Zaqlick

Chief Executive Officer

Adlega

Contractor

Salesforce Consultant

TRADEBYTE

Operational Excellence and Efficiency Team Leader

Knauf IT 

Salesforce Project Manager

Deloitte Digital

Senior Consultant

EPAM Systems GmbH

Salesforce.com Consultant/Product Owner

Project Manager

Building startups

I’ve started three companies, survived two pivots, and bailed countless founders out of spreadsheet purgatory.

Today, I’m the CEO of Adlega, a SaaS runway‑planning platform trusted by 1,200+ founders to model cash burn in under 90 minutes. Nights and weekends, I’m building Zaqlick, a “first‑mile” discovery layer that lets early‑stage teams chat with AI personas trained on real market data (think Reddit rants, competitor reviews, and support tickets). You ask, they answer, and your next customer interview starts miles deeper.

Why build both? I’ve wasted months guessing what users really cared about. The result: half‑built features, burned runway, and an inbox full of polite maybes. Adlega keeps the money math honest; Zaqlick flips the discovery script so you ship with confidence, or kill the idea early and save the coffee budget.

What lights me up:
- Digging through messy, unstructured data and surfacing non‑obvious opportunities.
- Translating growth metrics into plain‑English roadmaps.
- Swapping war stories with builders who still have more questions than answers.

Track record:
- Bootstrapped Adlega, now powering runway models for 1,200+ founders worldwide.
- Former CMO who turned a flat‑lining freemium app into a $2.4 M ARR engine in 18 months.
- Kellogg‑WHU EMBA (because numbers + narrative > numbers alone).

Let’s talk!
If you’re an indie hacker, accelerator grad, or curious VC who thinks discovery and finance should be equally data‑driven - DM me or join the Zaqlick waitlist 👉 zaqlick.com.

Elena Tsemirava

Startup lingo 101: 
 
-> Bootstrapped? Profit-making for the win.
-> Serial founder? More like, 'I can't hold a job'.
-> Big data: Brow furrows when it crosses 10GB.
-> AI? Basically, some good ol' linear regression.
-> Crushing it: The classy way to say 'barely making it'.
-> Automated? Just folks working their magic on Fiverr.
-> Visionary? Sounds like someone's feeling high and mighty.
-> Organic growth? Fancy talk for 'Google ads are draining my budget'.

Elena Tsemirava, MBA

Founder @ Zaqlick - AI-Enhanced User Research | 15+ yrs insight & marketing | I help PMs and Agencies run realistic AI focus groups to pressure-test concepts, journeys & messaging

𝗗𝗼𝗻’𝘁 𝗴𝗲𝘁 𝗳𝗼𝗼𝗹𝗲𝗱! 𝗡𝗼𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂 𝗵𝗲𝗮𝗿 𝗮𝗯𝗼𝘂𝘁 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗶𝘀 𝘁𝗿𝘂𝗲.

Everyone keeps repeating the same advice about how many interviews you “need”… but it’s outdated, slow, and usually wrong.

Watch this video before you schedule another interview. 

It breaks down the hidden mistake almost every team makes, and how to avoid wasting weeks talking to the wrong people.

🎥 𝗩𝗶𝗱𝗲𝗼 𝗯𝗲𝗹𝗼𝘄.

Rejections sting, but they're also your most honest feedback from the real world.

It is a critical topic about adequately assessing one's own competence in almost all areas of life!  

Life, work, business, and personal have one common element: constant judgment. 

Thumbs up or down, it's inevitable. 

The fear of a negative response is natural! 

This judgment is the only thing that can be called honest feedback from the real world. 

That's why there should be multiple attempts to get a verdict!

In startups, rejections are an integral part of all processes: any kind of outreach or, for example, applying to accelerators. Applying to a hundred accelerators is the best way to test yourself in the role of a founder. 

And rejections in personal life? Everyone here has plenty of their own examples!

Let's talk probabilities because understanding probabilities helps to accept rejections adequately:

 🎯  Getting into @Y-Combinator after Stanford is 1-2%. 

🤷‍♀️ And for us, international female founders - much less than 1%. 

At the same time, I often see messages like "We applied. We think they will admit us!"

Some only face reality from time to time, like job interviews. 

Dwell in an illusionary world, lose touch with competency. 

The more reality checks, the better the resilience and probability of success.

What to do? 

Enter competitions, write stellar posts, apply for jobs, launch small projects and try to sell (painful but worthy), and write innumerable emails seeking meaningful interactions. It's monitoring your competency outside the illusionary bubble.

My approach: rejection or ignorance is the default state. That is, this is the expected result of my contact with reality. Then, there are no worries about rejections. And if someone becomes interested - here is a reason to rejoice!  

