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Ahead of AI

科技

1 个榜单刚刚更新默认榜单

Ahead of AI

刚刚更新

01
Using Local Coding Agents
Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex SubscriptionsSebastian Raschka, PhD
02
LLM Research Papers: The 2026 List (January to May)
A curated roundup of notable LLM research papers that came out this yearSebastian Raschka, PhD
03
Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
From Gemma 4 to DeepSeek V4, How New Open-Weight LLMs Are Reducing Long-Context CostsSebastian Raschka, PhD
04
My Workflow for Understanding LLM Architectures
A learning-oriented workflow for understanding new open-weight model releasesSebastian Raschka, PhD
05
Components of A Coding Agent
How coding agents use tools, memory, and repo context to make LLMs work better in practiceSebastian Raschka, PhD
06
A Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architecturesSebastian Raschka, PhD
07
A Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026
A Round Up And Comparison of 10 Open-Weight LLM Releases in Spring 2026Sebastian Raschka, PhD
08
Categories of Inference-Time Scaling for Improved LLM Reasoning
And an Overview of Recent Inference-Scaling PapersSebastian Raschka, PhD
09
The State Of LLMs 2025: Progress, Problems, and Predictions
A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.Sebastian Raschka, PhD
10
LLM Research Papers: The 2025 List (July to December)
In June, I shared a bonus article with my curated and bookmarked research paper lists to the paid subscribers who make this Substack possible.Sebastian Raschka, PhD
11
From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
Understanding How DeepSeek's Flagship Open-Weight Models EvolvedSebastian Raschka, PhD
12
Beyond Standard LLMs
Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive TransformersSebastian Raschka, PhD
13
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code ExamplesSebastian Raschka, PhD
14
Understanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMsSebastian Raschka, PhD
15
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3Sebastian Raschka, PhD
16
The Big LLM Architecture Comparison
From DeepSeek-V3 to Kimi K2: A Look At Modern LLM Architecture DesignSebastian Raschka, PhD
17
LLM Research Papers: The 2025 List (January to June)
A topic-organized collection of 200+ LLM research papers from 2025Sebastian Raschka, PhD
18
Understanding and Coding the KV Cache in LLMs from Scratch
KV caches are one of the most critical techniques for efficient inference in LLMs in production.Sebastian Raschka, PhD
19
Coding LLMs from the Ground Up: A Complete Course
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.Sebastian Raschka, PhD
20
The State of Reinforcement Learning for LLM Reasoning
Understanding GRPO and New Insights from Reasoning Model PapersSebastian Raschka, PhD

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