Instructions for building an almost consumer hardware based prototype of a hearing aid
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Updated
Oct 20, 2021 - MATLAB
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
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.
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.
Distributed inference infrastructure for Mixture-of-Experts models. Run large MoE models on consumer GPUs.
Stream what shouldn't run.
Making new AI run on old hardware. Authoritative database for reproducible local-AI hardware benchmarks.
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.
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.
Out-of-core LoRA fine-tuning and expert-routing measurement for Kimi K3 (2.78T MoE) on a 7.6 GB laptop: 93 layers streamed from a USB disk, forward checked against an independent C implementation, gradients checked by finite differences
Real-time audio translation using Whisper + SeamlessM4T / NLLB-200
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.
A workbench for running large Mixture-of-Experts LLMs locally on consumer hardware with a tight VRAM budget.
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.
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
GRPO training that runs until you stop it on a single RTX4090 with vllm 0.26.0 (Linux Only).
Rabbit — independent third-party profile of a public API surface, by API Evangelist. Rabbit Inc. is a consumer technology company building AI-powered devices, best known for the Rabbit R1, a $199 handheld AI companion with industrial design by Teenage Engineering. The R1 runs rabbitOS and centers on a Large Action Model (LAM) that automates tasks a
Autonomous Knowledge Induction for LLMs: Prevent catastrophic forgetting with dynamic LoRA adapters and Bayesian clustering. Your Layer 2 engine for continuous learning
A fast ML library for experimentation and training on consumer hardware
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