Remember: Most life actions, particularly the ones that matter, have conversion rates lesser than 3%. 

Especially those that are particularly important to us! 

Let's call them "steps to the dream". 

Therefore, the most important thing is not to stop pouring your attempts into the funnel. 

Even with 1% odds, dreams do come true! 🤞

Most people think AI focus groups replace human research.

Wrong.

They amplify it.

I've run 200+ sessions now, and here's what I've learned:

• AI crushes it at scale and speed. You can test 50 variations in the time it takes to schedule one human interview.

But human intuition? That's where the magic happens.

Algorithms miss the pauses. The hesitations. The "wait, actually..." moments that completely flip your understanding.

They can't read the room when someone's being polite vs. genuinely excited.

The best insights don't come from choosing one or the other.

They come from layering both.

Here's my process:

1. Use AI to pressure-test ideas fast
2. Identify patterns and outliers
3. Validate with real users on the edges

AI tells you what. Humans tell you why.

AI finds the signal. Humans add the context.

Stop treating them as competitors. Start treating them as collaborators.

The companies winning right now aren't picking sides.

They're using AI to move faster and humans to move smarter.

I got my second resignation email before 9 am yesterday.

Saturday morning at 6:37, I was deep in spreadsheets while everyone else slept in.

By 3pm, I'd made 17 decisions. Big ones. Small ones. All on me.

The angry customer email? That's mine to handle.

Payroll's due Thursday. The bank balance isn't quite there yet.

Sunday afternoon and I can't switch off. My brain's still running scenarios.

This is the part they don't show you in the "entrepreneur lifestyle" posts.

No applause. No celebration. Just you and the weight of it all.

The highlight reels make it look easy.

They skip the sleepless nights and the constant pressure.

They forget to mention you're the first one blamed and the last one paid.

I love what I do. I wouldn't trade it.

But let's be real about what it actually takes.

Starting a business isn't for everyone.

And that's perfectly okay.

If you're in it, you already know.

If you're thinking about it, make sure you know what you're signing up for.

Because the reality hits different than the dream.

What 47 SaaS founders told us about their biggest pricing mistake:

𝟲𝟴% 𝗮𝗱𝗺𝗶𝘁𝘁𝗲𝗱 𝘁𝗵𝗲𝘆 𝘂𝗻𝗱𝗲𝗿𝗽𝗿𝗶𝗰𝗲𝗱 𝗶𝗻 𝘆𝗲𝗮𝗿 𝗼𝗻𝗲.

Why? They were "scared to ask for more."

The average time to first price increase? 𝟭𝟴 𝗺𝗼𝗻𝘁𝗵𝘀.

They all agreed: 12 months too late.

Most painful lesson learned:

"Free trials without qualification attracted tire-kickers, not buyers".

One B2B founder told us:

"I thought $49/mo was safe. Turns out, cheap pricing attracted cheap customers who churned fast".

Another shared:

"We doubled our price and lost 20% of signups, but revenue went up 60%. Should've done it sooner".

The pattern is clear.

𝗨𝗻𝗱𝗲𝗿𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗽𝗿𝗼𝘁𝗲𝗰𝘁 𝘆𝗼𝘂.

It attracts the wrong customers. Creates churn. And leaves money on the table.

Your pricing signals your value.

Price too low and you're telling the market you're not worth much.

Price confidently, and you attract buyers who see the value.

The founders who raised their prices early? They're the ones who scaled faster.

If you want help auditing your investor narrative, DM me.

Your customer just said "fine."

But your AI flagged it as high-risk frustration.

Here's what it saw that you didn't.

Modern conversational AI doesn't just read words.

It reads patterns humans miss in real-time.

Three signals that scream hidden frustration:

1️⃣  Response length collapse

When messages drop from 15+ words to 3-5 words, something shifted.

"I've been trying to resolve this billing issue for three days" → "Fine."

That's not agreement. That's resignation.

2️⃣  Punctuation intensity

Single periods carry weight.

"Ok." vs "Okay!" vs "Ok..."

Each dot, exclamation, or ellipsis changes meaning entirely.

AI tracks punctuation frequency across the conversation thread.

3️⃣  Word choice degradation

Formal language breaking down signals emotional shift.

"I appreciate your help" → "whatever works" → "fine"

The AI maps vocabulary complexity over time.

Why context windows matter more than keywords:

A single "fine" means nothing.

But "fine" after 6 back-and-forth messages, shorter responses, and dropped pleasantries?

That's a customer about to churn.

The AI doesn't just analyze the current message.

It analyzes the conversation arc.

This is why legacy chatbots failed and modern AI succeeds.

Rule-based systems saw "fine" as positive sentiment.

