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
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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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Experienced Tech Leader Seeking Engineering Leadership Role | Expert in Scaling Technology, Team Building, and People Management | Hands-On and Results-Driven

Building technology that scales.
Products. Teams. Companies.

3× founder and CTO with 15 years of experience.
Launched multiple SaaS and AI platforms.
Built digital solutions across industries.

Experienced in the messy middle: 
When products ship, teams grow, and clarity is essential.

At QUCOXX, leading technology and shaping strategy.
Creating an AI-powered platform that helps SMEs unlock performance.
Focusing on scalable foundations, turning technical complexity into business impact, and driving innovation across product and engineering.

Passionate about building systems and teams that deliver measurable value.
Sharing lessons on tech leadership, execution, and scaling.

Active in the startup ecosystem, supporting entrepreneurs navigating technology, product, and growth challenges.

Sanjeev Thapa

The CEO-CTO dynamics can be easy or challenging at the same time.

(If you do not manage it intentionally.)

A startup CTO asked me:
How to improve the CEO/CTO dynamics?

In the end,
It’s managing expectations.
It’s communication.

I have worked with many CEOs.
Very different.
Totally different background’s.

That's what makes it more beautiful.
At times could be challenging as well.

To begin with,
Don’t pressure yourself.
That’s a good start.

I met Dr. Karolina Sauer-Sidor via LinkedIn.
(Trust me, good things happen via strategic networking. :))

She is my CEO.
And aspiring partner.

What I like about working with Dr. Karolina Sauer-Sidor is:
- We both are willing to explain our THINKING.
- We respect our CONSTRAINTS.
- And we both APPRECIATE what we do for each other.

If you truly SUPPORT each other,
CARE & RESPECT each other,
Everything will follow.

It’s like every other relationships.
ONLY you can work on it.

Because every relationship is UNIQUE.

Do share some experience and tips.

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CTO at QUCOXX | 3× Technical Founder | Helping SMEs unlock performance through our AI-powered platform | Sharing lessons on tech leadership, execution & growth

I ran after the "best" tech stack.

(I was first time CTO, on my own business).

I was more obsessed about tech.

I was kinda proving my technical expertise.

I moved away from users.
Away from customers.
And actual problems.

Don't get me wrong.
Tech do matters.

But something else matters more.

Now I tell every tech leader the same.

Care about the business goals.
care about customers.

Fall in love with problem, not product.

Use simple tech which scale fine.

The best tech stack is the one that your team knows well.

Infrastructure matters. 
Don't overlook it.

But overthinking it wastes time.

What's your approach?

🔔 Follow Sanjeev Thapa for tech & startups

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Apple paid Google to power its AI.

(Pretty unusual.)

We have seen it the otherway in the past.

A couple of observations can be made:
- This is not just about gadgets.
- This is about where the value sits.

Apple believes it can leverage Google's foundational models.
Apple sees models as a commodity, not a differentiator.

The deal shows strategic partnerships are important.
For the new products, integrations offer so much.

The long-awaited Siri and Apple Intelligence might get revamped.

What I learned and found helpful for founders:
- AI isn't about who builds the model.
- It's about who integrates it into real workflows.

What's the deal telling us?

🔔 Follow Sanjeev Thapa for tech & startups

AI agents struggle to deliver ROI!

(It's rarely the technology.)

Most companies I talk to
investing heavily in AI tools & agents.

A big chunk of the budget goes to:
Models, infra, vendors.

Almost nothing goes into
people, education & process.

There is an imbalance.
And it's real.

Less than 10%
investment in training, ownership, education, and workflows.

Hardly, 
We consider security/compliance.

AI agents might be smarter already.
They might get smarter & better.

But if we fail to redesign
how works actually happens
nothing's gonna deliver.

In 2026, where do you invest more?
tech, people, or process?

CTO at QUCOXX | 3× Technical Founder | Get your personal business growth plan in 48 hours ↓ Featured

Founders getting defensive about their product.

(Faster than they realize.)

I have seen first-hand.

Teams spend weeks explaining,
Why a feature works.

Rather than noticing 
Why didn't users use it?

The moment you need a long explanation,
It's already telling you something.

All in all, 
It delays learning.

🔔 Follow Sanjeev Thapa for tech & startups

My first hires were people LIKE me.

(I thought it was the RIGHT fit.)

Amazing builders.

Possibly slightly impatient(like me).

It won’t take long.

All burned out.

I started then hiring for complementary skills.

As builders Hiring architects.

The best decision.

The better results.

You easily complement each other.

You fill the gaps.

My BIGGEST learning,
NO MATTER WHAT,
You need to RESPECT how each other THINKS.

That's what matters.

That's where it CLICKS.

What's your ideal first hire?

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Most SaaS founders don’t lack ideas.

(They lack something else.)

I talk to SaaS founders shipping constantly.
Features. Experiments. Improvements.

But when I ask:
“What’s the one thing that would unlock the next stage of growth?”
the answer is often fuzzy.

Not because they don’t think deeply.
But because everything feels interconnected.

Product affects sales.
Sales affect pricing.
Pricing affects churn.
And suddenly, every decision feels risky.

That’s usually the signal.
Growth didn’t stall.
Focus did.

What I’ve learned working with SaaS teams is this:
real progress starts when you identify the primary bottleneck
and stop treating everything else as equally important.

That’s why we built a simple growth diagnostic at QUCOXX.

In 10–15 minutes,
You answer questions about your business.

Within 48 hours, you get:
→ where growth is actually constrained
→ what to act on now
→ what matters over the next 90 days

No generic playbooks.
No “growth hacks”.
Just clarity for your stage.

