Helping startups to scale 🦄 ✦ Driving Halo Lab 7 → 8-figures ARR & having fun ✦ Founder with Design in ❤️

I run Design & Tech Agency helping brands become TOP 1%.

✦ $530 M raised + 9-figure exits by client startups 
✦ 500+ projects delivered
✦ 99+ international awards
✦ 4.9 / 5.0 average rating on Clutch from 80+ verified reviews

This is a real impact in numbers.

—

Over the past 15 yrs I’ve been shaping digital products and brands for category‑defining companies — from NOKIA and OPERA to high‑growth SaaS and Web3 start‑ups.

My core focus is design leadership at scale: I enter as a strategic partner, craft a vision, and lead multidisciplinary teams all the way from concept to launch.

—

I specialize in:

⤷  B2B SaaS Product Design | Guiding startups and enterprise spin‑ups from 0 to 1 — turning complex tech into intuitive, emotionally resonant experiences.

⤷  High‑Performance Digital Presence | Building conversion‑focused brand websites that turn first impressions into lasting growth.

—

Have BIG idea in mind?
Contact me directly: inquiry@halo-lab.com

Artem Borysenko

You can hire a designer.

Or you can hire a design brain.

One gives you beautiful screens.
The other gives you a product that thinks.

When you hire a single designer, you get skills.
When you hire a team that works as one brain, you get experience that compounds.

At Halo Lab — Your Digital Partner, our teams don’t work in silos.
The UX researcher sees what frustrates users.
The product designer connects it to business goals.
The UI designer turns it into clarity and trust.
The developer translates it into something fast and functional.

Each person covers a blind spot the others can’t.
And together, they don’t just design, they solve.

That’s the difference between design as a task and design as a system.
Between a pretty interface and a product that scales, converts, and feels effortless to use.

When founders realize this, everything changes:

1. They stop hiring portfolios.
2. They start hiring thinking systems - teams that anticipate problems before they appear, align design with ROI, and build products that last.

If you’re scaling your product, don’t just hire a designer.
Hire a brain that’s trained to think like your users and build like your business depends on it.

Find you team https://bit.ly/49wqB0p

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Founder @ Halo Lab ✦ Design & Tech Agency Helping Brands Become TOP 1%

You’ve raised your Series A.

Now you’re proving you can deliver at scale.

And every week of delay costs runway, users, and momentum.
The big question?
Who’s actually going to build this thing?
You have three choices and each comes with a hidden price.

1. The dedicated team.
(Agency/product squad model)
Fastest to launch.
Move as one unit.
Cross-functional by default.
→ Best for: founders who need traction now and can’t afford to spend 3 months hiring.

2. Freelancers.
They’re flexible, affordable, and perfect for low-risk experiments.
But you become the PM, the QA, and the glue holding everything together.
When people churn, rework kills speed.
→ Best for: quick feature spikes or marketing tests, not your core product.

3. In-house team.
The long-term bet.
You get culture, domain memory, and control.
But it’s slow and expensive.
Median SWE comp in the U.S. sits around $187 K/year, and that’s before benefits or churn.
→ Best for: protecting core IP and scaling once product-market fit is locked.

My take? Go hybrid by design.
1. Dedicated team: ship your core product in < 90 days.
2. In-house team: retain knowledge and own the moat.
3. Freelancers: plug niche gaps when speed matters.

Re-evaluate every quarter: velocity, cost per feature, defect rate.
Because at Series A, your biggest risk isn’t cost, it’s time.

✦ At Halo Lab — Your Digital Partner, we help Series A founders scale with dedicated product squads - ready to ship fast, adapt faster, and grow alongside your in-house team.

♻️ Share this to help make a good choice.
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 Dedicated Team vs. Freelancers vs. In-House 

“We need a redesign.”

This is how half of discovery calls begin.

However, 10 minutes into the discussion and it’s obvious - they need a problem statement.

The product is not broken. 
The understanding of the problem is.