Context-aware systems see "fine" as a red flag when the pattern shifts.

The result?

Priority routing before the customer explicitly complains.

Proactive solutions before they ask for a supervisor.

Better outcomes because the AI caught what the words hid.

Conversational AI isn't reading minds.

It's reading patterns we're too busy to notice. 

--- 👋 Hey, I’m Elena Mira.
I’m building Zaqlick, your AI-powered cofounder for customer discovery.

If you're tired of guessing what users want, I share teardown threads, battle-tested frameworks, and behind-the-scenes from building  Zaqlick.

Follow me for smart, scrappy ways to validate faster.
And join the waitlist to chat with your future customers before you build. (Link in bio)

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We tore up our own customer personas.

Best decision we ever made.

We thought we knew our customer: "Young founder. Likes tech. Wants to build ideas."

But that was too broad.

So we decided to test our own medicine. We ran our "ideal customer" through our own tool, Zaqlick.

We analyzed 1,522 public conversations from across the web.

We didn't look at age or job titles. We looked for 𝗽𝗮𝗶𝗻.

We found something surprising.

Our "one customer" was actually four different people:
𝟭. 𝗧𝗵𝗲 𝗪𝗮𝗻𝗱𝗲𝗿𝗲𝗿𝘀 (𝗣𝗮𝗶𝗻: 𝟳.𝟭/𝟭𝟬) They can't find their first user. They feel stuck. They will pay anything for help.
𝟮. 𝗧𝗵𝗲 𝗖𝘆𝗰𝗹𝗲-𝗕𝗿𝗲𝗮𝗸𝗲𝗿𝘀 (𝗣𝗮𝗶𝗻: 𝟲.𝟱/𝟭𝟬) They change their mind every few weeks. They are scared of running out of money.
𝟯. 𝗧𝗵𝗲 𝗡𝗶𝗰𝗵𝗲-𝗦𝗲𝗲𝗸𝗲𝗿𝘀 (𝗣𝗮𝗶𝗻: 𝟮.𝟵/𝟭𝟬) They are exploring 3 ideas at once. They want quick answers. They won't pay much.
𝟰. 𝗧𝗵𝗲 𝗣𝗶𝗼𝗻𝗲𝗲𝗿𝘀 (𝗣𝗮𝗶𝗻: 𝟯.𝟱/𝟭𝟬) They just need help picking an idea.

See the gap?

There is a huge difference between a pain score of 7 and a pain score of 2.

Here is the hard truth we learned:
-> Only 5% of the people had real, "bleeding" pain.
-> But that 5% is willing to pay 5x more than everyone else.

We were planning to build features for everyone. Which meant we were pleasing no one.

So we made a scary choice.

We killed half our planned features.

We rebuilt the roadmap just for 𝗧𝗵𝗲 𝗪𝗮𝗻𝗱𝗲𝗿𝗲𝗿𝘀 and 𝗧𝗵𝗲 𝗖𝘆𝗰𝗹𝗲-𝗕𝗿𝗲𝗮𝗸𝗲𝗿𝘀.
The results?

• We cut our roadmap in half.
• We saved 3 months of wasted dev time.
• We finally knew exactly what to build first.

The lesson is simple.

Don't group people by age or company size.

Group them by: "How badly does this hurt?"

A customer with low pain won't buy. A customer with high pain will.

Build only for the people who need you the most.

𝗣.𝗦. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝘀𝗲𝗲 𝘄𝗵𝗶𝗰𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗯𝗹𝗲𝗲𝗱𝗶𝗻𝗴? 𝗪𝗲 𝗼𝗽𝗲𝗻 𝘁𝗵𝗲 𝗱𝗼𝗼𝗿𝘀 𝗶𝗻 𝟮 𝘄𝗲𝗲𝗸𝘀. 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝘄𝗮𝗶𝘁𝗹𝗶𝘀𝘁 𝗵𝗲𝗿𝗲: 𝗵𝘁𝘁𝗽𝘀://𝘇𝗮𝗾𝗹𝗶𝗰𝗸.𝗰𝗼𝗺

CB Insights says "no market demand" kills more startups than anything else.

I wanted to know why.

So I spent 3 months analyzing 5,000+ Reddit posts from founders who failed.

Here's what I found:

Most founders can build. They just can't validate.

-> They're Googling competitors at 2am.
-> Scrolling Twitter for customer pain points.
-> Asking friends if their idea "sounds good."

Meanwhile, their enterprise competitors are using $15k tools like Klue and Crayon.

The gap isn't skill. It's access.

I kept seeing the same pattern in those Reddit posts:

"Spent 6 months building. No one wanted it."
"Thought there was demand. I was wrong."
"Wish I'd validated before I quit my job."

