A 35%-accurate model that still ranked well, and adding more features made it wo

<!-- SC_OFF --><div class="md"><p>I plotted the Expected Calibration Error (ECE) for an LLM (Gemini 2.5 Pro) forecasting 30 different real-world time-series targets over 38 days

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

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I am kind of desperate for help right now on my current project. I'll try and be as clear as possible.</p> <p>I'm working on

<!-- 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>After burning way too much money on GPT-4 API calls, I finally went all-in on local models with Ollama. The progress in 2026 is insane

<h2> The short version </h2> <p>I could not build a stock picker I can prove beats the S&amp;P 500, and the most useful thing I found was a list of bugs that made weak results

<!-- SC_OFF --><div class="md"><p>Been a UFC fan for years and recently had some spare time to build a web app that uses Machine Learning to predict which fighter will win as 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