Rivalz Storage is a cutting-edge distributed vector storage service, allowing users to securely store and access data from any location on the internet. Utilizing advanced decentralization, peer-to-peer (P2P) networking, artificial intelligence, and blockchain technology, Rivalz delivers a scalable, cost-efficient, and resilient solution for cloud-based vector storage.

In addition to its powerful storage capabilities, Rivalz offers an advanced AI platform that extracts valuable knowledge from your documents. We provide an easy-to-use API that allows you to vectorize your documents and integrate them into an AI model, creating a customized "knowledge base" tailored specifically to your application’s needs.

The Rivalz Developer Console is a user-friendly, intuitive web UI that allows developers to efficiently manage and monitor their uploaded files within the Rivalz Storage ecosystem. In addition, it helps manage billing information, generate and secure API keys.

Rivalz is a layer 2 blockchain creating the World Abstraction Layer for AI and AI Agents. The Rivalz Network is a decentralized market of Data, AI, DePIN and Human resources. Rivalz bridges AI and Agents to the real world. In the Rivalz AI World Hackathon we invite you to build on top of 1.0 versions of our Data Storage/AI RAG solution (OCY DePIN) + our Agentic Data and AI Oracle Network (ADCS).

This hackathon invites new and existing projects to bring exciting AI project to life using our infrastructure via 3 main tracks: OCY, ADCS, AI Agents. And a special ai16z sub-track.

Location and How to Participate:

This is a global online hackathon where anyone can participate, additionally there will be a special offline track in Manila, Philippines with a separate prize pool.

After submission you'll be given a special link for private developer chat.

Apply to the offline track in Manila, Philippines by The Block - .

Tracks and Prize Pool:

Total Prize Pool: $85,000. [50% USDT, 50% Rivalz Native Token $RIZ]

1. OCY - / - $20,000

This track will focus on Data Management and Usage for AI use cases.

1. ADCS - / - $20,000

This track will focus on creative DAPP or Agent design involving ADSC Oracles on-chain. Additionally, top creative Data and AI providers and Adapter creators will be rewarded.

1. AI Agents - - $45,000

General track for creation of AI Agents using OCY or ADCS. [$15,000]

Eliza Sub-tracks. For AI Agent developers using ai16z tech - Here you do not have to use any of our infra, although it will be a bonus if you do. For clarity: Rivalz is in no way associated with ai16z.

• [$15,000] Most creative/valuable use-cases of long-term autonomous planning and execution, by AI Agents. Supply the Agent with any resources from access to services to money, and have them perform as long as possible towards the end goal.

• [$15,000] Most creative/valuable use-cases of VMs or Web-access by AI Agents. AI Agent has full control over a virtual machine or just a web-browser.

• Additional points for using Waking Up .

Timeline

Week 1 (Nov 25 - 29):

• Introduction to OCY and ADCS

• Team formation

Weeks 2-3 (Dec 2 - Dec 13):

• Partner Workshops

• Business Talks

Weeks 4 (Dec 16 - Dec 20):

• Check-in & Feedback

Weeks 7 (Deadline Dec 20th- 9 PM UTC):

• Final Grant/Project Submission

The winner Announcement will take place within 2 weeks of the hackathon conclusion. Date TBA.

Qualifications

1. The GitHub repository, project description, and video presentation must all be submitted before the deadline. A link will be provided soon.

2. Participants must be 18+

Judging

The judging panel will primarily consist of the Rivalz Team, but we may also onboard additional judges during the hackathon. Stay tuned.

1. First part - Projects that have successfully submitted all the details will be screened. Top projects will be invited to second part.

2. Second part - Present your project on a video session with judges.

Projects will be selected based on merit of utility, creativity, depth of development, presentation.

Each track will have Gold, Silver, Bronze winners, with rewards worth 50%, 30%, 20% respectively of tracks reward pools.

Rewards

Rewards will be allocated within 4 weeks of winners announcements.

Winners will be provided with additional support in Resources, Marketing, Funding, Business and more. All participants will have a fast track for our incubator program -. For existing projects in the AI/Agentic fields we suggest to apply now.

