Highlights
- Pro
Stars
Adds MCP server to Blockbench
The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
📊 The Quant SDK for Python and Javascript. Written in Rust.
A Comprehensive Collection of Everything Related to Tradingview Pine Script.
🤖 Beautifully designed chatbot components based on shadcn/ui
Python framework for TradingView's Lightweight Charts JavaScript library.
Simple Windows desktop application for viewing & querying Apache Parquet files
Lightweight coding agent that runs in your terminal
Naive attempt at implementing TTT paper by letting autograd do the heavy lifting
Unlock your displays on your Mac! Flexible HiDPI scaling, XDR/HDR extra brightness, virtual screens, DDC control, extra dimming, PIP/streaming, EDID override and lots more!
Staging repo for development of native port of TypeScript
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
TypeScript's 1:1 validator, optimized from editor to runtime
An extremely fast Python package and project manager, written in Rust.
OpenAPI definition for the Fingerprint Pro server API
A massively parallel, high-level programming language
You like pytorch? You like micrograd? You love tinygrad! ❤️
Performant financial charts built with HTML5 canvas
Production-grade Rust-native trading engine with deterministic event-driven architecture
Free bot detection library that runs in the browser. Detects automation tools and frameworks. No server required, runs 100% on the client. MIT license, no usage restrictions.
Transpose your Excel calculations into python for better performances and scaling.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
An educational resource to help anyone learn deep reinforcement learning.