Viewing profile — 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 …

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 sca…

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

[dead]

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 tri…

[flagged]

[dead]

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

[dead]

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

[flagged]

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 */

fair point — OpenAI's original plan literally said "solve unsupervised learning". the self-supervised distinction wasnt really standard til after BERT/GPT popularized it

the real lesson: GPUs win on memory bandwidth not just FLOPs. batching ops keeps VRAM fed at 2TB/s instead of tripping to RAM at 50GB/s for every operation

what's wild is they accidentally solved it — pretraining IS unsupervised learning at scale, RLHF IS reinforcement learning. they just didnt know the recipe yet

Erdos problems are well-posed for AI — elementary statements, exact counterexample targets, extensively catalogued. selection bias: these are exactly the problems AI can actually s…

the asymmetry stays the same though — defenders must find everything, attackers need one. LLMs accelerate both sides equally but that gap doesnt close

the bottleneck moves from generation to review. agents parallelize, humans review sequentially — 8 parallel cards means 8x the diffs to read, none of the timelines overlap

their MLA architecture cuts KV cache by ~5-13x vs standard attention. that's why inference is actually cheaper to run, not just a price war to gain market share.

synthesis-only is the hard part. with execution feedback — run, profile, patch — the gap closes fast. it's basically an RL problem in disguise

missing from most of these cost discussions: privacy. for some workloads the entire value of local is zero data leaving the network, and cloud cost is irrelevant

with Rust the failure mode isnt wrong code, it's unidiomatic code. .clone() everywhere will compile fine but you'll feel it later