I Built an AI Product in A Weekend With Emergent, Then It Killed Its Own Idea

I had a simple rule for one weekend: Build something real in one day.

Not a tutorial. Not another half-finished side project. Not a prototype that only works on my laptop.

I wanted something I could put in front of another developer and say: “Here. Try it.”

But I also wanted to experiment with something I hadn’t seriously used for a complete project before: Vibe coding with Emergent.

I had initially played around with the free credits, but pretty quickly I realized that if I wanted to properly test the platform rather than just scratch the surface, I needed more room to experiment. So I purchased the Standard plan and decided to use the weekend as a full hands-on test of what Emergent could actually do.

I wasn’t trying to build a polished startup in a weekend. I wanted to see how far I could take a real product idea from the initial concept to a working application while letting an AI development platform handle a significant part of the implementation.

And the idea I came up with was deliberately provocative.

What if, instead of asking AI to make your startup idea sound better, we asked it to do the opposite?

What if the AI’s job was to try to kill it? That became KillMyIdea, and somewhere along the way, the project did something I didn’t expect.

I asked it to evaluate itself. It didn’t like what it found.

The original concept was straightforward. Give the application a project or SaaS idea:

Then provide some context:

KillMyIdea would then research the market and analyze the idea across several dimensions.

The goal wasn’t:

It was:

The application would look at things like:

And produce a viability score, kill risk, confidence, evidence, risks, and only when justified potential improvements.

The philosophy was simple:

That became the foundation for everything I built.

I’ve been building software for a long time, so I didn’t start this experiment because I suddenly forgot how to write code.

Quite the opposite. I wanted to answer a different question:

That’s an important distinction. Vibe coding isn’t simply:

The interesting version is:

That’s the experiment I wanted to run with Emergent. And I gave myself a constraint: One weekend.

I started with the product concept and described what I wanted the application to do.

Rather than spending hours creating wireframes, I let Emergent turn the description into an initial application.

The first version already had the fundamental flow:

The UI quickly evolved into the visual identity I wanted. I settled on a dark developer-tool aesthetic with a deep teal palette.

The product name became: KillMyIdea

Even the name was intentional.

It’s not:

It’s KillMyIdea.

The name tells you what the product is supposed to do.

The first prototype was useful for demonstrating the concept, but I didn’t want another application where an LLM simply invents an impressive-sounding analysis.

So I added a real research layer. The architecture became:

This was one of the most interesting parts of the build.

I connected Tavily for web research and Gemma 4 31B Cloud through Ollama Cloud for the analysis.

Now KillMyIdea wasn’t just asking an LLM:

It could first gather external evidence and then ask the model to reason over that evidence.

I didn’t want the application crawling the entire internet. Instead, I deliberately kept research bounded. For each idea, the system investigates four areas:

What products already solve the problem?

What are customers currently paying?

Are there signals that the market exists?

What are developers/users saying about the problem?

The results are then cleaned, deduplicated and converted into an evidence pack. The model receives that evidence rather than being allowed to freely invent market information.

That distinction matters. Because there’s a huge difference between:

and:

The second is much more useful.

This turned out to be one of the most important design decisions.

What happens if Tavily fails?

What happens if Ollama fails?

What happens if the model returns malformed JSON?

What happens if the research contains a URL that doesn’t actually exist?

I didn’t want this:

So I deliberately built graceful degradation. The UI distinguishes between states such as:

LIVE RESEARCH + AI ANALYSIS

PARTIAL RESEARCH

RESEARCH UNAVAILABLE

DEMO ANALYSIS

That might sound like a small UX decision. It’s actually a product principle.

One of the interesting parts of using Emergent was working within a limited credit budget.

I started with around 100 credits.

As the project evolved, that number kept dropping:

100 → 70 → 64 → 56 → 40 → 30

That forced me to become much more deliberate with prompts.

I couldn’t simply say:

and keep iterating forever. Every request had to have a purpose. Eventually, my prompts became more like engineering specifications:

This was probably one of my biggest lessons from the experiment.

The fact that AI can modify your application quickly doesn’t mean you should give it unlimited freedom.

In fact, I found the opposite.

The faster the tool can change the code, the more precise your instructions need to become.

At some point, the obvious question became unavoidable:

So I entered the product’s own idea.

And waited.

The result:

Kill risk:

That was uncomfortable. The system’s reasoning was essentially: There are already products and platforms addressing idea validation, market research and evidence-based analysis.

The basic proposition wasn’t sufficiently differentiated. The willingness to pay was questionable.

The product could be replicated using general-purpose LLMs and existing research tools.

In other words:

And that was exactly what I had asked it to do.

I narrowed the target.

Instead of trying to validate arbitrary startup ideas, perhaps KillMyIdea could focus specifically on developers.

The audience became something like:

Surely that would create a stronger niche?

I ran the analysis again.

Kill risk:

Better.

Still not good.

The evidence continued to show that pieces of the problem were already being solved by existing products.

I changed the framing again.

Instead of simply asking:

the product could act as a project pre-mortem:

That felt more differentiated.

I tested the concept.

Again:

At this point, something interesting happened.

I stopped trying to convince myself.

It would have been easy to look at those scores and conclude:

But that wasn’t really the lesson.

