Use expensive models to train cheap models." How far can this paradigm actually

<!-- SC_OFF --><div class="md"><p>I've been reading alot about AI compute stuff lately and something keeps bothering me.</p> <p>The total power used for cryptocoin mining around

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t47qbw/deepseek_v4_pro_matches_gpt52_on_foodtruck_bench/"> <img alt="DeepSeek V4 Pro matches GPT-5.2 on

<!-- SC_OFF --><div class="md"><p>Just went through a Stanford paper that tracked 51 actual AI deployments - not surveys or sentiment polls, real production systems across 41

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not

<!-- SC_OFF --><div class="md"><p>One thing we’ve been noticing lately is that a surprisingly large percentage of day-to-day AI workflows no longer seem to require frontier-scale

<!-- SC_OFF --><div class="md"><p>I’m trying to understand why models from major labs (GPT, Claude, etc.) dominate real-world usage? You might say it's due to the expensive

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p><strong>Noam Brown</strong> — OpenAI Research Scientist and co-creator of o1, Libratus, and Cicero — sat down with Dwarkesh Patel for a 1h 20m