These founders didn't lack talent or work ethic.

They lacked the tools to answer one question: Does anyone actually want this?

That's the problem I'm solving.

Market intelligence shouldn't cost $15k.

It shouldn't be locked behind enterprise sales calls.

Every founder deserves to validate their ideas before betting everything on them.

What's your validation process? I'd love to hear what's working for you.

We're watching the largest boom in micro-companies in history.

• 1-10 person businesses generating real cash.
• Serving tiny, passionate communities.
• Operating with leverage that used to need massive teams.

Organic social. AI. Tools that actually work.

The shift isn't just economic.

It's psychological.

Once people realize they can earn a meaningful living building something tiny and strange for a community they actually understand, everything changes.

Ambition looks different.
Lifestyle choices shift.
Career arcs stop being linear.

Even "success" gets redefined.

Some people will build two or three of these.
Some will build twenty.
Some will build one that accidentally scales big.

You can feel it happening right now.

An increasing number of people quietly opting out of traditional paths.

Choosing to build a life around small companies that actually work.

The infrastructure finally exists.
The playbook is getting clearer.
The barriers are gone.

What used to require venture capital and huge teams now requires clarity, leverage, and understanding your people.

The companies winning tomorrow won't be the biggest.

They'll be the ones that fit perfectly into spaces no one else bothered to look.

If your user interviews take 6 weeks, it's usually not because "research is slow".

It's because your process is broken.

Here's what actually eats your time:

Recruiting participants

✅ Finding 8-12 qualified users takes weeks. Screening, scheduling, no-shows.

✅ Synthetic interviews let you test assumptions in hours. Then recruit real users only for validation.

Calendar Tetris

✅ Coordinating schedules across time zones kills momentum. By the time you talk to user #8, insights from user #1 are cold.

✅ Run synthetic sessions back-to-back. Book real interviews only when patterns emerge.

Legal limbo

✅ NDAs, consent forms, compliance reviews. Each approval adds days.

✅ Synthetic research needs zero paperwork. Save legal for the final validation round.

Analysis paralysis

✅ Transcribing, coding, and synthesizing 10+ hours of recordings. You're drowning in notes while your team waits.

✅ AI handles the grunt work. You focus on what matters: finding patterns and making decisions.

The 48-hour plan:

Day 1: Run synthetic interviews, spot patterns

Day 2: Validate with 3-5 real users, ship insights


Would you trust a 48-hour run more than a 6-week zombie process? 

-- 👋 Hey, I’m Elena (Mira) Tesmirava.

I’m building Zaqlick, your AI-powered cofounder for customer discovery.

If you're tired of guessing what users want, I share teardown threads, battle-tested frameworks, and behind-the-scenes from building Adlega (1,200+ founders) and now Zaqlick.

Follow me for smart, scrappy ways to validate faster.

And join the waitlist to chat with your future customers before you build. (Link in bio)

What is the best price for my SaaS?

The best SaaS price is the one your customer complains about but still pays.

I've been analyzing hundreds of founder pain points.

And one pattern keeps showing up.

"Incorrect pricing" gets flagged constantly.

But here's what's really happening:

Founders aren't struggling with execution.

They're paralyzed by fear.

Two fears, specifically:

1. Charging too much and losing customers
2. Leaving money on the table

So they procrastinate.

They launch with "placeholder pricing."
They copy competitors without thinking.
They avoid the conversation entirely.

Until revenue forces them to care.

But by then?

They've already left thousands on the table.

Or worse, trained customers to expect low prices.

Here's what I discovered:

Most pricing tools give you qualitative data.

Surveys. Interviews. Competitor analysis.

All helpful.

But founders don't just need opinions.

They need numbers.

Quantitative modeling is the missing link.

What if you could see:

• Exact price points that maximize revenue
• How small changes impact your bottom line
• Which pricing tier drives the most growth

Not guesses.

Not best practices from another industry.

Actual data for your business.

The gap between qualitative insights and quantitative decisions?

That's where founders get stuck.

And that's where the real opportunity is.

Have you struggled with pricing your product?

I'd love to hear what made it click for you.

𝗔𝗜-𝗺𝗼𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵.

People keep repeating this.

Then you open Reddit and see a researcher who “tested all the tools” - 
𝗯𝘂𝘁 𝗼𝗻𝗹𝘆 𝘁𝗵𝗲 𝗳𝗿𝗲𝗲 𝘁𝗶𝗲𝗿𝘀 𝗮𝗻𝗱 𝗱𝗲𝗺𝗼𝘀 - 
and walks away, declaring the entire category useless.

Let’s be honest: free AI tiers are 𝘯𝘰𝘵 the product.

