Operationalall signals operational
Snapshot Aug 11, 06:01 UTCfetched just now
The numbers
Yeah reads YouTube’s running-review creators at scale, then distills the consensus into a graded catalog. Here’s the whole funnel — from raw reviews to buyable verdicts.
Updated Aug 11, 2026
1 / Scale
What we’ve watched
The whole corpus — every review ingested, across every creator we follow.
3.8K
Reviews analyzed
How this is computed
Videos analyzed
- Formula
- count of sources where source_type = 'youtube_video'
- Source
- sources table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
len([r for r in select(Source.source_metadata).where(Source.source_type == 'youtube_video')])722h
Hours watched
How this is computed
Video hours measured
- Formula
- sum(source_metadata.duration_s for youtube_video) / 3600, rounded 2dp
- Source
- sources table (source_metadata.duration_s JSON)
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
round(sum(duration_s) / 3600.0, 2)23.2K
Product mentions
How this is computed
Total mentions
- Formula
- count of all mentions rows
- Source
- mentions table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count()).select_from(Mention)110
Creators
How this is computed
Creator channels tracked
- Formula
- count of all creator_channels rows
- Source
- creator_channels table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count()).select_from(CreatorChannel)18.1M
Combined reach
subscribers (overlapping)
How this is computed
Total subscribers across channels
- Formula
- sum(creator_channels.subscriber_count), coalesced to 0
- Source
- creator_channels table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.coalesce(func.sum(CreatorChannel.subscriber_count), 0))4.5B
Lifetime views
across all creators
How this is computed
Total channel views
- Formula
- sum(creator_channels.view_count), coalesced to 0
- Source
- creator_channels table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.coalesce(func.sum(CreatorChannel.view_count), 0))2 / Graded
The published catalog
Only shoes with enough evidence earn a public verdict — scored, tiered, and written up.
301
Shoes graded
How this is computed
Published products with a verdict
- Formula
- count of products in the PUBLISHED scope (published=true AND deleted_at IS NULL) that have a latest-per-product verdict
- Source
- verdict_snapshots latest-per-product, filtered to published_ids
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
sum(1 for pid in latest if pid in published_ids)41
Brands
How this is computed
Distinct published brands
- Formula
- count(distinct brand) over products where published = true and brand is not null
- Source
- products table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count(func.distinct(Product.brand))).where(Product.published.is_(True)).where(Product.brand.is_not(None))82
Average score
of 100
How this is computed
Average verdict score (published)
- Formula
- mean(latest verdict.score) over published-scope scored products; N/A when none
- Source
- verdict_snapshots latest-per-product, filtered to published_ids
- Window
- all_time
- Missing data
- na_when_denominator_zero
- Freshness SLA
- 1d 2h
round(score_sum / n, 2) if n else None22.9
Mentions per shoe
How this is computed
Average mentions per scored product (published)
- Formula
- mean(latest verdict.mention_count) over published scored products; N/A when none
- Source
- verdict_snapshots latest-per-product, filtered to published_ids
- Window
- all_time
- Missing data
- na_when_denominator_zero
- Freshness SLA
- 1d 2h
round(mention_sum / n, 2) if n else None3.8
Creators per shoe
How this is computed
Average creators per scored product (published)
- Formula
- mean(latest verdict.creator_count) over published scored products; N/A when none
- Source
- verdict_snapshots latest-per-product, filtered to published_ids
- Window
- all_time
- Missing data
- na_when_denominator_zero
- Freshness SLA
- 1d 2h
round(creator_sum / n, 2) if n else None- S7
- A37
- B146
- C83
- D–F28
3 / Buyable
Price bridge
For graded shoes we track live prices across retailers, so a verdict comes with a place to buy.
271
Shoes priced
How this is computed
Products with a current price
- Formula
- count of product_current_prices rows
- Source
- product_current_prices table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count()).select_from(ProductCurrentPrice)758
Retailers tracked
How this is computed
Distinct priced retailers
- Formula
- count(distinct retailer) over price_snapshots
- Source
- price_snapshots table
- Window
- all_time
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count(func.distinct(PriceSnapshot.retailer)))Signal
What reviewers actually said
23.2K sentiment-tagged mentions, split by stance.
- Worth it45%
- Mixed15%
- Skip6%
- Neutral34%
Pulse
Still running
The pipeline keeps ingesting — here’s the recent activity.
1.7K
New reviews
published last 90 days
How this is computed
Videos published in the last 90 days
- Formula
- count of youtube_video sources with published_at >= now - 90d
- Source
- sources table (source_metadata.published_at JSON)
- Window
- last_90d
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
sum(1 for p in published_at if p >= now - timedelta(days=90))185
Videos ingested
last 7 days
How this is computed
Videos ingested in the last 7 days
- Formula
- count of youtube_video sources where created_at >= now - 7d
- Source
- sources table
- Window
- last_7d
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count()).where(Source.source_type == 'youtube_video').where(Source.created_at >= now - timedelta(days=7))477
Mentions extracted
last 7 days
How this is computed
Mentions in the last 7 days
- Formula
- count of mentions where created_at >= now - 7d
- Source
- mentions table
- Window
- last_7d
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count()).where(Mention.created_at >= now - timedelta(days=7))111
Verdicts written
last 7 days
How this is computed
Editorials generated in the last 7 days
- Formula
- count of editorial_reviews where generated_at >= now - 7d
- Source
- editorial_reviews table
- Window
- last_7d
- Missing data
- zero_when_empty
- Freshness SLA
- 1d 2h
select(func.count()).where(EditorialReview.generated_at >= now - timedelta(days=7))Over time
Growing
Tracking since Jun 20, 2026; updates daily.
+2.1K since tracking began
+10.8K since tracking began
+157 since tracking began