u/TransportationOne437

A question for founders:

How do you make important product decisions before you have enough data?

Pricing changes.
New positioning.
New features.
New customer segments.
New markets.

The advice is usually:

• talk to customers
• run experiments
• ship and learn

But many decisions happen before you have enough traffic, customers, or time to run meaningful tests.

I'm curious:

What's the biggest decision you've made recently where you wished you could have seen the likely consequences before committing?

Not asking how you solved it.

I'm asking what the decision was.

A question for founders:

How do you make important product decisions before you have enough data?

Pricing changes.
New positioning.
New features.
New customer segments.
New markets.

The advice is usually:

• talk to customers
• run experiments
• ship and learn

But many decisions happen before you have enough traffic, customers, or time to run meaningful tests.

I'm curious:

What's the biggest decision you've made recently where you wished you could have seen the likely consequences before committing?

Not asking how you solved it.

I'm asking what the decision was.

A question for founders:

How do you make important product decisions before you have enough data?

Pricing changes.
New positioning.
New features.
New customer segments.
New markets.

The advice is usually:

• talk to customers
• run experiments
• ship and learn

But many decisions happen before you have enough traffic, customers, or time to run meaningful tests.

I'm curious:

What's the biggest decision you've made recently where you wished you could have seen the likely consequences before committing?

Not asking how you solved it.

I'm asking what the decision was.

A question for founders:

How do you make important product decisions before you have enough data?

Pricing changes.
New positioning.
New features.
New customer segments.
New markets.

The advice is usually:

• talk to customers
• run experiments
• ship and learn

But many decisions happen before you have enough traffic, customers, or time to run meaningful tests.

I'm curious:

What's the biggest decision you've made recently where you wished you could have seen the likely consequences before committing?

Not asking how you solved it.

I'm asking what the decision was.

A question for founders:

How do you make important product decisions before you have enough data?

Pricing changes.
New positioning.
New features.
New customer segments.
New markets.

The advice is usually:

• talk to customers
• run experiments
• ship and learn

But many decisions happen before you have enough traffic, customers, or time to run meaningful tests.

I'm curious:

What's the biggest decision you've made recently where you wished you could have seen the likely consequences before committing?

Not asking how you solved it.

I'm asking what the decision was.

One thing I've noticed building products:

Most decisions happen long before you can run a real-world test.

Before the landing page.

Before the A/B test.

Before the first customer interview.

At that stage teams are often asking questions like:

• How might different customer segments react?
• What objections are we missing?
• What incentives are competing with ours?
• What second-order effects could emerge?

We're building Polyhyle to explore those questions through large-scale simulations.

Not to replace real users.

Not to replace experiments.

And definitely not to replace actual conversion data.

The goal is to give teams another tool for exploring possible outcomes before committing resources to a specific direction.

Curious:

Where do you think simulations are genuinely useful, and where do you think real-world testing remains irreplaceable?

Waitlist: polyhyle.com

We’re building Polyhyle.

The reason is pretty simple: knowledge work is still broken in a very unsexy way.

You don’t lose information because you didn’t store it. You lose it because you can’t retrieve it in the right context, at the right time, in a way that’s actually usable.

Notion stores it. Drive stores it. Search finds something. But none of them actually reconstruct “what you need to know right now” without friction.

We kept running into this while building products. Same pattern every time:

• context gets fragmented
• decisions lose their reasoning trail
• you end up re-deriving things you already figured out months ago

So we started building Polyhyle.

It’s an attempt to make knowledge behave less like a folder system and more like a living working layer you can actually interact with.

Right now:

• early development stage
• core architecture is still being iterated on
• no public release yet

We opened a waiting list for early access.

People who join the waitlist will get:

• first access when we roll out
• the product free for the entire early cohort

We’re not optimizing for scale yet. We’re trying to get the core loop right first.

If this problem sounds familiar, you’ll probably know why we’re building it.

The reason is simple: most knowledge work today is fragmented, slow to search, and relies too much on switching between tools, folders, and half-forgotten context. We kept running into the same problem ourselves while building and shipping products: information exists, but it is not actually usable when you need it.