Free versions use:
• throttled context
• cheaper models
• minimal logic
• no memory
• no guardrails
• watered-down prompting
• laggy avatars slapped on top of a chat window
  
Of course, it feels like a toy. It 𝘪𝘴 a toy.

But here’s the twist:
Even the paid versions - the real thing - are aiming at the wrong target.

They’re trying to 𝗶𝗺𝗶𝘁𝗮𝘁𝗲 𝗹𝗶𝘃𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀, when the true leverage of AI isn’t in 𝘮𝘰𝘥𝘦𝘳𝘢𝘵𝘪𝘰𝘯.

It’s in the 𝗳𝗶𝗿𝘀𝘁 𝗺𝗶𝗹𝗲 - the part of research where teams are blind, unprepared, and guessing.

Real researchers know this:
You don’t get insight from the 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯. You get insight from the 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻, the context, the assumptions, the weak signals, and the patterns you notice before the conversation even starts.

AI is insanely good at that. Better than humans, even:

• surfacing hidden pains
• analyzing thousands of posts
• clustering signals
• stress-testing a value prop
• pointing to blind spots
• preparing a team for the real interview
• turning chaos into shape
  
This is where AI should live. Not pretending to be a moderator with a synthetic face and a 2-second latency.

And yes, the Reddit post is right about one thing:

𝗔𝗜 𝘄𝗼𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗿𝗲𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗮𝗻𝘆𝘁𝗶𝗺𝗲 𝘀𝗼𝗼𝗻. 

But it 𝘸𝘪𝘭𝘭 replace bad surveys and blind discovery.

The future of research isn’t “AI acting like a human.” 

The future is 𝗵𝘂𝗺𝗮𝗻𝘀 𝘄𝗮𝗹𝗸𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝟴𝟬% 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 — 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗔𝗜 𝗿𝗲𝘃𝗲𝗮𝗹𝗲𝗱 𝘁𝗵𝗲 𝘁𝗲𝗿𝗿𝗮𝗶𝗻.

That’s the shift. That’s the leverage. That’s the part nobody is building for yet.

𝗠𝗼𝘀𝘁 𝗦𝗮𝗮𝗦 𝗳𝗼𝘂𝗻𝗱𝗲𝗿𝘀 𝗱𝗼𝗻'𝘁 𝗵𝗮𝘃𝗲 𝗮𝗻 𝗠𝗩𝗣 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 

They have a 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.

I saw a Reddit thread where a dev studio asked:

"SaaS founders, what's your biggest bottleneck in getting to MVP? Co-founder? Features? Budget? Validation?"

The answers were brutal.

The real bottleneck? 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗽𝗮𝗿𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗳𝗶𝗿𝘀𝘁.

Founders are building from imagination, not from 10 real conversations.

𝗟𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝗶𝘀 𝗲𝗮𝘀𝘆. 
𝗙𝗶𝗻𝗱𝗶𝗻𝗴 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗰𝗮𝗿𝗲 𝗶𝘀 𝗵𝗮𝗿𝗱.

One founder nailed it:
"Once I stopped guessing and started listening, the path to MVP became obvious. The rest (co-founders, budget, features) were excuses, not blockers."

Most MVPs aren't "too small." 

They're 𝘁𝗼𝗼 𝗱𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹𝗶𝘁𝘆.

So founders compensate:

➡️ Add features because they're scared of looking stupid
➡️ Polish UI to avoid honest feedback
➡️ Obsess over tech stack because it's safer than talking to users

Then wonder why a "finished" MVP can't hold 10 users.

Your bottleneck isn't code. 

‼️ It's 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁.

Talk to 10–20 real people before you obsess over your backlog. 

And if that feels impossible, overwhelming, or slow - 𝘂𝘀𝗲 𝗔𝗜 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝘀 𝘁𝗼 𝗯𝗿𝗶𝗱𝗴𝗲 𝘁𝗵𝗲 𝗴𝗮𝗽.

Not to 𝘳𝘦𝘱𝘭𝘢𝘤𝘦 real interviews. But to:

🌟 surface real pains from real data
🌟 test your assumptions fast
🌟 stress-test your landing page or pitch
🌟 clarify your direction before you talk to anyone

AI personas won’t give you product-market fit. 

But they will kill the fog that keeps you stuck.

If you can’t get clarity from conversations, whether human or AI-augmented, no amount of tickets in Jira will save you.

MVP isn't "the first version of your product."

It's 𝗽𝗿𝗼𝗼𝗳 that someone actually wants what you're building.

And today, you can get to that proof a lot faster.

-- 👋 Hey, I’m Elena (Mira) Tesmirava.
I’m building Zaqlick, your AI-powered cofounder for customer discovery.

