The Human is the Agent: How SolveIt Changed My Programming Journey After 25 Years - Chris Thomas Skip to content

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How SolveIt Enables Dialogue Engineering<br>Unexpected Applications and Iterative Refinement<br>Why This Approach Actually Works<br>Looking Ahead

The Human is the Agent: How SolveIt Changed My Programming Journey After 25 Years&para;<br>I have been programming for over 25 years off and on, with a background in pre-AlexNet AI and having been a technical reviewer for AI publications and courses. As an early adopter of AI tooling and LLMs I thought I had a good sense of what AI could do for programming. Then I joined the first cohort of SolveIt students and something unexpected happened. Despite my experience with AI tools, SolveIt changed how I approach programming in ways I did not anticipate.<br>The first sign came during the Advent of Code challenges. Initially, it reminded me of university days over 25 years ago solving logic puzzles with AI like the cannibals and missionaries problem. As I worked through the problems with SolveIt, I found myself enjoying programming more than I had in years. My Python skills improved and refined, I was tackling challenges that would have previously left me stuck on my own or in frustrating spirals with LLMs.<br>What made the difference was not just another AI tool - it was a fundamentally different approach to how humans and AI work together.<br>How to SolveIt with code&para;<br>The name SolveIt itself pays homage to George Pólya's classic mathematics book "How to Solve It" reflecting the platform's mission to help people tackle challenging problems, by combining practical skills with AI assistance rather than requiring a full computer science education.<br>Pólya's four-step problem-solving framework, first published in 1945, remains as relevant today as ever:<br>Understand the problem : You must clearly see what is required of you.<br>Devise a plan : Connect the data to the unknown to formulate a strategy for the solution.<br>Carry out the plan : Execute the strategy you have devised.<br>Review and extend : Look back at the completed solution to discuss it, check it and perhaps even use the same method to solve other problems.<br>What SolveIt does is embed these timeless principles along with much clever applied technology into a modern AI-assisted workflow, where each step becomes a collaborative dialogue between human insight and artificial intelligence capabilities.<br>Beyond Prompt Engineering&para;<br>Jeremy Howard first mentioned "dialogue engineering" in the context of AI Magic on the Latent Space podcast well before the SolveIt course launched and I was intrigued by the concept. The idea of moving beyond traditional prompting approaches to create ongoing conversations with AI seemed promising, I did not fully grasp its power until I experienced it first hand.<br>Traditional AI coding tools, even sophisticated ones, follow a familiar pattern: you describe what you want, the AI generates code and you either accept it or iterate through increasingly frustrated prompting loops. Sometimes this helped, although it often felt like negotiating with a black box.<br>SolveIt operates on an entirely different principle: the human is the agent. Rather than asking the AI to solve problems for me, I found myself working with it in small, deliberate steps. To see this approach in action, Jeremy Howard and Johno Whitaker demonstrate the SolveIt methodology in this detailed walkthrough of SolveIt: The Thinking Developer's Environment, showing exactly how the interactive dialogue process works in practice.<br>A few of my SolveIt projects&para;<br>When I wanted to build a context and style-aware image search engine combining CLIP, Stable Diffusion and FAISS with a FastHTML interface. SolveIt did not generate hundreds of lines of boilerplate code. Instead, we built it together piece by piece, with me understanding each component as we progressed. We implemented a prototype together in not too many hours and then improved upon adding in more search functionality. I learned enough FastHTML for the interface very quickly. Learning and implementing the technologies that were new to me would have taken much longer with traditional tutorials, reading and processing documentation. The iterative, dialogue-based approach made picking up all that was needed feel natural and easy, whilst leaving me with a more lasting understanding.<br>It was the same with my Stable Diffusion project, deploying previous research that used VGG features, style loss and latent MAE to steer/guide the difussion process. It was research I had started during the fast.ai part 2 course but I had not been easily able to deploy it. Yet SolveIt made the development process of the Gradio interface and deployment to Hugging Face spaces step by step, whilst being educational.<br>What struck me most was how this approach changed my thinking process. When I use SolveIt, I find myself initially pausing to carefully consider my prompts. Partly to anonymise my...