Forem: John Brawner

Build a Throwaway Prototype First: How I Use Vibe Coding to Avoid Tech Debt

John Brawner — Tue, 13 Jan 2026 10:20:00 +0000

TL;DR

Diving into Lovable without a plan turned my first build into an unusable mess. The fix: build throwaway prototypes first. I now spend a few hours creating high-level versions to explore layouts, flows, and components before starting production. When ready to build for real, I use Lovable's chat to summarize what I learned, then refine that into a foundation prompt with ChatGPT. The result is a cleaner starting point and fewer side effects.

The Problem with Building First

As a technical product manager, my role was to be an orchestrator. Most of my work involved setting others up for success in executing their tasks. Whether it was to work with designers to define a flow, developers to identify the table structure that wouldn’t put us into a corner later when we enhanced functionality, QA to help determine if something was a feature or a bug, customer success to make sure they had all their info and artifacts to roll out the release, or to provide stakeholders with a roadmap update. The job, while very rewarding, sometimes lacks the joy of taking something from start to finish and crossing it off the to-do list.

So when I finally tried Lovable.dev for the first time, I dove in headfirst without any planning and started building right away. I was like a kid in a candy store. I had a lot of fun and was building fast, but I wasn’t building anything stable. The constant switching things up and deciding on different approaches led to way too many unintended changes. When I tried to change something on one page, it would cause side effects on another page.

While I was building faster than I thought possible, I was also creating tech debt at an impressive rate. It was clear that the tools were remarkable, but if I didn’t put intention into my building process, the tech debt would blow up my product.

The Throwaway Prototype Approach

If you go into the pre-build stage with the understanding that you will throw things out, you can get the most value out of this step: identifying the high-level flows you want to build, what patterns you want to follow, where can you re-use functionality across your software, how you want to lay out the pages, and where you see future scaling issues. The power of vibe coding is that you can iterate and test out concepts and ideas rapidly, and then build cleanly and on a stable foundation when you are ready.

You can have multiple throwaway projects, too. Don't feel bad if you say, "I want to explore concept X on its own." The goal here is not perfection but iteration, so you can identify and build a foundation that sets up your product for where you want to take it. If you chase perfection, you will overarchitect your project and get nowhere. This process is a short exercise of a day or two at most, and in most cases, just a few hours.

I vibe-coded a few pre-build throwaway projects after my first attempt became too unwieldy. All the data was hardcoded, the buttons just automatically took you to the next step, but the flow was there. These prototypes revealed more insights into the user experience, allowing me to provide the product with greater intention.

What the Prototype Reveals

With a product called AssMan.ai, I needed to make it immediately apparent that it was related to the Football Manager game series. The origins of the name can be found here. I also needed to make the call to action apparent to the user so that, without thinking, they know what to do. This flow was one of the outliers where I spent days tinkering in the pre-build stage, rather than the usual handful of hours.

You can see one of my early smoke-and-mirror prototypes for the flow here. If you go through the flow on the actual website today, you will notice many of the same core components of the overall flow. There is an upload section that transitions to a spinner page while the analysis is running. The analysis page displays the feedback and provides an interactive chatbot at the bottom. Eventually, I landed on the following, which was heavily influenced by the pre-build prototypes.

Starting at the top of the page. I have my logo, a traditional soccer scarf with the product name, and a soccer ball to give users instant visual cues that it is soccer-related. Complete transparency, I put a beta tag there to buy some leniency from users if the experience isn’t perfect. Below that, I have the subtitle "Interactive Feedback," which instantly shows users the value of using this tool, as discussed in my article on product validation for Assman.ai.

Inside the box, there are three main aspects: a pulsing green button that makes a clear, identifiable call to action on what the user needs to do. An example tactic image so users know exactly what to capture in their screenshot. Finally, there is information on how to take a screenshot to reduce friction further if the user doesn’t already know.

