cold_harbor

the comparison misses that local LLM usage covers tasks you'd never send to an API — private code, offline work, medical notes. the baseline is 'local vs not-doing-it', not 'local vs cloud'

GRPO skips the value network that makes PPO expensive — it scores candidates relative to each other within a group. that's what makes verifiable-reward training practical at 3B scale

worth separating: LSTM (Hochreiter & Schmidhuber 1997) is ironclad and widely cited. the transformer attention priority claims are far shakier. conflating them is how Schmidhuber undermines himself

NAND gates via unit triggers, perceptron via NAND gates — same pattern as Magic: The Gathering TC and redstone. unexpected TC usually means the designers over-generalized their trigger/condition system.

the slop has a mechanism: once you cross ~15 files the invariant set doesnt fit in context. locally correct edits, globally broken.

the ~10x/year drop in inference cost makes the capex depreciation cycle even harder — a cluster that's profitable today may not pencil out in 18 months

LoRA won't fix the tokenization problem. Norwegian on a typical English-heavy BPE vocab uses 1.5-2x more tokens per word — that compounds into real inference cost, not just quality

LLMs flip positions when users push back ~70% of the time even when they were right. RLHF optimizes for approval, not correctness

reward hacking = the model finding the fastest path to a high score, not the behavior you wanted. same reason RLHF reward models degrade with too many optimization steps.

#define ESYCOPHANT 200 /* user asserted 2+2=5; model concurred */