Founders say: “Our checkout flow is poor.” 
Users say: “I don’t trust this payment form.”

The difference? One adds a feature. The other builds the right feature.

A good problem statement is not an educated guess.
It’s that moment that your research turns into clarity. 
It translates chaos into focus.

Here is what strong product teams do completely differently:

1. They do not start their process around “what to build.” 
They start their process around “who’s struggling – and why.”

2. They use data and real voices, not assumptions. 

3. They state the problem in a way that the whole team- designer, PM, and CEO can read and ingest in an instant.

When you get the problem right the design almost builds itself.
Your next big UX breakthrough will not come from adding another feature. 
It will come from rewriting the question you are trying to answer.

♻️ Share if it resonates.
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The hardest thing about building an AI product is getting people to trust what it does.

You can create a great model all you want, but as soon as users feel the slightest bit of uncertainty, they are going to ponder, hesitate, and leave.

When founders build trust into the product experience, three things happen fast.

1. Users stop second-guessing and start exploring. First session activation goes up because they understand the what and whys.

2. Every click of trust compounds itself into usage, retention, and upsell. Without trust, the best AI feature will remain unused.

3. You do not have to wait for product market fit to learn how users actually feel. Trust patterns can save you weeks of guessing, and reduce the launch chaos.

4. Transparent UX with design-based trust cuts confusion before it even reaches support. No more "I am left wondering why it said that?" tickets or bad reviews for hallucinations.

Most AI products are built for power. 
The real winners are built for confidence.

✦

At Halo Lab — Your Digital Partner, we help founders design AI products users trust enough to actually use and pay for.
If that’s the stage you’re at, let’s chat.

♻️ Share this to help others build AI products.
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UI for AI products

Most companies still treat a design system as a visual asset.

In reality, it behaves more like infrastructure.

When a design system is used by a company, it can help reduce costs for all the products the company already has. Also, using a design system can reduce the costs of making new products by different teams in the company.

Here’s where the real financial return shows up:

1. Reduction of duplicated efforts across teams.

When multiple teams are rebuilding and recreating similar elements, there are three new associated costs for each team: a new design, a new coded version, and the need for each team to perform QA. With the implementation of a design token system, this cycle is not necessary.

2. Component scalability across product portfolios.

When a company builds a system to support a group of products, they make more money by creating one design and using it many times for all the products in the group. This is why companies have been able to reduce the time it takes to get products to market by 20-40% without adding more staff.

3. Patterns decrease cognitive load and time spent deciding.

Once an organization sets up design patterns to support their onboarding, dashboards, permission systems, navigational structures, and error state designs, teams won't have to argue about the most important design principles. 

4. The cost of change decreases dramatically.

With no design system: A rebrand, accessibility update, or micro-interaction change triggers weeks of redesign and recoding..
With a design system: Update the source → every product updates automatically.

✦

A strong design system improves:

- Time to market.
- Cross-platform trust.
- Developer throughput.
- Conversion and activation.

✦

Build your design system for the next three products you launch. 
You will begin seeing returns on your investment in the early years.

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Your product already makes users feel something.

The question is what?

If your users don’t feel the right thing, they won’t do the right thing.
They’ll bounce, hesitate, mistrust, churn.

Over 500+ products, I’ve learned that the best products aren’t the ones with the most features. They’re the ones with the clearest emotional tone.

Every product leans into one of three emotional modes.

1️⃣ Calm → “I feel safe here.”
Perfect for: health, finance, productivity, wellness.
How it’s built: soft palettes, clear language, gentle interactions, no pressure.

2️⃣ Competent → “I trust this.”
Perfect for: SaaS, B2B, dashboards, enterprise.
How it’s built: structured layouts, predictable behavior, strong signals of reliability (performance, clarity, proof).

3️⃣ Empowering → “I can do this.”
Perfect for: fitness, learning, creativity.
How it’s built: energetic visuals, progress celebrations, milestones, personalization.

