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

Stop using JSON to cut your AI bill in half.

At least, that’s the promise of a new data format called Token-Oriented Object Notation (TOON).

If you’ve ever paid attention to token costs in AI, you know how fast they add up.
Every brace, quote, and comma in your JSON counts toward your token limit.
And when you’re passing structured data to an LLM, that bloat adds up fast.

TOON fixes that.

It’s basically the same data, just stripped down to no unnecessary formatting, no extra syntax.
Think of it as JSON without the noise.

Example:

{ "tags": ["jazz", "chill", "lofi"] }

Becomes

tags[3]: jazz,chill,lofi


That’s a 40–60% token reduction, without losing structure or meaning.
In other words, cheaper, faster, leaner AI.

We’ve optimized models. We’ve tuned prompts.
Maybe it’s time we optimized the data itself.

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.

fullscale-io

(30) LinkedIn

JSESSIONID

LinkedIn Login, Sign in | LinkedIn

li_at

Talks about #startups, #entrepreneurship, and #softwaredevelopment

Developers should love legacy code.
Heck, their ultimate job is to create legacy code. 

Startups and greenfield projects sound exciting.
You get to write shiny, modern code from scratch.

But here’s the truth: 90% of startups go out of business.
Their “clean” code never even had time to get messy.

Legacy code means the product was successful.
It means customers cared enough to keep using it.
And most importantly, it means your company is still in business and making money.

Legacy code is what success looks like.

If you’re lucky, your work will become legacy too.
Because something you built endured.

So the next time you open an old codebase, remember:
Legacy code means stability.
It means job security.
It means work-life balance.
It means the startup survived.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Adway

- 39 ATS analysis & reporting to clients; improving renewal success by 70%
- Automated monthly sales reports with Python, saving 15 hours per month. 
- On our way to 100M ARR in 2025, leveraging data and tech.
- Tech stack: M365, Excel, Python, PowerPoint, Loom

Data Scientist & BI Consultant

Alterfy

Advising the software and data team in building a robust SaaS web app leveraging AI,  analytics, and data. Perform data analyst and data scientist tasks by measuring KPIs and building machine learning models.

AI & Tech Advisor

ROCK

- Led the BI using tools like BrightGauge, PowerBI, and HaloPSA to create and update over 200 reports and dashboards.
- Managed the data transition for an MSP used internally called HaloPSA which delivers a 5x growth to the company.
- Data engineering, and data science work on Microsoft Fabric (pipelines, Lakehouses, Warehouses, Notebooks...)
- Data consulting in data projects for clients.

Data Scientist

Kadogoodiz

Head of data science and analysis with the goal of fetching, cleaning, analyzing, and reporting data to senior partners.

Uhubs

+ Analyzing the data sets to extract insightful information (Python, Pandas).
+ Cleaning and feature engineering on data sets.
+ Integrated natural language processing (NLP) to analyze textual data (nltk, stemming, tokenizing).
+ Visualized insightful information (Matplotlib, Seaborn, Plotly)
+ Deep research on benchmarking to increase sales workflow.
+ visualizations and reporting

Consultant Data scientist

Riad coaching

Performance Coach

Setexam – The Agar Specialists

+ Maintained databases using MySQL
+ Handled part of the AWS infrastructure and migration from on-premises to the cloud
+ Cleaned data prior to training and analysis using Python (Pandas, NumPy)
+ Visualized important metrics and outputs using Matplotlib and Seaborn
+ Been part of the machine learning team and made sure to apply best practices and optimize the code.

Assistant data scientist

+ Designed maintenance software ‘Coswin 7’ and trained technical staff to use it for preventive and corrective maintenance.
+ Trained a newly hired maintenance technician to manage and use Coswin 7 for troubleshooting and effective use.
+ Automated part of the process concerning the dosing and transfer of chemicals for cleaning purposes using Allen Bradley API language.
+ Supervised and visualized in real-time the automated process via RSLogix 500.
+ Worked with IO-Link, a modern technology of sensor/actuator that was added to the company’s workflow.

Engineer Intern

Syndic8

Collective created with a bunch of friends in 2015, eager to grow the Moroccan musical scene. 
As a co-founder and musician I dealt with :

+ 10+ High-quality events all over Morocco
+ Booking 16 International renowned artists. 
+ Budgets ranging from 1000-13500£.
+ Communication over Facebook and Instagram + Ads
+ Performing live as a professional around Morocco and France

Co-Founder

+ Exploring the industrial world, API's, sensors, programs, servers, machines, supervision, maintenance...

Stagiaire en ingénierie industrielle

🤖Data Scientist | Turning Raw Data into Money Generating Insights | Data-led Growth for Your Business 🚀

Freelance Data Scientist | ML Engineer | SQL | Python | Power BI | MLOps

Data analytics and Machine learning models built 5X faster - no quality compromise. Worked with 14 clients in 7 different industries in the last 3 years. Helped current company scale to $5M+ ARR. Featured TOP RATED PLUS talent on Upwork in 2025 with 100% job completion and satisfaction.