You can see the influence of testing this flow out on different operating systems and screenshot methods. Depending on the user’s operating system, the section defaults to their OS. There are many methods for capturing a screenshot; some save and download the file, while others only copy to the clipboard. Because of that, I made the primary CTA "Upload Tactic Screenshot,” as I expected that to be the core flow, and I also included “Paste from Clipboard” below it to not limit users.

The user can see all this information and take action all without any scrolling. That was very important to me to keep when I optimized the flow for mobile.

On mobile, you will notice two significant differences. First, the button changes from “Upload Tactic Screenshot” to “Take Photo”. This intentional change allows a mobile user to either instantly take a photo or, if they already have one, use the “Choose from Library” button below, which replaced “Paste from Clipboard”. I went back and forth a bit on whether it made sense to do this, since I basically moved the upload and save photo functions to different locations based on mobile or desktop. Still, I made the intentional decision to do this, as I would expect most users would not have a photo of their tactics on their phones, given the player base's overwhelming skew toward PCs.

The tactic feedback was something I worked on outside of Lovable. I used ChatGPT Playground, a tool that lets you test and compare different AI models and settings exactly as they would behave via the API. I used the tool to iterate until I found an output that would populate the frontend. You can see an example of the latest version here.

At this point, it was time for me to start my real production product, so I asked Lovable’s chat feature for an overview of where we stood on each of my prototypes. I then entered that data into ChatGPT. From there, I iterated until I found a prompt that would help lay a stable foundation.

I had my general layout defined: pages, functionality, common components, and core flow. The prototype gave me a blueprint. I wasn't guessing anymore. I knew what to scope, what to skip, and how to structure the product.

Closing Thoughts

Build a throwaway prototype first. Spend a few hours exploring how you really want the product to work so you don't spend days or weeks correcting an assumption. Limit iterations to hours, and stretch only to multiple days when necessary.

The barrier to building keeps getting lower. What took a lot of effort three months ago is easier now, and that gap will keep shrinking.

How to Validate Product Ideas Using Reddit and ChatGPT Before Building

John Brawner — Thu, 08 Jan 2026 02:19:39 +0000

TL;DR:

While lurking on r/footballmanagergames for the last few years, I could see I was far from the only one struggling to create the right tactics to get the most out of my team in Football Manager. As time went on, I started noticing more patterns around this and other similar questions and feedback requests. From here, I decided to use ChatGPT to analyze the subreddit at scale and quantify my assumptions.

The data was clear: tactics dominated at roughly 38% of posts during the period I reviewed. The findings led me to build the tactic analyzer as my MVP, and as of writing this, Assman.ai has analyzed 1891 tactics with 4979 follow-up messages. Player development came next at around 7% of posts, with 450 players reviewed and 819 messages. While the squad review and transfer assistant had a higher question frequency, they were both too large in scope for me to build at this time.

Starting Point

"Start where you are. Use what you have. Do what you can." - Arthur Ashe

Football Manager became an addiction when I found it in 2023. As someone who loves soccer and strategy games, it hooked me immediately. Steam says I have 1000+ hours in the series, doing playthroughs in England, the Netherlands, and Germany, where I take a low-tier team and work them through the league pyramid to get them into the top league in their country. I explain all of this to show that I already had some expertise and understanding of the ecosystem before I started this. I wouldn't name something AssMan.ai unless I had an actual reason for it.

I wanted to keep the scope limited. I was testing what I could build as a solo builder using no-code tools, so I needed to pick one feature to start with and validate it worked before moving to the next.

Validation and Prioritization

I had my hypothesis about tactics based on my own experience and confidence from following r/footballmanagergames since I started playing, but I didn't want to make assumptions. I needed to see if there was anything else I should consider before committing, so I went back to the subreddit to find any additional supporting data.

I went back through the previous month of posts to see what people were consistently asking about. FM24 had been out for nearly two years at this point. Sports Interactive missed their usual annual release, so FM25 was never released. My thought was that if the same questions were still showing up this late in the game's lifecycle, that meant the issues weren't going away unless FM26 made massive changes.

