GoingNext

Okay, let’s dive into conceptually designing the next way of building apps. This is a fascinating challenge, and it requires us to think about what frustrates developers now, what new technologies are emerging, and what the future of user interaction might look like.

Here’s my conceptual design broken down into key principles and ideas:

Core Principles:

1. Abstraction & Focus on Intent: Move away from low-level coding and towards describing what the app should do, not how. This means heavy reliance on:
   • High-Level DSLs (Domain Specific Languages): Languages that are specific to a problem domain, like UI design, data manipulation, or workflow automation.
   • Visual Programming & No-Code/Low-Code: Empowering non-programmers to participate in app creation.
   • AI-Powered Code Generation: Utilizing AI to translate high-level intents into actual code.
2. Component-Based, Modular Architecture: Embrace composability to the extreme. Think Lego bricks for apps:
   • Universal, Cross-Platform Components: Components that can be reused across web, mobile, desktop, and even embedded systems.
   • Micro-Frontend Architectures: Breaking down apps into independent, deployable units.
   • Open Component Marketplace: A vast library of reusable components with robust versioning and dependency management.
3. Data-Centricity: Apps should be viewed primarily as a way to interact with and transform data, not just as visual interfaces.
   • GraphQL-Like Abstractions: Allow developers to define their data needs declaratively.
   • Real-Time Data Syncing: Built-in mechanisms for seamless data updates across devices.
   • AI-Powered Data Analysis & Insights: Integrating data analytics directly into the app building process.
4. AI-Assisted Development: AI should be an active partner in the development process, not just a tool:
   • Intelligent Code Completion & Error Detection: More advanced than current IDEs, anticipating errors before they happen.
   • Automated Testing & Optimization: AI-driven testing to ensure quality and performance.
   • Personalized Development Experiences: Adaptive UIs and tools that learn developer preferences.
5. Security by Default: Security shouldn’t be an afterthought. It needs to be baked into the fabric of the platform:
   • Automated Vulnerability Scanning: Continuous security checks integrated into the development pipeline.
   • Secure Component Marketplace: Trusted source for components with verified security practices.
   • Granular Permission Models: Easily manage access to resources and data.

Conceptual Design Pillars:

Here’s how we can translate those principles into a concrete conceptual design:

1. The “Intent Canvas”:
   • Visual Interface: A drag-and-drop interface where you can “draw” the app’s architecture using logical building blocks.
     • Data Source Connectors: Connect to APIs, databases, and other data sources.
     • UI Layout Elements: Place and customize UI components.
     • Logic Blocks: Define workflows and business logic using visual connectors.
   • Textual DSL: An accompanying DSL that allows advanced developers to define complex interactions and logic using code.
   • AI-Powered Suggestions: AI actively suggests components, data transformations, and logic patterns based on your intent.
2. The Component Repository:
   • Centralized Hub: A vast library of pre-built and user-created components, categorized by function.
   • Version Control & Dependency Management: Robust systems for managing different component versions and dependencies.
   • Rating & Review System: Community-driven feedback to ensure component quality.
   • Security Badging: Components are vetted for security vulnerabilities and given security badges.
3. The Data Pipeline:
   • GraphQL-Inspired Definition: A declarative way to define the data your app needs.
   • Real-Time Data Synchronization: Automatic data updates across devices and user sessions.
   • Data Transformation Pipeline: A visual interface for manipulating and cleaning data before it’s displayed.
   • AI-Driven Data Insights: Built-in analytics tools that provide insights into your data.
4. The AI Co-Pilot:
   • Context-Aware Code Completion: AI understands the intent and suggests relevant code.
   • Error Detection & Prevention: AI identifies potential errors and offers suggestions for fixes.
   • Automated Testing Suite: AI automatically generates tests based on your app’s logic and data flows.
   • Personalized Recommendations: AI learns your development style and offers tailored suggestions.
5. Deployment & Management:
   • Automated Deployment Pipeline: One-click deployment to various platforms.
   • Centralized App Management Console: Monitor app performance, track usage, and manage updates.
   • Scalable Infrastructure: Automatically scale your app to handle increasing user load.

Example Scenario:

Imagine you want to build a simple to-do list app:

1. Intent Canvas: You drag a “Data Source Connector” and connect it to a cloud-based storage system.
2. Intent Canvas: You drag a “List Component” and a “Text Input” component and bind them to your data source.
3. Logic Blocks: You connect a “Button” component to a logic block that adds the text from the input field to the to-do list data source.
4. AI Co-Pilot: AI suggests adding a checkbox to mark items as completed and suggests a logic block for filtering completed and pending items.
5. Component Repository: You search for a UI component to make the app look more appealing.
6. Deployment: One click to deploy your app to web and mobile devices.

Challenges and Considerations:

• Complexity of Abstraction: Making these abstractions simple while still offering power to advanced developers will be a huge challenge.
• Performance: Ensuring performance while using higher-level abstractions will require smart design and optimization.
• The “Black Box” Problem: If too much is handled by AI, debugging could become opaque.
• Adoption: Getting the development community to embrace a radically different paradigm will require a lot of effort.

In Conclusion:

The next wave of app building needs to be about making the process more intuitive, efficient, and accessible. This means moving away from low-level coding and towards intent-driven development, powered by AI and composable components. It’s a significant shift, but one that I believe is essential to unleash the full potential of software creation.

