[{"data":1,"prerenderedAt":19},["ShallowReactive",2],{"post-day-2":3},{"status":4,"tags":5,"content":10,"excerpt":11,"publishedAt":12,"title":13,"coverImage":14,"updatedAt":15,"slug":16,"createdAt":17,"postId":18},"published",[6,7,8,9],"AI Product Development","Buy vs. Build. Product Strategy","Serverless SaaS","GanttBasic","## From Idea to Execution\n\nIn Day 1, I wrote about the conversation with my wife that started this experiment.\n\nShe had a specific project planning workflow in mind, and the available SaaS tools did not quite match what she needed. That led me to a larger question: if AI changes the effort required to build software, does it also change the buy vs. build decision?\n\nSo I gave myself three days to test it.\n\nDay 2 was when the experiment stopped feeling like an idea and started behaving like a system.\n\nBy the end of the day, what we’re now calling GanttBasic had started to behave like a real product. I could create a project, define tasks, organize them into a timeline, and work through the dependency logic that makes Gantt charts valuable. It was still early, but the important shift had happened: the system was no longer just being described. It was beginning to work.\n\nThe first thing I realized was that this was not a story about one AI tool. It was a workflow across multiple models, each doing a different kind of work. I started with ChatGPT because it was useful for shaping the messy early thinking: requirements, product behavior, edge cases, user flows, tradeoffs, and system structure.\n\nThat early step mattered more than I expected. If you hand an AI coding tool a vague request, you usually get vague software. But if you first create clear requirements, architecture notes, product decisions, and implementation guidance, the build process becomes much more accurate.\n\nBefore pushing Claude Code too hard on implementation, I used ChatGPT to turn the idea into a structured product foundation. We defined the workflow, core objects, planning behavior, dependency logic, and early user experience. Then we created a docs section and architecture decision records, or ADRs, that could live directly in the codebase.\n\nThat documentation became a kind of shared memory for the project. Instead of relying on a single prompt, the product had a reference system. The models could refer back to requirements, architecture decisions, naming conventions, preferred patterns, and product intent.\n\nChatGPT also helped create the handoff to Claude Code. The first prompts were not “build a Gantt chart app.” They included the product goal, expected user flow, architecture direction, data model assumptions, and immediate implementation priorities. Claude Code became much more effective because it was not starting from ambiguity.\n\nThe infrastructure foundation helped too. I have spent a lot of time building serverless systems with SST on AWS, and I already had preferred patterns for APIs, authentication, deployment environments, data storage, queues, and infrastructure as code. I have also created a custom SST skill to reinforce those patterns.\n\nThat matters because AI performs much better when it is not guessing at the architecture. When the system has clear patterns to follow, the output becomes more reliable.\n\nOnce those pieces were in place, the speed became hard to ignore.\n\nGanttBasic started taking shape quickly. It was not a clickable mockup or a design prototype. It was actual deployed software with a functioning application structure, core planning objects, timeline behavior, dependency logic, authentication, infrastructure, and a path toward billing.\n\nThe process still required judgment. I had to review decisions, correct assumptions, refine requirements, adjust flows, and decide where complexity belonged. AI made the work faster, but it did not remove the need to understand the system.\n\nThat was probably the most important realization of Day 2.\n\nAI does not make product thinking less valuable. It makes clear product thinking more useful because the distance between decision and implementation gets much shorter. If the requirement is vague, the software gets vague quickly. If the architecture is messy, the system gets messy quickly. But if the requirement is clear, the architecture is sound, and the implementation path is well guided, AI can move with surprising speed and accuracy.\n\nBy the end of Day 2, GanttBasic was no longer just an idea inspired by my wife’s workflow. It was something I could deploy, open in a browser, use, test, and refine.\n\nThat changed the question again.\n\nIf a product can move from requirement to working software this quickly, the next question is not whether it can be built. It is whether it can become a real product.\n\nDay 3 is where that question became much more concrete.","When AI shortens the distance between idea and implementation, the real work becomes designing the system clearly enough for software to emerge.","2026-05-15T19:40:10.792Z","Day 2","https://ericnparadis-com-prod-mediabucketbucket-baexchhz.s3.amazonaws.com/media/5e61296d-1f90-4bf2-8684-e6c101ed007f.png","2026-05-18T17:24:39.740Z","day-2","2026-05-15T19:40:10.794Z","a5b29432-133c-46c3-b1b5-bd1907679214",1779125217530]