The pattern around tactics and strategy was easily the most frequent. Post after post asking "why is my tactic not working," "why is my striker not scoring," "why is my tactic suddenly not working?" I struggled with tactics myself, and during my last playthrough, I used ChatGPT as an assistant manager to get feedback on my tactics. Seeing how often these questions came up confirmed that tactics were a significant issue, but I wanted to see if any other pain points made sense to tackle.

One common theme I kept seeing, besides tactics, was complaints about time commitment. Football Manager is called a spreadsheet simulator for a reason. During transfer periods, you can spend hours searching through databases of 100k+ players trying to find the perfect fit. Getting a new playthrough started is just as bad; you can spend multiple hours evaluating your squad, identifying weak spots, and planning changes before you even progress a single day. I'd experienced both myself too.

While less called out, player development was another recurring request. People asked how they should train a player and where they should play them to maximize their potential. It showed up less frequently than tactics or time-saving features, but it was consistent enough to consider.

Manual browsing gave me a strong signal, but I wanted numbers to back it up. Back in late July and early August, ChatGPT relied more on Reddit data, making it easier to pull extensive information from the platform. I asked it to analyze the last month of posts from r/footballmanagergames, create themes, and categorize the volume. After massaging the themes to match what I was looking for, I had it generate a table that showed the breakdown. The data was clear: tactics dominated at roughly 38% of all posts. Posts about tactics also received more engagement than posts about other topics.

The other patterns I'd noticed manually also showed up in the data. Time commitment across topics like transfers and squad management came to around 22% of posts. Player development came in at around 7%, which was lower volume than I'd hoped, but consistent enough that it felt worth exploring if tactics gained traction.

Before making my final call, I looked into the tools people already use. The most popular was ratemytactic.com, which requires users to input their tactics manually and then provides feedback based on an algorithm. There are other tools, like fmdatalab.com, that calculate player role proficiency, as well as a handful of forums.

While there are competitive tools and forums people already use, that actually makes me more confident, as it shows people will use tools and external resources for help. With that, I decided to build the tactic analyzer first, as there was an opportunity for instant, automated feedback that would enable interactive follow-up, differentiating it from the current tools.

What Got Built

As of writing this, Assman.ai has analyzed 1891 tactics and 4979 follow-up messages from users, delving deeper into suggested improvements. The research showed that, in this case, there was a high correlation between high Reddit volume and high product usage.

One of the first things I realized was that friction kills adoption. If I wanted people actually to use this, the flow had to be dead simple. Screenshot your tactic, upload it, and get feedback. No manual entry like ratemytactic.com required. The chatbot made it even better. Users could ask follow-up questions and iterate on the feedback in real time, something Reddit threads and static tools couldn't offer.

After releasing the tactic analyzer and seeing decent traction, I started prototyping solutions for transfer assistant, squad review, and player development. I eliminated the transfer assistant quickly. The user would need to manually export a file from the game, potentially with 100k rows, and I would need to parse it and provide answers. Not impossible, but it would require custom data processing that wasn't worth the effort for an MVP. I tried squad review and had some success, but I couldn't get more complex answers like suggested tactics and formations for the team based on the squad, even when I used more expensive models. There might be an opportunity for this in the future, but with current AI capabilities, it didn't deliver enough value.

I built the player development feature next. It had decent demand and the same upload flow: take a screenshot, get feedback. There wasn't much friction, and I could reuse a lot of what I'd already built for tactics. As of writing this, Assman.ai analyzed 450 players with 819 messages. The throughput was significantly lower than tactics, but that tracked with the research. Player development accounted for about 7% of Reddit posts, compared to tactics at 38%. Lower volume, but consistent enough to validate it was worth building.