When the emotional tone doesn’t match the product’s purpose:
1. A data tool feels playful → trust drops.
2. A wellness app feels intense → stress rises.
3. A learning app feels sterile → motivation dies.

Emotion either reduces friction or adds it. 
What to do before your next sprint.

✦ Pick your emotional tone before UI design.
✦ Build it into your design system (colors, spacing, motion, language)
✦ Test the gut reaction early (“How did this flow make you feel?”)
✦ Make sure every new feature reinforces that emotional mode.

We design products that make users feel the right thing.
It’s about building experiences users want to return to.

♻️ Share this to help others catch hearts.
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You didn’t just hire a team.

You hired a risk.

That’s what most founders realize by Sprint 2, but by then, momentum is already bleeding out.

What starts as “a good fit” quickly turns into:

→ Delayed updates.
→ Vanishing senior talent.
→ Quiet budget drift.
→ Process that feels more defensive than proactive.

It rarely breaks all at once.
It just slowly stops working.

At Halo Lab — Your Digital Partner, we’ve inherited dozens of these “almost there” projects.
And we’ve learned to spot the early signals that most teams ignore.

Here’s how experienced founders filter for risk before it shows up on a roadmap:

1. Ask who stays after kickoff.
Bait-and-switch team structures are common. 
Real partners show you the core team from day one.

2. Low prices don’t remove cost. 
Into rework. Into scope creep. 
Into things no one “thought about” until it’s too late.

3. Their process should flex around your product.
If their workflow can’t adapt to your stack, roadmap, or internal rhythm, you're buying overhead.

4. Silence isn’t a “working phase.” It’s a liability.
Communication isn’t a nice-to-have. 
It’s part of delivery.

5. IP, code ownership, and access - settle it early and in writing.
No smart founder leaves this vague.

6. Domain experience is a requirement.
A capable team isn’t enough. 
You need people who’ve solved your kind of problems before.

7. If there’s no post-launch plan, you’re the plan.
Support, iteration, and continuity should already be mapped.

We partner with founders who know that reliable execution is rare and valuable.
If that’s where you are, you’ll feel the difference from day one.

♻️ Share this to help others fint a right partner.
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I can’t remember the last time I used an app that didn’t ask me to share, rate, or “engage.”

Every product today (even in B2B) is designed for an algorithm.
Google’s. LinkedIn’s. App Store’s. TikTok’s.

We’ve quietly shifted from designing for people to designing for distribution.
The algorithm rewards speed, volume, stickiness.
And so we chase it - with notifications, widgets, and “viral moments.”

But something gets lost along the way.

When was the last time a product was designed for:
✦ Silence?
✦ Reflection?
✦ A human mistake that wasn’t punished instantly?

You've trained our teams to ask,
“How will this perform in search?”
“Will this trigger engagement?”
“Will this help retention metrics?”

Not:

“Will this make someone feel seen, understood, or at ease?”
We measure time-on-page, not peace of mind.
We optimize funnels, not flow.

And in doing so, we risk turning our users into an interface between two algorithms, feeding signals back and forth.

Maybe the next wave of great design won’t be about more engagement.
Maybe it’ll be about creating digital spaces that breathe.
Moments that respect our attention instead of stealing it.
Because the best products don’t just perform, they pause.
They make us feel something.

What’s one product you’ve used lately that still feels designed for humans, not algorithms?

♻️ Share this if it resonates.
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I sat in a meeting where a CEO said something I'll never forget:

“I don’t need it to look beautiful. I need it to make money.”

At the time, it stung a little.
We’d poured weeks into crafting the perfect interface.
But he was right.

If your design doesn't move revenue or retention, it's wallpaper.
That moment changed how we work at Halo Lab — Your Digital Partner.
Now, every design decision starts with a business question:

1. What does this screen earn?
2. How does this feature reduce churn?
3. Can this interaction build trust faster?

We stopped chasing “nice-to-have” visuals and started designing for impact: shorter time to value, higher conversion, more clarity.