My Toolkit:
- Data analytics: Python, SQL, Excel
- ML Engineering: FastAPI, AWS, Streamlit, great_expectations
- Data visualization: PowerPoint, PowerBI, Plotly, Matplotlib
- Data modeling: Scikit-learn, TensorFlow, Keras
- MLOPs: CI/CD (GitHub Actions), Docker
- Additional tools: Notion, n8n, Canva, Git/GitHub

My Stats:
- 5-stars reviews on 99% of projects delivered.
- 100% Job Success Score for on-time, budget delivery.
- Helped scale currently business to $5M ARR with ATS analysis & Reporting.
- 1 year TOP RATER PLUS on Upwork.
- 1200 hours billed.
- $40k+ earned.

What You’ll Get Working With Me:
- A data analyst who is a real problem solver.
- Quick responses (within the hour on working hours)
- Clean, maintainable code and detailed documentation
- A clear “NO” if something isn’t feasible; no wasted time
- A well-crafted report or dashboard as per the requirements

My clients think:
- "Anas is great and highly knowledgeable. He is passionate about solving data science challenges and has helped me significantly streamline some of my processes, enabling better scalability.
- "Anas, was prompt and thorough and I would def use him again in the future."
- "Anas is great to work with. He made the job so fluid and his knowledge of data science makes him someone worth listening to. I’m glad I chose him for my small business. I’m looking forward to what’s coming next, and I would definitely hire him again."

Popular Clients & Products:
- Uhubs: Financial company based in London
- AllAboutGroup: Law recruitment firm based in London
- Growth Dynamics: Marketing and data strategist based in London.
- Adway: Tech and recruitment company based in Gotheborg, Sweden

About me:
- Football fan since I can remember (Real Madrid fan)
- Tennis lover and practitioner (one-handed backhand style)
- Mangas fan (One Piece, Naruto, Bleach)
- Travel addict (South Asia, Europe, Africa, America (soon))
- Reading is something I enjoy as much as coffee (oat latte fan recently)

-----

YouTube Channel
→ GO HERE: https://www.youtube.com/anas_riad

Request services via LinkedIn or Email below:
r.riadanas@gmail.com

Anas Riad

Generative AI is shifting fast.

These are the skills companies are already hiring for.

Been reading “Learn Mistral” by Pavel Cherkashin, and wow!

This book goes way beyond Mistral.

Yes, it walks you through how to run and build with Mistral models…
But what I really liked?

It gives you the practical GenAI foundations every builder needs right now.
No fluff, no hype, just theory + code that actually works.

Here’s a quick breakdown of what’s inside:

🤖 Local LLMs
→ How to run Mistral models locally (e.g. via Ollama)
→ Getting started with manifests, weights, and custom prompts

🧠 Tokens & Parameters
→ Understand logits, softmax, temperature, top_k…
→ Why your model outputs what it outputs

🔍 Embeddings
→ How to build your own with PyTorch or SentenceTransformers
→ Semantic search, clustering, and similarity scoring

🛠️ Agents
→ Build your own agent using the Mistral API
→ Or use n8n to drag, drop, and automate the smart way

🔎 RAG (Retrieval-Augmented Generation)
→ Covers retrieval, indexing, chunking, and generation
→ Includes real examples, not just concepts

💻 Code Assistants
→ Use Mistral inside VS Code as your coding buddy
→ Tips for better prompting and context windows

🔐 AI for Security
→ Detect anomalies, threats, and geo-based patterns
→ Use GenAI for real-world security use cases

☁️ Cloud Deployments
→ How to run Mistral on AWS (Bedrock) and GCP (Vertex AI)
→ Permissions, endpoints, and integrations that work

-- -- -- 

This book will help you start your GenAI journey.

📌 Save this for your next project.
♻️ Share this to help your network in data.

Data Scientist & BI Consultant @Adway • ML Engineer • YouTube @ Anas Riad

10 Must-Know Data Analytics Tips 

(from combined 8+ years of experience)

1- Fix column types before merging
2- Use .info() and .describe() early
3- Deal with missing values
4- Use .assign() for method chaining in EDA
5- Group By summary and aggregation
6- Pivot tables for multidimensional summaries
7- Simple bar chart with Plotly
8- Apply log transform for skewed data
9- Time-based analysis (Time series)
10- Detect and visualise correlation clusters

What would you add?

♻️ Save this carousel and repost with your network to benefit

PS: Collab with Cornellius Yudha W., give him a follow 🌟

taplio

The Battle

Matt Watson

Anas Riad

Followers

Posts

Engagements

Likes

Performance ScoreMatt Watson's LinkedIn Performance

Performance ScoreAnas Riad's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Matt Watson

Anas Riad

Followers

Posts

Engagements

Likes

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);}Matt Watson's LinkedIn Performance

Performance ScoreAnas Riad's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreMatt Watson's LinkedIn Performance