I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.
Qwen3.8 27B Quantization BenchmarksBoth involve running Qwen3.x models efficiently in resource-constrained local environments, with quantization and performance tradeoffs being directly relevant to training tiny decision models on Qwen3.5
DeepSeek-V4.1-Flash: Smaller, Faster AI ModelDeepSeek-V4.1-Flash shares the 'smaller, faster' design philosophy with tiny decision models, both representing a trend toward lightweight specialized models over monolithic large ones
Supports
LLM Classification Is Feature EngineeringLLM Classification as Feature Engineering aligns directly with decision model use cases — tiny decision models are essentially structured classifiers, and this framing supports their architectural rationale
Mini-AGI: Continual Learning on 8GB VRAMBoth pursue the same core goal of enabling capable AI model training and inference on consumer/local hardware with tight VRAM constraints, validating the viability of small local model approaches
Develops into
Introducing System One Models and JevExplicitly states inspiration from 'Jev-like architectures' — Jev is the decision model architecture introduced by System One, making this a direct derivative implementation
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