Last quarter, applying this thinking helped a SaaS client cut trial-to-paid time by 40%. Design decisions became revenue decisions.

What's one screen in your product that looks great but doesn't move the needle?

🔔 Follow Artem Borysenko for more updates!

Director, Software Engineering | Actionable content for leaders in tech. Software engineering, engineering leadership, online business.

15+ years building production systems. Former Director of Engineering at Oracle, leading public cloud services and working with engineers from Microsoft, AWS, Google, Netflix.

My mission: help businesses and engineers build AI systems that create real leverage.

We're entering a world where AI agents do the work and humans direct. The question is which side of the gap you're on.

Tool user: Prompts ChatGPT, starts from scratch every time.

System builder: Has agents that research, write, and code - creating output while they sleep.

Most AI content teaches you to copy paste. I teach the patterns underneath - so you can build any system, for any problem.

One person with the right systems can do the work of 10. I help people become that person.

What I do:
→ AI Engineer: Community for AI engineers. Courses, resources, downloadable AI projects
→ GradientWork: Custom AI development for businesses
→ Newsletter: Free value every week

📩 newsletter.owainlewis.com
👥 skool.com/aiengineer
💼 gradientwork.com

Owain Lewis

5 AI frameworks worth learning in 2026:

(And when to use none of them):

Here's the dirty secret nobody talks about:

AI frameworks are wrappers over provider SDKs with extra features bolted on.

Many professional developers I respect actively avoid them.

The complexity. The over-abstraction. The breaking changes. It adds up fast.

I call this framework tax: the ongoing cost of maintaining someone else's opinions about how AI apps should work.

Start with the SDK. Build until you hit a wall. Then reach for a framework.

When you do, here's what's worth learning:

1. Pydantic AI (Python)

If you've used FastAPI, this feels instantly familiar.

Types, validation, contracts, retries on malformed responses. Works across all major providers.

Best for: Python engineers who already think in terms of types.

2. LangGraph (Python)

Treats AI applications as graphs, not chains.

Branches, retries, parallel execution, human-in-the-loop become first-class citizens instead of afterthoughts.

Best for: Production systems with complex workflow logic.

3. Vercel AI SDK (TypeScript)

Provider-agnostic with a clean developer experience.

Swap models and build agents with minimal code.

Best for: TypeScript developers building AI apps.

4. Google ADK

The first framework that takes polyglot teams seriously.

Official support for Python, Java, TypeScript, and Go with the same mental model. Ships with observability out of the box.

Best for: Teams building on the Google ecosystem.

5. Spring AI (Java)

Exposes LLM services through familiar Spring patterns.

The value isn't innovation. It's integration.

Best for: Enterprise teams already familiar with Spring. 

The uncomfortable truth?

The best framework is often no framework at all.

The goal isn't to master frameworks. It's to build reliable systems.

Sometimes a framework helps. Sometimes it just gets in the way.

What's your current recommendation?

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Founder, GradientWork | AI Engineer | I build custom software to make your business more profitable

5 AI frameworks worth learning in 2026

The easiest way to build AI agents in 2026.

(My micro-agent architecture explained):

I built an AI agent that searches YouTube, analyses thousands of videos in seconds, downloads transcripts, writes scripts, and uploads content.

It completely changed my workflow and how I think about "personal AI assistants". I use it every day. 

The whole thing? A folder with some files and about 150 lines of Python code.

Here's the architecture:

1. The folder IS the agent

A Micro Agent is a folder where files define everything:

AGENT md = who it is and what it can do
tools/ = scripts it runs
context/ = knowledge it reads
workspace/ = where it saves files

Point any terminal coding agent at the folder. Claude Code, Codex, Amp, OpenCode. Works perfectly with all of them. 

2. Scripts are a superpower

Software engineers have been writing scripts and CLIs for decades. Nothing new.

But when you give them to an AI agent, magic happens.

Terminal-based agents are exceptional at invoking scripts. This eliminates 90% of the complexity you get with setups like MCP.

