A zero-dependency, Markdown-native autonomous ML research workflow system covering the full research lifecycle from idea discovery to rebuttal via cross-model adversarial collaboration.
An AI Agent-driven automated experiment framework that points at any code repo, autonomously analyzes, designs, runs experiments, and keeps improvements that work
The Open Source AI Engineering Platform for Agents, LLMs & Models, providing experiment tracking, model registry, LLM observability, evaluation, prompt optimization, and a unified AI gateway.
A self-learning vector database integrating GNN-driven search optimization, local LLM inference, Cypher graph queries, and a PostgreSQL vector extension, deployable from WASM embeddings to Raft-distributed clusters.
An open-source, all-in-one platform combining web analytics, session replay, feature flags, and A/B testing for product teams to build successful products.
An interactive open-access textbook on Machine Learning Systems engineering from Harvard University, integrating the TinyTorch framework with hands-on edge deployment labs, covering the full spectrum from ML fundamentals to system optimization.
An AI-powered data science team of agents that automates data loading, cleaning, feature engineering, EDA, visualization, and machine learning modeling (H2O + MLflow) through specialized agent collaboration, featuring a Streamlit visual pipeline studio to perform common data science tasks 10X faster.
An AI-driven multi-agent research assistant based on LangGraph that automates the entire research workflow from hypothesis generation, data analysis, and visualization to comprehensive report writing.
The official inference framework for 1-bit Large Language Models by Microsoft. It features optimized kernels for lossless, high-speed inference on CPUs and GPUs, drastically reducing energy consumption and enabling 100B+ parameter models to run on local consumer hardware.
A curated collection of recent research papers on autonomous agents, focusing on both reinforcement learning-based and large language model-based approaches, helping researchers quickly understand the cutting edge of the field。
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