Apply to the hackathon .

OCY TRACK

The developer guides are designed to equip you with a comprehensive range of resources, tools, and support, empowering you to build applications within the Rivalz ecosystem—especially AI-focused solutions. Through these guilds, developers gain access to detailed technical documentation, SDKs, and development frameworks, simplifying the journey from concept to deployment, making it easier to create, innovate, and excel in building applications on Rivalz Infrastructure.

Next Steps

• Read for an overview of Rivalz's distributed vector storage service, learn how to create a knowledge base and use it for AI RAG systems.

The design of the Dapp creator off-chain components includes the Adaptor and ADCS Nodes, each playing a vital role in the overall architecture of the system

With access to trusted and reliable data, AI Agents can unlock their full potential – from making smarter decisions to streamlining processes and drastically improving operational efficiency. ADCS enables you to seamlessly integrate data providers directly into your AI Agents within an off-chain environment, offering unparalleled flexibility and performance.

One of the most powerful applications enabled by Large Language Models (LLMs) is sophisticated Question-Answering (Q&A) Chatbots. These chatbots can answer questions based on specific source information, offering more relevant and accurate responses.

To achieve this, these applications use a technique known as Retrieval Augmented Generation (RAG). RAG enhances the model's ability to generate responses by retrieving relevant information from a database or document, allowing the chatbot to answer questions with greater precision and context.

What is RAG?

RAG is a technique used to augment the knowledge of Large Language Models (LLMs) by providing additional, relevant data.

While LLMs are capable of reasoning about a wide range of topics, their knowledge is restricted to the public data available up until the point they were trained. This means they may not have up-to-date information or may lack knowledge of private or specialized data. To build AI applications that can reason about new data or private information, it's essential to

The billing page provides a comprehensive overview of your financial activities within the Rivalz platform. It allows you to easily monitor and manage your invoices, track your subscription status, and update or add new payment methods. This page ensures that you stay on top of your account’s billing cycle and payments, making it easier to manage your costs.

Payment System

Rivalz implements a robust and secure payment system by leveraging Stripe, a leading global payment processing platform. This integration offers users a seamless, efficient, and familiar payment experience while ensuring the highest standards of security and compliance.

ADCS nodes plays a crucial role in aggregating and processing data from various providers, ensuring that the data ecosystem operates effectively and securely, No matter If the user's request involves inference or not. Here’s a breakdown of the key components of ADCS nodes:

• Fetcher Node

• Data Storage

• Data Providers

Read the DataProviders section to learn more

Oracles act as intermediaries that connect AI agents to external data sources, facilitating the flow of information between blockchain-based applications and off-chain data. In the Dapp-Agentic Data Coordination System (ADCS), oracles receive data from reporters (data providers) and relay it to the AI agents or smart contracts.

However, sending raw data back to oracles in ADCS can introduce security risks, especially when handling sensitive or proprietary information. Such data could be vulnerable to tampering, interception, or unauthorized access during transmission, posing a significant threat to the integrity and privacy of the system.

To mitigate these risks, Zero-Knowledge Proofs (ZKPs) are employed. ZKPs are cryptographic techniques that enable the reporter to validate the accuracy of the data being sent to the oracle without revealing the actual content of the data. This method ensures:

• Data confidentiality: The sensitive information remains hidden, even as its validity is verified by the oracle.

• Trust without exposure: The oracle can trust that the data is correct without needing access to the raw information, preventing leaks or breaches.

• Protection against tampering: The data cannot be manipulated or altered without detection, as ZKPs ensure that only valid, authenticated data is processed.

The Oracle Router is responsible for directing the data requests to the appropriate Oracles based on the specifications outlined by the user.

• When the Coordinator contract receives a request from a Consumer contract, it performs a thorough analysis of the request details. This includes understanding the type of data required, the specific parameters defined by the user.

• After the Coordinator has evaluated the request, it routes it to the corresponding Oracle Router.

• The Oracle Router has the capability to dynamically select oracles based on the type of data being requested and the criteria set forth by the user.

