We help create an authoritative platform for vinyl collectors and underground music enthusiasts—one that doesn’t just report on releases but actively shapes a culture of mindful listening by merging physical media with digital tools (APIs, databases). Our mission is to provide expert reviews and analytics on rare and new vinyl pressings, build a community where users can exchange opinions and find like-minded peers, integrate release data via APIs (Discogs, MusicBrainz) to build a seamless catalog, and establish editorial standards that reject clickbait and superficial judgments.

Segment: Music Industry / Hobbies and Collecting. Niche Topic: «Vinyl as Art Object and Investment: A Guide to Underground Electronic, Jazz, and Rock.»

Justification for Selection: The vinyl market has been growing for 15 consecutive years (Statista data). The audience is fatigued by algorithmic playlists and there is a demand for expertise and «curated» selection. The niche topic allows us to avoid competition with industry giants (Pitchfork, Rolling Stone) and focus on deep analysis.

Goal: To ensure the editorial content is informed by accurate information regarding releases, pricing, and trends, while also creating a seamless user experience (catalog, search).

1. External APIs (Machine Data):

Discogs API — A database for releases, presses, secondary market pricing, and tracklists. Used for: automatically enriching articles (rarity, average price, release year).

MusicBrainz API — An open music encyclopedia. Used for: establishing relationships between artists, labels, and genres.

Spotify / Apple Music API — Streaming links. Used for: adding a «Listen» button to reviews (for users who do not own vinyl).

YouTube Data API–Video statistics. Used for: analyzing trends and engagement on competitor reviews.

2. Internal Sources (Human Data):

Editorial reviews and interviews — Expert-written pieces from DJs and collectors.

UGC (User Generated Content) — Stories from subscribers (finds, collection photos), submitted via a bot.

Social Media Analytics (LiveDune) — Engagement and reaction data on posts to determine which topics resonate with the audience.

3. Partner Data:

Vinyl stores (e.g., «Sound Point») — Exclusive announcements of stock arrivals and press releases.

Labels — Promotional materials and early access to new releases.

Verification: The editor checks the data received from the API (e.g., if the API shows a price of $10,000, but this is a parsing error—the editor corrects it).

Contextualization: Adding historical background, analyzing cultural influence, and assessing rarity (why this press release is more valuable than another).

Content Creation: Writing copy, scripts for videos, and podcasts based on the collected data.

UGC Moderation: Reviewing subscriber stories for accuracy and compliance with editorial standards.

The Intersection: Data from the API is automatically pulled into the article draft. The editor opens the draft, sees the prepared release card (machine work), and writes their expert commentary (human work).

Ingestion: Python scripts query the Discogs API and MusicBrainz API every six hours to retrieve new releases and update pricing.Parsing and Normalization: Standardizing data into a uniform format (for example, converting «LP, ” „12“, ” and „7“ all to the „Vinyl“ type).Automatic Generation: Creating release records (title, artist, year, label, average price) in the database.Filtering: Excluding irrelevant items (for example, CDs or cassettes if the focus is solely on vinyl).

Goal: To ensure transparency, ethics, and security in the use of automation so as not to lose audience trust or violate copyright.

1. Citation and Attribution Rules:

When using data from Discogs API or MusicBrainz, the source must always be indicated (via logo or link). Copying user reviews from Discogs without the authors' written permission is prohibited. Only factual data (year, label, matrix) may be used.

2. Autogeneration Limitations (Anti-AI Slop):

Automatically generated text (e.g., by a neural network) cannot be published without editorial review. Using neural networks to write «personal» stories on behalf of subscribers is forbidden. Automated Telegram posts (e.g., «New Release: [Title]») must include a link to the full expert review, and cannot merely be empty placeholders.

3. Frequency and Rate Limits:

API limits must be respected (e.g., Discogs — 60 requests per minute). This is managed using caching (data is saved to a database and refreshed once daily). Automated social media posts should not exceed one or two per day to prevent cluttering the subscriber feed.

4. Data Quality Control:

Automated checks must be implemented to flag broken links and inconsistencies (for example, if the release year from the API does not match the year in the article text—the system should highlight this for the editor). For pricing data, the average value over the last 30 days should be used instead of the current lot price to avoid speculative spikes.

5. Privacy and Security:

Subscriber data (UGC) is not shared with third-party APIs without their explicit consent. All API keys are stored securely (not in open code).