v1.0

DJINN PROTOCOL

The Genius-Idiot Network

Intelligence X Execution

A Decentralized Intelligence Marketplace

Built on Bittensor

White Paper v1.0

January 2026

© Djinn Inc. 2026


Abstract

The global sports betting market exceeds $2 trillion annually, yet a fundamental inefficiency persists: skilled bettors with profitable information are systematically excluded from the market, while recreational bettors without edge are actively encouraged to lose more. Sportsbook operators have perfected the art of identifying and banning "sharp" bettors while maximizing exposure to "square" money.

The Djinn Protocol introduces a decentralized, permissionless marketplace that connects these two disparate populations - Geniuses (information holders who cannot execute) and Idiots (executors who are not profitable) - through a novel mechanism built on the Bittensor network. By leveraging Bittensor's native infrastructure, Djinn creates a trustless environment where betting intelligence becomes a tradeable commodity with built-in quality assurance threshold.

This white paper presents the technical architecture, economic model, and incentive mechanisms that enable the Djinn Protocol to unlock billions of dollars in previously inaccessible value while creating a self-sustaining ecosystem that rewards genuine predictive and execution capability.

1. Introduction

1.1 The Broken Sports Betting Market

The modern sports betting landscape is characterized by a critical market failure. Sportsbook operators, armed with sophisticated player tracking systems and behavioral analytics, have become exceptionally skilled at identifying winning bettors. Once identified, these bettors face severe restrictions: reduced betting limits, account closures, and outright bans. The industry term for this practice is "limiting", and it affects virtually every bettor who demonstrates consistent profitability.Meanwhile, the same operators aggressively court losing bettors with deposit bonuses, VIP programs, and enhanced odds promotions.

The result is a bifurcated ecosystem: Geniuses possess valuable predictive models and analytical frameworks but lack the ability to execute on their knowledge, while Idiots have unlimited betting access but no systematic edge. Both groups are worse off than they would be in an efficient market.

1.2 The Bittensor Opportunity

Bittensor provides the ideal infrastructure for solving this market failure. As a decentralized network designed to incentivize and reward intelligence production, Bittensor's subnet architecture enables specialized markets for different forms of machine intelligence. The Djinn Protocol operates as a Bittensor subnet specifically designed to connect intelligence with execution, leveraging the network's native capabilities and incentive framework:

  • Native Staking Infrastructure: TAO (Alpha) staking provides the collateral mechanism for Genius performance bonds without requiring custom smart contract development.
  • Decentralized Validation: Bittensor's validator network can verify information quality against authoritative data sources without centralized trust.
  • Permissionless Participation: Anyone can become a Genius or Idiot without KYC, credit checks, or approval from centralized gatekeepers.
  • Proven Tokenomics: TAO's deflationary mechanics and emission schedule provide a stable economic foundation for the marketplace.

2. Problem Statement

2.1 The Information-Execution Gap

Consider a professional sports bettor who has developed a proprietary model for predicting NBA point spreads. After two years of profitable betting, generating a 4% ROI (“Return on Information”) on over $10 million in volume, this bettor has been limited at every major sportsbook. Their maximum bet is now $20 at most venues, down from $5,000 previously. Despite possessing profitable information, they can now only extract insignificant profits relative to the value of their edge.

Simultaneously, thousands of recreational bettors place the same NBA bets daily with no restrictions whatsoever. These bettors have full market access but lack any systematic approach. They represent execution capacity without intelligence.

Traditional solutions fail for several reasons. Syndicate betting (where sharps direct squares' accounts) is illegal in most jurisdictions, creates significant counterparty risk, and requires a large amount of trust in a centralized authority. Tipster (“tout”) services suffer from misaligned incentives and lack of accountability - the tout gets paid win or lose, and there is no trustworthy mechanism to maintain accurate records of performance. Copy-betting platforms lack accountability mechanisms and are plagued by fraudulent "experts."

2.2 Trust and Verification Challenges

The core challenge in any betting intelligence marketplace is verification. How can an Idiot trust that a Genius actually possesses profitable information before paying for access? Historical track records can be fabricated. Even legitimate track records face survivorship bias - of 1,000 random bettors, some will appear skilled by chance alone.  Beyond that, edges disappear over time: it is common for a once-profitable bettor’s edge to erode as the market becomes more sophisticated.

