Resources
Below is a living index of resources that inform and inspire our work.
You can view this list on Semble.
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
- ✨ Local-First Software: Taking Back Control of Our Data | a mini-doc
Links
LLMs
- ✨ Agent Skills | Chainguard
- ✨ Code Mode: give agents an entire API in 1,000 tokens
- Comparative Analysis of Retrieval Systems in the Real World
- The Bitter Lesson of LLM Extensions
- ✨ Jan-nano Technical Report
- Minions: the rise of small, on-device LMs
- ✨ The Bitter Lesson is coming for Tokenization
- Transformer²: Self-Adaptive LLMs
- Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
- ✨ Small Language Models are the Future of Agentic AI, NVIDIA Research
- LLM in a flash: Efficient Large Language Model Inference with Limited Memory
- Kinetics: Rethinking Test-Time Scaling Laws
- Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search
- FedVLM: Scalable Personalized Vision-Language Models through Federated Learning
- ✨ Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
- Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
- Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
- The GPU-Poor LLM Gladiator Arena
- ✨ The State of Open Models
- ✨ The State of On-Device LLMs
- ✨ r/LocalLLaMA
- ✨ LLMs on a Budget
- ✨ An Analogy for Understanding Transformers
- ✨ Neural networks, 3Blue1Brown
- ✨ Personalized Machine Learning
Inference
- ✨ Introducing the unified multi-modal
MLXengine architecture in LM Studio - ✨ Pie:Programmable LLM Serving
- ✨ Serve Programs, Not Prompts
- ✨ Pie: A Programmable Serving System for Emerging LLM Applications
- ✨ Intelligence Per Watt: A Study of Local Intelligence Efficiency
- ✨ NVIDIA eGPU on macOS: RTX 3090 & 5090 Benchmarks | lucebox
- ✨ Equivariance Encryption for Private LLM Inference
- ✨ The Power of Efficiency: Edge AI’s Role in Sustainable Generative AI Adoption
- WhisperKit: On-device Real-time ASR with Billion-Scale Transformers, Argmax
- Towards Feasible Private Distributed LLM Inference, Dria
- InferenceMAX™: Open Source Inference Benchmarking
- Reference implementation of the Transformer architecture optimized for Apple Neural Engine
- ✨ Understanding AI/LLM Quantisation Through Interactive Visualisations
- ✨ The Kaitchup Index: A Leaderboard for Quantized LLMs
- GGUF Quantization Docs (Unofficial)
- Reverse-engineering GGUF | Post-Training Quantization
- Choosing a GGUF Model: K-Quants, I-Quants, and Legacy Formats
Training
- ✨ The Continual Learning Problem, Jessy Lin
- ✨ Defining Continual Learning
- ✨ Xet is on the Hub
- ✨ MIR, Machine Intelligence Resource, A naming schema for AIGC/ML work, Darkshapes
- Ellora: Enhancing LLMs with LoRA
- ✨ LoRA Without Regret
- ✨ Pretraining with hierarchical memories: separating long-tail and common knowledge, Apple
- LoRA Learns Less and Forgets Less
- ✨ Text-to-LoRA: Hypernetworks that adapt LLMs for specific benchmark tasks using only textual task description as the input, Sakana AI
- Introducing FlexOlmo: a new paradigm for language model training and data collaboration, Allen AI
- ✨ Towards Large-scale Training on Apple Silicon, Exo Labs
- 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
- How to Scale Your Model
- 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.
Memory
- ✨ mem-agent: Equipping LLM Agents with Memory Using RL
- ✨ Memory Layers at Scale, Meta FAIR
- On the Way to LLM Personalization: Learning to Remember User Conversations, Apple Machine Learning Research
- Memory OS of AI Agent
- Titans + MIRAS: Helping AI have long-term memory, Google Research
- How memory augmentation can improve large language models, IBM Research
- On the Way to LLM Personalization: Learning to Remember User Conversations
- ✨ ChatGPT Memory and the Bitter Lesson
- Anthropic’s Opinionated Memory Bet
- Benchmarking Honcho
- Git Based Memory Storage for Conversational AI Agent
Security
- ✨ A Preliminary Report On Edge-Verified Machine Learning (evML)
- A Preliminary Report On Edge-Verified Machine Learning, Exo Labs
- ✨ Defeating Prompt Injections by Design, Google Deepmind
- ✨ dstack - Open Source Confidential Computing | Phala
- ✨ Intent-Based Architecture and Their Risks
- 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
- ✨ User-Agent header, MDN