I made the classic mistake.

I convinced myself that if I just added one more feature, then I'd be ready to show people.

That "one more feature" turned into weeks of work.

Eventually I forced myself to stop coding and start talking to people who actually use AI every day.

The conversations were a bit humbling.

A feature I thought would be the biggest selling point barely came up.

Meanwhile, almost everyone mentioned the same annoyance:

They were constantly jumping between AI tools, copying context from one chat to another, and trying to remember what each conversation was about.

That wasn't even the problem I originally thought I was solving.

So I changed direction.

Instead of trying to build another "AI that does everything," I'm focusing on making AI workflows feel less chaotic. The idea behind my micro SaaS (FlexoraAI) is that different AI agents handle different jobs, while keeping the workflow in one place.

I'm still very early, and honestly I'm trying hard not to fall back into feature-building mode.

Right now I'm spending more time talking to people than writing code.

It's uncomfortable, but it feels like the right trade-off.

I'm curious how other solo founders handle this.

At what point do you stop building and say, "This is good enough—I need feedback now"?

I feel like that's a lesson I should've learned much sooner.

I've been experimenting with AI workflows over the past few months, and I realized something that I think a lot of builders overlook.

At first, I tried making one AI assistant do everything:

• write content
• analyze documents
• brainstorm ideas
• research
• answer support questions
• summarize meetings

It worked... until the context got huge.

The model would forget earlier instructions, mix tasks together, and responses became less reliable as conversations grew.

So I changed the architecture.

Instead of one giant assistant, I created separate AI agents, each responsible for one job. One researches, another writes, another summarizes, another reviews, etc. Each starts with a clean context and only receives the information it actually needs.

The improvements were noticeable:

• Better consistency
• Faster responses
• Lower token usage
• Much less context pollution

It's a bit like having a small team instead of asking one employee to do every department's work.

I'm building this into my micro SaaS (FlexoraAI), but I'm curious whether other founders have reached the same conclusion.

If you've built AI products:

• Do you prefer a single general-purpose assistant or multiple specialized agents?
• Have you noticed context degradation in long conversations?
• What's been your biggest lesson when building with LLMs?

I'd genuinely love to hear how others are approaching this problem.