Matt Watson

Full Scale

Helping companies scale with affordable talent in the Philippines. Provided $50M worth of dev services to over 200 clients since 2018.

Our 300+ employees are helping dozens of companies build their software quickly and affordably.

* Philippines based, English fluency is superb!
* Staff augmentation model, our team works directly for you!
* Focused on senior level developers
* No middle man project managers in the way

Founder & CEO

Product Driven

A must read guide for anyone working in tech or startups on how to build teams that think about customers and take ownership of their work.

Author

Full Scale Ventures

Venture Studio creating new SaaS companies

Chief Executive Officer

Startup Hustle

Podcast about entrepreneurship with over 6 million downloads! We interview entrepreneurs and others related to the startup ecosystem.

Podcast Host

At Capacity

The only smart ads platform for home services. We turn search ads off when our customers have full schedules or are "At Capacity". We turn their ads on and advertise special offers when they need more customers.

Founder, CEO, CTO

CAMP Digital

Leading the development and product teams to help take CAMP to the next level. CAMP Digital is the industry leader in digital marketing for home services. We have the unique ability to dynamically change ad campaigns based on the capacity of our customers.

Stackify

Stackify (sold 2021 to Netreo, then to BMC in 2024) provides the ultimate combination of tools for developers to monitor and troubleshoot their applications. We combine application performance monitoring (APM) with server monitoring, app metrics, advanced error tracking, and log management in a single, easy to use platform for developers.

Founder, CEO

VinSolutions

One of two original founders of the company in 2003. Started the company with the simple goal of helping car dealers upload car photos to the internet and syndicate them to internet classified websites. Over many years the product rapidly expanded in to an inventory management, website, CRM, and internet marketing product. VinSolutions became the market leading CRM solutions for car dealers with thousands of customers. I provided most of the product vision for the entire suite of products and as CTO was in charge of all product and software development.

Founder, Chief Technology Officer

Physicians Reference Laboratory

Designed and developed laboratory information systems

Senior Architect & Lead Developer

Ticket Solutions

Designed and developed software for tracking and retailing tickets

Senior Application Developer

Serial Entrepreneur, Startup CTO, Podcaster, Blogger @ VisionaryCTO.com

I’ve spent over 20 years as a founder and CTO, starting in 2003 when I co-founded one of the earliest SaaS companies. We bootstrapped VinSolutions to $35M ARR and sold it for $150M in 2011.

Since then, I’ve built and scaled multiple companies. I’ve led engineering teams through every phase of growth. I’ve also learned firsthand what breaks when leadership, product, and engineering aren’t aligned.

In 2018, I started Full Scale. It began as a way to help myself scale faster with top-tier engineering talent in the Philippines. Today, Full Scale has over 300 developers supporting fast-growing tech companies that need to move efficiently and with clarity.

I recently launched Full Scale Ventures, a venture studio focused on solving real problems for startup founders and engineering leaders. Our first product is EngagePath, a tool designed to help thought leaders identify warm leaders from their followers.

Most recently, I wrote Product Driven, a book for engineering leaders who want to stop playing whack-a-mole with tickets and start building real product momentum. The book is about aligning culture, leadership, and customer focus. Product thinking isn’t just for product managers anymore.

I also host a podcast and write a weekly newsletter focused on the gap between business and engineering and how to close it.

Read the book and subscribe here: https://productdriven.com/connect

Learn more about Full Scale: https://fullscale.io

We started Full Scale to do software development the right way from the Philippines. Our 300+ employees are helping dozens of companies build their software quickly and affordably.

* Philippines based, English fluency is superb!
* Staff augmentation model, our team works directly for you!
* Focused on senior level developers
* No middle man project managers in the way

AI has created a new kind of engineering paradox.

Some less-experienced engineers have mastered AI tools and can “vibe code” their way to fast results. On the surface, it looks like the productivity every company wants.

Meanwhile, some senior engineers have not fully embraced these tools yet, even though they could benefit the most.

But here is the catch:

The people vibe-coding their way through tasks are also the ones most likely to ship code they do not fully understand. And when that code breaks in production, they are stuck.

That is the paradox:
AI makes it easy to write code fast, but even easier to get in trouble if you do not understand it.

It is just like aviation.
Pilots rely heavily on autopilot, but they still train for years because when the system fails, real skill matters.

