Local or frontier? Start with the task.
A task-based framework for comparing model quality, device constraints, data boundaries, and review effort.
NEURO BROWSER LAB / TAG
Model evaluation
A useful answer must survive review.
Compare model configurations against clearly defined tasks, representative examples, and meaningful failure cases. This reading path connects model choice with the label handbook and reviewer process that make a comparison interpretable. Avoid substituting a single attractive output for a repeatable evaluation.
Begin with “Local vs. frontier AI models: compare the whole workflow” for a focused method you can apply to a small reading or planning exercise. Keep the original reference open beside the article, note which recommendations fit your task, and preserve any unresolved questions for review. Follow the related topic guides to change perspective without losing the source context.
2 articles in this reading path
Better labels begin with better questions.
Build a label handbook, preserve supporting evidence, and route ambiguous outputs to a reviewable decision process.
Continue through the topic guides
Keep the larger question in view. These complete guides connect the reading path to definitions, limitations, and practical review questions.
Follow a question, not just a category.
Use these connected reading paths to explore source literacy, evaluation, data quality, and the decisions around a research workflow.