Question about the new models and the data they're built on

<!-- SC_OFF --><div class="md"><p>I read this sub every day and I keep seeing benchmarks and discussions focused almost entirely on tokens/s generation speed. Prompt processing

<!-- SC_OFF --><div class="md"><p>Long time Claude Opus user, but after the recent session limit changes by Anthropic, I am seriously considering trying Chinese models for coding.

<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>I am trying to understand the current consensus and legal landscape regarding how public data and developer contributions are being utilized by

<!-- SC_OFF --><div class="md"><p>I just had a question. I use the free mode of GPT, I don’t use it terribly often so I don’t see a point in paying for it, can someone explain the

<!-- SC_OFF --><div class="md"><p>is it possible that a moe model can decide how many billion parameters to activate per token according to the task. eg if qwen 3.6 35b a3b - if a

<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1srx4i1/new_llm_position_bias_benchmark_does_an_llm_keep/"> <img alt="New LLM Position Bias Benchmark: does

<!-- 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