIS and STPLS3D.

[CVPR 2023] Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis

[CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"

Laspy is a pythonic interface for reading/modifying/creating .LAS LIDAR files matching specification 1.0-1.4.

PyViz3D is a web-based visualizer for 3D objects and point clouds.

A PyTorch Library for Accelerating 3D Deep Learning Research

A versatile framework for 3D machine learning built on Pytorch Lightning and Hydra [looking for contributors!]

MINSU3D: MinkowskiEngine-powered Scene Understanding in 3D

Code for "Hitting the Target: Stopping Active Learning at the Cost-Based Optimum"

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.