Language Models for Text Classification: From Bag-of-Words to Jev
A Visual Guide to RNNs, CNNs, Transformers, and Calibration, with Hands-On Experiments on Accuracy and Efficiency
Ahead of AI (Raschka)33 min read
Ahead of AI
21 stories
GPT-6 Astra, Looped Transformers, and Hidden Reasoning
A Look at Recurrent Depth, Hidden Chains of Thought, and Recent Research on Looping Transformer Blocks
Sebastian Raschka26 min read
How Claude Watermarks AI-Generated Text
A 48-minute video walkthrough of token sampling, watermark detection, and removal
Sebastian Raschka34 min read
Building an AI Text Detector From Scratch
An End-to-End Project With Dataset Construction, Model Training, Local Deployment, and RLVR
Sebastian Raschka3 min read
Controlling Reasoning Effort in LLMs
How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes
Sebastian Raschka25 min read
200,000 Subscribers
Short note celebrating Ahead of AI reaching 200,000 subscribers.
Sebastian Raschka1 min read
Using Local Coding Agents
Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions
Sebastian Raschka29 min read
LLM Research Papers: The 2026 List (January to May)
A curated roundup of notable LLM research papers that came out this year
Ahead of AI (Raschka)6 min read
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 Costs
Ahead of AI (Raschka)22 min read
My Workflow for Understanding LLM Architectures
A learning-oriented workflow for understanding new open-weight model releases
Ahead of AI (Raschka)1 min read
Components of A Coding Agent
How coding agents use tools, memory, and repo context to make LLMs work better in practice
Ahead of AI (Raschka)14 min read
A Visual Guide to Attention Variants in Modern LLMs
From MHA and GQA to MLA, sparse attention, and hybrid architectures
Ahead of AI (Raschka)18 min read
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 2026
Ahead of AI (Raschka)21 min read
Categories of Inference-Time Scaling for Improved LLM Reasoning
Inference scaling has become one of the most effective ways to improve answer quality and accuracy in deployed LLMs.
Ahead of AI (Raschka)3 min read
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.
Ahead of AI (Raschka)28 min read
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.
Ahead of AI (Raschka)2 min read
From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
Understanding How DeepSeek's Flagship Open-Weight Models Evolved
Ahead of AI (Raschka)24 min read
Beyond Standard LLMs
Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers
Ahead of AI (Raschka)30 min read
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