Thought graph

The AI transformation is a tangible reality now, and the pressing challenge is to democratize the power of AI for all, not exclusively for tech specialists. Over the last three years, our team at Lexie.ai has been dedicated to developing ‘Thought Graph’—an AI-native programming framework. This framework is grounded in the innovative “Visitor Workflow Model,” which we will explore in depth in this and subsequent blog posts.

In our journey with Thought Graph, we challenge the conventional boundaries of software development. We believe and have steadily worked towards a future where the creation of robust, AI-driven applications isn’t confined to those with traditional coding expertise. Thought Graph is our answer to this challenge, a platform that empowers individuals, especially those with rich domain knowledge, to develop not just prototypes or proof of concepts, but fully-functional, complex applications.

Understanding the Thought Graph Model with Visitor Workflow

The Thought Graph with Visitor Workflow empowers citizen developers to assemble complex systems through an intuitive, no-code approach. This process is akin to a customer visiting an organization with a complex request. The customer is navigated through the workflow and necessary service employees get involved to accomplish the task. Every aspect of the request is broken down into simpler tasks, each managed by different employees. This method mirrors the capabilities of Thought Graph: if a developer possesses the critical thinking skills required to construct an organization, define workflows among employees, and specify each individual’s role, then that developer can adeptly use Thought Graph to become an efficient software developer.

In Thought Graph, the no-code user interface offers a visual tool where citizen developers can seamlessly create workflows using a drag-and-drop mechanism. This is similar to outlining an organization’s structure and the workflow interactions between various employees to complete the tasks. Additionally, developers can use the TG’s AI assistant to develop and modify their workflows using natural language. The visitor model in Thought Graph serves as an algorithm, ensuring each component performs its designated role and efficiently passes tasks to other components, maintaining a cohesive and streamlined workflow.

DMV Analogy

Imagine a Department of Motor Vehicles (DMV) and an individual arriving to obtain their driving license. This person (the visitor) first encounters a receptionist who  figures out what the user request is and guides the user for the first step in the process. Then the visitor will visit multiple different agents to go through the process of getting the driving license. He should provide necessary documents and information to an agent, undergo an eye exam and take a photo with another agent, complete a written test with yet another agent, and finally take a driving test. Meanwhile, unseen staff at the DMV perform background checks and document audits, integral to the license issuance process.

Now, imagine we want to create a software to automate the experience in DMV. Without going into too much detail and showing background workflows our high level TG would like the following. 

DVM analogy

manageable steps, each executed by a component (akin to a DMV agent). Some components interact with the user (the visitor) by asking the user to fill up a form (to collect the visitor’s information), while others may work independently or delegate tasks, all adhering to the application’s defined workflow in Thought Graph. Note that in the digital version of the DMV, still some tasks need to be done by real agents – at least for now – like an eye exam or proctoring the written exam or giving the driving test.

Understanding the parallels between orchestrating DMV workflows and constructing a software application is key:

1. Reception:
DMV –  The journey begins with a receptionist who guides you and sets the stage for subsequent steps.
Thought Graph – The root component in Thought Graph figures out the user request and routes it to the right component.

2. Workflow-based Task Management:
DMV – You navigate through various agents for tasks like document verification and eye examinations. Each DMV agent helps the visitor to find the next agent he/she should visit.
Thought Graph – Distinct software components sequentially manage parts of the task, akin to the workflow facilitated by DMV agents.

3. Defining the roles and responsibilities:
DMV – Each DMV agent knows exactly what are his/her roles and responsibilities, including the information needed to gather from the visitor (using forms perhaps) – or the documents he/she needs from the user.
Thought Graph – We also define the description of each component and the type of interaction the agents need to have with the user through the UI.

4. Specialized Agents for Targeted Tasks:
DMV – Certain agents specialize in specific tasks, such as conducting eye exams.
Thought Graph – The application includes specialized components for different functions, similar to DMV specialists.

5. Behind-the-Scenes Operations:
DMV – While you engage with front-office agents, others work quietly in the background, crucial to the DMV’s functionality.
Thought Graph – Certain components function behind the scenes, handling operations like background checks and reviewing the validity of the information.

6.Natural Language Interaction:
DMV – The process involves natural language communication with agents.
Thought Graph – when you build a software with thought graph, the software is AI-native – you  can talk to the agents in the app using natural language.

Conclusion: A New Era for Citizen Developers

Thought Graph with Visitor Workflow Model ushers in a new era in software development. It breaks down the barriers of traditional programming, allowing citizen developers to create complex, AI-native applications with ease. This inclusive approach ensures that software solutions are not only technically sound but are also deeply rooted in domain-specific knowledge. As Thought Graph continues to evolve, it stands as a testament to the democratization of technology creation, heralding a future where everyone has the potential to be a developer.

 

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Natural Language Code Revolution AI Leading the Way For Citizen Developers

Historically, attempts to replace traditional programming languages with no-code visual tools have fallen short. Real-world applications still predominantly rely on powerful programming languages like C++, Python, JavaScript, and React for two main reasons:

  • Beyond a certain complexity level, managing software with visual tools alone becomes impractical.
  • Customization, essential for serious applications, often demands more control than traditional no-code tools offer.

The following shows some of the existing no-code solutions – credit to Sacra research group.

The Amplified Flywheel Effect in No-code Platforms with AI Integration

The flywheel effect in no-code platforms, a self-reinforcing cycle that propels the platform’s growth and efficiency, is profoundly enhanced by AI. This can be visualized in a model with seven element in it:

  • More Developers: The cycle starts with an increase in the developer base, leading to:
      • More Components – A diverse range of building blocks.
      • Better Composability -Enhanced ability to integrate these components.
      • Improved Customizability: Making components more adaptable to specific needs.
  • Generality and Ease of Use (Boosted by items 1.a, 1.b, and 1.c): As components become more versatile and user-friendly, the platform’s general applicability and ease of use improve, fueling:
  • Higher ROI: Enhanced generality and usability lead to a higher return on investment, which in turn attracts more developers, thus completing the cycle.