We currently support for both Python and Node.js.

In the context of Rivalz, the DApp Creator represents a seamless integration of AI and blockchain technology, transforming how data is processed and decisions are made. AI's capacity to analyze extensive data in real-time, combined with blockchain's decentralized, secure, and immutable architecture, is revolutionizing large-scale, data-driven decision-making.

A key innovation in this space is the development of Onchain AI Agents—intelligent systems that function directly on blockchain networks. These agents enable decentralized AI services that are transparent, secure, and highly efficient, unlocking new possibilities for applications in a wide range of fields.

Design

Dapp creator consists of both on-chain and off-chain components that work together to provide off-chain data access to on-chain applications. This architecture facilitates efficient data flow between decentralized applications (dApps) and external data sources, ensuring that AI-driven decision-making processes are supported by reliable data.

Next Steps

The following sections provide a more in-depth look at its key components.

You can find all the relevant information here: https://github.com/ai16z

The CoordinatorBase Contract plays a pivotal role in the Dapp-Agentic Data Coordination Service (ADCS) architecture, serving as the bridge between on-chain smart contracts and off-chain AI agents. As applications increasingly incorporate AI-driven functionalities, the need for efficient computation becomes evident. However, implementing AI computations directly on the blockchain poses significant challenges, primarily due to the high computational costs involved.

Many applications opt to perform these computations off-chain on centralized servers, submitting only the final results to the blockchain. While this approach is practical and efficient, it compromises the core principles of decentralization, raising security concerns and potentially undermining the trust and transparency foundational to the blockchain ecosystem.

To address these challenges, We propose a solution where AI agents perform computations or inferences off-chain. Here’s how it works:

1. Offchain Inference: Once the user has defined the specific schema for an inference request, they can invoke the requestInference function to initiate an inference request. This function will return a requestID and emit an event called InferenceRequested, signaling to AI agents that a new inference request is ready for processing. Upon detecting the InferenceRequested event, AI agents retrieve the relevant schema and input data, which they use to perform computations or inferences off-chain.

2. Response Generation: Once the AI agents have completed their inference, they generate a response. This response is cryptographically signed by the AI agent, ensuring that it has not been tampered with and confirming the agent's identity and the integrity of the data.

3. Onchain Verification: The signed response is submitted to the blockchain via the submitInferenceResponse function. Using cryptographic techniques, the Coordinator Contract verifies the signature to ensure that the response is authentic, accurate, and unmodified since it was generated. Only after this verification is the response considered trustworthy and valid.

The Adaptor is a critical parameter for off-chain inference that acts as a template, dictating the structure and content of inference requests. Users define the specific parameters for the schema used in off-chain inference as follows:

• Request Instructions: This contains detailed guidelines about the nature of the request, including various parameters such as variables, consumer contracts, event requests, and required user fees.

• Reference Data: The data that the inference should reference or utilize, including any necessary context or historical data.

• Response Format: The expected structure of the inference response, which ensures the output is formatted correctly for subsequent workflows. This could be a boolean, JSON, or Integer format depending on the use case.

Users can define multiple Adaptor, each tailored to specific requirements, with each adaptor being uniquely identified by a randomly generated jobID during the creation process. These jobIDs serve as crucial identifiers in Consumer contracts, ensuring accurate referencing.

The Dashboard page helps you monitor the status of your uploaded files, including the total bytes uploaded. It provides detailed insights and visual representations of your data, breaking it down into daily, weekly, and monthly views. This allows you to track your storage usage, identify trends, and make data-driven decisions to optimize your storage needs.

Submissions are NOW CLOSED for the Rivalz AI World Hackathon! Deadline: December 20, 2024

• $85,000 in Rewards

• Get fast-tracked into the Rivalz Alliance

• Join 100+ developers, showcase your talent, and take your project to the next level

Submissions are NOW CLOSED! Thank you for your participation. Stay Tuned for more on our socials: | |

The Upload History page within the Rivalz Developer Console provides a detailed log of all the files you have uploaded to the Rivalz storage system. It allows you to easily track your file uploads, and see important information related to your uploads, such as:

• File Name: The name of the uploaded file, which allows you to easily identify your files.

