Domain Apps

Strix deploys autonomous LLM agents to attack your applications and validate findings with working proof-of-concepts, trading the false-positive noise of static scanners for actual exploits.

A research framework that assigns LLMs to trading-floor roles—analyst, researcher, trader, risk manager—to debate and execute simulated stock decisions.

MiroFish builds a parallel digital society of autonomous LLM agents so you can rehearse futures—from PR crises to lost novel endings—before they happen in reality.

It exists to bundle quantitative analytics, AI research, and live trading into one native desktop app for users who would rather not rent a Bloomberg terminal.

OpenBB normalizes proprietary and public financial data so engineers can feed the same sources to Python scripts, REST APIs, Excel, and AI agents without rebuilding integrations.

Scientific Agent Skills packages 142 curated research workflows into agent-readable modules so AI coding assistants can stop hallucinating bioinformatics APIs and start executing real experiments.

Shannon autonomously attacks your running web apps and APIs to prove vulnerabilities exist, because annual pentests leave the other 364 days of shipping exposed.

A PyTorch toolkit that treats robot learning like Hugging Face treats NLP: standardized datasets, pretrained policies, and one interface for many arms.

TimesFM is a pretrained decoder-only transformer that turns historical sequences into point and quantile forecasts without training from scratch.

MAA automates the daily chores of Arknights by treating the game screen as a computer vision problem, using OpenCV and OCR to handle farming, recruitment, and base shifts without human tapping.

This Chinese-localized fork rebuilds a multi-agent LLM trading framework in FastAPI and Vue, adds A-share data and domestic model support, and explicitly refuses to place live orders.

Automates the full pipeline from multi-market data ingestion to LLM-generated decision reports pushed straight to your team chat.

An AI-powered resume builder that tailors your CV to specific job descriptions instead of spraying the same PDF everywhere.

Dexter turns vague financial questions into structured research plans, executes them against live market data, and iterates until it trusts its own answer.

Genesis World wraps rigid-body, FEM, particle, and fluid solvers—plus a photo-realistic renderer and cross-platform compiler—into a single Python simulation stack for embodied AI research.

A framework-free Python multi-agent system built to scrape, debate, and report on public sentiment across 30+ Chinese and international social platforms.

Qlib provides a full-stack ML pipeline for quantitative finance—from alpha research to order execution—and now plugs into an LLM agent to automate R&D.

To explore AI-driven trading, it orchestrates a committee of LLM agents cast as famous investors who analyze stocks and generate orders that never reach a market.

It gives LLM agents real brokerage access, then cages live trading inside strict mandates, kill switches, and audit ledgers.

DeepTutor was built to turn generic LLM chats into persistent, agentic tutoring that can research, co-write, and remember across sessions.