quality

Translation is seq2seq generation served across hundreds of language pairs under tight latency and quality budgets. The signal is dodging the quadratic pair explosion, rescuing low-resource languages, and serving it cheaply. Here is the design.

Labeled data is the fuel for ML, and a labeling platform lives or dies on quality control. The signal is the workflow plus the quality math: consensus, gold honeypots, inter-annotator agreement, and active learning to spend the budget where it counts.

RLHF and evals are only as good as the preference data behind them, and that data is generated by humans whose quality varies wildly. The platform that produces trustworthy labels is itself a serious system. Here is its design.