• File Size: The size of the uploaded file, measured in bytes or GB, so you can monitor your storage usage.

• Upload Hash: A unique hash is generated for each uploaded file, providing a way to verify file integrity and ensure the file hasn't been altered or corrupted.

• Upload At: The date and time the file was uploaded, enabling you to track when files were added to your storage.

The Profile page is where you can manage your personal information, access your API Key, and also create credits or subscribe to Rivalz storage service packages.

API Key

An API is a JWT token, formatted as a string like: eyJhbGciOiJIUzI1NiJ9.eyJpZCI6IjY2ZmI3ZTY..., which is needed to authenticate your requests to the Rivalz storage service.

The Agentic Data Coordination System (ADCS) is the connectivity module and the next evolution in data infrastructure tailored specifically for AI Agents. Designed to revolutionize the future of Artificial Intelligence, ADCS builds an expansive data network that emphasizes rapid validation and ultra-low latency.

Key components

• Providers

To access the Rivalz storage service, you need to have an account. This section walks you through the process of creating an account and obtaining the credentials required to access the Rivalz storage service. Follow the step-by-step instructions to get started quickly and easily.

1. Create an account

Refer to learn How to sign up for an account

If you already have a account, you can use those credentials to sign in.

• Structured data refers to information that is organized in a format that is easily understandable by both humans and machines. In structured data, the elements or fields are clearly defined, and there is a well-defined schema or model that governs the relationships and properties of these elements. This organization allows for efficient storage, retrieval, and analysis of data.

  Structured data is typically found in relational databases, spreadsheets, and other tabular formats. Each piece of data is assigned to a specific category or field, and relationships between different pieces of data are explicitly defined. The use of schemas ensures that the data adheres to a specific structure, which simplifies operations like querying, filtering, and aggregating data.

  Examples of structured data include tables with rows and columns, where:

To claim your free credits, simply visit the and click on the "Create Credit" button in the Profile tab.

Fetcher Nodes are vital components of the Agentic Data Coordination Service (ADCS) Node, designed to intelligently retrieve, process, and aggregate data from diverse external sources. By leveraging a structured data adapter framework, these nodes ensure that requestors have access to standardized data structures.

In the ADCS system, Fetcher Nodes serve as data provision points for oracles, ensuring they are supplied with high-quality, reliable information necessary for efficiently fulfilling consumer requests.

In addition, to further ensure data integrity and bolster network security, we are committed to open-sourcing our Fetcher Nodes. This initiative empowers anyone to run a node, fostering a collaborative environment where the community actively participates in maintaining the integrity of our data ecosystem.

Each Fetcher Node consists of three primary components:

• Fetcher: This component is responsible for intelligently fetching data from designated sources based on predefined configurations. It ensures efficient retrieval of information while adhering to the specifications set in the data adapter.

• Data Adapter: The idea behind our adapter framework is to ensure that users can request various types of data from diverse sources while maintaining compatibility with standardized formats. It acts as the critical interface that standardizes the interaction between external data sources and Fetcher within the ADCS framework.

  Each data adapter is identified by a unique adapterHash that ensures compatibility with the aggregator. This hash guarantees the data retrieved by Fetcher is correctly structured and compatible for aggregation, maintaining data integrity throughout the process.

• Aggregator: Aggregates the data retrieved from the fetchers and provides the final, aggregated data. Each node stores both a local aggregate and a global aggregate.

The Aggregator consolidates this data according to the unique specifications of each Adapter, ensuring accuracy and relevance. When data is fetched from a fetcher, it is processed using different aggregation methods, such as median or majority voting, depending on the type of data. This aggregated information is then stored in the local aggregate. Subsequently, all local aggregates are synchronized with each other to become global aggregates. When the consensus is reached, the global aggregate will be the final data for storage.

We use the Raft consensus mechanism to ensure that all nodes in the network agree on the global aggregate data, maintaining consistency and reliability while effectively managing data replication and fault tolerance.

