Below is a living index of resources that inform and inspire our work.

Motivations

• ✨ This is Cuba's Netflix, Hulu, and Spotify – all without the internet
• Cuba's Underground Gaming Network
• ✨ Weathering Software Winter
• ✨ The Memory Walled Garden
• ✨ The Resonant Computing Manifesto
• ✨ Why Tech Needs Personalization
• File over app

Research

• ✨ Evolution of Apple M Series Chips | From M1 to M5
• The Bitter Lesson of LLM Extensions
• ✨ The Continual Learning Problem, Jessy Lin
• ✨ mem-agent: Equipping LLM Agents with Memory Using RL
• ✨ Xet is on the Hub
• ✨ MIR, Machine Intelligence Resource, A naming schema for AIGC/ML work, Darkshapes
• ✨ Jan-nano Technical Report
• ✨ Memory Layers at Scale, Meta FAIR
• ✨ Self-driving infrastructure
• ✨ LoRA Without Regret
• ✨ A Preliminary Report On Edge-Verified Machine Learning (evML)
• ✨ Pretraining with hierarchical memories: separating long-tail and common knowledge, Apple
• LoRA Learns Less and Forgets Less
• ✨ The Bitter Lesson is coming for Tokenization
• On the Way to LLM Personalization: Learning to Remember User Conversations, Apple Machine Learning Research
• ✨ Text-to-LoRA: Hypernetworks that adapt LLMs for specific benchmark tasks using only textual task description as the input, Sakana AI
• Transformer²: Self-Adaptive LLMs
• How memory augmentation can improve large language models, IBM Research
• Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
• ✨ The Power of Efficiency: Edge AI's Role in Sustainable Generative AI Adoption
• ✨ Small Language Models are the Future of Agentic AI, NVIDIA Research
• ✨ Defeating Prompt Injections by Design, Google Deepmind
• LLM in a flash: Efficient Large Language Model Inference with Limited Memory
• Introducing FlexOlmo: a new paradigm for language model training and data collaboration, Allen AI
• WhisperKit: On-device Real-time ASR with Billion-Scale Transformers, Argmax
• ✨ Towards Large-scale Training on Apple Silicon, Exo Labs
• Kinetics: Rethinking Test-Time Scaling Laws
• Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search
• LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
• AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air
• Comparative Analysis of Retrieval Systems in the Real World
• FedVLM: Scalable Personalized Vision-Language Models through Federated Learning
• On the Way to LLM Personalization: Learning to Remember User Conversations
• A Preliminary Report On Edge-Verified Machine Learning, Exo Labs
• ✨ Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
• ✨ Intent-Based Architecture and Their Risks
• Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
• Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
• Towards Feasible Private Distributed LLM Inference, Dria
• ✨ ChatGPT Memory and the Bitter Lesson
• ✨ OpenPoke: Recreating Poke's Architecture
• Anthropic's Opinionated Memory Bet
• Mind the Trust Gap: Fast, Private Local-to-Cloud LLM Chat
• MCP Colors: Systematically deal with prompt injection risk
• New physical attacks are quickly diluting secure enclave defenses from Nvidia, AMD, and Intel

Reference

• ✨ Foundation Models, Apple Developer Documentation
• ✨ User-Agent header, MDN
• ✨ Modelfile Reference, Ollama Documentation
• Dria Inference Arena – Compare Benchmarks Across LLMs, Inference Engines & Hardware
• The GPU-Poor LLM Gladiator Arena
• ✨ The Kaitchup Index: A Leaderboard for Quantized LLMs
• InferenceMAX™: Open Source Inference Benchmarking
• RFT, DPO, SFT: Fine-tuning with OpenAI — Ilan Bigio, OpenAI
• ✨ Hand-picked selection of articles on AI fundamentals/concepts that cover the entire process of building neural nets to training them to evaluating results.
• ✨ The State of Open Models
• ✨ The State of On-Device LLMs
• How to Scale Your Model
• ✨ r/LocalLLaMA
• ✨ An Analogy for Understanding Transformers
• ✨ Neural networks, 3Blue1Brown
• GGUF Quantization Docs (Unofficial)
• Reverse-engineering GGUF | Post-Training Quantization
• Choosing a GGUF Model: K-Quants, I-Quants, and Legacy Formats
• Reference implementation of the Transformer architecture optimized for Apple Neural Engine
• ✨ LLMs on a Budget
• ✨ Personalized Machine Learning