No protocols. No complexity. Just stdin/stdout.

3. Skip the infrastructure

The pain of building AI agents is the harness. The UI. The orchestration layer.

There are hundreds of coding agents now. They already have perfect UX out of the box.

Don't rebuild what exists. Focus your energy on what makes your agent unique: powerful tools and rich context.

4. Version control native

Your agent is just files. Share the folder with your team and they can use the agent.

Track changes. Roll back mistakes. Collaborate on improvements. No deployment pipeline needed.

The craft is knowing what you don't need:

Most agents don't need vector databases. Or distributed orchestration. Or complex memory systems.

They need clear instructions, relevant context, and powerful tools that do useful things.

Is this suitable for every case? No. 
Is it suitable for multi user production situations? No
Is it a great way to quickly build useful agents for your own use? Yes.

What's your current setup for running AI agents?

---

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The easiest way to build AI agents in 2026

How I use Claude Code. 

7 tactics to make you a power user:

Claude Code is an AI agent that lives in your terminal. People from all industries are using it for far more than just writing code. Imagine it as the smartest personal assistant you've ever had. 

But the tool alone isn't enough. Here are 9 tactics I use daily:

1. Design before you build (think first)

Separate planning from execution. Use plan mode (Shift+TAB) to think through what you're building before Claude writes any code. What's the approach? What are the edge cases? Think first, code second. This is how senior engineers work. 

2. Break big work into tasks (plan the work)

I take a large spec/plan and automatically break it into smaller tasks to feed to an agent. Each includes what to build, why it matters, and what success looks like. Claude executes one at a time with a clean context window.

3. Iterate, don't one-shot (refine as you go)

No engineer writes code perfectly first time. So why expect AI to? 

After any first draft, I ask: "Review this and suggest improvements" or "What edge cases are we missing?" "How would you improve this code?".

4. Save commands you repeat (systematize patterns)

Use slash commands: I have a .claude/commands/ folder. /design starts a planning session. /review checks code before shipping. /commit writes proper commit messages. Write once, run forever.

5. Set up your project brain (configure once)

CLAUDE {dot} md sits in your project root. Claude reads it at the start of every session. Put your tech stack, code style rules, and things Claude should always (or never) do. You stop repeating yourself. Claude just knows.

6. Automate your standards (automate quality)

I use hooks to run tasks on every edit (tests, linting etc). Quality happens automatically. Problems get caught immediately, not discovered later.

7. Document more things (the mindset shift)

LLMs have no memory. Store important context in static docs. I keep a docs/ folder with SOPs, examples of good output, and rules for how I want things done. 

Claude is a powerful builder with amnesia. My job isn't writing better prompts. It's building docs and systems that make every session start strong.

The future of work = humans using AI agents to become superhuman. 

Which of these are you using yourself?

---

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7 Claude Code tactics

Most AI agents never make it to production.

Because no one knows how to deploy them.

Everyone is vibe-coding great looking demos on YouTube.

But how do you take a demo and make it work for real customers?

Most production AI systems fit three patterns:

1. API service

User calls your endpoint, gets a response.

→ Chat APIs for customer support
→ Document analysis endpoints
→ AI-powered search

Use when you need synchronous responses or human-in-the-loop.

Pro tip: Stream the response. Standard request/response feels too slow for LLMs.

2. Scheduled job

Runs on a schedule. No trigger needed.

→ Daily report generation
→ Hourly data sync
→ Weekly newsletter drafts

Use for batch processing at regular intervals. Set it and forget it.

3. Event-driven

External event triggers your automation.

→ New email arrives, process and respond
→ File uploaded, analyse and categorise
→ Webhook fires, take action

Use when reacting to the world in real-time.

The pattern you choose determines your infrastructure.

On Google Cloud, for example:

API service → Cloud Run Service (scales to zero, scales to thousands)
Scheduled job → Cloud Run Job + Scheduler (runs to completion, stops)
Event-driven → Pub/Sub + Cloud Run (decouples source from processing)

AWS and Azure have equivalents. The patterns stay the same.

