MARKET RESEARCH
Log every new HuggingFace release from tracked authors into a Notion tracker
Checks tracked HuggingFace organizations and authors for newly published models on a schedule and appends a structured row for each to a Notion database with metadata and an LLM…
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled poll every few hours
- ActionList recent models from tracked authors and orgsHugging Face
- LogicDrop models already present in the Notion tracker
- ActionExtract metadata and assign a categoryOpenAI
- OutputAppend a structured row to the Notion databaseNotion
What it does
Maintains a living catalog of model releases from the organizations and authors your team follows (for example Meta, Mistral, BAAI, or specific researchers). Each new release becomes a structured Notion row with the model name, task, license, parameter size, and an LLM-assigned category, so your team has a searchable history rather than scattered links.
When to use it
Use it when you want an auditable, filterable record of what specific labs are shipping over time, not just a momentary alert. Good for competitive tracking and for building a knowledge base of candidate models to evaluate.
How it works
- 1A scheduled run fires every few hours.
- 2HuggingFace is queried for each tracked author's recently published models.
- 3A filter drops models whose IDs already exist in the Notion tracker so nothing is logged twice.
- 4OpenAI reads each new model card and extracts task, license, and size, then assigns a category tag.
- 5A new row per model is created in the Notion database with all fields and the Hub URL.
Set it up
What you configure once, before turning it on.
- 1Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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