u/_killam

I just came across a YC-funded self-healing software startup, and it genuinely gave me hope.

For the past few months I've been building something in a similar direction called Tero.

The biggest bottleneck in software today isn't shipping an MVP anymore. AI has made building products faster than ever. The real challenge begins after deployment, when users start dropping off and founders are left trying to understand what broke, what needs fixing, and what should be prioritized next.

Tero isn't just another LLM wrapper. It acts as an evolution layer for post-MVP software by analyzing user behavior and analytics, identifying friction points, generating multiple fix variants, simulating how different user archetypes react to those changes, selecting the best-performing option, and opening a pull request ready for review.

Seeing companies in this space getting funded feels like validation that this problem is real and worth solving.

Feels like we've reached a point where analytics alone aren't enough anymore.

Ten years ago the problem was not having enough data. Today the problem is having dashboards full of data and still not knowing what change to make on Monday morning.

User churn isn't getting better because founders can see the problem. Most of the time they're staring right at it.

The hard part is figuring out what to do next.

If anything, AI has accelerated this. More products are getting built faster than ever before, especially by smaller teams and non-technical founders. Shipping is becoming easier.

AI has made building cheaper and faster. It hasn't made product decisions easier.

If anything, the gap between shipping features and knowing which features to ship next feels bigger than ever.

Feels like we've reached a point where analytics alone aren't enough anymore.

Ten years ago the problem was not having enough data. Today the problem is having dashboards full of data and still not knowing what change to make on Monday morning.

User churn isn't getting better because founders can see the problem. Most of the time they're staring right at it.

The hard part is figuring out what to do next.

If anything, AI has accelerated this. More products are getting built faster than ever before, especially by smaller teams and non-technical founders. Shipping is becoming easier.

AI has made building cheaper and faster. It hasn't made product decisions easier.

If anything, the gap between shipping features and knowing which features to ship next feels bigger than ever.

I have been coding for quite sometime and experimenting with multiple AI's as well using claude for a while now what are some other AI's platforms that are better when it comes to research ?

I've noticed that the hardest part for many founders isn't building anymore vibecoding has made it sm easier to build and test.

It's the transition period after launch.

Lovable gets you the MVP.
Vercel gets it live.

Then comes the part nobody really talks about.

Everything works until it doesn't.

A random Tuesday deployment breaks onboarding.
Users quietly leave without reporting anything.
Analytics tell you where they disappeared but not why.

Building and hosting have become dramatically easier over the last year, but post-deployment maintenance and understanding why users churn still feel as painful as ever.

That gap between shipping and evolving a product is what led me to build Tero

The idea is simple: reduce the time between a problem appearing, understanding why it happened, and getting a fix shipped.

Would genuinely love to hear how others are handling this stage today.

I've noticed that the hardest part for many founders isn't building anymore vibecoding has made it sm easier to build and test.

It's the transition period after launch.

Lovable gets you the MVP.
Vercel gets it live.

Then comes the part nobody really talks about.

Everything works until it doesn't.

A random Tuesday deployment breaks onboarding.
Users quietly leave without reporting anything.
Analytics tell you where they disappeared but not why.

Building and hosting have become dramatically easier over the last year, but post-deployment maintenance and understanding why users churn still feel as painful as ever.

That gap between shipping and evolving a product is what led me to build Tero

The idea is simple: reduce the time between a problem appearing, understanding why it happened, and getting a fix shipped.

Would genuinely love to hear how others are handling this stage today.

Lately I've noticed that the hardest part for many founders isn't building anymore.

It's the transition period after launch.

Lovable gets you the MVP.
Vercel gets it live.

Then comes the part nobody really talks about.

Everything works until it doesn't.

A random Tuesday deployment breaks onboarding.
Users quietly leave without reporting anything.
Analytics tell you where they disappeared but not why.

Building and hosting have become dramatically easier over the last year, but post-deployment maintenance and understanding why users churn still feel as painful as ever.

That gap between shipping and evolving a product is what led me to build Tero

The idea is simple: reduce the time between a problem appearing, understanding why it happened, and getting a fix shipped.

Would genuinely love to hear how others are handling this stage today.

When I started building Tero, the idea was huge.

Analytics.
User behavior.
Feature generation.
Code changes.
Product evolution.
Post-deployment maintenance.

