On-device, AI memory runtime for continual learning.
We envision a future shaped by many small, task-specific models, each finely tuned to its purpose and context. Tiles is our step toward enabling this vision. We are building on-device AI infrastructure designed for continual learning through model chaining and parameter-efficient fine-tuning. Continual learning only makes sense when it runs on edge and consumer devices, where the model can adapt to each user privately, contextually, and with additional redundancy layers that safeguard their data.With advancements like MatFormer architecture, PLE caching, and Mixture of Experts (MoE), this is now practical on consumer devices.
We’re in the prototype stage and seeking design partners among early-stage companies. Connect with us in the #tiles channel on the User & Agents Discord, or reach out via [email protected]. Subscribe to our blog Neuron for updates on on-device AI and personalization research.
Below is a living index of resources that inform and inspire our work.
- ✨ Unternet Kernel
- Ollama JavaScript library
- ✨ Modelfile Reference - Ollama English Documentation
- ✨ Introducing Gemma 3n: The developer guide
- Foundation Models adapter training - Apple Intelligence - Apple Developer
- ✨ Unsloth AI - Open Source Fine-tuning & RL for LLMs
- ✨ Mistral.rs, a cross-platform, highly-multimodal inference engine
- Osmosis, Unlocking AI self-improvement at production scale
- Supermemory MCP
- ✨ Introducing the v0 composite model family, Vercel
- Agent Reinforcement Trainer, OpenPipe
- Universal Quantized File Format: UQFF
- GGUF Tool Suite
- uqff_maker
- Minions, Big & Small LLMs working together
- ✨ The Kaitchup Index: A Leaderboard for Quantized LLMs
- Pipecat Cloud: Enterprise Voice Agents Built On Open Source - Kwindla Hultman Kramer, Daily
- Serving Voice AI at $1/hr: Open-source, LoRAs, Latency, Load Balancing - Neil Dwyer, Gabber
- 📏RULER: Easy Mode for RL Rewards
- ART·E: How We Built an Email Research Agent That Beats o3
- ✨ LoRA's Limitations: Head-to-Head with Full RL
- A DSPy rewrite to Rust
- ✨ A case for client-side machine learning, Christopher Fleetwood
- ✨ Ratchet Architecture
- ✨ How Tailscale works
- Democratizing Al: The Psyche Network Architecture, Nous Research
- ✨ 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: Instant Transformer Adaption, Sakana AI
- ✨ Small Language Models are the Future of Agentic AI, NVIDIA Research
- ✨ Defeating Prompt Injections by Design, Google Deepmind
- 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
- ✨ r/LocalLLaMA
- ✨ The State of On-Device LLMs
- ✨ An Analogy for Understanding Transformers
- ✨ Neural networks, 3Blue1Brown
- MCP 201: The power of protocol, Anthropic
- GGUF Quantization Docs (Unofficial)
- Reverse-engineering GGUF | Post-Training Quantization
- Reference implementation of the Transformer architecture optimized for Apple Neural Engine
- H100 PCIe vs SXM vs NVL: Which H100 GPU Is Fastest and Most Cost-Effective for Fine-Tuning LLMs?
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✨ Everything is ugly, so go build something that isn't — Raiza Martin, Huxe (ex NotebookLM)
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✨ Machines of Buying and Selling Grace - Adam Behrens, New Generation
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✨ The Rise of Personal LLM "Cognitive Core", Andrej Karpathy
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Fun stories from building OpenRouter and where all this is going - Alex Atallah, OpenRouter
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✨ Something Pretty Right: A History of Visual Basic | Retool
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