The Djinn Protocol solves this through economic commitment. By requiring Geniuses to stake capital that is forfeited upon poor performance, the protocol creates a self-selecting mechanism where only genuinely skilled bettors can sustainably participate. The stake requirement transforms cheap talk into costly signaling. The bonding mechanism creates a market-based Service Level Agreement (SLA) where data providers are economically penalized for delivering low-fidelity signals.


3. Protocol Architecture

3.1 Participant Roles

The Djinn Protocol defines three primary participant roles, each with distinct rights, obligations, and economic incentives.

3.1.1 Geniuses (Information Providers)

Geniuses are participants who possess betting intelligence they wish to monetize. To become a Genius, a participant must stake TAO tokens as collateral, establishing both their credibility and their exposure to performance-based penalties. The stake amount determines the Genius's capacity—specifically, the number of signals they can issue and the notional value each signal represents.

For example, a Genius posting a 1,000 TAO SLA bond for 100 signals establishes a notional value of 10 TAO per signal. This notional value serves as the basis for performance calculations and Idiot compensation.

When registering, Geniuses must specify several key parameters that define their offering:

  • Quality Assurance Threshold (X%): The minimum ROI the Genius commits to achieving. If actual performance falls below this threshold, Idiots are compensated from the Genius's stake. A 0% threshold means the Genius only promises not to lose money; a 2% threshold means the Genius commits to at least 2% profitability.
  • Fidelity Rebate Factor (P%): A dynamic pricing adjustment mechanism that issues a retroactive credit to Consumers when signal accuracy exceeds the Quality Assurance Threshold. A P of 20% indicates that a value equivalent to 20% of the surplus signal delta is credited back to the Idiot. This functions as a contractual value adjustment, ensuring the net cost of the data service is automatically calibrated to incentivize liquidity provision for high-precision signals.
  • Minimum Fee: The floor price for any signal sold, ensuring the Genius receives baseline compensation for their information.
  • Maximum Fidelity Rebate: An optional ceiling on fidelity rebate payments, typically expressed as a percentage of fees paid (e.g., cannot exceed 50% of the fee). This protects Geniuses from scenarios where exceptional performance would erode all fee income.
  • Evaluation Window: The number of signals over which performance is measured. (Set to 100 in the above example, but this is configurable to the Genius’s preference.)
  • Liquidated Damages: Schedule of how damages are refunded to Idiot if results are below the quality assurance threshold X%. Default value is linear damages, but Genius can select from other options to send stronger signal to Idiots about Genius confidence level.

3.1.2 Idiots (Information Consumers)

Idiots are participants who purchase betting intelligence from Geniuses. The term is used colloquially within the sports betting community to describe losing bettors—here, we reclaim it to describe participants who recognize their informational disadvantage and seek to leverage that disadvantage by executing on Genius information.

When registering, Idiots provide two key inputs that the Djinn Algorithm uses for pricing:

  • Self-Reported Liquidity: The amount of capital the Idiot has available for betting. This determines the notional value of information to that specific Idiot.
  • Preference Parameters: Constraints on pricing, such as "never pay more than half the reported edge" or maximum fee thresholds. These preferences guide the Djinn Algorithm in setting personalized prices that allow Idiots to pay a price that allows them to extract value off-chain.

Idiots retain full discretion over whether and how to act on purchased information - the protocol facilitates information transfer, not bet execution. The Djinn Protocol provides data signals. The protocol is agnostic regarding the utility of these signals. Consumers may use this data for academic research, fantasy sports, media commentary, or personal entertainment. The Protocol does not facilitate, settle, or clear wagers on real-world sporting events.

3.1.3 The Djinn Network (Protocol Validators)

The Djinn Network comprises Bittensor validators who perform essential protocol functions: executing the Djinn Algorithm for price discovery, verifying sporting event outcomes against authoritative data feeds, calculating Genius performance metrics, executing stake slashing and fee distribution, and facilitating signal resale. Validators earn a percentage of protocol fees proportional to their stake and performance.

3.2 The Signal Lifecycle

Each signal progresses through a defined lifecycle from issuance to settlement.

Stage 1 - Issuance: The Genius submits a signal to the protocol, specifying the event, the predicted outcome, and an (optional) estimated edge. The estimated edge reflects the Genius's assessment of value and informs (but does not bind) the Djinn Algorithm's pricing.

