If you're building coding agents, this breaks down exactly how they work under the hood—from LLM harnesses to tool calling patterns to invisible prompts. Understanding these architectural patterns helps you make better decisions about which agent frameworks to use and how to customize them for your specific engineering workflows.
Establishes a clear framework for understanding 'agentic engineering'—the practice of developing software with AI coding agents as active collaborators rather than just tools. This conceptual foundation helps engineers think systematically about integrating agents into their development workflows and understanding the methodological shifts required.
This 30B parameter model with only 3B active parameters achieves frontier-level reasoning performance, demonstrating that efficient architectures can match much larger models. The cascade reinforcement learning and multi-domain distillation techniques offer practical insights for teams building high-performance models with resource constraints.
A visual catalog of LLM architectures that helps engineers understand the structural differences between major models like GPT, BERT, T5, and newer variants. This reference is invaluable for making informed decisions about which model architectures best fit your specific use case requirements.