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.
Real-time audio translation using Whisper + SeamlessM4T / NLLB-200
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
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.
GRPO training that runs until you stop it on a single RTX4090 with vllm 0.25.1 (Linux Only).
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.
Making new AI run on old hardware. Authoritative database for reproducible local-AI hardware benchmarks.
Autonomous Knowledge Induction for LLMs: Prevent catastrophic forgetting with dynamic LoRA adapters and Bayesian clustering. Your Layer 2 engine for continuous learning
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.
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.
PotatoCs: local AI for ordinary computers — OCR, RAG, diagnostics, benchmarks, and transparent traces for small local models.
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