Ahead of AI · 实时热榜

How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes

Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions

A curated roundup of notable LLM research papers that came out this year

From Gemma 4 to DeepSeek V4, How New Open-Weight LLMs Are Reducing Long-Context Costs

A learning-oriented workflow for understanding new open-weight model releases

How coding agents use tools, memory, and repo context to make LLMs work better in practice

From MHA and GQA to MLA, sparse attention, and hybrid architectures

A Round Up And Comparison of 10 Open-Weight LLM Releases in Spring 2026

And an Overview of Recent Inference-Scaling Papers

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

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

Understanding How DeepSeek's Flagship Open-Weight Models Evolved

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

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

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

And How They Stack Up Against Qwen3

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

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

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

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.