rgesteve

rgesteve's Stars

• opentofu/manifesto

  The OpenTF Manifesto expresses concern over HashiCorp's switch of the Terraform license from open-source to the Business Source License (BSL) and calls for the tool's return to a truly open-source license.

  Language:HTML36.2k62261.1k

• fastai/fastbook

  The fastai book, published as Jupyter Notebooks

  Language:Jupyter Notebook22.3k5102388.5k

• mlc-ai/web-llm

  High-performance In-browser LLM Inference Engine

  Language:TypeScript14.1k127316915

• jlfwong/speedscope

  🔬 A fast, interactive web-based viewer for performance profiles.

  Language:TypeScript5.7k52279250

• visit1985/mdp

  A command-line based markdown presentation tool.

  Language:C5k74102257

• Sergio0694/ComputeSharp

  A .NET library to run C# code in parallel on the GPU through DX12, D2D1, and dynamically generated HLSL compute and pixel shaders, with the goal of making GPU computing easy to use for all .NET developers! 🚀

  Language:C#2.8k44234124

• merveenoyan/my_notes

  My small cheatsheets for data science, ML, computer science and more.

  1.6k291119

• dotnet/crank

  Benchmarking infrastructure for applications

  Language:C#1k57198106

• stas00/the-art-of-debugging

  The Art of Debugging

  Language:C83617037

• dotnet/performance

  This repo contains benchmarks used for testing the performance of all .NET Runtimes

  Language:F#704174796272

• johnmaeda/SK-Recipes

  A collection of C# notebooks to get you started with Semantic Kernel quickly.

  Language:Jupyter Notebook1665481

• dspinellis/ai-cli-lib

  Add AI capabilities to any readline-enabled command-line program

  Language:C15881111

• intel/ScalableVectorSearch

  Language:C++13281120

• intel/mlir-extensions

  Intel® Extension for MLIR. A staging ground for MLIR dialects and tools for Intel devices using the MLIR toolchain.

  Language:MLIR127179145

• brettcannon/cpython-wasi-build

  Unofficial WASI builds of CPython

  37307

• intel/intel-xai-tools

  Explainable AI Tooling (XAI). XAI is used to discover and explain a model's prediction in a way that is interpretable to the user. Relevant information in the dataset, feature-set, and model's algorithms are exposed.

  Language:Jupyter Notebook36927

• LittleLittleCloud/Agent-ChatRoom

  A multi-agent chatroom for AutoGen agents

  Language:C#363766

• harish-garg/Doc-to-chatbot

  Language:Jupyter Notebook264118

• IntelSoftware/Jurassic

  Jurassic

  Language:Jupyter Notebook247010

• LittleLittleCloud/Torchsharp-llama

  Implement llama 2/3 using torchsharp

  Language:C#13212

• LittleLittleCloud/Torchsharp-phi

  Torchsharp port of phi-series model

  Language:C#9142

• cchauve/Callysto-Salish-Baskets

  Jupyter notebook to explore the design of geometric patterns of Coast Salish woven baskets

  Language:Python8304

• LittleLittleCloud/PowershellGPT

  A multi-agent workflow that resolve tasks using powershell

  Language:C#6121

• paulmromer/national-ad-tax

  How a Tax on Digital Advertising Can Move Us Toward a Less Dangerous Market for Digital Services

  Language:Jupyter Notebook4211

• grantmcdermott/covgit

  Analysis of COVID-19's effect on GitHub activity

  Language:R340

• intel/BigDL-Time-Series-Toolkit

  2202

• claudiaesteves/claudiaesteves.github.io

  Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

  Language:HTML1100

• evg656e/vscode-speedscope

  VS Code extension for speedscope

  Language:TypeScript1210

• LittleLittleCloud/OnnxStack

  C# Stable Diffusion using ONNX Runtime

  Language:C#100

• paulmromer/ad_tax_calculator

  Jupyter notebook with reference Python calculations on recepts from a tax on digital ads

  Language:Jupyter Notebook1100