This conceptual design is a starting point, and the specifics would need to be refined and iterated upon. But, the core principles of abstraction, composability, data-centricity, AI-assistance, and security are what I believe will drive the future of app development. What do you think? What ideas does this spark for you?

But wait! What current companies or projects are the closest to this vision?

That’s a great question! Isn’t it?
While I do not know of any single entity that has perfectly captured the entire vision laid out here, several companies and projects are making significant strides in various aspects of it. Here’s a breakdown of who’s closest and where they’re making progress:

1. No-Code/Low-Code Platforms:

• OutSystems: A leading low-code platform that excels in building enterprise-grade applications. It uses visual development tools and provides a good level of abstraction, allowing for rapid development. They’re strong on the “Intent Canvas” and component reusability for enterprise use cases, but still require some coding for complex logic.
• Mendix: Another prominent player in the low-code space, known for its visual modeling tools and focus on business applications. Similar to OutSystems, they provide a strong abstraction layer for UI and workflows, but are less focused on a full spectrum of app types beyond enterprise.
• Bubble: A very popular no-code platform with a visual drag-and-drop interface, focusing on building web applications. It’s accessible to non-programmers and promotes visual programming, pushing the envelope in “Intent Canvas” and visual programming, though it has its limitations with scalability and performance for complex use cases.
• Webflow: Focuses on visual web design, offering a powerful way to build websites and web apps. It excels in the visual aspects of UI creation with a great degree of control. Though not a full application platform, it embodies the principles of visual construction and component reusability.
• Glide: A strong player in transforming data into mobile applications. They enable users to quickly build apps directly from spreadsheets, showing a focus on data-centricity.

2. GraphQL and Data-Centric Approaches:

• GraphQL: While not a full platform, GraphQL itself is a core enabling technology for the data-centric aspects of my vision. Companies like Apollo and Hasura are building tooling and infrastructure to leverage GraphQL for building applications that prioritize data definition and access. They are very close to implementing the “Data Pipeline” concept.
• Supabase/Firebase (with their latest features): Cloud platforms that provide robust backend-as-a-service solutions with real-time databases, serverless functions, and now GraphQL support. They are bringing data-centric approaches to a wider audience.

3. Component-Based Architectures:

• React, Vue, Angular (and associated component libraries): While these are JavaScript frameworks, they promote component-based development, a critical concept in the vision I outlined. They foster reusable UI components but require more code to assemble the “Lego blocks”.
• Bit: A tool that focuses on component sharing and versioning across different projects. They’re actively pushing the boundaries of composability and component reuse.
• Storybook: Primarily a UI development tool, but it is key in enabling component-driven development by providing a way to build, organize, and test UI components in isolation.

4. AI-Powered Development:

• GitHub Copilot (and similar AI code assistants): This is a huge step towards the “AI Co-Pilot” vision. It provides intelligent code suggestions and can help developers write code more quickly and with fewer errors.
• Tabnine/Codiga: Similar AI code completion tools that work in various IDEs. These demonstrate the potential for AI to assist developers in code creation.
• Various Auto-ML platforms: While mostly used for AI model training, AutoML tools showcase the capability of AI to automate the creation of software artifacts, which is a key element of the long-term vision.
• Replit Ghostwriter: Replit is working on AI tools for code explanation, conversion, generation and bug detection.

5. “Intent-Based” Development (emerging):

• Some emerging startups: Several startups are experimenting with natural language or visual input to generate apps. While not as mature as the platforms mentioned earlier, they are early examples of the “Intent Canvas” idea.

Where They Fall Short (and where there’s room for innovation):

• Holistic Approach: Most platforms focus on one aspect (e.g., UI, data, low-code) and lack the holistic vision of combining them all.
• AI Integration: While AI tools are improving, full AI-powered development assistants that actively anticipate developer needs are still in their infancy.
• Universal Component Marketplace: We don’t yet have a widely adopted, well-curated marketplace of cross-platform, secure, and trustworthy components that are built with reuse in mind.
• Security By Default: While security features are improving, very few platforms have made it an integral part of the entire development process.

Key Takeaways:

• The pieces of the puzzle are being built, but they’re often fragmented across different companies and projects.
• No-code/low-code platforms are closest to the “Intent Canvas” vision but can be limited in complex logic and performance.
• GraphQL and modern backend-as-a-service offerings are driving data-centricity.
• Component-based frameworks and tooling are enabling reusability, but the vision of a universal marketplace is still nascent.
• AI-powered development is rapidly evolving, but the “AI Co-Pilot” concept is still aspirational.

The Future:

I believe the next wave of innovation will come from platforms that:

• Integrate these technologies seamlessly: Bringing together visual development, powerful data handling, AI assistance, and component marketplaces in a cohesive ecosystem.
• Prioritize developer experience: Make the development process more intuitive and enjoyable, enabling a wider range of people to participate in creating software.
• Focus on true abstraction: Allowing developers and creators to focus on what they want to build, not how to build it.

These companies and projects are certainly driving us closer to a future where building software is more accessible, efficient, and powerful. The next step will likely involve a consolidation of these technologies into more unified and complete platforms. It’s a really exciting time to be in this space!

What do you think of this analysis? Are there any specific companies or projects you believe are further along or are missing from the list?