Learn how to interact with this file using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get file metadata including name, visibility, description, file size, and other asset properties.
Get a URL to download or embed the file. For private assets, the URL is temporary and will expire after 1 hour.
Update file metadata (name, description, visibility, etc.) and optionally replace the file data with a new file. Requires write or admin permission.
Permanently delete a file from the platform. Requires admin permission. This action cannot be undone.
# Delete a file (requires admin permission)
ouro.files.delete(id=file_id)# Get signed URL to download the file
file_data = file.read_data()
print(file_data.url)
# Download the file using requests
import requests
response = requests.get(file_data.url)
with open('downloaded_file', 'wb') as output_file:
output_file.write(response.content)# Update file metadata
updated = ouro.files.update(
id=file_id,
name="Updated file name",
description="Updated description",
visibility="private"
)
# Update file data with a new file
updated = ouro.files.update(
id=file_id,
file_path="./new_file.txt"
)import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
file_id = "e4c07c19-6a29-41dd-bfa1-440a0bcdbc74"
# Retrieve file metadata
file = ouro.files.retrieve(file_id)
print(file.name, file.visibility)
print(file.metadata)## Overview
This is a highly curated, high-density Supervised Fine-Tuning (SFT) dataset focused explicitly on product launches, competitor positioning, and market sentiment within the **Marketing Automation, CRM, and SaaS Operations** sectors on Product Hunt.
Constructed in a clean `{"instruction": "...", "output": "..."}` JSON array structure, this data is formatted out-of-the-box to train Large Language Models (LLMs) in competitive analysis, marketing strategy generation, and SaaS market research.
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## Dataset Features
* **Niche Core Focus:** Dedicated to marketing automation tools, open-source CRMs, email infrastructure, and AI-driven growth platforms (featuring high-profile launches like Graphy, Customer.io, Loops, Twenty, and PhantomBuster).
* **Granular Analytics Output:** Every training response maps complete analytical metrics including day/week launch rankings, historical upvote volume, total follower traction, and launch timestamps.
* **Aggregated User Sentiment:** The dataset captures synthesized qualitative signals from real-world comments and reviews, cataloging core product strengths, user themes, and UX friction points.
* **Production Ready:** 100% compliant JSON formatting with zero placeholder text, missing variables, or messy website script noise.
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## Technical Specifications
* **Format:** Valid JSON Array (`.json`)
* **Data Schema:**
* `instruction`: Targeted prompts directing the model to analyze a product’s positioning and launch vectors.
* `output`: Comprehensive, data-dense responses written in an expert, objective tone.
* **Primary Use Cases:** Fine-tuning LLMs for tech market intelligence, building autonomous competitive-intelligence agents, RAG pipeline injection, and growth marketing model calibration.