An enterprise AI platform that turns real business needs into ready-to-use AI solutions.
Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 30 days −11.75%: protocol buys 0.00%, root sells −1.21%, users net −8.83%, cross-subnet −1.71%, unattributed +0.0003%.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-07 UTC, as of ≈11:32 UTC (block 9,453,952)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
Developing commercial AI capabilities at scale requires clear, objective benchmarks and real-world validation. Traditional approaches either rely on closed evaluation processes that lack transparency, or use generic benchmarks disconnected from actual business workflows. This makes it difficult for organizations to identify which AI solutions truly perform in production or for developers to optimize toward genuine business needs rather than academic metrics.
A decentralized, incentive-aligned network of developers competing to build solutions for specific business workflows—with autonomous, transparent evaluation against published benchmarks—creates competitive pressure around AI that businesses actually need. Open-source graders, published datasets, and deterministic scoring allow any developer to audit results and understand why their submission ranked as it did, while objective benchmarking surfaces the highest-performing capabilities for commercial integration.
SN121 implements this model on Bittensor by publishing structured Challenges (a business workflow brief plus evaluation suite with scenarios, rubric, and graders) and rewarding developers whose AI skills score highest across multiple scenarios evaluated by independent, weighted graders. The evaluation harness is fixed; submissions are skill bundles (teaching documentation plus optional reference files); and results include per-scenario grader rationales so developers can inspect and improve. High-performing skills can then be integrated into sundae_bar's commercial platform, creating a direct loop from business demand to deployed capability.
Auto-generated from the GitHub repository and may not reflect the latest changes.
SN121 is an incentivized AI development network on Bittensor designed to create and benchmark commercial AI capabilities for real business use. Developers compete to build AI Skills that solve structured Challenges informed by real business demand, with submissions autonomously evaluated against benchmarks. High-performing skills can be integrated into the sundae_bar commercial platform, creating a direct loop between business demand, competitive AI development, and commercial deployment.
SN121 operates a rolling Challenge cycle: (1) sundae_bar publishes a Challenge (brief + evaluation suite including suite.yaml, dataset.jsonl, rubric.txt) on the Lab; (2) Miners submit skill bundles (SKILL.md up to 10KB plus optional references up to 30 files, 128KB total); (3) Validators run each submission through a fixed harness against published scenarios and graders, updating an on-chain leaderboard; (4) The #1 miner receives 40% of validator weights; (5) When a qualifying score (80%) is achieved, a review begins and a closing window opens; (6) At Challenge close, submissions stop, emissions end, and high-performing skills may be integrated into sundae_bar's commercial platform. The base agent loads SKILL.md and can read references on demand via read_reference tool. Grader results include rationales and token usage per scenario. sundae_bar's validator posts on-chain weights (setWeights); some validators child-key to amplify those results.
Emissions settle at subnet epoch boundaries (~72 minutes).
Each Challenge remains open until a submission achieves a qualifying score (currently 80%). After that, a closing window begins (typically a few hours) before submissions stop and emissions end. High-performing skills may be integrated into sundae_bar's commercial platform.
The evaluation harness is fixed and uses published suite.yaml, dataset.jsonl, and rubric.txt files. Pre-gates (kept deliberately opaque) catch gaming or answer-key-encoding before paid judges run. All grader weights, rationales, and per-scenario results are published for full auditability.
Validators child-key to sundae_bar's scoring validator to amplify the same rankings.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
An enterprise AI platform that turns real business needs into ready-to-use AI solutions.
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Hardware requirements: see the subnet's repository (github.com, opens in a new tab, external site)
| Day (UTC) | Share |
|---|---|
| 2026-11-06 | 0.0008% |
| 2026-11-05 | 0.0002% |
| 2026-11-04 | 0.0007% |
| 2026-11-03 | 0.0009% |
| 2026-11-02 | 0.0002% |
| 2026-11-01 | 0.00% |
| 2026-10-31 | 0.0007% |
| 2026-10-30 | 0.0008% |
| 2026-10-29 | 0.0002% |
| 2026-10-28 | 0.00% |
| 2026-10-27 | 0.00% |
| 2026-10-26 | 0.00% |
| 2026-10-25 | 0.00% |
| 2026-10-24 | 0.00% |
| 2026-10-23 | 0.0011% |
| 2026-10-22 | 0.0004% |
| 2026-10-21 | 0.0011% |
| 2026-10-20 | 0.0004% |
| 2026-10-19 | 0.00% |
| 2026-10-18 | 0.00% |
| 2026-10-17 | 0.00% |
| 2026-10-16 | 0.0012% |
| 2026-10-15 | 0.0004% |
| 2026-10-14 | 0.00% |
| 2026-10-13 | 0.00% |
| 2026-10-12 | 0.00% |
| 2026-10-11 | 0.00% |
| 2026-10-10 | 0.00% |
| 2026-10-09 | 0.00% |
| 2026-10-08 | 0.00% |
| 2026-10-07 | 0.00% |
| 2026-10-06 | 0.00% |
| 2026-10-05 | 0.00% |
| 2026-10-04 | 0.00% |
| 2026-10-03 | 0.00% |
| 2026-10-02 | 0.0011% |
| 2026-10-01 | 0.0014% |
| 2026-09-30 | 0.001% |
| 2026-09-29 | 0.0015% |
| 2026-09-28 | 0.0004% |
| 2026-09-27 | 0.00% |
| 2026-09-26 | 0.00% |
| 2026-09-25 | 0.00% |
| 2026-09-24 | 0.00% |
| 2026-09-23 | 0.00% |
| 2026-09-22 | 0.00% |
| 2026-09-21 | 0.00% |
| 2026-09-20 | 0.00% |
| 2026-09-19 | 0.00% |
| 2026-09-18 | 0.00% |
| 2026-09-17 | 0.00% |
| 2026-09-16 | 0.00% |
| 2026-09-15 | 0.00% |
| 2026-09-14 | 0.00% |
| 2026-09-13 | 0.00% |
| 2026-09-12 | 0.00% |
| 2026-09-11 | 0.0015% |
| 2026-09-10 | 0.0016% |
| 2026-09-09 | 0.0027% |
| 2026-09-08 | 0.0008% |
| 2026-09-07 | 0.00% |
| 2026-09-06 | 0.0025% |
| 2026-09-05 | 0.0036% |
| 2026-09-04 | 0.0028% |
| 2026-09-03 | 0.0029% |
| 2026-09-02 | 0.003% |
| 2026-09-01 | 0.0006% |
| 2026-08-31 | 0.00% |
| 2026-08-30 | 0.0018% |
| 2026-08-29 | 0.0026% |
| 2026-08-28 | 0.0006% |
| 2026-08-27 | 0.0017% |
| 2026-08-26 | 0.0027% |
| 2026-08-25 | 0.0013% |
| 2026-08-24 | 0.00% |
| 2026-08-23 | 0.00% |
| 2026-08-22 | 0.0009% |
| 2026-08-21 | 0.0037% |
| 2026-08-20 | 0.0034% |
| 2026-08-19 | 0.0039% |
| 2026-08-18 | 0.0037% |
| 2026-08-17 | 0.0013% |
| 2026-08-16 | 0.00% |
| 2026-08-15 | 0.00% |
| 2026-08-14 | 0.0033% |
| 2026-08-13 | 0.0038% |
| 2026-08-12 | 0.0042% |
| 2026-08-11 | 0.0021% |
| 2026-08-10 | 0.0042% |
| 2026-08-09 | 0.0042% |
