CTO turned CMO | My code is making $3M+ ARR | 2x products #1 of the day on ProductHunt

Hey, I'm Igor. 👋

I've co-founded Momentum Page and ClipWing (both products #1 of the day on ProductHunt)

I share what I can to help community around me build $1M startups and beyond (our biggest partner's exit is $1.2B)

Some of the startups we helped build and scale
• VeroSkills (700K ARR reached within 4 months since first users)
• goLance.com (100s of thousands of users)
• Robomongo (1M+ downloads)
and more

I believe that every startup is a media company

You can also find me on twitter: https://twitter.com/igormomentum

Thanks!

Igor Krasnik

AI agents won't replace developers. Developers who use AI agents will. 

Here are 10 tips to build better with AI:

10 tips to build better with AI agents

Last week was super hot in the world of databases. All because of the release of a new database called SpacetimeDB.

Check out this article to learn what's so special about SpacetimeDB and why it spawned an active discourse in the database world:

Last week was super hot in databases! All thanks to the release of SpacetimeDB 2.0, and their viral release video: What’s about the video? The video i

Open article: What is going on with databases? SpacetimeDB release by Igor Krasnik • 5 min read

What is going on with databases? SpacetimeDB release

Shadcn became the most popular UI library. Here’s why:

Shadcn broke through 100k GitHub stars last November. It took it 2.5 years to surpass other popular UI libraries. Now, Shadcn is baked into AI agents 

Open article: Shadcn became the top #1 UI library. Here’s why by Igor Krasnik • 4 min read

Shadcn became the top #1 UI library. Here’s why

If you're working with Lovable or starting to, check out these slides, useful tips inside:

Lovable is one of the most popular AI-powered no-code tools. Want to get more accurate results faster? In this post, we’re sharing our proven efficiency hacks for Lovable. What hacks do you use when working with Lovable?

Lovable Hacks

CEO @ Fotando | Building AI-ready systems for non-technical companies | Founder @ SalesRing

We help non-technical companies use AI the right way: by simplifying their business first.

Most growing companies want to use AI. 

But the business already feels hard to manage.

1. Information is spread everywhere.
2. Processes aren’t clear.

Adding AI on top of that usually makes things worse…  More tools, more activity, but not much actually changes.

That’s why so many AI projects get stuck in “almost there” for months.

 At Fotando, we help non-technical companies simplify how their business works first and then apply AI where it actually makes a difference.

The goal is real change in 8 weeks, not 18 months. 

If AI feels important for your company but also confusing or overwhelming, feel free to reach out.

Happy to have a straightforward conversation and see if this makes sense for you.

Uladzimir Ausianikau

Today I run a 7-figure business.
But in 2020, I got fired for a weird reason:

I got fired for being too direct.
One day I disagreed with an executive.
I thought I was solving a problem.
They thought I was being insubordinate.
Fired the next day…

But in the end, it was the best thing that ever happened to me.

It taught me 2 lessons:
1. Firstly, how to be honest without being rude.
2. But also, I learned that most people optimize for not getting fired instead of solving the actual problem.

They tell their boss what they want to hear.
And that's why projects fail.

I built my entire business on the opposite:
Honesty and transparency.

I want honest feedback from my employees and they expect the same in return.
This way we build faster, and fix problems.

CEO @ Fotando | AI implementation partner for companies stuck between slow consulting and generic AI tools

Some businesses are still stuck in the 2020 consulting model:
- Hire a “Big Four”
- Wait 6-12 months
- Get a strategy deck
- Find vendors to implement
- Hope it works
By the time you're done, the market has moved.

Here's what changed in 2026:

GenAI collapsed the timeline.

Smaller teams can now outperform bigger companies with bigger budgets.

But only if they learn FASTER.

They need to replace the old model:
- 18 months for "digital transformation"
- Analysis that took quarters, not weeks
- Innovation only enterprises could afford

With the new model:
- Fixed timelines instead of open-ended engagements
- Working solutions instead of strategy decks
- Senior practitioners doing the work

The companies getting ahead are the ones learning fastest.

PS: I built a free assessment tool to show you how fast diagnosis actually works. 

Comment "Need assessment"  for access

In 2026, everyone is building “AI agents” for everything.
This makes me think about what Elon Musk said:

“The most common error of a smart engineer is to optimize a thing that should not exist.”

And this is true in businesses too.

I’ve been building products for 10 years.

And here’s a question I always get back to:

“Does this problem need intelligence, or does it need elimination?”

I've seen companies spend months building AI solutions when the real fix was simpler:

- Change 1 step in the process
- Delete the report nobody reads
- Have a conversation with a collaborator

AI shines when it can 5X your process or free up people for higher-value work.

It’s NOT here to add extra work or steps.

So here’s what you should do:

1. Map the process first.
2. Find the constraint.
3. Then ask: What's the simplest thing that fixes this?

Sometimes it's AI but sometimes it's a “delete button”.

PS: That’s why I builtFotando Global. We start with diagnosis, not technology.

Last week I shared my learnings on LLMs on stage.
This revelation made people the most excited:

“In 2026, every company can operate like a high-growth startup with a small team.”