During this time, I also built out functionality around account creation and account benefits. In total, 125 people created accounts. This number increased when I added the option to create a Gmail account, which significantly reduced friction. I also added the ability for users to see all their historical tactics and player development feedback.

As of this writing, Assman.ai has analyzed a total of 2341 images, and users have sent 5798 messages to the chatbot. I ran ads for about a month and spent around $2000. I stopped once I'd learned what I wanted to about ad campaigns and using Google Ads as a platform. Once I stopped advertising, traffic dropped to a few people a day.

For now, I'm holding off on more development around AssMan.ai. It proved I could build and validate a product solo using no-code tools, and that was the goal. I'll be adding a Patreon in the near future. If it gets enough traction, I'll return to development, but for now I'm focused on other adventures.

Closing Thoughts

The research method worked. Manual browsing gave me direction, ChatGPT gave me confidence, and the data held up in production. Tactics led Reddit at 38% and usage with 1891 tactics reviewed. Player development represented 7% of posts and reviewed 450 players. The numbers weren't perfect, but they were directional, and that was enough.

Every subreddit has pain points that get discussed over and over. It might not be evident at first, but they're there. I found one in a space I knew well, quantified it with actual data, and avoided building something no one would use.

You probably have a space you know well, too. A hobby, a profession, a problem you've dealt with personally. That's your advantage. You understand the context, you can spot the patterns, and you can tell the difference between real pain and noise. This method works when you lean into that knowledge and let the data confirm what you're seeing.

How I Built an AI Product With Lovable.dev (No Code): Full Breakdown, Costs, and Insights

John Brawner — Sun, 28 Dec 2025 01:18:56 +0000

TL;DR

If you are looking to build software and you don’t have any coding experience, go with Lovable.dev. It is at a point where anyone can quickly build no-code/low-code with few issues and minimal cost. That said, as complexity increases, if you don’t make intentional decisions about your architecture and database structure, things can turn into a spiderweb of complexity that becomes unmanageable. For these reasons, I would suggest non-technical founders skip learning how to code. Instead, focus on learning system architecture, database design, a conceptual understanding of how software works, and how UI component libraries function.

Sipping the AI Kool-Aid

“It’s like a boulder rolling down a hill—you can watch it and talk about it and scream and say shit, but you can’t stop it. It’s just a question of where it’s going to go.”

—Ken Kesey, quoted in The Electric Kool-Aid Acid Test by Tom Wolfe (1968)

Around the end of July, I decided to start experimenting with vibe coding. I have done some programming for fun and random tinkering here and there, but I haven’t programmed professionally for over 8 years since switching to product management. I wanted to see, with my background, what I could still build with the tools available and, if so, how much complexity it could handle before the system broke.

I started by researching options, but Lovable.dev quickly became the obvious answer. While not perfect, it has by far the easiest learning curve for non-technical users. It has many integrations that make complex functionality, such as authorization, manageable while enabling you to use platforms like Gmail, Twitch, and Patreon as authorizers. That is a single-case use, but with their recent integrations with Shopify, Atlassian, and Figma, it shows that they have, or are creating, the tools to let users build quickly.

In the five months I have been tinkering, I haven’t used any other tool as much as Lovable for building software products. Given how far ahead of the competition it is at the moment and how they keep adding on, I believe this trend will continue. I will eventually do a deeper dive into others, but right now, Lovable is my tool of choice and the one I recommend to others, as it continues to get better and better.

First Prototype

Required Context:

As this project was more about learning than profit, I wanted to keep it contained, so I created a tool for the popular video game series Football Manager (FM). The player manages a professional soccer team, controlling things like roster, training schedule, tactics, strategy, and more.

When a product requires too much context before you explain the name…

The FM community jokingly calls their in-game assistant manager their “AssMan”… so when I was searching for a domain, AssMan.ai seemed like the perfect domain name because it's both memorable and product-significant… So, yeah, this is why, rightfully so, my last boss removed my ability to name features.