Steps Raft Takes to Achieve Consensus for a Global Aggregate:

1. Leader Election: Initially, a leader must be elected among the nodes. This node will be responsible for managing the log replication process and ensuring that all nodes (followers) are in sync.

2. Log Entry Creation: Once a leader is elected, it proposes the global aggregate data to be stored. This is done by creating a new log entry that contains the aggregated data and then sends this log entry to all follower nodes

3. Log Replication: Each follower node receives the proposed log entry from the leader and appends it to its own log. Followers send an acknowledgment back to the leader to confirm they received and stored the log entry.

1. Adaptor Creator

The Adaptor Creator represents a foundational tool that defines the structure of off-chain inference requests, creating a standardized framework for AI agents, smart contracts, or other consumers to communicate effectively with off-chain data sources.

2. Parameter Specification

To create an adaptor, you need to specify several essential parameters that define its functionality, capabilities, and communication requirements. These parameters ensure that the adapter is correctly configured to facilitate interactions between different systems, particularly between on-chain and off-chain environments. Here are the critical parameters:

• Name (String): Name of the adaptor.

• Provider: Choose a data provider (Currently, We provide a list of providers. You can choose one that fits your requirements).

• Network: The network you wish to deploy on (Base, Rivalz, Arbitrum).

• Description(String): Provides a brief description of what the adapter does.

During the generation process, the system will automatically create a unique JobID in a format compatible with Solidity's bytes32 type. This JobID serves as a unique identifier and must be generated before the creation of the adaptor.

For example jobID: 0x1b364865ca3e6bb5ada098d0ea96f9e9369b5693cacede79d1352334c4213ac2

3. How to create an Adaptor

Step-by-step guide on how to approach building an adaptor

1. Visit the Rivalz ADCS at . Next, select "Connect Wallet" in the header to login the website.

1. Click "Your Adaptor" and then Click the "Create Adaptor" button.

1. Create your adaptor

In this example, I would like to build a simple application designed to gather real-time token price data. From this foundation, you can expand the functionality by integrating AI agents to analyze the data, providing users with actionable insights.

For example, by leveraging AI-driven models, you can develop PriceIntel, an advanced system that evaluates current and historical token price trends to help predict future movements. Based on this analysis, the AI agent can assess whether the price of a specific token is likely to increase or decrease, offering actionable predictions for users in the context of:

• Trading: Deciding when to buy or sell tokens based on predictive trends.

After defining all the necessary information for your adaptor, click the 'Create Adaptor' button.

Congratulations! You have successfully created an adaptor !

Providers are crucial to the Agentic Data Coordination Service (ADCS) ecosystem, supplying the essential data that power AI-driven applications and services. Based on the specific data requirements defined within the Adaptor, they can deliver various types of data. However, there are two primary providers: Classic Data and Inference Data (AI Data). Classic Data and Inference Data, each serving different purposes and use cases within the network.

1. Classic Data

Classic data typically consists of two primary types:

Before engaging in AI-driven decision-making within the Dapp - Agentic Data Coordination Service (ADCS), users must first deploy a Consumer Contract. This contract acts as a crucial intermediary between the user’s specific requirements and the AI inference capabilities of the system. Operating similarly to a class in object-oriented programming, the Consumer Contract inherits key functionalities from the Coordinator Contract, enabling it to manage the intricate request and response processes essential for effective inference operations.

The primary responsibility of the Consumer contract is

1. Request Inference: The Consumer contract requests an inference from the Coordinator contract, providing the necessary input data.

2. Handle Request: The Consumer contract receives and processes the inference response to make a decision.

To access the Rivalz Console, you need to have an account. If you don’t have one, go to to sign up.

Enter your email and password. Please make sure to enter the same password in both the 'Password' and 'Repeat Password' fields. Then click the "Sign up" button.

Congratulations! You have successfully signed up for an account on Rivalz.

In Rivalz, Knowledge is a collection of documents. A knowledge base can be integrated into an application as a retrieval context.