A lot of people who vibe-code into engineering roles got into tech for the paycheck, not the craft. And when you treat engineering as a shortcut rather than a discipline, AI becomes a crutch instead of a multiplier.

Personally, I want engineers at heart flying my plane and shipping our software.

fullscale-io

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Talks about #startups, #entrepreneurship, and #softwaredevelopment

5x Founder & CTO | Author of Product Driven | Bootstrapped to 9-Figure SaaS Exit | CEO of Full Scale | Teaching Product Thinking to Engineering Leaders

You hire smart engineers
You tell them to think like product leaders
Then you get frustrated when they challenge bad ideas, skip pointless meetings, or ask “why” too often

But that’s the whole point.

Real product engineers aren’t quiet.
They don’t just code what’s handed to them.
They think deeply about what they're building—and why it matters.

And if your culture isn’t ready for that, you don’t want product thinkers.
You want quiet executors who won’t rock the boat.

The fix?
Start leading like you actually want the people you hired.

✅ Give context, not control
✅ Share problems, not tasks
✅ Invite pushback, not silence
✅ Reward outcomes, not effort

If you want engineers who think like owners, you have to create a culture that treats them like owners.

Anything less is just lip service.

People ask me how I raised millions of dollars for my startup.

We had a $150M exit and people are curious how we did it.

We tied up our bootstraps and chased customers, not investors.

We did not raise anything.
Nothing.

We gave $0 of that $150M to any institutional investors.

Bootstrapping is the default path for most startups.
It just does not get the press or the glamour.

Today it is even more possible.
AI makes it incredibly fast to build a product.
Global talent makes it affordable to scale.

It has never been a better time to bootstrap a startup.

Vibe coding works just well enough to fool you.

You give it a prompt.
It builds a decent app.
The UI looks fine. The code runs.
Everything seems okay… until it isn’t.

There’s always something weird—logic errors, data leaks, missing validations.

If you’re a senior dev, you’ll catch it.
You’ve seen enough code to know what “wrong” smells like.
You know when to override the AI and when to trust it.

But if you’re newer?
You assume it’s right—because it runs.

That’s where things get dangerous.

AI is changing the way we build. No question.
But too many companies are cutting junior engineers under the assumption that AI will replace them.

It won’t.

What AI lacks is judgment.
Pattern recognition.
An understanding of what good actually looks like.

And the more we treat AI as a shortcut instead of a collaborator, the more tech debt we’re going to rack up.

Mark my words:
The companies skipping junior talent today will be writing six-figure checks to clean up the mess in two years.

2015: Copying code from StackOverflow
2025: Vibe coding with AI

Different decade. Same problem.

The real question isn’t where the code came from.
It’s whether they can explain what it does.

A real software engineer can.
A basic coder can’t.

The average time-to-hire for a senior developer is 90 days.

That’s three months of interviews, background checks, and onboarding… before a single line of code gets written.

So if you start hiring in January, your new devs don’t start shipping until April.

That’s *one quarter of your year gone* before your roadmap even moves.

We see it every year at Full Scale.

Some CTOs start planning in November or December.

Others wait until “after the holidays.”

The difference isn’t subtle.

The early planners are reviewing candidates before Christmas.

Their new developers are sprinting by the first week of January.

By February, they’re shipping features while everyone else is still waiting for résumés.

That’s the real ROI of early capacity planning — you buy time that can’t be recovered later.

I just vibe-coded myself straight into the exact mess every engineer tries to avoid… but absolutely can’t.

I’ve been building internal tools with Claude Code at breakneck speed.

It’s insane how fast you can move.

But as I keep iterating, tweaking, regenerating, and “just trying one more thing,” the codebase keeps expanding.

And now?

It’s a disaster.

⚡ Disorganized files
⚡ Duplicate logic
⚡ Dead code everywhere
⚡ Functions doing whatever they want
⚡ Full spaghetti-mode

I’m an engineer. I should know better, right?

But when you’re flying, you’re not thinking architecture. You’re just shipping and stacking the mess higher and higher.

I cooked this spaghetti… and now I’m cooked. 🍝🔥

The only real way to avoid this? Clean as you cook.
Otherwise you end up with spaghetti code and no fork big enough to untangle it.

There is only one kind of person who believes Vibe Coding is the future.

It’s the person who hasn’t built an app yet with 10,000 lines of code... and now has to figure out WTF the code actually does.

Last week at a funding conference I kept hearing the same thing.
VCs want seven or eight months of revenue before they even care.