Basically everything that happened after an MVP went live.

The problem was simple:

If you're solving everything, people don't know what you're solving.

So I started speaking to founders and reading discussions from people shipping products.

The same themes kept showing up:

• Users leaving without saying why.
• Bugs getting discovered too late.
• Analytics showing what happened but not what to do next.
• Spending more time diagnosing problems than fixing them.

That changed how I thought about Tero.

The goal stopped being "build more features."

The goal became reducing the time between a problem appearing and a fix reaching production.

Ironically, making the problem smaller made the product much clearer.

Still early and still learning.

Would genuinely love feedback from founders here it will help me understand the user market better since there isnt really an established category for this lmao

A year ago the bottleneck was building. apart from the obvious distribution XD

Today I can go from idea to deployed product in a weekend.

Between Lovable, Cursor, Claude, GPT and every other tool dropping weekly, building an MVP has become almost absurdly cheap.

The weird thing is that this shifted the bottleneck somewhere else.

Users leave without telling you why.

Analytics tell you where they disappeared but not what assumption broke before they left.

A small bug quietly kills onboarding for days before anyone notices.

Features get shipped because they sound good rather than because users asked for them.

And suddenly you're spending more time figuring out what to fix than actually fixing things.

The more founders I speak to, the more it feels like we're entering a new phase:

We solved building.

Now we have to solve evolution.

That realization is what eventually led me to build tero

Not another builder.
Not another code generator.

Something focused on what happens after launch: understanding churn, catching issues faster, figuring out what users actually need, and reducing the gap between a problem appearing and a fix reaching production.

Tbh, I don't think this observation is uncommon anymore. Most founders know the MVP isn't the business.

What still feels unsolved is what comes next.

A year ago the bottleneck was building. apart from the obvious distribution XD

Today I can go from idea to deployed product in a weekend.

Between Lovable, Cursor, Claude, GPT and every other tool dropping weekly, building an MVP has become almost absurdly cheap.

The weird thing is that this shifted the bottleneck somewhere else.

Users leave without telling you why.

Analytics tell you where they disappeared but not what assumption broke before they left.

A small bug quietly kills onboarding for days before anyone notices.

Features get shipped because they sound good rather than because users asked for them.

And suddenly you're spending more time figuring out what to fix than actually fixing things.

The more founders I speak to, the more it feels like we're entering a new phase:

We solved building.

Now we have to solve evolution.

That realization is what eventually led me to build Tero.run

Not another builder.
Not another code generator.

Something focused on what happens after launch: understanding churn, catching issues faster, figuring out what users actually need, and reducing the gap between a problem appearing and a fix reaching production.

Tbh, I don't think this observation is uncommon anymore. Most founders know the MVP isn't the business.

What still feels unsolved is what comes next.

Vibecoding made building easy. Maintaining the product is the hard part.

Everyone talks about how AI lets you ship an MVP in a weekend.

What nobody talks about is what happens after deployment.

Users start churning.

Bugs show up in production.

Analytics tells you what happened but not why it happened.

You spend more time figuring out what to fix than actually shipping fixes.

I ran into this myself while building products. The MVP wasn't the bottleneck anymore. Understanding user behavior, finding issues before users left, and deciding what to build next was.

That's actually why I started building Tero.run

The idea is simple: connect your Git repo, monitor your product, get notified when things break, understand what users are struggling with, and ship improvements faster even from your phone.

Maybe the new challenge isn't building software anymore.

Maybe it's everything that comes after launch.

Anyone else feeling this?

Vibecoding made building easy. Maintaining the product is the hard part.

Everyone talks about how AI lets you ship an MVP in a weekend.

What nobody talks about is what happens after deployment.

Users start churning.

Bugs show up in production.

Analytics tells you what happened but not why it happened.

You spend more time figuring out what to fix than actually shipping fixes.

I ran into this myself while building products. The MVP wasn't the bottleneck anymore. Understanding user behavior, finding issues before users left, and deciding what to build next was.

That's actually why I started building Tero.

The idea is simple: connect your Git repo, monitor your product, get notified when things break, understand what users are struggling with, and ship improvements faster even from your phone.

Maybe the new challenge isn't building software anymore.

Maybe it's everything that comes after launch.

Anyone else feeling this?

