MiroThinker is a deep research agent optimized for complex research and prediction tasks. Our latest models, MiroThinker-1.7, achieves 74.0 and 75.3 on the BrowseComp and BrowseComp Zh, respectively.
- agent
- browsecomp
- deep-research
- futurex
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MiroThinker is a deep research agent optimized for complex research and prediction tasks. Our latest models, MiroThinker-1.7, achieves 74.0 and 75.3 on the BrowseComp and BrowseComp Zh, respectively.
Standards for developing consistent, flexible, and sustainable HTML and CSS.
Project management skill system for Agents that uses GitHub Issues and Git worktrees for parallel agent execution.
You Know, For WEB Fuzzing !
This project is based on the scripting capabilities of several excellent iOS proxy tools (e.g. Surge, Quantumult X)
GEF (GDB Enhanced Features) - a modern experience for GDB with advanced debugging capabilities for exploit devs & reverse engineers on Linux
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Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
Finds files and directories via an intuitive fluent interface
FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
Rapid Web Development w/ Go
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Leading free and open-source face recognition system
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Performant React Native image component.
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Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.
Deepsec is a security harness for finding vulnerabilities in your codebase powered by coding agents
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A network load-balancer implementation for Kubernetes using standard routing protocols
System prompt builder. Assembles agent identity from workspace config and spark.toml. Part of Unicity AOS.
bika 哔咔 全平台客户端。A comic browser,support Android / iOS / MacOS / Windows / Linux.
A collection of inspiring resources related to engineering management and tech leadership
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.