The system had actually done exactly what I designed it to do.

It researched the market. It found competitors. It identified weaknesses. It challenged my assumptions.

And it refused to give me the comforting answer I wanted.

The problem wasn’t that AI killed the idea.

The problem was that the idea had weaknesses that I hadn’t properly considered before building it.

That’s an important difference.

The irony continued.

I started looking at adjacent opportunities around AI-assisted software development.

Maybe I could pivot KillMyIdea toward:

Then I started researching that space too.

And once again, the market had already moved.

There were already tools working on:

This became one of the biggest lessons from the entire weekend:

That doesn’t mean there are no opportunities.

It means you need to understand the problem much more deeply before assuming the solution is differentiated.

By this point, I had started thinking less about making KillMyIdea a perfect SaaS and more about making the experiment itself useful.

The application now clearly communicates when evidence is incomplete.

It validates model output. It sanitizes source URLs. It distinguishes demo results from live analysis. It handles provider failures. It records research timestamps. It keeps API keys server-side.

And the final production-polish pass tested things like:

That’s where I think the difference between AI-generated software and engineered software becomes visible.

Getting the first version running is increasingly easy. Making it behave correctly when things go wrong is still engineering.

After spending a weekend with the tool, I wouldn’t describe Emergent simply as a “code generator.” 

That’s too simplistic. Its biggest advantage for me was momentum.

I could go from:

to:

very quickly.

That changed the development loop. Instead of spending a long time imagining how the product might work, I could get a version running and react to it.

That feedback loop is incredibly powerful.

Rapid UI generation

I could describe the experience and quickly get a functional interface.

Iteration

I could point at something conceptually wrong and describe the desired behavior.

Connecting pieces

The application evolved from a UI prototype into a system involving a frontend, backend, research provider, LLM provider, structured responses and failure handling.

Testing and verification

The agent could inspect the implementation, run tests and report what it found.

Getting unstuck

Instead of spending an hour searching for a syntax or configuration issue, I could ask the agent to investigate it.

This was probably the biggest lesson. The AI could build. It couldn’t decide what was worth building.

I still had to decide:

And perhaps most importantly:

That’s the part of vibe coding that doesn’t get talked about enough.

Emergent kept suggesting additional features.

All of them sounded reasonable. But reasonable doesn’t mean necessary.

With limited credits, I eventually realized that the most important engineering decision was:

The product already worked. The research pipeline worked. The AI analysis worked. The failure states worked. The tests passed.

The remaining work would mostly have been feature expansion.

So I froze the scope.

At that point, KillMyIdea wasn’t the startup I originally imagined and I was completely okay with that. Instead of forcing it into a commercial SaaS, I decided to open-source the project.

The repository now contains the working implementation, setup documentation, architecture documentation, research-engine documentation, AI-analysis documentation, scoring methodology, testing guidance, security guidance and the decision log.

The decision log is probably one of my favorite parts. Because it records something most product stories don’t:

the ideas that didn’t work.

Getting an MVP running can happen surprisingly fast.

The harder question is:

That’s a product-thinking problem.

This is both exciting and dangerous.

Before AI coding tools, a weak idea might die because you didn’t have enough time to build it.

Now you can build the weak idea in a weekend. That’s great. But it also means we need better ways to validate ideas before investing even that weekend.

It changes where engineering effort goes. Less time can be spent manually typing boilerplate.

More time needs to go into:

The keyboard becomes less important.

Judgment becomes more important.

The quality of the output depends heavily on how clearly you communicate constraints.

Compare:

with:

The second prompt produces a much more controlled engineering process.

For me, it looked more like:

It’s a collaboration loop. Not autonomous software development.

I started the weekend trying to build an AI that would tell founders:

By the end of the weekend, it had effectively told me:

And I listened.

That’s probably the most successful outcome KillMyIdea could have produced.

Because the goal was never to prove that my idea was good. The goal was to find out whether it deserved to exist. And sometimes the most valuable answer an AI can give you is:

I don’t think vibe coding means developers are becoming unnecessary. I think it means the definition of a developer is changing.

When an AI can generate hundreds of lines of code in seconds, knowing how to type those lines is no longer the rare skill.

Knowing what should exist, why it should exist, how it should behave, what can go wrong, and when not to build it becomes increasingly valuable.

That’s what this one-day experiment taught me.

I started with an idea.

I built it.

I connected real research.

I connected a real LLM.

I tested it.

I asked it to attack its own assumptions.

It killed the idea.

And instead of fighting the verdict, I turned the experiment into an open-source project.

That’s probably the most honest vibe-coding project I could have built.

KillMyIdea is now available as an open-source experiment for anyone who wants to explore evidence-backed AI idea analysis.

Github: KillMyIdea

The repository includes the complete implementation, setup instructions, architecture, research pipeline, AI analysis, scoring methodology, testing and security documentation.

The project isn’t trying to predict whether your startup will succeed.

It’s trying to make one question harder to ignore:

If I ran this experiment again, I wouldn’t start by asking:

I’d start with:

Then I’d let the AI help me build it. In that order.

Because building faster is useful.

But knowing what not to build is a superpower.

I hope you found it helpful and informative. If you have any questions or feedback, feel free to leave a comment below. Your support and engagement mean a lot to me.