That's the gap most people miss.

The engineers getting hired aren't the ones with impressive demos.

They're the ones who can answer: "How do we give this to real customers?"

Which pattern are you building with?

__

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3 production AI pattersn

If you want to build AI applications, you need to know how to deploy them.

Most tutorials skip this part entirely.

They end with "and now you have a working chatbot." Running on localhost. Useless for real customers.

Here's what they don't show you:

- TLS certificates
- Load balancers
- Database connections
- Environment variables and secrets
- CI/CD pipelines
- Networking and DNS
- Async work and message queues
- Container orchestration

This is real architecture. Even if a platform team handles it at your company, understanding how software gets to production matters.

I deploy AI applications three different ways depending on the situation:

1. PaaS (Render, Railway)

Push code. They handle infrastructure.

Best for: Side/solo projects, MVPs, "just get it live today."

Trade-off: Costs add up quickly once you scale.  

2. Cloud Platforms (AWS, GCP, Azure)

Managed services with real infrastructure control.

GCP Cloud Run is my default: containerised, scales to zero, pay for what you use. AWS has ECS and Lambda. Azure has Container Apps. All three work - pick one and learn it deeply.

Best for: Client work, production apps, anything enterprise.

Trade-off: Steep learning curve. Each platform has its own approach to IAM, networking, and secrets. Budget real time to learn.

3. VPS (DigitalOcean, Hetzner)

Rent a server. SSH in. Run everything yourself.

Best for: Cost optimisation, GPU workloads, learning how it actually works.

Trade-off: You're the ops team. Security, backups, uptime: all you.

There's no universal right answer.

Shipping a side project? Start with Render.

Building for clients who need scale? Learn a cloud platform.

Want full control and lowest cost? VPS.

Is this the complete picture? No. Every app has different needs.

What's your go-to deployment path?

---

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How to deploy AI apps to production

Qoder just launched their Teams Plan which brings agentic coding to enterprise software teams:

One of the challenges with agentic coding is working on larger real-world codebases. 

Qoder is an agentic coding platform that can search and reason across 100,000+ files, giving you deeper codebase understanding - not just individual files.. 

I use it as both a dedicated IDE and a CLI.

Here are the features worth knowing about:

1. Quest mode

This is a killer feature. Describe what you want. Qoder generates a spec, breaks it into tasks, and executes asynchronously.

You delegate, walk away, review completed work later.

This matches how we work as engineers: Plan > [tasks] > execute.

2. Repo wiki

Auto-generates documentation and keeps it in sync as code changes. Architecture overviews, module summaries, dependency maps.

3. Smart model routing

Qoder routes to the right model based on task complexity which makes token usage way more efficient. 

4. Teams Plan

→ $30/seat/month (minimum 2 seats)
→ 2,000 credits per seat
→ Centralized billing and admin console
→ SSO for team-wide access
→ Shared credit packages coming soon

Qoder is moving agentic coding from “nice to have” to serious infrastructure for enterprise software teams.

Check it out here: https://aisecret.co/Owain

If you want to get reliable data from LLMs, learn this essential technique:

The problem is simple:

LLMs return text. Your application needs structured data.

You ask for JSON. Sometimes you get JSON. Sometimes you get JSON wrapped in markdown. Sometimes you get a paragraph explaining the JSON.

Your code expects one format. The model gives you three.

This is why structured outputs exist.

What are structured outputs?

A way to force an LLM to return data in an exact format you define.

Instead of asking the model to return any old answer, you give it a schema. The model's output must match that schema. No exceptions.

When to use them:

→ Extracting information from text (names, dates, entities)
→ Classifying content into categories
→ Routing decisions in AI workflows
→ Any time downstream code expects specific fields

If your AI application needs predictable data from an LLM, don't rely on instructions.

Use structured outputs.

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