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Coding agents routinely fail to build agents that truly meet user requirements, with the gap between demonstration and delivery being the norm.
An 80B model that runs like a 3B? Qwen3-Coder-Next shows you can get competitive coding agent performance with a fraction of the active parameters, thanks to smart training.
LLMs can now capture an author's unique voice in translations, thanks to a multi-agent system guided by a "Stylistic Feature Spectrum" derived from wavelet transforms.
LLMs can solve competitive coding problems much more reliably by actively searching for the *right* test cases, rather than relying on random or pre-defined inputs.