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A course on aligning smol models.
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization
The Open Cookbook for Top-Tier Code Large Language Model
Open-source no-code web data extraction platform. Turn websites to APIs & spreadsheets with no-code robots in minutes.
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
RAG that intelligently adapts to your use case, data, and queries
A simple screen parsing tool towards pure vision based GUI agent
Automate browser-based workflows with LLMs and Computer Vision
独立开发者出海工具收集站（Awesome Independent developer sea tool collection station）
SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled.
OCR, layout analysis, reading order, table recognition in 90+ languages
Example profiling with gunicorn
A diffusers pipeline for zero shot stylised portrait creation
Open deep learning compiler stack for cpu, gpu and specialized accelerators
State-of-the-art CLIP/SigLIP embedding models finetuned for the fashion domain. +57% increase in evaluation metrics vs FashionCLIP 2.0.
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
AutoMQ is a cloud-native alternative to Kafka by decoupling durability to S3 and EBS. 10x Cost-Effective. No Cross-AZ Traffic Cost. Autoscale in seconds. Single-digit ms latency.
Command line tool to generate idiomatic Go code for SQL databases supporting PostgreSQL, MySQL, SQLite, Oracle, and Microsoft SQL Server
Simple static web-based mask drawer, supporting semantic segmentation and video segmentation with interactive Segment Anything Model 2 (SAM2).
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…
Easiest and laziest way for building multi-agent LLMs applications.
《Pytorch实用教程》（第二版）无论是零基础入门，还是CV、NLP、LLM项目应用，或是进阶工程化部署落地，在这里都有。相信在本书的帮助下，读者将能够轻松掌握 PyTorch 的使用，成为一名优秀的深度学习工程师。
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.