Instructions for building an almost consumer hardware based prototype of a hearing aid

CodeUp: A Multilingual Code Generation Llama-X Model with Parameter-Efficient Instruction-Tuning

Cross-architecture LLM internal observation database (23 models, 13 architecture families). Exposed as MCP tools for any AI coding agent.

Capable, auditable coding that runs fully offline on a 16 GB machine. A verification-first layer (hard test execution, symbolic checking, agentic repair) that takes a local 7B to parity with its 671B teacher on verifiable tasks. MIT, pre-registered, reproducible.

GLM-5.2, a 744 billion parameter mixture of experts model, in a pure C inference engine: quantized to int4, experts streamed from disk, deployed and benchmarked. Generates in 16 GB of RAM.

Real-time audio translation using Whisper + SeamlessM4T / NLLB-200

PERSPECTIVE v2 — A 1.05 trillion parameter sparse Mixture-of-Experts language model that runs on consumer hardware (4 GB VRAM + 32 GB RAM). Features O(1) perspective decay recurrence, 3D torus manifold routing, native ternary {-1,0,+1} weights, holographic distributed memory, and hard geometric safety constraints. Built in Rust.

A workbench for running large Mixture-of-Experts LLMs locally on consumer hardware with a tight VRAM budget.

实时追踪 Kickstarter 上中国背景的消费硬件项目 · 每日 cron · prelaunch / live / 已结束 · Editorial design

Run giant mixture-of-experts LLMs on a 16 GB consumer GPU, losslessly — a streaming inference engine you can verify yourself.

Consumer brain-computer interface for inner speech decoding. 8-channel EEG headband ($800) outperforms 128-channel clinical systems ($50K). EEGNet 35.5% accuracy (p=0.0006), cross-subject generalization (p=0.003). Real-time demo included.

A fast ML library for experimentation and training on consumer hardware

A 13-module AGI cognitive architecture built on Global Workspace Theory, with alignment embedded as structure. Runs on consumer hardware via Ollama.

Enables building routed collections of task-specific language models with LoRA adapters that run on consumer hardware, routing queries to specialized models for improved performance without expensive API access.

Autonomous Knowledge Induction for LLMs: Prevent catastrophic forgetting with dynamic LoRA adapters and Bayesian clustering. Your Layer 2 engine for continuous learning

Compress large LLMs (MoE 200B+) on consumer hardware: structured depth + expert pruning and 4-bit quantization, with zero-RAM GGUF export for llama.cpp / ollama. Runs on 16GB RAM + 4GB VRAM. No retraining. [BETA]

414 tests, 7 models, one 2017 iMac. Full benchmark suite behind ableandrew.com/gemma-4. Every prompt, every grading function, every raw result.

Pooling frontier LLMs across an NVIDIA RTX 4070 + a 5-year-old M1 MacBook over a $40 Thunderbolt cable. Honest, measured field records — a 70B run across both machines, and a day-old frontier MoE generating content cross-machine (framework-confirmed weight residency, byte-proven over the cable). Reproduction included.

Peloton Interactive is a connected fitness company that produces interactive exercise equipment and a streaming subscription service. The company sells connected stationary bikes, treadmills, rowers, and a digital app offering live and on-demand fitness classes.

Builds a conversational harness for small local language models that amplifies human thinking through verified task offloading, using a four-subsystem architecture for orchestration, context management, tools, and inference.