Stage 2 - Pricing and Initial Sale: The Djinn Algorithm calculates a personalized fee for each subscribed Idiot based on their liquidity, preferences, the Genius's parameters, and the estimated edge. The signal is sold to the Idiot with the highest computed bid (fee willingness adjusted for Idiot value).

Stage 3 - Resale Window: At the Genius's discretion, the signal may continue to be resold for a specified number of blocks. Each subsequent sale follows the same pricing mechanism, with the signal information transferred to new Idiot purchasers. This enables broader distribution of valuable information while generating additional fee revenue.

Stage 4 - Settlement: After the event concludes, validators verify the outcome and record whether the signal won or lost. Individual signal results accumulate toward the Genius's overall performance record within the settlement window.

Stage 5 - Distribution: At the conclusion of a Genius's settlement window (all signals in the window settled), the protocol calculates final performance against the threshold and executes distributions: stake adjustments, fee transfers, refunds or fidelity rebates to Idiots, and protocol fees to validators.


4. Economic Model

4.1 The Quality Assurance Threshold Mechanism

The quality assurance threshold is the cornerstone of the Djinn Protocol's trust model. By requiring Geniuses to commit to a minimum ROI (X%), the protocol creates accountability that is absent from traditional tipping services.

Let X represent the Genius's threshold minimum ROI and Y represent the Genius's actual ROI over the settlement window. Two scenarios arise:

Scenario A: Underperformance (Y < X)

When the Genius fails to meet their quality assurance threshold, the protocol triggers full Idiot protection:

  1. Fee Refund: All fees paid by Idiots during the settlement period are fully refunded.
  2. Shortfall Compensation: Each Idiot receives additional compensation according to the Genius’s pre-agreed liquidated damages policy.  Default is that this will equal to (X - Y)% × notional value per signal. This payment comes from the Genius's stake.
  3. Djinn Fee Waiver: The Djinn Network collects no fees when Geniuses underperform, eliminating any incentive for the protocol to favor losing Geniuses.

This mechanism ensures that all incentives are aligned: when a Genius fails to deliver, the Genius is penalized, Djinn receives no fees, and Idiots are made whole - and then some. The compensation formula means an Idiot betting proportionally to the notional value would have their losses fully covered by the Genius's stake.

Scenario B: Outperformance (Y > X)

When the Genius exceeds their quality assurance threshold, value is shared between all participants:

  1. Fidelity Rebate: Each Idiot receives a fidelity rebate equal to (Y - X)% × P × notional value, where P is the fidelity rebate percentage set by the Genius. This payment comes from the Genius's stake.
  2. Fee Collection: The Genius receives all accumulated fees from the evaluation period minus the amount of Djinn’s fee and any fidelity rebates returned to Idiots.
  3. Stake Return: The Genius's stake is unlocked.
  4. Djinn Fee: The Djinn Network collects its percentage F% from total fees.

4.2 The Fidelity Rebate Mechanism

The fidelity rebate serves multiple economic functions. It aligns Idiot and Genius incentives: Idiots benefit when Geniuses outperform, creating a collaborative rather than adversarial dynamic. It also provides Idiots with upside participation beyond just receiving profitable information.

The fidelity rebate formula is: Fidelity Rebate = (Y - X)% × P × Notional Value

For example, if a Genius with a 2% threshold (X=2) achieves 5% ROI (Y=5) with a 20% fidelity rebate percentage parameter (P=0.2) and $1,000 notional per signal, each Idiot receives: (5-2)% × 20% × $1,000 = $6 per signal purchased.

Fidelity Rebate Caps and Protection Mechanisms

Without constraints, exceptional Genius performance could theoretically result in fidelity rebates exceeding fee income, causing a profitable Genius to lose money. The protocol provides two protective mechanisms:

  • Maximum Fidelity Rebate as Percentage of Fee: Geniuses can specify that the fidelity rebate cannot exceed a certain percentage of the fee paid. For example, a 50% cap means an Idiot who paid $20 can receive at most $10 as a fidelity rebate, regardless of performance.
  • Minimum Fee Floors: Geniuses can set minimum fees that ensure baseline compensation even when the Djinn Algorithm would price lower.

These mechanisms work together to ensure that a winning Genius does not provide valuable information but still lose money.

4.3 The Djinn Algorithm

The Djinn Algorithm is the protocol's intelligent matching engine, responsible for setting personalized fees that balance value capture for Geniuses with affordability for Idiots.

