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LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.
Eleven Claude skills that run a YouTube channel: scripts off 21 hook formulas with a scored hook, title and thumbnail linted as one pairing, an edit decision list from your transcript, a retention reader that finds where viewers actually leave, a Shorts cutter, and a virality engine. Free, MIT.
Open-source AI Security Operations Center: alert fusion, LLM-agent triage, MITRE ATT&CK investigation, and a replayable decision ledger for every agent step. Self-hostable, runs with no API keys, MIT licensed. Ships an MCP server for Claude, Cursor and Continue.
Sellbird (formerly Harvey): the open-source outreach platform that runs on your Claude or ChatGPT plan. Finds companies and decision makers, verifies emails, writes personal cold email, sends it and handles the replies.
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-making in a user-friendly environment.
Jev-like family of decision models built on top of Qwen3.5/3.8 you can train and run on your own
The chat decision assistant: before you reply, Jev reads the chat, judges intent and risk, and drafts replies you fill in with one tap. You press send. Android · Windows · macOS · iOS | 聊天辅助决策工具：先看懂对方再回复，发不发由你。
Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Turn local language models into Jev-style structured decision models. Get results from text and images with prefill alone—no token-by-token decoding required.
Jeeves – Reasoning improves Jev-like decision models
A framework for building decision-model harnesses for any kind of task. Your program offers the model a short list of next steps, and the model picks one at a time until it can answer.
Open, lightweight multimodal decision models for text, images, and video. Train and run them yourself.
One AI trade decision every Monad block. Jev on Kuru MON-USDC.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Epistemic protocols for Claude Code — structure human-AI interaction quality at every decision point - https://epistemic-protocols.com
非自回归System 1决策引擎，专为结构化类型决策场景设计 DeepOpen Multilingual, non-autoregressive System 1 decision engine.
This project applies machine learning techniques in Python to analyze data, visualize feature relationships, and perform classification using models such as Decision Tree and Support Vector Machine. Model performance is evaluated through confusion matrices and graphical analysis.
Loomio is a collaborative decision making tool
MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.
Coordinates asynchronous decision making on Rust repositories. Status of tracked issues and PRs can be viewed at https://rfcbot.rs.
Architecture decision record (ADR) examples for software planning, IT leadership, and template documentation
Universal model exchange and serialization format for decision tree forests
A cloud native Identity & Access Proxy / API (IAP) and Access Control Decision API that authenticates, authorizes, and mutates incoming HTTP(s) requests. Inspired by the BeyondCorp / Zero Trust white paper. Written in Go.
A collection of environments for autonomous driving and tactical decision-making tasks
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
An ontology-governed decision intelligence platform for humans and AI agents.
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
Grid2Op a testbed platform to model sequential decision making in power systems.