Proves we don't need frontier models for complex multi-agent systems. The resource constraints of 3B models forced better coordination patterns and more efficient agent communication. These techniques scale up to larger

If we're shipping agents to production, we need governance frameworks before regulators force them on us. The 60% adoption vs 40% compliance barrier shows most teams are flying blind. Docker's guide gives us practical starting

If we're building RL training environments, this guide prevents the mistakes that actively harm model performance. Broken environments don't just add noise, they teach models wrong behaviours. The trajectory analysis approach

Shows how domain experts can improve LLM performance on specialised tasks. The NMR spectrum work and molecular representation techniques give us patterns for training models on scientific data. Useful if we're building

Agent-Reach tackles the fundamental limitation that's been frustrating since ChatGPT plugins disappeared. The zero-API-cost approach using native scrapers is more sustainable than paying platform fees for every query.

Docker's finding that 60% have agents in production but 40% cite compliance barriers reveals a dangerous gap. We're shipping first and asking permission later, which rarely ends well when regulators catch up.