The algorithm considers multiple inputs:

  • Idiot Liquidity: Higher liquidity Idiots can extract more value from signals and thus pay higher fees.
  • Estimated Edge: The Genius's reported edge on the signal informs expected value calculations.
  • Idiot Preferences: Constraints like "never pay more than 50% of reported edge" bound the algorithm's pricing.
  • Genius Parameters: Minimum fees, quality assurance threshold, and fidelity rebate percentages all influence pricing.
  • Historical Performance: Geniuses with strong track records command premium pricing.

Example: A signal has an anticipated 4% edge. An Idiot has $1,000 liquidity and a preference to never pay more than half the reported edge. The Djinn Algorithm sets the fee to $20 (2% × $1,000), capturing half the expected value while respecting the Idiot's constraints.

The algorithm must also prevent pathological outcomes where Geniuses lose money despite positive performance. This requires ensuring that total expected fidelity rebates (given likely performance distributions) do not exceed expected fee income. The fidelity rebate caps and minimum fees serve as safety valves, but the algorithm should price to avoid hitting these constraints under normal conditions.

4.4 Fee Flow Summary

Scenario

Genius Receives

Idiots Receive

Djinn Network

Y < X (Underperformance)

Stake - [(X-Y)% × Notional × N]

Full refund + (X-Y)% × Notional each

Nothing

Y > X (Outperformance)

Fees + Stake - Total Fidelity Rebate - Djinn Fee

Fidelity Rebate: min[(Y-X)% × P × Notional, Cap]

F% of Fees

Where N = number of Idiots who purchased signals during the settlement window.

5. Scenario analysis

5.1 Example 1: Outperformance with Moderate Fidelity Rebate Percentage

A Genius stakes $100,000 for a 100-signal settlement window with a 2% quality assurance threshold and 20% fidelity rebate percentage.

Idiots pay fees of $50 each for signals 1-50 and $100 each for signals 51-100. The Genius achieves a 3% ROI. Djinn fee is 5%.

Component

Calculation

Amount

Notional per Signal

$100,000 / 100

$1,000

Fidelity rebate per Idiot

(3% - 2%) × 20% × $1,000

$2

Total fidelity rebate (100 Idiots)

$2 × 100

$200

Genius Stake Return

$100,000 - $200

$99,800

Fee Pool

(50 × $50) + (50 × $100)

$7,500

Djinn Fee

$7,500 × 5%

$375

Genius Net Proceeds

$99,800 + $7,500 - $375

$106,925

The Genius profits $6,925 (6.9% on stake). Each Idiot receives a $2 fidelity rebate. The Djinn Network earns $375.

5.2 Example 2: Underperformance

Same Genius parameters: $100,000 stake, 100-signal window, 2% threshold, 20% fidelity rebate percentage. But the Genius realizes a -2% ROI (loss).

Component

Calculation

Amount

Shortfall from Threshold

2% - (-2%)

4%

Compensation per Idiot

4% × $1,000

$40

Total Compensation

$40 × 100

$4,000

Fee Refund

Full refund to all Idiots

$7,500

Genius Stake Return

$100,000 - $4,000

$96,000

Djinn Fee

Waived on underperformance

$0

Every Idiot receives their fee back plus $40 compensation. The Genius loses $4,000 from stake. The Djinn Network earns nothing, maintaining aligned incentives.

5.3 Example 3: High Performance with No Threshold

A different Genius stakes $100,000 with a 0% quality assurance threshold and 50% fidelity rebate percentage. Fees are lower ($5 for signals 1-50, $10 for signals 51-100) reflecting the weaker threshold. The Genius achieves 5% ROI.

Component

Calculation

Amount

FIdelity rebate per Idiot

(5% - 0%) × 50% × $1,000

$25

Total fidelity rebate

$25 × 100

$2,500

Genius Stake Return

$100,000 - $2,500

$97,500

Fee Pool

(50 × $5) + (50 × $10)

$750

Djinn Fee

$750 × 5%

$37.50

Genius Net Proceeds

$97,500 + $750 - $37.50

$98,212.50

Despite 5% ROI, the Genius loses $1,787.50 due to low fees and high fidelity rebate obligations. This illustrates why the Djinn Algorithm must price appropriately—the fidelity rebate ($25) far exceeds the fees paid ($5-10). Each Idiot receives $25, making this an exceptional deal for information buyers.

