The ML Blog
Deep dives and interview guides to help you ace your next role.
Laguna S 2.1: How Poolside Built the Best 118B Agentic Coding Model
Poolside's Laguna S 2.1 is a 118B MoE model that scores 70.2% on Terminal-Bench 2.1 - beating models ten times its size. Here is what changed under the hood.
The Architecture Behind Kimi K3: Open 3T-Class Intelligence
Kimi K3 scales to 2.8T parameters using Kimi Delta Attention and Stable LatentMoE. Discover how this open model achieves a 1M token context window and 2.5x scaling efficiency.
Understanding Kronos: How a Foundation Model Reads the Language of Financial Markets
Generic Time Series Foundation Models fail on financial data. Kronos fixes this by treating K-line data as a discrete language. A stage-by-stage breakdown of its architecture, from BSQ tokenization to detokenization.
The Brutal Truth About ML Trading: Why Your XGBoost Model Keeps Failing (And What Actually Works)
We built the "perfect" XGBoost trading model — walk-forward validation, 26 features, intraday data. Result: 50.2% accuracy, Sharpe 0.18, 0 trades. Every failure documented, and what the DRW Kaggle 1st place winner did instead.
The Complete ML Interview Guide 2026: Topics, Tips & Mock Tests
A complete machine learning interview guide for 2026 requires mastering system design, fundamental algorithms, and modern deep learning frameworks.
Trainer vs SFTTrainer: The Complete LLM Training Stack for Financial Services
How Hedge Funds and Quant Teams Navigate 15+ Training Libraries to Build Custom AI Models