If you're tired of guessing what users want, I share teardown threads, battle-tested frameworks, and behind-the-scenes from building Zaqlick.

Follow me for smart, scrappy ways to validate faster.

And join the waitlist to chat with your future customers before you build. (Link in bio)

Nobody tells you the truth in a focus group.

Not because they're lying.

But because they're being watched.

• The pauses before answering.
• The contradictions in their stories.
• The shift in tone when something hits close to home.

Traditional focus groups miss this.

Because the moment someone knows they're being watched, they edit themselves.

• They give you the "right" answer.
• The polite version.
• The one that makes them look good.

AI focus groups changed this.

• No moderator staring at them.
• No group pressure to conform.
• No fear of judgment.

Just honest reactions captured in real time.

You see the hesitation before they admit what they really think.

You catch the contradiction between what they say they want and what they actually choose.

You notice the emotional shift when a question touches something real.

This is where the learning happens.

Not in the rehearsed responses.
Not in the surface-level feedback.

But in the unfiltered moments people don't even realize they're revealing.

The observer effect disappears.

And suddenly, you're not just hearing what people think you want to hear.

You're seeing what they actually feel.

That's the difference between data and insight.

Between asking questions and understanding people.

The best research doesn't just collect answers.

It captures the truth people don't know how to say.

Smart4Diagnostics GmbH

Duties:
 🧪 responsible for the design and architecture of the S4DX cloud solution
 🧪 leading cloud team
 🧪 requirements gathering from internal and external stack holders
 🧪 designing infrastructure solution
 🧪 design of deployment and release strategy
 🧪 design of monitoring solution
 🧪 all of the above while still staying hands-on and actively contributing to the code base 

Achievements:
 🧪 improved scalability of the system
 🧪improved performance of solution by 3x
 🧪 optimizing cloud running cost by 3x
 🧪 improving and speeding up deployment procedures
🧪 successfully passing audits for ISO 27001, ISO 9001 certifications
🧪 successfully went through a penetration test

Tools:
🛠️Java
🛠️Spring Boot and all-around Spring
🛠️PostgreSQL
🛠️Kafka
🛠️AWS
🛠️Terraform
🛠️Jira

Lead Cloud Developer/ Architect

FERNRIDE

My duties here @Fernride:
 - Architecture design and implementation of Fernride's backend
 - Managing cloud infrastructure and solution deployment
 - Maintenance and monitoring
 - Leading backend team

Tech stack:
🪛 JVM world: Java, Spring Boot, JPA and all around
🪛Azure cloud
🪛Postgresql, Hyperscale
🪛Terraform
🪛Docker
🪛Linux

Lead Backend Developer 

relayr

Building a streaming analytics platform for processing IoT generated data using various Machine Learning models, which includes REST APIs built using Spring Boot and processing pipeline built with Apache Spark.

Day to day responsibilities
 • design and build the streaming analytics platform
 • continuous improvement of development practices and infrastructure
 
Main tools/frameworks used:
 ⚒ Spring Boot
 ⚒ Apache Spark
 ⚒ Apache Kafka
 ⚒ Docker
 ⚒ AWS
 ⚒ Kubernetes
 ⚒ PostgreSQL

Senior Software Backend Developer

HYLA Mobile - Extending the Life Cycle of Mobile Devices

Day to day responsibilities: leading a team of 5 developers, which includes: discussion, design and implementation of new features as well as providing code review. Other than that also making sure current process is followed and constantly making improvements to process. Also handling cross team coordination. Technologies used: Spring, (Spring MVC, Spring security, Spring Boot and other Spring libs) Hibernate, PostgreSQL, Redis, RabbitMQ, AWS, Linux, JUnit (Spring-test, Mockito DBunit and other testing libs), Gradle, Ansible, NewRelic

Tech/Team Development Lead

Ciklum

Development Team Lead

Self Employed

Working on own projects, contributing to open source projects

Software Developer

EPAM Systems

Software Development Team Lead - leading a team of 10-17 members. Working on big Case management system with built in Process, Document management functionality, called Case360 for OpenText (ex. Global360).

Software Development Team Lead

Playing a key developer role in a team, working on big Case management system with built in Process, Document management functionality, called Case360 for OpenText (ex. Global360).

Senior Software Engineer

Java Software Engineer - developing different software applications in a team using Java programming language and various Java based frameworks.

Software Engineer

⚙️ I help engineering teams solve complex performance, scalability, and security challenges — from backend architecture to build pipelines.

I specialize in optimizing systems and infrastructure to handle scale, improve reliability, and accelerate delivery. Whether you're dealing with application slowdowns, CI/CD inefficiencies, or compliance bottlenecks, I bring practical, hands-on expertise to fix them.