Most founders get stuck right there.
Too early for institutional funding and not enough traction to get it.

I lived that gap.
I worked full-time during the day and built my startup every night and weekend until we had enough revenue to stand on our own.
It took eighteen months before I could quit my job.

And here’s the truth.
We never raised a dollar from VCs.
We didn’t need to because of those eighteen months of grind.

If you’re in that same gap today, keep going.
Traction will open the doors that funding won’t.

The core job of a startup founder isn't the idea or building the product.

The core job of every single startup founder is distribution.

Sales.
Marketing.
Partnerships.
Business development.

Whatever you want to call it.

It's what turns a side hustle and idea into a real business.

It's what turns inventors into entrepreneurs.

Distribution.

Stop building more features. Go sell something.

It's why I write these posts every single day. 😂

"Don't hire a software engineer, use AI instead!" is terrible advice.

Non-technical people usually aren’t good at explaining what they really need.

They are experts in their craft. They just want help solving real business problems.

What they actually need is someone who can read between the lines.

Someone who understands users, constraints, architecture, systems, tradeoffs, and risk.

Someone who can turn vague ideas into real products.

Handing them AI and saying “figure it out” is like telling a CEO to replace their CMO or CFO with AI.

Sure, AI can help. Sure, it can generate a prototype.
But expertise still matters. A lot.

A CEO can make a basic financial model in Excel.
A founder can sketch a marketing plan in a Google Doc.

But no one confuses that with the depth of real professionals.

AI is the same.
It’s a tool.

What people actually want is someone with experience that they can collaborate with.

They don’t want more work. They want real help.

Hiring engineers at the end of 2025 still feels like a logic puzzle.

We put candidates through timed whiteboard tests and Leetcode marathons.
We grade them on how fast they solve problems with clear inputs and perfect answers.

Then we hire them…
And hand them a task full of missing context, shifting requirements, and trade-offs that don’t have a “right” answer.

Engineering in the real world requires navigating messy ambiguity.
What’s unclear?
What’s missing?
What trade-offs are worth it?
Who needs to be looped in?

That’s the actual job.

So stop hiring engineers based on puzzles AI could solve on day one.

Start hiring for the human side of engineering.

Vibe Coding is not low-code. It’s the opposite.

I used to describe vibe coding like low-code.
But that’s completely wrong.

Low-code platforms are point-and-click, drag-and-drop, configuration-based systems.

They force structure.
They limit chaos.
They give you guardrails whether you want them or not.

Vibe coding?

None of that. ‼️

Vibe coding will give you all the spaghetti dumpster fire code you want.

There are no guardrails.

It's not low code. Do not call it that!

It's more like unlimited bad code.

It's amazing for prototypes, but it needs an engineer in the loop to turn it into vibe engineering.

The Code Monkey is extinct.

It was replaced by Claude Code Monkey.
And GPT Code Monkey.
And every other AI tool that can generate working code in minutes.

Everyone now has an army of code monkeys at their disposal.
All available on demand.
All faster than any junior engineer could ever be.

Some days it feels like we are zoologists managing a troop of AI code monkeys. We give them tasks, they produce results, and then we sift through the output to decide what needs to happen next.

And yes, sometimes they throw shit at us.

This shift changes everything about the role of the engineer. If AI can generate the code, the value moves to understanding the problem, clarifying what matters, and choosing the right direction. The hardest part is no longer typing the solution. It is knowing which solution is worth building.

The engineers who thrive will not be the ones who wait for the next Jira ticket. They will be the ones who understand the product, the customer, and the context well enough to guide the work that the AI produces.

The extinction of the Code Monkey is not the end of engineering. It is the beginning of a higher level of engineering.

We are watching evolution happen in real time.

AI can write the lines of code.
It cannot provide the judgment or the vision.

That part is still the human’s job.

Using Claude Code to build software that uses OpenAI is kind of ironic.

Is everyone else doing this too?

Everyone says it’s impossible.
Your recruiter probably told you it’s too late.
Your HR director agreed.

They’re all wrong.

If you need to hire developers quickly for a January 1 start, you can make it happen. 
And I’ll prove it.

Because while they’re prepping job descriptions for Q1…
We’re already onboarding engineers.

Not theoretical hires.
Not "let's circle back in January" conversations.
Real devs.
Writing real code.
For real teams.
Before the holiday cookies go stale.

