olokoji

Project Owner

Self Modifying IDE using MeTTa

Expert Rating

n/a

Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Develop interesting demos in MeTTa

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Overview

We propose to develop an AI-powered Integrated Development Environment (IDE) that leverages MeTTa's metaprogramming capabilities to enable self-modifying source code. This IDE will allow non-developer and developer users to interact with software programs that adapt themselves to fulfill user requirements dynamically. By integrating MeTTa into the IDE, we will create a platform where the source code adjusts itself based on user inputs, making programming more accessible, efficient, and alive. The project will involve forking and customizing an existing IDE (e.g., VS Code, Cursor AI, or Void) and incorporating MeTTa's features to achieve self-modification of programs.

RFP Guidelines

Develop interesting demos in MeTTa

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $100,000 USD
Proposals 21
Awarded Projects n/a

SingularityNET

Aug. 12, 2024

Create educational and/or useful demos using SingularityNET's own MeTTa programming language. This RFP aims at bringing more community adoption of MeTTa and engagement within our ecosystem, and to demonstrate and expand the utility of MeTTa. Researchers must maintain demos for a minimum of one year.

Proposal Description

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Total Milestones
5
Total Budget
$25,000_USD
Last Updated
8 Dec 2024

Milestone 1 - Requirements Analysis and Environment Setup

Description

Conduct a detailed analysis of requirements finalize the selection of the base IDE and configure the development environment. This includes setting up tools frameworks and repositories for efficient collaboration.

Deliverables

Project Plan Document. Forked IDE repository with initial configurations. Detailed Requirements specifications on GitHub

Budget

$6,000 USD

Success Criterion

Approval of the project plan by stakeholders. Functional development environment with all dependencies installed. Repository setup with initial version control practices.

Milestone 2 - Integration of MeTTa and Core Functionalities.

Description

This phase focuses on integrating MeTTa’s metaprogramming capabilities into the IDE and developing its core functionalities. It includes creating a plugin or module for MeTTa enabling dynamic code generation and showcasing self-modifying capabilities. Logical reasoning and error correction features are implemented to support intelligent program modifications.

Deliverables

1. MeTTa Plugin/Module: Integrated into the IDE supporting metaprogramming and logical reasoning. 2. Demonstration Use Cases: Scripts for scenarios like dynamic program creation (e.g. a calculator application that self-adjusts based on user input). 3. Error Detection & Correction Module: A feature to identify and rectify common coding errors dynamically.

Budget

$8,000 USD

Success Criterion

1. Validation of MeTTa integration through successful execution of at least two demonstration scenarios. 2. Stable operation of the error detection and correction feature in test environments.

Milestone 3 - UI/UX and NLP Integration

Description

This milestone delivers an intuitive and user-friendly interface designed specifically for non-developers and integrates NLP capabilities to enable natural language input. The goal is to make complex programming tasks accessible through visual programming and conversational commands.

Deliverables

1. Interactive UI with Visual Programming Support: Includes drag-and-drop elements real-time visual feedback and guided workflows for common programming tasks. 2. NLP Integration Module: Processes user inputs in natural language and translates them into actionable programming instructions. 3. Predefined Workflow Examples: Demonstrations of NLP-driven workflows (e.g. "Create a to-do list app" or "Add a save functionality to a text editor").

Budget

$4,000 USD

Success Criterion

1. Usability testing results with 80% of participants rating the interface as intuitive and user-friendly. 2. Successful execution of predefined NLP-driven workflows, with accurate code generation and modifications.

Milestone 4 - Testing Optimization and Feature Finalization

Description

Rigorous testing ensures the IDE is stable reliable and efficient. Performance optimizations are applied to enhance usability scalability and responsiveness. This milestone ensures all core functionalities are production-ready.

Deliverables

1. Comprehensive Testing Report: Documentation of test cases results and resolved issues covering functional integration and stress testing. 2. Optimized Performance Metrics: Detailed benchmarks (e.g. under 500ms response time for code generation and editing tasks). 3. Feature-Complete IDE Prototype: Fully functional IDE with stable operation across test cases.

Budget

$3,000 USD

Success Criterion

1. Resolution of 95% of identified critical bugs. 2. Over 50 testers with diverse profiles rating above 70% 3. Performance benchmarks achieved, ensuring smooth and responsive operation under typical use scenarios.

Milestone 5 - Documentation Tutorials and Deployment

Description

Comprehensive documentation and educational resources ensure users and developers can effectively utilize and contribute to the IDE. The milestone concludes with the deployment of the prototype for public access.

Deliverables

1. User Guide and Developer Manual: Step-by-step instructions for using and customizing the IDE. 2. Tutorial Videos: Three short videos demonstrating installation basic workflows and advanced customization. 3. Open-Source Release: IDE repository published on a platform like GitHub including proper open-source licensing and contribution guidelines.

Budget

$4,000 USD

Success Criterion

1. Repository available to the public, complete with all supporting materials. 2. Early adopter feedback indicating ease of understanding and usefulness of provided documentation and tutorials.

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The weighted average of the 4 perspectives Overall

0.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

0.0

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.

Overall (Primary)
This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).

Feasibility (secondary)

This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.

Viability (secondary)

This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary)

Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?

Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary)

This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”

For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

olokoji

Project Owner

Oversees project timelines, resources, and stakeholder communication. Creates project plans, user manuals, and technical reports, ensuring alignment with objectives and clear documentation.

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Oluwalana Ajayi

Backend/DevRel

Develops and optimizes backend systems and APIs. Integrates MeTTa/NLP, and creates tutorials and tools to promote the IDE's adoption and effective use. Link: GitHub.com/lanacreates

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Ridwan Amokun

Data/ML engineer

Builds NLP models for user input parsing, optimizes ML algorithms, and analyzes user data. Enhances IDE features with intelligent code generation and adaptive program behavior. Link: github.com/MITXy

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