How EVERYWEAR Works
EVERYWEAR is a daily wearable tech intelligence feed. Not a blog, not a news site -- an aggregator that scores, ranks, and tracks every wearable article from 34 sources using the EWEAR scoring algorithm. Updated twice daily at 6:30am and 6:30pm UTC.
Every article that enters the feed is scored on five factors, filtered through a wearable context gate, and categorised across eight wearable domains. The result is a ranked, searchable feed of the most relevant wearable tech coverage on the internet -- updated automatically, without editorial bias.
The EWEAR Score (0-100)
Every article receives an EWEAR score between 0 and 100. The score is calculated from five weighted factors, each measuring a different dimension of article quality and relevance. Higher scores surface to the top of the feed.
How many wearable category keywords the article matches. An article covering smartwatches AND fitness tracking AND health sensors scores higher than one mentioning only smartwatches. More categories = higher relevance. This is the heaviest-weighted factor because cross-category articles tend to be the most substantive.
How recent the article is. Same-day articles score the full 25 points. Articles from yesterday score less. Anything older than a few days scores progressively lower. This keeps the feed current and ensures breaking news surfaces above evergreen content.
Wearable-specialist sources score highest. An article from Wareable or DC Rainmaker gets maximum authority points because wearables are their entire focus. General tech sites like The Verge or CNET score in the middle. Broader publications score lower. The weighting rewards deep expertise over general coverage.
The number of tracked wearable brands mentioned in the article. EVERYWEAR monitors 21 brands (listed below). An article comparing Apple Watch, Garmin, and WHOOP scores higher than one mentioning a single brand. Multi-brand articles tend to be comparisons, roundups, or market analysis -- all high-value content.
Title length as a proxy for article substance. Longer, more descriptive titles typically indicate in-depth coverage rather than brief news hits. A title like "Garmin Fenix 8 Review: GPS Accuracy, Battery Life, and Training Features Tested" signals more depth than "New Garmin Watch Announced." It is a lightweight heuristic, but it works.
Score Breakdown Example
8 Categories Tracked
Every article is classified into one or more of eight wearable technology categories. Articles that span multiple categories score higher on the Relevance factor.
21 Brands Tracked
EVERYWEAR monitors mentions of 21 wearable technology brands across every article. Brand mentions feed into the Brand Signal score and power the brand-specific filtering on the dashboard.
The Wearable Context Gate
Not every article that mentions a tracked brand is actually about wearables. "Apple" appears in thousands of articles about iPhones, iPads, and MacBooks. "Samsung" shows up in TV and phone reviews. "Google" is everywhere.
The Wearable Context Gate solves this. When an article matches only on a brand name (and no wearable category keywords), it must also contain at least one wearable context word to pass through the gate. These context words include:
- Device types: watch, ring, tracker, band, earbuds, headphones, glasses, headset
- Health terms: fitness, health, sensor, heart rate, sleep, SpO2, HRV, steps
- Wearable concepts: wearable, on-body, biometric, strap, charging cradle
An article titled "Apple Reports Record Q4 Revenue" with no wearable context words gets rejected. An article titled "Apple Watch Ultra 3 Gets New Health Sensors" passes immediately. This gate keeps the feed focused and prevents noise from flooding the rankings.
34 Data Sources
EVERYWEAR monitors 34 RSS feeds across three tiers: wearable specialists, major tech publications, and health tech sources. Specialist sources receive the highest Source Authority scores.
Weekly Features
Beyond the daily feed, EVERYWEAR produces three weekly outputs that synthesise the most important wearable tech coverage from the past seven days.
Audio Briefing
A narrated weekly summary generated with ElevenLabs. Top stories, brand trends, and what to watch -- in under five minutes.
Email Digest
Delivered via Buttondown. The week's highest-scoring articles, category highlights, and brand movement -- straight to your inbox.
Weekly Summary
Published on the site every week. Top stories, brand mention trends, category breakdowns, and the single highest-scoring article.
Built By
One Person, AI Tools, Real Product
EVERYWEAR is built and maintained by Mike Litman -- a strategy director turned builder, exploring what one person can create with AI tools. The entire pipeline -- RSS ingestion, scoring algorithm, category classification, site generation, audio briefings, email digests -- is built with Claude Code and runs on automated schedules via GitHub Actions.
EVERYWEAR is part of a broader series of experiments in AI-assisted building. No team, no funding, no traditional development background. Just taste, tools, and the question: what happens when one person can build something that used to require a team?