Klysera

Build exceptional products...

[Klysera @klysera_ai]

This is why we exist

[TG @ossaijaD]

Hiring today feels like drinking from a poisoned chalice.

Every job posting attracts hundreds, sometimes thousands, of applications that all look and sound the same, as everyone now uses AI agents to apply.

🧵

[Klysera @klysera_ai]

We’ve all hired someone who looked incredible on paper, only to watch them execute at 60% capacity.

And it’s rarely a lack of technical skill. Most times, the work simply doesn’t hold their attention.

When a candidate isn't interested in the core problem you're solving, you get task execution.

They’ll clear tickets, but they won't push past obvious solutions or bring the persistence needed when things get messy.

How do you screen for genuine interest during your interview process?

[Klysera @klysera_ai]

Some engineers interview brilliantly but are frustrating to work with once they join the team.

You usually see the difference when something breaks, priorities change or no one has mapped out the next step.

Do they wait for instructions? Hand you a list of possibilities? Or take ownership and help get things moving again?

That response often tells you more about how they’ll work in a startup than another technical interview question.

We put together a simple framework to help you spot the difference.

Swipe through to see the five levels of ownership and which one your team needs.

[Klysera @klysera_ai]

A fast build is only valuable when the product can keep working after launch.

That means cleaner architecture, fewer fragile workarounds and an engineer who can take ownership beyond the first version.

[Klysera @klysera_ai]

Hiring an "AI Engineer" without knowing which type you need is one of the most expensive mistakes you can make right now.

Treating "AI Engineer" as a single role is like hiring a cardiologist when you need a surgeon. They are both doctors, but their specialties, toolkits, and day-to-day work are completely different.

To build faster and spend smarter, you have to match their specific superpower to your immediate product struggle:

• Pre-PMF & need a fast MVP? AI-Native Product Engineer. Their superpower is shipping at 10x speed using AI-native coding workflows. Hire them when you need to test ideas fast and get an MVP out in weeks.

• Adding smart features to an existing app? AI Product Engineer. Their superpower is leveraging foundational models (like OpenAI or Claude) into production-ready software. Hire them to add smart search, autonomous agents, or chat-with-your-data capabilities.

• Building custom models on proprietary data? ML Engineer. They can train, fine-tune, and optimize custom architecture. Hire them when the model itself is your core IP and market differentiator.

• API bills spiraling as you scale? You need LLMOps Engineer. Their superpower is system reliability, latency reduction, and cost control. Hire them when you have traction and need to keep your AI fast, secure, and financially sustainable.

Start with the problem you’re trying to solve today, and hire the specialist built to fix it.

[Klysera @klysera_ai]

We almost hired the wrong engineer.

A candidate solved our technical problem in just twelve minutes. Clean code passing each test. We were ready to make an offer on the spot.

Then we asked one question:

"What are you trading off here?"

He could explain what the code did, but not why it was the right choice for our constraints. We didn't hire him.

Here is the part that should worry you: AI already writes code that passes tests faster than any candidate you will ever interview.

What AI cannot do is understand when a technically "correct" answer is the wrong business decision.

That is still the human job.

So we completely changed how we interview. We stopped asking candidates to solve problems.

Instead, we hand them a working solution and ask them to find what's broken.

That is what separates someone who owns outcomes from someone who just writes code.

[Klysera @klysera_ai]

The environment you build decides whether a hire's ownership survives.

Give someone end-to-end ownership without a clear target, a readable culture, or leaders who live the standard, and they’ll stop running.

They'll just wait for permission.

[Klysera @klysera_ai]

You can hire the most self-directed engineer on the market and still watch them freeze up.

If they're constantly waiting for permission instead of acting on judgment, you don't have a talent problem.

You have an environment problem.

Here is how to fix it: 👇

[Klysera @klysera_ai]

Leaders must be culture champions.

Culture only holds when the people at the top actually practice it.

A standard that leaders don't live by quickly becomes optional for everyone else. Rule by example, not by decree.

[Klysera @klysera_ai]

Exceptional engineers are not just comparing offers anymore.

They are asking what kind of company they would be joining, how clear the product is, how strong the team feels, and whether the environment will help them do work worth staying for.

That is why the talent crisis has moved beyond recruitment.

The competition now is for the conditions that make strong engineers want to join, stay, and do their best work.

[Klysera @klysera_ai]

Day 47 of being a tech recruiter. 😮‍💨

[Klysera @klysera_ai]

A good recruiter can help you find engineers who look qualified.

But for founder-led teams, that’s not always enough.

You need to know who can work well when things are moving, when the brief is still taking shape, priorities change, and when the work needs more thinking than the ticket explains.

That’s where we look deeper.

We pay attention to how they’ve made decisions before, handled unclear problems, talk through tradeoffs, and whether they’ve actually owned outcomes, not just tasks.

Because the right engineer makes the team lighter.

[Klysera @klysera_ai]

AI adoption gets messy when the rollout creates more work for the people already trying to keep the business moving.

A smarter approach starts with the people who are already curious.

Let them test what fits, shape it around the workflow, and turn what works into something the rest of the team can use without stress.

[Klysera @klysera_ai]

That is where leadership matters most: making the change feel useful, practical, and easier to carry.

How is your team handling AI adoption right now?

[Klysera @klysera_ai]

Read the full breakdown here: https://t.co/TWWpJW5oxQ

[Klysera @klysera_ai]

Today’s strongest engineers do more than write clean code.

They understand context, make smart tradeoffs, think in product outcomes, work well with AI, and keep learning fast.

That’s why we use the IKE Framework to identify high-value engineers across 5 core pillars.