MEKA is an open source implementation of methods for multi-label learning and evaluation, based on the WEKA Machine Learning Toolkit. It includes dozens of multi-label methods from the scientific literature and a wrapper to the MULAN framework. The project is distributed via SourceForge, with code hosted on GitHub.
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QuestDB is an open-source time-series database engineered for low latency. It uses a column-oriented, time-partitioned storage engine with memory-mapped files and vectorized execution for high-throughput ingestion and millisecond-level analytical queries. SQL is extended with time-series operators such as SAMPLE BY, LATEST ON, ASOF JOIN, WINDOW JOIN and HORIZON JOIN.
Jan is an open-source alternative to ChatGPT. It runs open-source AI models locally or connects to cloud models such as GPT and Claude. The download is offered for Mac. A memory feature is listed as coming soon.
Kilo is an open source AI coding agent for VS Code, JetBrains, CLI, desktop and cloud. It offers access to 500+ models at zero markup, bring-your-own keys, or local models for privacy. Kilo Gateway provides a unified API to access AI models through a single endpoint.
Yomu AI is an academic writing assistant for essays, papers, literature reviews and theses. It offers a document assistant for drafting sections, AI autocomplete, editing commands such as paraphrase and summarize, citation finding and formatting, a plagiarism checker, and chat with PDFs and images. Sources can be saved to a library and reused across documents. It is described as free to try with no credit card required.
SciSummary is an AI tool for researchers and students that summarizes and helps users understand scientific articles and research papers, and acts as an AI-powered reference and document manager. It supports bulk paper summarization, comparing papers, organizing a library with folders and tags, and chat messages. A free trial, a student plan and a pro plan are listed.
Spinning Up is OpenAI's educational resource on deep reinforcement learning. Its documentation covers installation, included algorithms, running experiments, and an introduction to RL, along with algorithm pages for methods such as DDPG and PPO. The DDPG page describes an algorithm that concurrently learns a Q-function and a policy for continuous action spaces. The PPO page describes on-policy first-order methods, including PPO-Clip.
