7 posts tagged with "deepseek"

Your AI pipeline generates a parts list for a LEGO castle MOC. It says you need 12x "Brick 2 x 4" in Light Bluish Gray, 8x "Arch 1 x 4" in Dark Tan, and 4x "Slope 45 2 x 1" in Sand Green. The text looks plausible. But does the part image next to "Arch 1 x 4" actually show an arch? Does the quantity make sense for a castle build? Would this list genuinely help someone source bricks for the build?

These are multi-modal evaluation questions — they span text accuracy, image-text coherence, and practical usefulness. Standard unit tests cannot answer them. This article walks through a production evaluation pipeline built with DeepEval that evaluates AI-generated LEGO parts lists across five axes, using image metrics that most teams haven't touched yet.

The system is real. It runs in Bricks, a LEGO MOC discovery platform built with Next.js 19, LangGraph, and Neon PostgreSQL. The evaluation judge is DeepSeek — not GPT-4o — because you don't need a frontier model to grade your outputs.

Multi-agent AI engineering has become a core discipline in production software development. The interesting question is no longer whether to build multi-agent systems. It is how — and specifically, which architectural pattern to reach for given the nature of the work. The clearest demonstration is that multiple fundamentally different paradigms live inside the same codebase.

This article documents the complete OpenRouter integration implemented in Nomadically.work, using DeepSeek models exclusively through a unified API.

This article documents an evaluation harness for a Remote EU job classifier—but the real focus is AI observability: how to design traces, spans, metadata, scoring, and run-level grouping so you can debug, compare, and govern LLM behavior over time.

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