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Research · Intermediate

# Curating video datasets for generative model training

Inspect clip samples, score visual quality, and maintain filtered dataset manifests

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askProvide dataset directory
- ThenScore clips and draft prune list
- ResultConfirm pruning and log manifest

Filter raw video batches to prepare high-quality training data for visual generative models. The bot runs evaluation passes across sample batches, flags low-quality artifacts or poor motion, and records dataset curation stats into your tracking database.

## Prompt to start with

BotBentoRecommended

```
When given a dataset directory or storage location, execute analysis scripts via terminal to score visual aesthetics and motion quality. Summarize the score distribution and list clips flagged for removal. Always ask for user approval before deleting any files from disk or publishing updates to the Notion training manifest.
```

Copy

## Tools it uses

TerminalNotionDatabase

## Works with

BotBento

## Tags

- machine-learning
- dataset
- video
- data-curation

## Source

[Show HN: Text-to-video model from scratch (2 brothers, 2 years, 2B params)](https://news.ycombinator.com/item?id=46721488)
Hacker News · checked 2026-10-16

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-16 · Updated 2026-10-16 · [Suggest a correction](/contact/)

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