🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Repository Index
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📷 Instagram Bot - Tool for automated Instagram interactions
Create web-based user interfaces with Python. The nice way.
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view
Talk to any LLM with hands-free voice interaction, voice interruption, and Live2D avatar running locally across platforms
Pioneering Automated GUI Interaction with Native Agents
A Python library for automating interaction with websites.
Official repository for the Boltz biomolecular interaction models
Automatically mock your HTTP interactions to simplify and speed up testing
Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.
Python low-interaction honeyclient
AUR helper with minimal dependencies. Review PKGBUILDs all in once, next build them all without user interaction.
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
🔥 The first-ever interactive streaming video generation benchmark.
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Source code for "DeepDTA: deep drug-target binding affinity prediction"
Easily bring your Python applications online with the world's lightest GUI toolkit.
A medium interaction printer honeypot 🍯
Automation of some MAPT activities and interaction with the mobile Android device.
Low interaction honeypot designed for Android Debug Bridge over TCP/IP
Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses | 500+ Papers | Perception, Cognition, Planning, Interaction, Agentic System
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
A Macau food recommendation Agent built on Thompson Sampling + StackedPareto weight fusion, with a full evaluation pipeline (5-fold CV, bootstrap CIs, stress tests) on 5,000 simulated users and 100k interactions. Competition entry for the QwenPaw AI Developer Challenge.
Learning cell communication from spatial graphs of cells