How to create your first knowledge base

1. Prerequisites

Rivalz Python SDK for developers

from mimetypes import knownfiles

Rivalz Python Client

Rivalz Nodejs SDK for developers

Rivalz client Node.js SDK

This is a TypeScript library that provides functionalities for Rivazl AI

To help you better understand how a Dapp - ADCS works, let’s dive into the following example together Meme Coin Trend Application.

The Meme Coin Trend application utilizes cutting-edge AI and blockchain technology to monitor trending meme coins and analyze market data in real time. With an integrated AI agent, the platform autonomously trades meme coins using insights gathered from the ADCS network, ensuring efficient and data-driven trading decisions.

Step-by-step guide on how to approach building the application

1. Adapter Definition:

Visit to learn How to create an adaptor.

You need to define an adapter that outlines the necessary parameters.

• Provider: Meme Coin Trend

• Network: Rivalz

• Category : Meme Coin

• Output Type : StringAndBool

Once an adaptor is created, the system will generate a unique JobID.

Oxd7....2352 is the JobID for your Meme Coin Trend Adaptor.

2. Setup Your Consumer Contract:

Depending on the type of outputData you have defined in the adaptor, your consumer contract must inherit from the appropriate ADCS fulfillment contract. Here the outputData is StringandBool so the consumer contract will inherit the ADCSConsumerFulfillStringAndBool.

Struct

MemeCoin is a custom data type used to to represent a meme coin with three properties:

Contract Variables:

The memeCoins variable represents an array of MemeCoin structs, allowing the contract to store multiple meme coins

Constructor:

ADCSConsumerBase is an abstract contract that serves as the base for consumer contracts interacting with the ADCS coordinator. It initializes data requests and verifies the fulfillment.

The constructor takes the Coordinator contract's address (_coordinator) as input and passes it to the base contract. The Coordinator manages the interaction between oracles and consumers, facilitating the flow of data requests and responses.

_weth: the address of the WETH (Wrapped Ether) token contract.

_swapRouter: the address of the Uniswap V3 swap router contract, used to interact with the Uniswap decentralized exchange (DEX) to swap tokens

Main functions

Request Functions:

This function Initiates a data request to the ADCS network and requests :

• _callbackGasLimit: The maximum amount of gas a user is willing to pay for completing the function.

• _jobId: The jobID that the consuming contract is registered to.

• The function uses the buildRequest() function to create a request, adds the necessary parameters, and sends it to the Coordinator.

Fulfillment Functions:

This function is called by the ADCS coordinator to fulfill the data request and trigger a trade.

TradeMemeCoin Function:

The tradeMemeCoin function executes the buy or sell trade on Uniswap V3 based on the data received and triggered by the fulfillDataRequest function.

After processing the received data, the tradeMemeCoin function is called with the tokenName and the result (true/false).

• If result is true, the contract performs a buy trade by swapping WETH (Wrapped Ether) for the specified meme coin on Uniswap V3.

• If result is false, the contract performs a sell trade by swapping the meme coin for WETH on Uniswap V3.

addMemeCoin Function: Adds a new memecoin to the list of tradable tokens

setWethAmountForTrade Function: Sets the amount of WETH to use for trading.

3. Deploy the consumer contract

After defining all necessary functions, the global state, and the Coordinator contract for your consumer contract, the next step is to deploy the contract to the blockchain. .

4. How the Meme Coin Trend Application works

When receiving a request from the consumer contract, the system listens for the request and then identifies an appropriate AI to process the data.

1. The AI system processes the request, identifying the meme coin (e.g., "Shiba Inu") and making a recommendation (e.g., buy or sell).

2. The Coordinator contract receives the result from the AI system and calls the fulfillDataRequest() function in the consumer contract, passing the processed result (token name and recommendation).

3. The consumer contract processes the result in the fulfillDataRequest() function, which identifies the meme coin (via the name) and calls the tradeMemeCoin() function to execute the buy or sell action.

FullCode Example

We've also built some examples to help you easily understand what you can do with ADCS network

.