personalization

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Memory library for building stateful agents

Universal memory layer for AI Agents. It provides scalable, extensible, and interoperable memory storage and retrieval to streamline AI agent state management for next-generation autonomous systems.

A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine

[EMNLP 2025 Oral] MemoryOS is designed to provide a memory operating system for personalized AI agents.

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

[ICLR 2026] LightMem: Lightweight and Efficient Memory-Augmented Generation

[ECCV 2024 Oral 🔥] Arc2Face: A Foundation Model for ID-Consistent Human Faces ------------------------ [ICCVW 2025] ID-Consistent, Precise Expression Generation with Blendshape-Guided Diffusion

A personal AI assistant for everyone

A curated solutions to building a self-evolving second brain that helps AI agents understand your personal and team context.

Papers and resources on Controllable Generation using Diffusion Models, including ControlNet, DreamBooth, IP-Adapter.

Official implementation of the NeurIPS 2023 paper "Photoswap: Personalized Subject Swapping in Images"

Anti-DreamBooth: Protecting users from personalized text-to-image synthesis (ICCV 2023)

Official Implementation for "A Neural Space-Time Representation for Text-to-Image Personalization" (SIGGRAPH Asia 2023)

Memory that remembers the story not just the facts. File System Memory and Three layer sentence graph for AI agents -> Facts, Episodes, raw Sentences. One DB. Zero config.

A Survey of Personalization: From RAG to Agent

🏞️ Official implementation of "Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition"

Local-first vector similarity search for Jazz Tools

Python SDK for FirstBatch: Real-time personalization using vectorDBs

Get familiar with different fine-tuning techniques for text-to-image models, and learn how to teach a diffusion model a concept of your choosing