Ahead of AI

“Ahead of AI” delves into the intricacies of architecting enterprise-level AI systems, focusing on the challenges and solutions in implementing Generative AI technologies. The document explores the importance of understanding and utilizing live data sources, such as APIs, to enhance the capabilities of AI models. It emphasizes the need for AI systems to move beyond static data libraries and adapt to the dynamic nature of Big Data. Additionally, the document highlights the significance of precision in AI applications, showcasing the critical role of context and rich data indexes in optimizing AI performance.

Visit Ahead of AI

Categories of Inference-Time Scaling for Improved LLM Reasoning

Ahead of AI

And an Overview of Recent Inference-Scaling Papers

The State Of LLMs 2025: Progress, Progress, and Predictions

Ahead of AI

A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.

LLM Research Papers: The 2025 List (July to December)

Ahead of AI

In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.

A Technical Tour of the DeepSeek Models from V3 to V3.2

Ahead of AI

Understanding How DeepSeek's Flagship Open-Weight Models Evolved

Beyond Standard LLMs

Ahead of AI

Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers

Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)

Ahead of AI

Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples

Understanding and Implementing Qwen3 From Scratch

Ahead of AI

A Detailed Look at One of the Leading Open-Source LLMs

From GPT-2 to gpt-oss: Analyzing the Architectural Advances

Ahead of AI

And How They Stack Up Against Qwen3

The Big LLM Architecture Comparison

Ahead of AI

From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture Design

LLM Research Papers: The 2025 List (January to June)

Ahead of AI

A topic-organized collection of 200+ LLM research papers from 2025

Understanding and Coding the KV Cache in LLMs from Scratch

Ahead of AI

KV caches are one of the most critical techniques for efficient inference in LLMs in production.

Coding LLMs from the Ground Up: A Complete Course

Ahead of AI

Why build LLMs from scratch? It's probably the best and most efficient way to learn how LLMs really work. Plus, many readers have told me they had a lot of fun doing it.