Technical Guides & Articles
Deep dives, algorithm breakdowns, and industry interview preparation guides written by machine learning practitioners.
Top 50 Tokenization & Vocabulary Interview Questions (2026 Master Guide)
The definitive 2026 technical interview guide covering the Top 50 tokenization and vocabulary questions: BPE vs WordPiece vs Unigram, vocab sizing (32K to 256K), special tokens, multilingual fertility, domain adaptation, and concrete input-to-output examples.
LLM Architectural Specifications & 120-Step Industry Application Pipeline (2026 Master Guide)
The definitive 2026 master guide to LLM architectural specifications, 15-category model card taxonomies, and the complete 120-step enterprise AI engineering pipeline from raw data to autonomous production.
Vector Databases for RAG: 2026 Master Guide & Architecture Landscape
The definitive 2026 master guide to LLM RAG vector databases: full 34-provider comparison, indexing algorithms (HNSW, IVF, ScaNN, DiskANN), financial domain use cases, and RAG DBs vs Frameworks.
Financial Data Providers for AI Agents, Quant & Algo Trading (2026 Master Guide)
The definitive 2026 master guide and directory of 60+ financial data providers, enterprise terminals, alternative data sources, and broker APIs for AI agents and quantitative trading.
Agentic AI Frameworks (2026): Open-Source & Enterprise Landscape Guide
Comprehensive Q3 2026 analysis of open-source agent frameworks (LangGraph, MAF, CrewAI, PydanticAI V2) and enterprise platforms with comparative breakdown.
Library Combinations for Building LLM Foundation Model Architectures from Scratch (2026)
A complete evaluation of 12 framework stacks for building LLM foundation model architectures from scratch in 2026. Explore frontier lab choices, PyTorch vs HuggingFace roles, and new layer trends like MLA, Gated DeltaNet, and SSMs.
smolagents Framework: The Complete Python Guide to Building AI Agents (2026)
Master the smolagents framework: CodeAgent, tools, multi-agent systems, and agentic RAG with real Python examples. Prep for the 80+ MLQuiz questions.
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