Why use low code tools in today's world with AI assistants?

Generally speaking:

“Low code tools” are ideally deterministic, and have restricted problem space

“AI assistants” are often creative, unrestricted problem space and non-deterministic

They both serve different purposes, AI hasn’t replaced programming yet…

I find low-code pipelines to be somewhat self-documenting in a way that code often isn't, especially for tools like Zapier, IFTTT, and Make.

They provide a visual pipeline/flowchart that any stakeholder can follow as an outline, even if they can't necessarily understand every node.

That's a lot easier than trying to show them code, even we'll-commented code. AI can help you write code and explain it step by step, but I don't think it's great at making visual flowcharts yet (or is there one out there?) Maybe you can get it to write Mermaid markup, but that's still not as integrated as an actual visual logic editor.

I don't think low-code is going to take over or anything, I just think they're a useful subset of serverless for certain use cases. In event driven pipelines like email or telephony, for example, they're pretty handy!

I suppose the biggest thing for me would be maintenance and interface updates when downstream APIs update and deprecate certain functions or frameworks require you to implement a change. This becomes tedious at time, with a managed solution like a no-code platform, the accountability shifts to that platform (e.g. IFTT) to keep those interfaces upto date. This saves some headache down the road, but as with everything convenience comes at a cost.

With low code or no code, we work within the constraints of the platform. With the added benefits of not worrying about infrastructure, operations, and with built-in error handling, retries/back off, and parallelization.

Code is just a small part of the solution. The code could be developed by developers or be generated by AI assistants.

It depends on the use case.

AI assistants like Copilot and ChatGPT serve a different purpose than low-code platforms, and they are not necessarily direct replacements.

Low-Code Platforms: Purpose: Low-code platforms are designed to enable rapid application development by minimizing the need for manual coding. They allow users (including non-developers) to create applications using visual interfaces, pre-built components, and drag-and-drop functionality. Target Audience: Low-code platforms primarily cater to citizen developers, business analysts, and professionals who want to automate processes or build simple applications without extensive coding knowledge. Strengths: Speed: Low-code platforms accelerate development by reducing the time spent on manual coding. Accessibility: They democratize software development, allowing more people to participate. Visual Modeling: Users can create workflows and UIs visually. Limitations: Complexity: For more complex applications, low-code platforms may fall short. Customization: Some use cases require custom code that low-code platforms cannot handle.

AI Assistants (Copilot, ChatGPT): Purpose: AI assistants like Copilot and ChatGPT are designed to augment human creativity and productivity in various domains, including coding, writing, and problem-solving. Target Audience: They primarily target professional developers, writers, and individuals seeking assistance in specific tasks. Strengths: Code Generation: Copilot assists developers by suggesting code snippets, improving code quality, and speeding up development. Natural Language Interaction: ChatGPT engages in conversations, answers questions, and provides creative content. Complementary: AI assistants complement existing skills and knowledge. Limitations: Contextual Understanding: While AI models have improved, they may not always fully understand context or domain-specific intricacies. Dependency on Input: AI assistants rely on user input; they don’t independently create applications.

Coexistence and Synergy: Collaboration: Rather than replacing each other, low-code platforms and AI assistants can collaborate. For instance: Developers can use Copilot to speed up coding within a low-code environment. Citizen developers can leverage low-code platforms while seeking guidance from AI assistants. Hybrid Solutions: Future platforms may integrate both approaches, allowing users to switch seamlessly between visual modeling and AI-generated code. Skill Enhancement: AI assistants can help citizen developers learn coding concepts, bridging the gap between low-code and traditional development.

AI assistants and low-code platforms serve distinct purposes, and their coexistence can lead to more efficient and creative solutions. The future likely involves a blend of both approaches, empowering a broader range of users to participate in software development.