Use a cheaper model to develop a prototype, then use an expensive one to finish

<!-- SC_OFF --><div class="md"><p>Everyone keeps saying the future is using high-capacity frontier models to systematically train and distill more efficient, low-cost models. And

<!-- 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>Statcounter tracks which AI chatbots send traffic to websites across 1m+ sites. April 2026 numbers:</p> <p>• ChatGPT: 76.85% (was 84.21% a year

<!-- SC_OFF --><div class="md"><p>There are so many options now — GPT, Claude, Gemini, etc.</p> <p>I find myself switching depending on what I’m doing (coding, writing, research),

<!-- SC_OFF --><div class="md"><p>Link to the paper:</p> <p><a

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s1ckkm/why_llms_sound_right_but_fail_to_actually_do/"> <img alt="Why LLMs sound right but fail to actually

<!-- SC_OFF --><div class="md"><p>AI skills and agents feel like the units you can build real things with. Skills are portable, composable, they travel with the model. One skill