This pathological outcome demonstrates the need for protective mechanisms as discussed in the next example.

5.4 Example 4: Fidelity Rebate Caps Preventing Loss

Same Genius as Example 3, but now with protective parameters: minimum fee $20, maximum fidelity rebate capped at 50% of fee paid. The Genius achieves 5% ROI.

Component

Calculation

Amount

Base Fidelity Rebate Calculation

(5% - 0%) × 50% × $1,000

$25

Fidelity Rebate Cap (Signals 1-50)

50% × $20 fee

$10 max

Fidelity Rebate Cap (Signals 51-100)

50% × $30 fee

$15 max

Actual Fidelity Rebates Paid

(50 × $10) + (50 × $15)

$1,250

Genius Stake Return

$100,000 - $1,250

$98,750

Fee Pool

(50 × $20) + (50 × $30)

$2,500

Djinn Fee

$2,500 × 5%

$125

Genius Net Proceeds

$98,750 + $2,500 - $125

$101,125

With fidelity rebate caps in place, the Genius profits $1,125 despite the high fidelity rebate percentage. Each Idiot receives 50% of their fee back as a fidelity rebate.

5.5 Example 5: Balanced Parameters

A Genius optimizes parameters: $100,000 stake, 0% threshold, 20% fidelity rebate percentage, $25 minimum fee, 20% fidelity rebate cap. All Idiots pay the $25 minimum. The Genius achieves 5% ROI.

Component

Calculation

Amount

Base Fidelity Rebate Calculation

(5% - 0%) × 20% × $1,000

$10

Fidelity Rebate Cap

20% × $25 fee

$5 max

Total Fidelity Rebate

$5 × 100

$500

Genius Stake Return

$100,000 - $500

$99,500

Fee Pool

$25 × 100

$2,500

Djinn Fee

$2,500 × 5%

$125

Genius Net Proceeds

$99,500 + $2,500 - $125

$101,875

The Genius profits $1,875 (1.875% on stake) while achieving 5% ROI. The gap reflects conservative fee pricing. Each Idiot receives $5. This demonstrates how parameter choices affect profit capture. The more confident a Genius is in his edge, the higher the quality assurance threshold can be offered, which in turn commands a higher price to the Idiot and allows the Genius to capitalize on his edge.  The more conservative a Genius is (protecting himself against downside losses), the less they will capture from Idiot fees.  Ultimately, the market forces will determine the equilibrium between Genius settings and Idiot willingness to pay for information.


6. Technical Implementation on Bittensor

6.1 Subnet Architecture

The Djinn Protocol operates as a dedicated Bittensor subnet optimized for sports betting intelligence. The subnet defines custom roles that map to the standard Bittensor architecture:

  • Miners: Geniuses register as miners, with their mining "work" being the production of betting predictions. Mining rewards are replaced/supplemented by the fee-based compensation model described above.
  • Validators: The Djinn Network validators execute the Djinn Algorithm, verify sporting outcomes, calculate performance metrics, and execute distributions. They earn protocol fees proportional to their stake and validation quality.
  • Delegators: Idiots function as consumers who stake TAO to participate and delegate trust to validators for pricing and outcome verification.

6.2 Data Oracles and Outcome Verification

Accurate verification is essential to protocol integrity. The Djinn Network integrates multiple authoritative sports data APIs and requires validator consensus before evaluating any signal. A minimum number of independent data sources must agree on an outcome, with a specified delay post-event (e.g., 4 hours) to allow for corrections and official reviews.

Validators who submit incorrect outcome data are slashed, with slash amounts exceeding potential gains from manipulation. This economic security model leverages Bittensor's native slashing infrastructure while adding sport-specific verification requirements.

6.3 The Djinn Algorithm Implementation

The Djinn Algorithm runs as a deterministic function executed by validators, ensuring all participants can verify pricing decisions. The algorithm is updated through governance proposals, with changes requiring supermajority validator approval.

Key algorithmic components include:

  • Value Estimation: Calculating expected value to each Idiot based on their liquidity and the signal's estimated edge.
  • Constraint Satisfaction: Respecting Idiot preferences and Genius minimums while maximizing market efficiency.
  • Dynamic Adjustment: Updating pricing based on Genius track record, with proven performers commanding premium prices.
  • Fidelity Rebate Sustainability Check: Ensuring expected fee income exceeds expected fidelity rebate obligations given performance distributions.

