Trading activity:
Reported 24-hour volume equals 0.29% of market capitalization. The local markets snapshot lists Subnet Tokens among venues with observed trading activity.
YearBull indicators:
YearBull Rank #8,511. Bull Score 23/100.
YB Market Risk Low. This relative market-volatility label is not an investment-safety assessment.
Cycle Early. Observed price change: 24h -0.43% · 7d -14.36% · 30d -37.90%.
Values are descriptive and should be read together rather than as a price forecast.
Read the YearBull methodology.
Snapshot date: 2026-10-27.Data history: 90 days available in the latest 90-day window.
Methodology responsibility:
YearBull’s analytical methodology and presentation rules are developed and maintained by
Alan Zelvin, Founder & Lead Crypto Researcher.
This note identifies responsibility for the methodology; it does not attribute authorship of this data snapshot.
What is Bitrecs (SN122)?
Source description
“Bitrecs (subnet 122) is a protocol built on the Bittensor network that powers product recommendations specifically for ecommerce websites. Its primary function is to suggest existing product sets (SKUs) to online shoppers using simple rules like {1, 2, 3} >= {1, 2}, which appear as familiar sections such as "Similar to this" or "You may also like" on product pages. The network leverages a consortium of large language model (LLM) calls from miners to generate a 'best guess' of what a customer might be interested in. Miners receive queries with shopper context, such as viewed products, cart contents, or browsing history, and use prompting techniques (e.g., "Suggest complementary products to X") to create personalized suggestions. Validators then evaluate these responses based on criteria like relevance, diversity, latency, and potential for increasing conversions, selecting the top recommendations. Feedback loops from real user interactions refine the system over time, improving accuracy and boosting average order value (AOV) for merchants, particularly Shopify store owners. Bitrecs is entirely opt-in, with a simple plugin that merchants can easily adopt to enable subnet inference for predictions directly on their product pages. This decentralized approach ensures scalable, AI driven recommendations without relying on centralized data silos.”
This source-supplied description may contain old, promotional, or unverified claims and is not YearBull editorial analysis.
Official links and contract records appear in Key Facts. Project details can change, so verify current information with the project.
Bitrecs (SN122) FAQ
How does Bitrecs create product recommendations for online shoppers?
Bitrecs uses shopper context—such as viewed products, cart contents, or browsing history—to generate suggestions for existing product sets, or SKUs. Miners use language-model calls and prompting techniques, including requests to suggest complementary products, to produce a best guess of what a customer may want. These recommendations can appear in familiar ecommerce sections such as “Similar to this” or “You may also like.”
What role do miners and validators play in Bitrecs?
Miners receive ecommerce queries containing shopper context and generate personalized product recommendations. Validators assess those responses using criteria described by the project, including relevance, diversity, latency, and potential to increase conversions. The highest-performing recommendations are selected, creating a process in which multiple responses are evaluated rather than relying on a single recommendation source.
How does Bitrecs use feedback from customer interactions?
The project describes feedback loops based on real user interactions as a way to refine its recommendation system over time. These interactions are intended to help improve the accuracy of suggestions and support higher average order value for merchants. The stated focus is on learning from how shoppers respond to recommendations rather than treating the initial model output as final.
How can merchants enable Bitrecs recommendations on their stores?
Bitrecs presents itself as an opt-in service that merchants can activate through a plugin. The plugin is described as enabling subnet inference for predictions directly on product pages, allowing recommendations to be integrated into an ecommerce site without requiring merchants to build the full recommendation process themselves. The project specifically highlights Shopify store owners as a target audience.
What types of product relationships can Bitrecs recommend?
Bitrecs supports recommendation patterns based on existing product sets and simple set relationships, including examples such as one group of products containing or extending another. In practical storefront terms, this can support complementary-product suggestions, similar-item sections, and related-product displays. The project’s stated goal is to match these sets to shopper context and present potentially relevant SKUs.
Bitrecs metric comparison
This comparison is a stored snapshot generated 2026-10-24 06:30 UTC from 294 daily observations available from 2025-12-30 through 2026-10-24. It is separate from the latest analytical cards above. Percentiles compare the snapshot value with that day's analytical universe; a higher percentile means a larger observed value, not necessarily a better investment characteristic.
Metric
Snapshot
30d before
90d before
Change vs 30d
Universe percentile
Price
$0.8347
$1.21
$1.41
-31.0%
n/a
Market cap
$230.4K
$335.1K
$388.8K
-31.2%
P46.5
YearBull Rank
#5,540
#874
#4,798
Lower by 4,666
P34.3
Bull Score
30/100
45/100
19/100
-15.0 pts
P22.1
Turnover
4.94%
52.25%
0.74%
-47.3 pts
P76.4
YB Market Risk
Low
Low
Low
Unchanged
n/a
Cycle
Early
Early
Early
Unchanged
n/a
Median absolute daily movement 4.57%; distance from the highest local daily price -80.9%; circulating supply change 0.0%. These measurements are descriptive and do not predict future direction.
YearBull Rank context
Most recent YearBull Rank reading for bitrecs is #8511.
Rank timeline (last 365 days)
Rank movement (time windows).
Reading rule: a smaller rank number indicates stronger placement.
7d window (2026-10-20): #4033 → #8511 (down by 4478).
30d window (2026-09-27): #1474 → #8511 (down by 7037).
Downside posture: the same move can be stable in one market and fragile in another.
Market depth: a quiet tape can still re-rank the pack.
Venue read: improvement with higher churn can be a rotation phase.
Trend context: a single week rarely defines a phase on its own.
Practical note: treat the line as positioning context over time.
YearBull Rank is an internal ordering on YearBull that positions a coin relative to the rest of the tracked universe. It is meant for comparison and tracking, not certainty.
Editorial note:
This analysis was prepared by the YearBull research team under the direction of
Alan Zelvin,
Founder and Lead Crypto Researcher.
The assessment follows YearBull’s internal research methodology and editorial standards.
Methodology ·
Editorial Policy
Related Research
Continue with contextually connected profiles and category data:
Stored venue snapshot. Markets last checked: 2026-09-30. Updated on a rolling basis; no fixed refresh date is promised. Venue listings and volumes are stored snapshots, not live quotes.
