Write HTML. Render video. Built for agents.

Google Workspace CLI — one command-line tool for Drive, Gmail, Calendar, Sheets, Docs, Chat, Admin, and more. Dynamically built from Google Discovery Service. Includes AI agent skills.

SGLang is a high-performance serving framework for large language models and multimodal models.

The memory harness for proactive AI agents — structured storage, intent capture, 10x token reduction.

An unofficial Model Context Protocol (MCP) server for accessing Malaysia's Open Data APIs

Access large language models from the command-line

FastAPI framework, high performance, easy to learn, fast to code, ready for production

🤗 smolagents: a barebones library for agents that think in code.

The book every data scientist needs on their desk.

A roadmap to guide you through mastering SQL for Data Science in just 6 weeks for free

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…

From the Tensor to Stable Diffusion, a rough outline for a 10 week course.

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

OCR, layout analysis, reading order, table recognition in 90+ languages

Creating beautiful plots of data maps

Courses, Articles and many more which can help beginners or professionals.

Preparation material and resources for the ML (including DL) and Quant Research interviews

A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

Jupyter Notebooks to help you get hands-on with Pinecone vector databases

Starter code for Stanford CS224n default final project on SQuAD 2.0

Efficient Retrieval Augmentation and Generation Framework

ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Here I test a simple ML trading strategy