Polyhyle is our attempt to change that. The goal is to make working with knowledge feel more like interacting with a living system rather than digging through static storage.

Right now we are still early stage.

Status:

• Core system is under active development
• We are iterating on the main experience and architecture
• No public product yet

We have opened a waiting list for early users. People who join the waitlist will get access first, and the product will be free for all users in the waitlist cohort when we start rolling it out.

We’re not trying to optimize for hype at this stage. We’re focused on getting the core experience right before scaling anything.

If you’re interested in early access or want to follow the build, you can join the waitlist. The Idea is still in progress and open to accept feedbacks.

Why do early-stage startups make so many important decisions before they have enough evidence?

I keep seeing this pattern.

A founder needs to decide whether to change pricing, rewrite positioning, launch a new feature, adjust onboarding, or focus on a different customer segment.

But at the early stage, the usual options are limited:

talk to a few users

look at a small amount of data

run an A/B test without enough traffic

copy what competitors are doing

or trust founder instinct

None of these are bad.

The problem is that early-stage teams often need to make dozens of these decisions before they have enough traffic, time, or budget to validate each one properly.

So a lot of startup strategy becomes educated guessing.

And sometimes one wrong guess costs weeks.

I think there is an underrated step missing between “idea” and “real-world test”:

stress-testing the decision before shipping it.

Not to replace real users.

Not to pretend you can predict the market perfectly.

But to force yourself to ask better questions before spending time, money, or traffic.

For example:

What objections might appear if we increase price by 20%?

Would a non-technical buyer understand this landing page?

Which customer segment would react badly to this positioning?

Would this feature actually change intent, or just sound nice?

What assumptions are we making that could be wrong?

I’m curious how other founders handle this.

When you have to make a product, pricing, or positioning decision with limited data, what do you usually do before shipping it?

One thing I keep seeing in early-stage startups is that many decisions are still made with a weird mix of intuition, small sample feedback, and hope.

Pricing changes, landing page copy, onboarding flows, positioning, new features.

Most teams either ask a few users, run a slow A/B test, ship and wait, or ask a generic AI model for feedback.

None of these are ideal when you’re still early.

You often don’t have enough traffic for proper experiments. User interviews are useful, but slow. Surveys can be shallow. And a single LLM answer is usually too generic to be trusted for a specific market or audience.

So I’m building Polyhyle.

The idea is to let founders simulate user behavior before shipping product decisions.

You define the context, the audience, the assumptions, and the scenario. Then different synthetic users react based on their profile, needs, constraints, and behavior.

Use cases I’m focusing on:

• testing pricing changes
• comparing landing page copy
• stress-testing positioning
• validating onboarding flows
• testing product ideas before building
• understanding how different segments might react

To be clear, this is not meant to replace real users.

The goal is to catch weak assumptions before spending weeks on interviews, ads, A/B tests, or building in the wrong direction.

I’m opening early access for free to a small group of founders and builders in exchange for feedback.

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

One thing I keep seeing in early-stage startups is that pricing, copy, onboarding, and feature decisions are often made with very little data.

You can talk to users, run interviews, look at analytics, or run A/B tests, but early on you often don’t have enough traffic or enough signal.

I’m building Polyhyle to explore a different workflow:

Define a scenario, simulate how different user segments might react, then use that to decide what is actually worth testing with real users.

Not trying to replace real validation. More like a way to surface objections, compare segments, and avoid wasting time on obviously weak directions.

Would this be useful before making product or pricing decisions, or would you not trust behavioral simulation at all?

I’m trying to understand how founders handle decision-making before they have enough traffic or users for proper experiments.

For example:

• changing pricing
• changing landing page copy
• launching a feature
• changing onboarding
• testing a new ICP
• repositioning the product

At that stage, A/B tests are often useless, interviews are slow, and analytics are too sparse.

I’m exploring whether simulated behavioral segments could help founders generate better hypotheses before doing real-world validation.

Not as a replacement for users, but as a way to ask:

“What are the likely objections?”
“Which segment would react badly?”
“What should I test first?”
“What decision has the highest risk?”

Curious how others think about this.

Would this be useful, or would you not trust simulation for this kind of decision?