My work spans:

    🔧 Performance tuning (applications, databases, infrastructure)

    🚀 Build process optimization (CI/CD pipelines, automation, release flow)

    ☁️ Cloud infrastructure (AWS-focused, IaC, monitoring, auto-scaling)

    🔒 Security & compliance (ISO 27001, SOC 2 readiness, secure design)

I work closely with teams to identify root causes, design scalable solutions, and implement improvements that last — both technically and operationally.

📈 If your systems are slowing you down, your compliance goals feel overwhelming, or your delivery pipeline needs streamlining, I can help you move faster without breaking things.

🔗 Let’s connect if you need help scaling securely and efficiently — or if you just want to swap notes on building better systems.

Viktar Patotski

𝙔𝙤𝙪𝙧 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝙨𝙝𝙤𝙪𝙡𝙙 𝙚𝙫𝙤𝙡𝙫𝙚 𝙖𝙩 𝙩𝙝𝙚 𝙨𝙖𝙢𝙚 𝙥𝙖𝙘𝙚 𝙖𝙨 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 — 𝙣𝙤𝙩 𝙛𝙖𝙨𝙩𝙚𝙧, 𝙣𝙤𝙩 𝙨𝙡𝙤𝙬𝙚𝙧.

I often see engineering teams solving problems that don’t exist yet (and in many cases, never will). It usually comes from good intentions... and a strong desire to play with shiny new technologies.

Real examples I’ve seen:

🔹 A full-scale, failover Kafka cluster to process ~10k messages/day — costing ~$20k/year — when a simple SNS queue for a few hundred dollars would do the job perfectly.

🔹 A Kubernetes cluster with two microservices… built long before the company had its first real customers.

🔹 Over-engineered CI/CD pipelines for products that aren’t even close to reaching production.

🔹 Redis cluster for caching when you barely have traffic

🔹 Over-sized RDS clusters. I've seen db.r5.xlarge used for a database that stores 2 GB of data and handles ~10 queries per minute. Because "if there will be a demand".

These systems are fun to architect and build. But at an early stage, they add cost, complexity, and operational overhead — while bringing zero business value.

Great engineering isn’t about using the most advanced tools.
It’s about making the right trade-offs at the right time.

Your architecture must serve your business goals — not the other way around.

Backend & Infrastructure Architect |Cloud | Scalability & Performance Expert | ISO 27001 & SOC 2 Compliance | Secure Systems at Scale

𝗬𝗼𝘂𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 𝗜𝘀𝗻’𝘁 𝗦𝗹𝗼𝘄. 𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗱𝗲 𝗜𝘁 𝗜𝘀.

Most teams jump to “we need more servers” when users complain about performance.

99% of the time, the system isn’t slow — a specific bottleneck is.
Here are the usual suspects I keep finding during performance reviews:

🐌 1. Overloaded or poorly-indexed database
Missing indexes, inefficient queries, table scans — the classics.

🐌 2. Ineffective or missing caching strategy
Caching the wrong things, caching too little, or caching nothing at all.

🐌 3. Chatty microservices
20 calls to get what could be returned in one. The silent killer of latency. 

🐌 4. Blocking I/O in high-traffic flows
Synchronous calls where async would unblock half the system.

🐌 5. Bad ORM usage
N+1 queries, unnecessary data loading, heavy abstractions causing hidden cost.

🐢 6. Architectural latency
Services calling services calling services... because “that’s our pattern.”

🐢 7. Oversized payloads
Returning full objects when the client needs only 2 fields.

🐢 8. Unbounded background jobs or queues
Workers that scale horizontally without limits and crush the DB.

🐢 9. Misconfigured thread pools
Too few → everything waits
Too many → everything thrashes.

🐢 10. Debug/Trace-level logging in production
I’ve seen logs add hundreds of milliseconds per request.

🦥 11. Excessive ORM migrations / schema evolution issues
Locks, timeouts, and blocked writes during peak hours.

🦥 12. Inefficient API gateways or load balancers
Routing, throttling, transformation — all slowing everything down.

🦥 13. Lack of connection pooling strategy
DB or Redis accessed like it's localhost.

🦥 14. Overly complex “enterprise” middleware
Every request passing through 12 layers to do something trivial.

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗰𝗼𝗺𝗲 𝗳𝗿𝗼𝗺 𝗯𝗶𝗴𝗴𝗲𝗿 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 — 𝗶𝘁 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻.

Seen any of these bottlenecks lately? 
Drop your own war stories or tips in the comments, and follow for more real talk on backend performance.🐌

🔥 A Tiny Mistake That Causes HUGE Performance Issues

🔥 “Weekend Tech Myth-Buster”

𝗠𝘆𝘁𝗵: Kubernetes will solve your scaling issues. 