The same small team can now:
1/ Deep dive into operations (marketing, sales, delivery, finance, legal)
2/ Map the bottlenecks and risks actually limiting growth
3/ Build working experiments in days, not months

Now here's the part that excites me the most:
This isn't just for funded startups or big corporations anymore.
These techniques are now available for traditional businesses.

Here’s what it means for you:
You now have access to the same playbook corporations invest millions in.

2026 is going to be wild!

In 1986, Duracell was stuck at $300M revenue.
In 1996, they got acquired for $7B thanks to this martial artist:

David Shaner was an Aikido master.
Duracell hired him in 1986 to solve a problem no one else could.

The battery industry was broken.
- Every brand squeezed suppliers to near-bankruptcy
- No money left for R&D
- Everyone sold identical products

Shaner changed one thing:
“Stop fighting suppliers, partner with them.”

So Duracell made the first move:
- Stopped demanding impossible discounts
- Gave suppliers room to breathe
- Pooled R&D budgets together

They built a battery that lasted twice as long at twice the price. Same cost per hour for consumers. Dramatically higher margins for everyone.

The result?
They built their entire positioning on “longer batteries”.
The revenue went from $300M to $2.3B.
And in 1996, Gillette acquired Duracell for $7 billion.

This story had a great impact on my life.

One line has stayed with me for 20 years:
"𝘙𝘦𝘭𝘢𝘹𝘢𝘵𝘪𝘰𝘯 𝘤𝘳𝘦𝘢𝘵𝘦𝘴 𝘮𝘰𝘳𝘦 𝘱𝘰𝘸𝘦𝘳 𝘵𝘩𝘢𝘯 𝘧𝘰𝘳𝘤𝘦."

Most AI projects follow the same pattern:

- Month 1: kickoff, demos, excitement
- Month 3: "we're making progress"
- Month 6: "almost there"
- Month 12: it quietly disappears

Companies try to add AI on top of a mess.

Random Excel sheets, unstructured Google Drive, no ownership, no SOPs, and no visibility into what's actually happening.

When I jump into an AI project, the real problem shows up in the first conversation:

"Where is the bottleneck in your business?"

Most executives can't answer because they haven’t audited the workflows.

So they end up taking months to automate chaos.

What they should do instead is use a 2-week sprint methodology:

Week 1 - Diagnose before you build
- Map the entire business flow
- Find where work actually piles up
- Define the ONE constraint worth solving

Week 2 -  Build the smallest thing that moves the metric
- No strategy decks. No 6-month roadmaps.
- A working solution tested against real data
- A clear decision: scale, pivot, or kill

Here, the two weeks of learning beats six months of assumptions.

That’s the power of learning from speed.

I’ve been building products for 8 years.
In 2026, GenAI changes the main bottleneck:

The bottleneck isn't execution anymore, it's knowing what to execute.

Because here’s the truth:

It’s easy to execute fast with AI.

So in a post-AI world, speed of learning is the new unfair advantage.

Here are the differences:

1/ Speed of doing
- Speed of doing means spending 6 months building the wrong thing.
- Then starting over when you realize it doesn't work.

2/ Speed of learning
- Speed of learning means spending 2 weeks discovering it's wrong,
- Then pivoting immediately.

This is why 2-week sprints beat 6-month projects.

You find the constraint, build the smallest thing that should move the metric, and see if it works.

- If it does, keep going.
- If it doesn't, you learned something in 2 weeks instead of 6 months.

That’s how you build AI projects in 2026.

A company of around 400 people spent 6 months collecting AI feedback from every department. The CEO looked at the report and said:

"Maybe next year."

The truth was that almost nobody wanted it.

I was speaking with one of their managers when I understood what had actually happened.

A few months before the research, the company had implemented AI in one of their departments. 

The entire team was let go. 
Then they did the same in another.

Every manager who witnessed that, and was then asked "should we use AI in your area?", gave the same answer: NO.

The reports they submitted were detailed: 

• AI is unreliable
• It makes mistakes
• It's too expensive

Every argument was technically defensible. 
None of it was the real reason.

They weren't lying, but protecting their teams.

That company delayed their AI rollout by 8 months because of research they commissioned themselves.

The problem with internal AI diagnostics: the people you ask have skin in the game. 
When their team's jobs are on the line, honest answers become impossible.

When we ran an external assessment, the resistance was fear, not opposition to AI. 

That one finding changed everything: not build a business case for AI, but address what employees had watched happen to their colleagues.

The honest assessment can only come from someone with nothing to lose by giving it.

PS: This is the first thing we establish before any Sprint begins.

My laptop broke during a trip abroad. No replacement, only my phone.
This forced me to do something I'd never done before:

I called my team leads and said: "Run it without me. Here's what I expect. Here's best practice. The rest is yours."

They did.

When I came back, I realized I didn't want to go back to those meetings.

I'd learned something I couldn't un-learn.

A few months later, a different problem appeared : 

1/ Certain decisions only moved when I was in the room

2/ Certain clients would only talk to me

3/ Certain processes had no written rule, just a habit that lived in my head

I wasn't running a business built on systems.

I was the system.

Answer a question instead of documenting it : you become the answer.

Make a call instead of writing a process : you become the process.

Disappear for two weeks, and the company shows you exactly what it can't do without you.

PS: One of first things we do in every workshop is ask founders to map every process that stops when they stop.

No words on Earth can describe my feelings... 87% filled project ...

#Claude come back 😅

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