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Ahead of AI

GPT-6 Astra, Looped Transformers, and Hidden Reasoning How Claude Watermarks AI-Generated Text Building an AI Text Detector From Scratch Controlling Reasoning Effort in LLMs Using Local Coding Agents LLM Research Papers: The 2026 List (January to May) Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention My Workflow for Understanding LLM Architectures A Visual Guide to Attention Variants in Modern LLMs A Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026 Categories of Inference-Time Scaling for Improved LLM Reasoning The State Of LLMs 2025: Progress, Progress, and Predictions LLM Research Papers: The 2025 List (July to December) A Technical Tour of the DeepSeek Models from V3 to V3.2 Beyond Standard LLMs Understanding the 4 Main Approaches to LLM Evaluation (From Scratch) Understanding and Implementing Qwen3 From Scratch From GPT-2 to gpt-oss: Analyzing the Architectural Advances The Big LLM Architecture Comparison LLM Research Papers: The 2025 List (January to June) Understanding and Coding the KV Cache in LLMs from Scratch Coding LLMs from the Ground Up: A Complete Course The State of Reinforcement Learning for LLM Reasoning
Components of A Coding Agent
Sebastian Raschka, PhD · 2026-04-04 · via Ahead of AI
How coding agents use tools, memory, and repo context to make LLMs work better in practice