This week's featured content highlights how open-source tooling and iterative model training are reshaping the way developers build and refine AI systems. Two deep-dive resources offer complementary perspectives on the infrastructure behind modern machine learning.
First, Hugging Face presents a video walkthrough of DeepSeek Harness (DSH), DeepSeek's open-source agent harness built on the Cordis plugin system. The core philosophy is striking in its simplicity: everything is a plugin, including the agent loop, the available tools, and even the system prompt. The video begins with a quick tour of the new framework before exploring how this modular architecture enables greater flexibility and customization for agent-based workflows.
Next, the PyTorch Foundation shares a keynote from PyTorch Conference North America featuring Cody Mazza-Anthony of Shopify. The talk examines Shopify's continual learning loop built with PyTorch and vLLM, illustrating how the company turns product expertise into robust evaluations. Low-scoring conversations are mined, critiqued, and repaired to train better models, with the Buyer Profile case study demonstrating how small models can deliver meaningful results through this iterative flywheel approach.
Together, these posts underscore a shared theme: whether through plugin-driven agent design or continuous evaluation and retraining, the
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