AI Benchmarking and Testing Methods

<!-- SC_OFF --><div class="md"><p>I've been putting together a test system for agentic harnesses against local models. Actually running the harnesses/getting baseline metrics is

<!-- SC_OFF --><div class="md"><p>I saw a post on a new LinkedIn translator that turns text in &quot;English&quot; like: I took a massive dump at work just now, into something

<!-- SC_OFF --><div class="md"><p>Figured this out by running 4 models: Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro, and Grok 4.20, on a benchmark of 1,417 binary forecasting

<!-- 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've always been under the impression that open models were closely trailing behind closed source models on nearly every benchmark from LM Arena,

<!-- SC_OFF --><div class="md"><p>I’ve been exploring how to build a fully on-prem document AI pipeline for handling confidential data — no cloud APIs, no external processing.</p>

<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>Link to the paper:</p> <p><a