Recently, and only recently, I realized that the biggest bottleneck for most startups isn't building the product or even post-deployment issues it's distribution.

When I started marketing my product, I followed the usual Reddit playbook. Pretty quickly, I learned that Reddit users don't care about your product. They care about their problem. If your post doesn't solve something or provide value immediately, it gets ignored.

X felt very different.

On X, founders are much more comfortable talking about what they're building. Product discussions are common, and people are often willing to engage with the journey behind the product.

That said, getting attention on Reddit felt easier than getting attention on X.

The irony is that Reddit has become crowded with founders using the exact same marketing strategies. Everyone is trying to disguise promotion as value, which makes genuine posts harder to stand out.

My takeaway so far:

Reddit rewards solutions.

X rewards stories.

Both can work, but they require completely different approaches.

Vibecoding is probably the most exciting shift in software I’ve seen in years.

You can go from idea to working product in a weekend now. No waiting for a team, no months of planning, no agency, no funding round. Just an idea, an AI, and enough energy to stay awake until 3am.

But nobody really talks about what happens after you ship.

At the start, everything feels insanely fast. You ask for features, they appear. You move files around, redesign flows, rewrite entire sections without thinking twice.

Then one day the codebase reaches a certain size and the vibe starts collapsing under its own weight.

You fix one thing and something completely unrelated breaks. The AI loses context between files. Old decisions conflict with new ones. Half the prompts become “don’t break the existing logic please.” You stop building new things and start managing chaos.

And honestly, users don’t even report bugs anymore.

That’s the part I think most people underestimate.

A few years ago, users would hit an issue, maybe send feedback, maybe wait for a fix. Now they just leave. There are ten other products that do almost the same thing and switching takes less than a minute. The second your onboarding feels confusing or your checkout flow feels slightly off, they disappear and never come back.

No angry email. No support ticket. Just silence.

I think that’s going to become the real challenge of this era. Not shipping faster, because everyone can ship fast now. It’s noticing the quiet failures before they slowly kill the product.

Do yall resonate to this as well or is it only me ?

I got a few users from Reddit posts that weren’t even directly promoting my product and honestly that surprised me.

I used to wonder how people here were getting traction without doing some massive launch or posting their startup everywhere 24/7.

But after being on Reddit for a while I realized most of them weren’t really selling.

They were just talking about a real problem they noticed or were dealing with.

And that naturally makes people curious because chances are other people are facing the same thing too.

Tried doing the same recently and it worked way better than I expected. A few people checked my profile on their own and ended up trying what I’m building.

Made me realize people here are pretty good at spotting forced marketing, but if something genuinely relatable gets pointed out, they’ll pay attention.

For the curious ones, my product’s in my bio.

Would genuinely love to know how y’all have been marketing your own products and how it’s been going so far.

Recently I was going through a few websites trying to understand why there had been a sudden drop in users and conversion rates.

What stood out was that most of them weren’t facing major crashes or server issues.

It was small things.

A button not responding properly on mobile
A flow breaking midway for certain users
Confusing wording after a quick UI change
Pages technically loading, but feeling off enough for users to leave

Most of these sites were heavily vibe coded, so changes were being pushed really fast and a lot of these bugs just slipped through unnoticed

The interesting part was that nobody even realized these bugs existed because users rarely complain anymore they just leave

There are error notifications , no obvious crashes just a "silent drop-off"

this made me realize that building on top of vibe coded products is becoming a completely different skill set

Shipping fast is easy now however keeping conversion rates stable while constantly introducing AI-generated changes is the hard part

Getting to V1 with AI is easier than ever now.

The hard part starts once people actually begin using the product.

Suddenly you're dealing with:

• messy logs
• production bugs
• regressions
• infra issues
• deployment mistakes
• multiple “optimized” versions floating around

A lot of vibe-coded projects can ship fast.

But maintaining and improving them after V1 becomes chaotic very quickly.

Most problems only appear once real users arrive and rapid iteration begins. Until then, you're mostly taking blind shots in the dark.

That’s why agentic AI for post-deployment workflows feels far more valuable to me than AI that only generates the MVP itself.

Things like:

• monitoring production
• analyzing failures
• reviewing deployment diffs
• rollback suggestions
• regression detection
• comparing optimized variants before deployment

If you're already using AI for post-deployment workflows, what does your current setup look like?