AI-driven tools bring unprecedented efficiency to component development, integration, and customization, making the no-code platform more powerful and appealing to a broader user base. This AI-enhanced flywheel effect promises a new era of growth and innovation in no-code development.

Flywheel efect

We are on the cusp of an AI-driven revolution in no-code development, a shift that promises to simultaneously tackle the complexities and enhance customization in unprecedented ways. This synergy between expansion and enhancement ignites a virtuous cycle, catapulting the return on investment (ROI) into a realm far beyond the reach of traditional no-code tools.

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Redefining AI-Assisted Software Development – How Thought Graph is Different From Other Solutions?

In the burgeoning field of AI tooling and AI-assisted code generation, Lexie stands out with its innovative approach. We reviewed more than 50 AI/agent tooling platforms. E2B presented the landscape of these tools in a well structured figure as below – although there are many more projects that are not covered in this diagram.

Our extensive interaction with customers has revealed that to truly democratize AI’s power, a solution must embody certain key characteristics. Here’s how Lexie meets these crucial demands:

1. Genuine No-Code Innovation

At Lexie, ‘no-code’ is more than a buzzword—it’s a practical reality. We have transcended the limitations of random code generation that often necessitates programmer intervention. Our platform is designed with the simplicity of team organization in mind. If you can define roles and responsibilities within a team, you can seamlessly build software with Lexie. It’s intuitive, user-friendly, and eliminates the need for any coding skills.

2. Scalability from Simple Workflows to Complex Full-Stack Applications

Lexie isn’t confined to simple task management. It shines in crafting sophisticated, full-stack applications. Whether it’s developing intricate e-commerce systems or custom CRMs, Lexie transforms daunting complexity into manageable simplicity. Our platform is versatile, catering to a broad spectrum of development needs from basic workflows to intricate, full-stack applications.

3. Robust and Analyzable AI Reasoning

A key differentiator for Lexie is its powerful AI reasoning capabilities. This feature ensures that application development is not only reliable but also straightforward, catering to users of varying expertise levels. Our robust AI framework underpins a solid and analytical foundation for development, allowing for deep insights and reliable outputs.

Despite the presence of several solutions in the market, none have fully addressed these multifaceted requirements. Understanding and tackling the complexities of this challenge was a journey for us. However, we are confident that the innovation of Thought Graph by Lexie.ai is a game-changer. It has the potential to significantly alter the landscape of software development and the creation of intelligent systems on a broader scale.

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Citizen developer with AI Vs Traditional developers

In 2017, Jensen Huan, CEO of NVIDIA, stirred up the tech world with his statement about “AI eating the software.” In recent years, AI code generation has gained significant traction, with OpenAI’s custom GPTs leading the way. This technological leap has sparked a debate between two extremes: some believe it’s “game over” for traditional software development, while others see AI as suitable only for simple tasks. At Lexie, we take a more nuanced view, recognizing the transformative potential of AI in programming while acknowledging its current limitations.

The Nuanced View

Rather than prematurely declaring the end of traditional software development or dismissing AI’s capabilities, we believe we are witnessing a paradigm shift in programming. This shift, which we consider more significant than previous technological revolutions like personal computers, the internet, mobile, or cloud computing, introduces a new era of “citizen developers.”

Citizen developers do not require expertise in standard programming languages. Instead, they excel in:

  1. Understanding Customer Pain Points: They have a knack for comprehending the pain points of customers and discerning their preferred mechanisms for resolving those issues.
  2. Specifying Software Requirements: Citizen developers are adept at specifying software requirements using natural language, wireframes, flowcharts, or a combination of these methods.
  3. Breaking Down Complex Problems: They excel at breaking down intricate problems into modular agents that collaborate seamlessly to accomplish complex tasks.
  4. Leverage existing software libraries: Citizen developers can harness the vast array of existing software components using natural language invocation and parameterization.  
  5. Establishing Guardrails and Checks: They specialize in specifying guardrails, checks, and balances to ensure that these agents align precisely with the specified workflow.

qualities of citizen developers

Challenges and Opportunities

This paradigm shift presents significant opportunities for individuals who deeply understand specific industries and workflows, regardless of their technical background. Identifying pain points and optimizing workflows remains a valuable skill. 

Despite the exciting prospects, several challenges must be addressed for this technology to scale effectively:

  1. Alignment and Analyzability: Ensuring that all AI-driven reasoning remains transparent and safe, addressing concerns about AI decision-making.
  2. Data Quality: Acknowledging that the quality of AI models heavily depends on the data used for training. Addressing inaccuracies and conflicts in data to avoid poor AI decisions is crucial.
  3. Compensation for Domain Experts: Implementing mechanisms to fairly compensate domain experts and data providers who play a pivotal role in fueling AI systems with intelligence.
  4. Meta Reasoning: Establishing a self-improvement cycle of meta-reasoning over time, reducing the need for extensive human intervention while continually enhancing the system’s intelligence.

 

Conclusion

The software industry is on the brink of a significant transformation, driven by AI and the rise of citizen developers. While challenges lie ahead, the potential for improving workflows and collaborations between AI and domain experts is immense. By addressing these challenges and embracing this paradigm shift, we can unlock new possibilities and usher in a more efficient and innovative era of software development. The future may not be the end for the software industry, but rather a promising new beginning.

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