𝗥𝗲𝗮𝗹𝗶𝘁𝘆: It will only amplify whatever mess you already have.

Why Your AWS Bill Doubles Every 6 Months?

My observations:
 - Over-provisioned instances
 - Wrong storage classes
 - Zombie resources
 - Misconfigured autoscaling
 - Over-engineered architecture
 - Dev/test environments left running

What have I missed?

From Firefighting to Predictable Delivery: Why Architecture Reviews Save Money

Most teams don’t realise how much money they burn before a single customer even sees the product.

Not because developers are slow.
Not because features are complex.
But because the architecture silently bleeds cash every day.

Here’s where companies lose money without noticing:

🔥 Slow Development
Poor boundaries, unclear ownership, and tangled dependencies turn every “small change” into a week-long task. Your roadmap slips, developers get frustrated, and delivery slows down.

🔁 Endless Rework
If the foundation is shaky, everything built on top cracks. Refactoring features 2–3 times is not a process problem — it’s an architecture problem.

🚨 Incidents & Outages
Every incident isn’t just downtime. It’s developer time, customer trust, and opportunity cost being burned in real-time.

📈 Scaling Issues
Features that work for 100 users collapse at 1,000. Teams then rush into expensive rewrites that could have been avoided with proper design.

⚙️ Ops Overload
Over-engineered systems, confusing deployments, and unclear monitoring force DevOps to babysit infrastructure instead of improving it. Your “platform team” becomes a “putting out fires team.”

The fix? A proper Architecture Review.

Not a theoretical document.
Not a 100-page consulting artefact.
But a practical, surgical audit of the real bottlenecks.

No alternative text description for this image

𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗥𝗲𝘃𝗶𝗲𝘄 𝗶𝗻 𝟲𝟬 𝗠𝗶𝗻𝘂𝘁𝗲𝘀: 𝗪𝗵𝗮𝘁 𝗜 𝗟𝗼𝗼𝗸 𝗔𝘁 𝗙𝗶𝗿𝘀𝘁

Most teams think an architecture review takes days or weeks.
In reality, the first 60 minutes already expose 80% of the risks, bottlenecks, and cost drains.

Here’s my quick-audit checklist I use during the first hour when reviewing any cloud-based system

✅1. Business ↔ Architecture Alignment

I always start here.
If the architecture doesn’t match the current business stage, nothing else matters.
 - Common issues I see:
 - Enterprise-level complexity for an MVP
 - Over-engineered data flows
 - Tech chosen for hype, not for needs

Architecture must match the business needs. Otherwise it’s expensive noise.

⚙️ 2. Critical Paths & Bottlenecks

I map the request flow end-to-end:
 - What’s the slowest component?
 - Where are single points of failure hiding?
 - Is there unnecessary synchronisation between services?

This already explains 50% of performance issues.

💰 3. Cloud Cost Leaks

Quick wins often come from:
 - Oversized instances
 - Over-provisioned Kubernetes clusters
 - Databases scaled for traffic that doesn’t exist
 - No lifecycle policies for storage or logs

Most companies burn thousands monthly here without noticing.

🔐 4. Security & Compliance Red Flags

Fast checks I perform:
 - Public resources that shouldn’t be public
 - Missing IAM boundaries
 - Unencrypted data stores
 - Are code quality guards and vulnerability scanners in place?
 - Risks analysis is part of development process

Most vulnerabilities aren’t complex — they’re just unnoticed.

🧪 5. Deployment & Release Safety

I look at:
 - How fast someone can deploy
 - What happens if a deployment goes wrong
 - Whether rollback is possible in seconds, not hours
 - How “repeatable” the delivery pipeline is

You can’t scale a team without stable delivery.

📈 6. Observability & Feedback Loops

You can’t fix what you can’t see.
I check:
 - Logging structure
 - Metrics Dashboards
 - Alerts (too many or not enough?)
 - Tracing coverage
Most architecture problems are actually visibility problems.

Which of these areas gave you the biggest headache in the past?
𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? 𝗖𝗼𝘀𝘁𝘀? 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆?
Drop a comment — I’m curious what others see most often.

🔥 Weekend Tech Myth-Buster

𝗠𝘆𝘁𝗵: More microservices = better architecture. 
𝗥𝗲𝗮𝗹𝗶𝘁𝘆: Most SaaS products don’t need microservices until they reach real scale.

taplio

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Elena Tsemirava

Viktar Patotski

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The Battle

Elena Tsemirava

Viktar Patotski

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Elena Tsemirava's LinkedIn Performance

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