The longer you wait, the longer your team stays stuck in hiring limbo.
Meanwhile, your competitors are using December to build actual momentum.

Need devs shipping features in January?
Book them now.
We’ve got availability.

"We need funding to figure out our business model."

I hear this all the time.
But it’s backwards.

You don’t need funding to figure out your model.
You need customers.

Funding only helps you hire people.
And the day they show up, they’ll have the same problem you have right now.
No clarity about what the business should be.

Customers give you that clarity.
Not money.

Start there.

Startup founders never know if they have lightning in a bottle or if they are about to get struck by lightning.

At our venture studio, our first product looks amazing and demand is starting to show up.
So now we ask the million dollar questions.

Does that mean we are on to something?
Do we go all in?
Or are we just excited and reading too much into early feedback?

Every founder faces this moment.
You feel the spark and want to bet everything.
The ones who get it right slow down just enough to validate the signal.

More importantly, they keep listening to their customers.

Founders should not assume they already have it all figured out. Nobody does.

That is how you find out if it is lightning in a bottle or just a flash in the sky.

By listening to what customers actually show you.

You can buy more software.

You can buy more infrastructure.

But you can’t buy time.

Teams try to “fix” velocity in Q1 with new tools or bigger budgets.

But by then, the clock’s already against them.

The only real advantage? Starting earlier.

When you pre-book developers in December, you’re building runway.

Your team hits January shipping, not scrambling.

You skip the hiring bottleneck, avoid the backlog, and gain momentum before everyone else even logs back in.

How do we go faster?

As a startup founder, this is the question I’m always thinking about. I ask it of myself and I ask it of the team.

Getting things done quickly and focusing on the right things is critical to success, especially in startups.

At the same time, people can’t live in constant urgency or chaos. That pace wears everyone down.

But asking how we go faster is always fair.

It forces us to look at what slows us down. It reminds us that speed comes from clarity, focus, and removing friction.

It is a question worth asking every day.

CEO Rebase | DevOps co-pilot | No YAML, No Terraform | Angel Investor

Building Rebase - 10x DevOps and Platform Engineering Teams.

Mudassir Mustafa ⌥

Code review says “looks good.” Production says “wake up.”

The gap isn’t logic. It’s correlation.

Why “good code” still causes incidents:

- Single‑domain review: code correctness ≠ deploy safety
- Invisible context: infra drift, hot paths, noisy neighbors
- Hidden coupling: one “safe” change shifts a shared dependency
- Stale assumptions: yesterday’s baseline, today’s traffic

Close the correlation gap with cross‑domain signals:

1) Pre‑deploy risk score

Code diff × infra state × dependency health × recent incidents → gate or guardrails

2) Evidence chains by default

Auto‑link PR → service → infra → monitors → incidents for 5–10 min RCA

3) Blast‑radius mapping

Identify which users, services, regions, and budgets a change can touch

4) Human‑in‑the‑loop guardrails

Approvals include rollback plan, time window, and owner—not just LGTM

Tell‑tale smells during review:

- “Small change” in a hot path
- Config toggles without traffic context
- Library bump with transitive impact
- Friday 4pm infra mutation with no rollback plan

If your pipeline can’t correlate, it can only congratulate.

Add intelligence so “looks good” also means “ships safe.”

Mudassir Mustafa

"You're capturing 70% unnecessary data. And paying for it."

Research on observability trends revealed something that explains your cloud bill.

Organizations are capturing 70% unnecessary observability data.
70%.

That means:

70% of your log storage is noise
70% of your metrics aren't used
70% of your traces never get analyzed

But you're paying for 100% of it.

Storage costs.
Ingestion costs.
Query costs.
Retention costs.

All accumulating for data you'll never look at.
The pattern is always the same:

"Let's log everything. We might need it someday."

So you enable debug logging in production.
Capture every metric from every service.
Store every trace for 90 days.

Then when an incident happens:
You still can't find what you need.

Because the signal is buried in noise.
The log analytics challenge:

Long-term storage costs overshadow query value.
You're paying thousands per month to store logs that:

You'll never search
Don't help debug incidents
Exist "just in case"

So teams make impossible choices:
Which logs do we keep?
Which do we archive?
Which do we just discard?

This is the observability paradox:
Capture everything = Find nothing
Pay for everything = Afford nothing

Your monitoring costs shouldn't exceed your infrastructure costs.
But for some teams, they do.

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