AI Coding Is Not Just for Coders

Lessons Learned from the Forest Gump of Artificial Intelligence

In early August, I wrote about the rise of No Code AI, tools that democratize software development by allowing non-developers to create complex applications. While the broader AI landscape appears to be stabilizing, one corner of the market—AI coding—is showing no signs of slowing down. If anything, it's gaining momentum, fueled by open source innovation and an expanding toolkit of specialized large language models (LLMs).

In my opinion developers shouldn’t just be using AI for autocompleting snippets of code—they should be orchestrating it to write entire applications. Tools like GitHub Copilot may have paved the way for AI-assisted coding, but the real leap forward is in leveraging advanced models that generate high-quality code based on specifications, not just filling in blanks. Watching my friend rUv (Reuven Cohen) at work this week was a revelation: he wasn’t writing code in the traditional sense; he was conducting it.

Instead of piecing together individual components or relying solely on auto-suggestions, modern AI tools allow developers to describe what they want, and the AI does the heavy lifting. This isn’t just faster—it’s transformational. AI can now create architecture, define libraries, and even solve for edge cases, leaving developers to focus on guiding the vision rather than executing every detail. And he was doing it at rate that was easily 10x that of a normal developer.

For non-coders, this shift is equally groundbreaking. AI now enables anyone to create microapps without touching a single line of code. Whether you’re a developer or not, the message is clear: the days of manually building everything from scratch are over. It’s time to harness AI to work smarter and unlock a new level of productivity.

For Developers: AI as a First-Class Citizen in the IDE

If you’re a developer, AI should already feel like a natural extension of your toolkit. Tools like Cline and Aider enable seamless interactions with large language models (LLMs), and frameworks like LiteLLM give you the flexibility to direct development to the LLM of your choice. These tools aren’t just for auto-completing lines of code or speeding up rote tasks—they’re redefining the entire development process.

Unlike first-generation “no-code” tools, which were akin to digital Legos for non-coders, today’s AI-driven systems are far more capable. They can generate high-quality code from plain-language specifications, skipping the need for architecture diagrams or pre-defined libraries. This agentic AI approach empowers developers to work smarter, not harder.

The takeaway here? Unless privacy concerns or other constraints make it unfeasible, every developer should embrace AI-driven tools—not just GitHub Copilot but well-trained models that integrate directly into their Integrated Development Environment (IDE). These tools amplify creativity, streamline problem-solving, and reduce time-to-deployment.

For Everyone Else: Microapps Without the Code

But what about the rest of us—the non-technical users who don’t write code? AI has opened up unprecedented opportunities for productivity, allowing users to create microapps without writing a single line of code.

[I think this video on LinkedIn by Allie K. Miller provides a few great examples of what I am describing here.]

Unlike earlier no-code tools, which required users to piece together pre-built components, today’s AI systems take a more agentic approach. You provide the specifications—what you want the app to do, what data it needs to work with—and the AI handles the rest, generating everything from the codebase to the necessary integrations.

This shift means that non-technical professionals can create custom tools for niche tasks, vastly improving efficiency in areas where pre-built software often falls short. Whether it’s automating a specific workflow, building a bespoke data analysis tool, or creating lightweight internal apps, AI microapp creation is no longer a luxury; it’s a productivity game-changer.

The Takeaways

Developers Should Fully Embrace AI: AI isn’t just a helper—it’s an integral part of modern development. If you’re not leveraging tools like Cline, Aider, and LiteLLM to switch between modes to build smarter, faster, and better, you’re leaving potential on the table.

Non-Technical Professionals Should Explore Microapps: Identify tasks where a bespoke application could dramatically amplify your productivity and investigate how today’s AI models can help you create those apps without traditional coding.

AI is for Specifications, Not Just Execution: The new agentic AI systems are built to understand what you need, not just how to build it. They generate architecture, libraries, and code on demand, freeing users to focus on outcomes, not implementation.

And there you have it—coding the old-fashioned way is starting to feel, well, a little cheugy. Whether you’re a seasoned developer or someone who’s never written a line of code, the tools at our disposal today are rewriting the playbook.

AI is no longer just assisting with the grunt work—it’s enabling anyone to bring their ideas to life, faster and smarter than ever.

Further Reading and Resources

Joining communities and forums can provide valuable support, resources, and networking opportunities for no-code app developers:

• Makerpad - One of the largest communities for individuals developing software and automating processes without code.
• NoCode.tech - Known for its supportive environment, providing resources and guidance for no-code enthusiasts.
• Indie Hackers - While not exclusively a no-code community, it includes many no-code developers sharing their experiences and projects.
• Reddit (r/nocode) - A subreddit where users discuss various aspects of no-code development, share projects, and seek advice.