Every idea gets four questions, five frameworks, a real-world evidence check, and one verdict: build, validate first, or skip.
The FL Method is the home court scorer. It breaks every idea into four weighted pillars that together answer one question: is this worth a solo builder's time?
Are people actively searching for solutions, complaining online, or paying for workarounds? Real pain leaves evidence.
Is the current market underserving this need? Are existing solutions clunky, overpriced, or missing entirely?
Would the target audience actually pay for a better solution? Evidence of spending behavior in adjacent categories matters more than survey answers.
Can a solo builder ship an MVP in weeks, not months? Technical complexity, API availability, and existing tools all factor in.
Builder's note
These four questions are the ones I kept coming back to before starting anything. Not “is it cool?” or “could it go viral?” but “is the pain real, and can I actually build this?” The point system just formalized what was already a gut check.
Beyond the FL Method, every idea goes through four more frameworks, each scoring 0 to 100. None of them are endorsed by or affiliated with the people who inspired them. They are our interpretation, adapted for solo builder idea evaluation.
Dream outcome multiplied by perceived likelihood of achievement, divided by time delay and effort.
Inspired by the value equation framework. Adapted for solo builder idea evaluation.
Personal edge. Looks at whether a solo creator audience would care about the idea, and whether it positions the builder as someone others want to learn from.
Inspired by the one-person business philosophy. Adapted to score personal edge.
Hook strength and shareability. Optimizes for the first 3 seconds and the impulse to send it to a friend.
Inspired by micro-SaaS and viral content frameworks. Adapted for idea-level evaluation.
Builder credibility. Looks for real usage, real metrics, and real decisions. Penalizes vaporware.
Inspired by startup evaluation criteria. Adapted for pre-launch idea assessment.
Scores mean nothing without evidence. After scoring, every idea goes through a validation layer. We search for real-world evidence that either supports or contradicts the score. This is what separates an AI opinion from a data-backed recommendation.
01
Google Search for competitors, pricing, and market signals. Reddit for community sentiment. Real data from real conversations, not surveys.
02
How many competitors exist? Are they well-funded or vulnerable? What are users complaining about in their reviews? Where are the gaps?
03
Each idea gets a confidence level (high, medium, low) based on how much evidence was found. Low evidence means the score is less reliable, not that the idea is bad.
After the questions, the frameworks, and the evidence check, everything folds into one of three calls.
Strong signal across the board. The idea clears enough bars to justify building an MVP. Ship something small and test with real users.
The signal is there but not conclusive. Talk to potential users, run a landing page test, or find more evidence before investing build time.
Not enough signal to justify the time investment. Move on to the next idea. The best builders are fast at saying no.
Strong signal across all dimensions. This is rare. If you see it, pay attention.
Solid fundamentals with minor gaps. Worth exploring with a focused MVP.
Promising but unproven. Needs more research or a different angle before committing time.
Significant gaps in the scoring. Could work with a major pivot, but risky as-is.
Weak signal across most dimensions. Not worth pursuing without a different approach.
Do your own research
The scoring system is a starting point, not a business plan. AI models can miss context, overweight trends, or undervalue niche markets. Use the scores as one input alongside your own research, conversations with potential users, and judgment. The best ideas still require a human who cares enough to build them well.
Every idea is free to browse, scores included.