Superduper: End-to-end framework for building custom AI applications and agents.
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Updated
Sep 1, 2025 - Python
Superduper: End-to-end framework for building custom AI applications and agents.
deploy ML Infrastructure and MLOps tooling anywhere quickly and with best practices with a single command
Nerlnet is a framework for research and development of distributed machine learning models on IoT
🍎👉🍏 Everything you need in order to get started building distributed machine learning with Apple's MLX
An AI agent that teaches itself to fix bugs — MCTS explores debugging strategies, DPO trains on what works. Pipelined across two H100 nodes: one for 4-bit inference and trajectory collection, one for full bf16 LoRA fine-tuning. Built on Qwen2.5-Coder-7B, evaluated on DebugBench. Inspired by Agent Q (Putta et al., 2024).
Network-Aware Federated Optimisation (NAFO): a 5G-native framework that jointly optimizes wireless channel conditions, differential privacy, and multi-modal healthcare AI. Demonstrates robust federated learning under realistic 3GPP network constraints with AoI-aware dynamics.
🌍 Generate realistic, dynamic environments for robotics research using Isaac Sim. Manage simulations, control robots, and customize workflows easily.
A simulation framework for federated learning experiments, allowing researchers to test and evaluate privacy-preserving machine learning algorithms on decentralized datasets.
This project demonstrates how to build a recommendation system using Apache Spark for distributed data processing, Python for machine learning, and common data science libraries. It uses the Alternating Least Squares (ALS) algorithm for collaborative filtering.
Federated learning utilities for distributed threat detection — Flower integration with differential privacy and secure aggregation
AllReduce/AllGather scaling in ASTRA-sim across torus vs switch topologies on the analytical + ns-3 backends — latency-bound vs bandwidth-bound over message size and node count. Reproducible Docker/Chakra harness + write-up.
Federated phishing detection platform combining privacy-preserving learning and distributed cybersecurity analytics.
Privacy-preserving medical diagnosis system using Federated Learning (Flower), Differential Privacy (Opacus), and FastAPI.
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