6.4 Signal Resale Mechanism

Geniuses can specify a resale window (in blocks) during which signals continue to be sold to additional Idiots. Each sale generates new fees while the same signal information is distributed more broadly.

The resale mechanism enables: broader information distribution for high-value signals, additional revenue for Geniuses from popular predictions, and price discovery as later buyers observe early purchase activity.

Signal information is encrypted and only revealed to purchasers, preventing information leakage to non-paying observers.

7. Risk Management and Security

7.1 Genius Risks

Geniuses face stake risk when they fail to meet their quality assurance threshold - the shortfall compensation can be significant for large underperformance. A Genius with a 2% threshold who achieves -3% ROI loses 5% of their stake to Idiot compensation.

Geniuses also face parameter risk if they set fidelity rebate percentages too high without adequate caps. As Example 3 demonstrated, a 50% fidelity rebate percentage with 5% outperformance can result in losses despite winning signals. Careful parameter selection is essential.

The protocol mitigates these risks by providing simulation tools that help Geniuses understand the distribution of outcomes under different parameter choices before committing.

7.2 Idiot Risks

Idiots are substantially protected by the quality assurance threshold mechanism. If a Genius underperforms their threshold, Idiots receive full refunds plus compensation. The primary Idiot risk is paying fees for a Genius who barely meets their threshold—profitable enough to avoid refunds but not enough to generate meaningful fidelity rebates. But even in this case, the Idiot is protected from long-term loss.

7.3 Protocol Risks

The Djinn Protocol inherits Bittensor's security model and adds sport-specific considerations. Key protocol risks include:

  • Oracle Manipulation: Mitigated by multi-source verification and validator slashing.
  • Algorithm Gaming: Mitigated by deterministic execution and governance-controlled updates.
  • Regulatory Risk: Mitigated by decentralized architecture and information-only scope- the protocol does not facilitate actual betting, as Idiots are at liberty to use information however they deem appropriate.

8. Governance

The Djinn Protocol employs progressive decentralization, beginning with foundation-led parameter setting and transitioning to full DAO governance as the ecosystem matures.

8.1 Governable Parameters

  • Minimum stake requirements for Geniuses
  • Djinn Algorithm coefficients and pricing formulas
  • Djinn Network fee percentages
  • Supported sports and data oracle integrations
  • Settlement window constraints
  • Resale window parameters

9. Conclusion

The sports betting industry's systematic exclusion of skilled bettors represents one of the largest market inefficiencies in the global economy. Billions of dollars in predictive value are stranded because the participants who create that value cannot access the markets where it would be rewarded.

The Djinn Protocol provides the infrastructure to unlock this value. By creating a trustless marketplace where Geniuses can monetize their information through quality assurance thresholds and Idiots can purchase verified intelligence with downside protection, the protocol enables both populations to capture value that currently accrues entirely to sportsbook operators.

Built on Bittensor's robust decentralized infrastructure, the Djinn Protocol combines cryptoeconomic incentive design with intelligent algorithmic pricing to create a self-sustaining ecosystem. The quality assurance threshold mechanism ensures accountability. The Djinn Algorithm enables efficient price discovery. The fidelity rebate mechanism aligns incentives and rewards successful collaboration.

The Genius-Idiot Network isn't just a protocol - it's a paradigm shift in how predictive intelligence is executed upon. We invite Geniuses, Idiots, and validators alike to join us in building a more efficient, more accessible, and more rewarding sports betting ecosystem.

Disclaimer

This white paper is for informational purposes only and does not constitute financial, legal, or investment advice. The Djinn Protocol facilitates the exchange of information and does not operate or facilitate actual gambling activities. Users are responsible for compliance with applicable laws in their jurisdictions. Past performance of Geniuses does not guarantee future results. TAO token values fluctuate and staked amounts may lose value independent of protocol performance.

The Djinn Protocol is a decentralized software utility. It holds no custody of funds, places no wagers, and operates no order books. TAO tokens are used strictly for network utility and access rights.

The protocol architecture, parameters, and mechanisms described herein are subject to change based on technical requirements, security considerations, and governance decisions. This document does not constitute an offer or solicitation to purchase tokens or participate in any investment scheme.

© Djinn Inc. 2026