Overview:

Prototype Goals:

With my prototype with Lovable, I had a few goals:

Build something contained for MVP that:
- uses AI as a core component
- uses AI to handle an interactive conversation
Try out PostHog
Run my first Google Ads campaign
Experiment with Google AdSense

Product Opportunity:

One of the best tools for user research is Reddit. In many subreddits, you will notice that users will ask a similar question or make a similar request over and over again. That is a signal that there may be an opportunity. In my case, I noticed people kept requesting feedback on their tactics. I, myself, have struggled with tactics in FM and previously used ChatGPT for feedback.

Executing:

Lovable built-in Integrations Used:

Lovable + Ionos = Simple domain purchasing and set up

Lovable + Supabase = Turnkey backend and DevOps, basic account management

Lovable + Gmail = Standard account creation (OAuth)

Manual Integrations Set Up:

OpenAI Platform = Analyzed images to produce feedback/suggestions, and an interactive chatbot

Misc Tools:

ChatGPT = Iterated constantly to assist with problem-solving, Created Logo, $20/m

PostHog = Amazing Product Tool for all your tracking needs, free

Google AdSense = I made $19.63 by showing ads on the website

Google Ads = I spent $1080.12… it doesn’t quite offset the AdSense revenue

Reddit Ads = I also spent $267.73… it furthers the offset…

GitHub = Code repository and branch management

If you use this link for Lovable, you will get 10 free credits added to your Lovable account, and earn me a small commission if you end up purchasing credits.

Results

I set out to:

Build something contained for MVP that:
- uses AI as a core component - Done
- uses AI to handle an interactive conversation - Done
Try out PostHog - Done, Love it
Run my first Google Ads campaign - Done, a lot of good learnings
Experiment with Google AdSense - Done, a lot of good learnings

All objectives achieved.

Analysis:

105,046 visitors to the website
125 users have created accounts
1,848 tactics analyzed
445 player snapshots reviewed
5,704 AI chats interactions

Cost:

Domain: $200 for 2 years; usually, you can get things for a lot cheaper, but .ai domains are more expensive right now.

Lovable: $400 is my rough estimate; I worked on a few things at once, so the exact cost isn’t clear.

ChatGPT: $44.21, I felt pretty reasonable

Ads: $1347.85

Google Ads = $1080.12, 1.5 months
Reddit Ads = $267.73, 1.5 months

Supabase: $51.26, I forgot the importance of paginating, which caused some extra fees before I realized and fixed it

PostHog: $27.62, should have been free, but didn’t set a proper limit for recordings

Total: $2,070.94

Profit?:

This project was always for learning first, and profit second. The exposure to cutting-edge software and experience I gained during this project were well worth the cost. This project gave me the confidence to see what can be created with the tools out there, take the leap of faith and leave my job, and start up some actual projects that I will discuss more in the future.

Closing Thoughts

This project showed me what is possible when you build solely through prompting. It is not a silver bullet, but it does move the line for when you genuinely need developers. A lot of what once required an developer can now be built, tested, and validated by anyone who knows how to use AI and prompting correctly.

It is extremely easy to build simple things or personal tools, but the moment you create something for customers, expectations change. Without real intention behind system design, architecture, and structure, complexity starts to make things fall apart.

If I were a non-technical founder trying to build software with AI, I would skip learning how to code. I would focus on system architecture, database design, a conceptual understanding of how code and data flow through a system, and how UI component libraries function. That is the foundation you need to use these tools intentionally.

The goal is not to become a developer. The goal is to get the product to a point where a developer can take over while you focus on customer value and product growth.

For transparency, I built this entire project in Lovable without any developer help. I was comfortable shipping because I avoided sensitive data and monetized through ads. If I were collecting anything sensitive or taking payments directly, I would have had a developer review the code before fully releasing it. You can control risk in an alpha or beta, where users understand the constraints, but once the product is in the wild and expectations shift, the margin for error disappears.