Sweeps
Scalable, customizable hyperparameter tuning
Sweeps launched on January 30, 2020 with 92 votes, #363 of 3,486 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 6 years on.
What it does
Hyperparameter tuning is a way to find the best machine learning model. We make it ridiculously easy to run hyperparameter sweeps using simple algorithms like grid search, to more modern approaches like bayesian optimization and early stopping.
Discover scalable hyperparameter tuning and AI workflow integration with W&B Sweeps, boosting efficiency and customization in AI projects.
Does a similar job
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A radically more scalable alternative to Bayesian Optimization2018 · github.com · ▲96Experimental Global Optimization Algorithm. Contribute to chrisstroemel/Simple development by creating an account on GitHub.

Sweep, Open-weights 1.5B model for next-edit autocompleteJan 2026 · huggingface.co · ▲534Hey HN, we trained and open-sourced a 1.5B model that predicts your next edits, similar to Cursor. You can download the weights here (https://huggingface.co/sweepai/sweep-next-edit-1.5b) or try it in our JetBrains plugin (https://plugins.jetbrains.com/plugin/26860-sweep-ai-autocomp...). Next-edit autocomplete differs from standard autocomplete by using your recent edits as context when predicting completions. The model is small enough to run locally while outperforming models 4x its size on both speed and accuracy. We tested against Mercury…



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