If you’re planning your next growth move,
Which part of your SaaS feels hardest to prioritize right now?

If this sounds familiar,
DM me and I’ll share how we help founders get that clarity in 48 hours.

🔔 Follow Sanjeev Thapa for tech & startups

Most SMEs chase growth the wrong way.

(Knowingly, unknowingly).

Hustle hard.
Hire more.
Push faster.

In no time, 
They burn out.

The good ones do something different.

They get clarity on:
What's actually bottlenecking growth?

Revenue?
Operations?
Team?

Not guessing. 
Actual root cause diagnosis.

Then they focus. 
One lever at a time.

They execute. 
They iterate. 
They adopt.

You have limited resources.
You pull the wrong lever, 
You waste even more.

With QUCOXX you won't end up getting the generic advice/plan.
We help you get to your specific situation.
We help you with a personalized plan & initiatives.

And not in 90 days.
And not with expensive projects.

In no time.
Tailored and adaptable.

The best part:
You can execute,
Wherever and whenever you want.

What's your growth blocker right now?

P.S. 💡 Executive Masterclass: “How can innovation accelerate your growth?”
 📅 January 21, 2026 | 🔒 Limited seats
 👉 DM or register via the link in the comments 👇

Big reason why you might not be getting results from agentic coding!

(It’s not the tools.)

Be it cursor or
Be it claude or
Other tools.

Most spend more time in agent mode.

But often plan mode,
Or planning in general is overlooked.

You end up with messy results.
Blockers during the process.

Spend 2,3x in planning mode
And see for yourself.

My fav mode is for sure planning mode.

What’s yours?

The EU Inc announcement has been getting a lot of attention lately.

(And honestly, it should.)

But the question is still there.

Is this actually meant to compete?
Or is it mostly symbolic?

Yeah, less friction is always better for startups and founders.

But if this really moves from paper to action, does it suddenly create great startups?

Maybe. Maybe not.

What is pretty clear though is that bad policy can block them for sure.

So what matters next is execution.
Not just faster defaults.
Better defaults.

That’s the real test.

So should we actually be hyped about this announcement?

Most "growth plans" are just wish lists.

(Not action maps that move revenue.)

Founders see a dozen frameworks.
Templates. 
Playbooks. 
Generic advice.

What gets missed through is:
The context and your specific needs.

Real growth starts with clarity.
"What's the next thing I should do? Not hypothetically someday."

Are you on the hypothetical end?
Or the first one?

That's why we build something valuable & different.
Something actionable.

"A personalized growth plan for your business",
delivered in less than 48 hours.

Actionable immediately afterwards.

It is more than a static document.
It's a diagnostic + execution map built for your situation.

If you are interested,
let's chat.

If you could change one thing in your growth engine by Friday,
What would it be?

P.S. Link to upcoming masterclass:

- "How can business model innovation drive growth? Masterclass February 11, 2026" https://lnkd.in/dkd4QTuE

- "Wie können Innovationen des Geschäftsmodells das Wachstum vorantreiben? Masterclass, 11. Februar 2025" https://lnkd.in/duJCXQAd

Founders paying themselves isn't the problem.

(The guilt around it is.)

I have seen and known many founders
avoid paying themselves early on.

Not because they can't.
But because it feels wrong.

Let's talk about the guilt.

The guilt usually comes from the wrong question.
"Am I being selfish?"

We have seen founders end up
stressed, distracted, or quietly burned out.

Paying yourself does not mean excess.
It means sustainability.

The real issue is never whether founders get paid.

It's whether incentives stay aligned
with the risk, the runway & the stage.

No guilt needed.
Just the honesty.

All founders have different circumstances.
Different situation.

They might like to operate & build differently.
Let's not tag them.

We were doing well.

(That's exactly why the next decision slightly backfired.)

In my first business,
We built a solid company.

Good customers.
Clear value.
Decent execution.

We were a service company.
We were good at it.

Then came the idea
that slows us significantly down.
"What if we also build a product?"

in paper, it all sounded smart.

In reality, 
it blurred a lot.

Delivery priorities conflicted.

The product never got the focus it needed.

Service work starts losing its sharpness.

We didn't fail.

Luckily, 
the service revenue kept us going.

But we lost momentum.

Looking back,
The mistake was not ambition.

It was mixing 2 different models
that require very different systems.

Trying to be two things at once
made us worse.

So anytime you're considering a new direction
Consider if that might dilute what you're already good at.

Is vertical integration becoming less relevant?

(Or just unnecessary for most teams now.)

Not long ago,
owning a full stack seemed mandatory.

Model, infra, platform. Everything.

Fast forward to today,
The game looks different.

You can pick the best model.
Run on the best infra.

Focus on delivering value on top.

The hard part moved for sure.

It's no longer about building everything.
More about choosing well.

If you're building today:
- What actually do you need to own?
- and whatnot?

🔔 Follow Sanjeev Thapa for tech & startups

I was watching a few AI videos lately.

(Agents everywhere.)

Creators wiring up AI agents.
Autonomous workflows.
“Fully hands-off” setups.

Then I opened Reddit.
And saw Moltbook and OpenClaw going wild.

What surprised me isn’t the tech.

It’s how quickly people are okay with giving agents access to:
→ their machines
→ their emails
→ internal tools

All because someone trusted a hype.
Someone finds it interesting.

I'm concerned we may be skipping a step.
Not “does it work?”
But “are we ready to delegate responsibility?”

Do they all have the details?

We talk a lot about autonomy.
Much less about ownership
when things go wrong.

Are we being careful enough?
or are we caught up in creator hype right now?

🔔 Follow Sanjeev Thapa for tech & startups

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