Study: LLM Wiki with governance approach hits 97% accuracy, at ⅓ cost — with Emo

<!-- SC_OFF --><div class="md"><p>I ran 90 research queries through 8 AI models simultaneously. 86% of findings were unique to one provider... not rephrasing, literally different

<!-- SC_OFF --><div class="md"><p>Cursor dropped their new product yesterday and its not a code editor anymore. its an agent orchestration console that happens to have an editor

<!-- SC_OFF --><div class="md"><p>I've been working on MDA (Modular Dynamic Architecture), an online associative memory system for LLMs. Here's what I learned building it.</p>

<!-- SC_OFF --><div class="md"><p>Most of my LLM cost was on the wrong tier of work.</p> <p>Classification, extraction, JSON formatting, summarization I'm going to review anyway.

<!-- SC_OFF --><div class="md"><p>I’m starting to think most “agent bugs” aren’t bugs. They’re mismatches between what we think we asked and what the agent thinks we asked.</p>

<!-- SC_OFF --><div class="md"><p>Study link: <a href="https://ojs.aaai.org/index.php/AAAI/article/view/41259">https://ojs.aaai.org/index.php/AAAI/article/view/41259</a></p>

<p>The quadratic attention problem has quietly shaped everything you've built with LLMs. RAG pipelines, agentic decomposition, hybrid architectures — these aren't the natural

<!-- SC_OFF --><div class="md"><p>Largest empirical study of AI penetration in academic publishing ever conducted—51%-by-2025 is the most authoritative quantitative marker yet of