Category: Ashes of Time

Both Sides Now – Cloud and AI

“I’ve looked at clouds from both sides, now 

From up and down, and still somehow 

It’s cloud illusions, I recall 

I really don’t know clouds at all”

Those hauntingly beautiful lyrics from Joni Mitchell – recorded back in her 1966 live performance at the Second Fret – resonate with me today in a way they never did when I first heard them.

It has been 15 years since I led my first cloud implementation. Back then, the “cloud” felt like a radical experiment. I have vivid memories of the early days – days when I had to use my own personal credit card to shoulder the team’s cloud expenses just to keep our projects running while we fought for internal buy-in.

Back then, I thought I knew exactly what the cloud was. I was wrong.

Today, looking back from 2026, I realize that “knowing the cloud” isn’t a destination; it’s a continuous, evolving journey. We have moved from simple infrastructure migration to complex, distributed architectures, and now, we are in the era of AI-driven cloud computing.

If the last 15 years taught me anything, it’s that the technology will always outpace our current understanding. The “cloud” isn’t just about servers or storage anymore – it’s the foundation upon which the intelligence of tomorrow is being built.

In this era of rapid AI acceleration, the biggest risk isn’t the technology failing; it’s our own willingness to stop learning. Staying relevant means constantly “looking at the clouds from both sides” – the cost side and the innovation side, the technical debt and the architectural opportunity, the legacy systems and the generative future.

I started with a credit card and a dream of agility. Today, I’m still learning, still iterating, and still finding that the more I know, the more I realize there is to explore.

How has your relationship with the cloud evolved over the last decade? Are you finding the AI era to be the most challenging (or exciting) shift yet?

#CloudComputing #ContinuousLearning #AI #DigitalTransformation #CloudJourney

Clear Thinking > Fast Typing

I came across this “relic” today – my old IBM Flowcharting Ruler.

In the “good old days,” before typing a single line of code onto a punch card, we had to be incredibly intentional. Computer time was expensive and punch cards weren’t free. We used these rulers to meticulously map out program logic, followed by hours of “desktop debugging” before the hardware ever saw our work.

Fast forward to 2026, and the game has changed. We are in the age of Chat-to-Code. With a simple Product Requirements Document (PRD) or a solid implementation plan, AI can generate entire application with hundreds line of code in seconds.

But here is the reality: The tool has changed, but the “Golden Rule” hasn’t.

Clear Thinking > Fast Typing

Just because we can generate code instantly doesn’t mean we should do it blindly. In the age of AI, your clear thinking is still your most valuable asset.

➡️ Garbage In, Garbage Out: If your requirements or logic is fuzzy, your AI-generated code will be a hallucinated mess.

➡️ Architectural Integrity: AI is great at writing functions; humans are still the masters of designing systems.

➡️ Efficiency: A well-structured prompt born from a clear plan saves hours of “prompt-tweaking” and debugging later.

The Lesson from the Ruler

Back then, we planned to save punch cards. Today, we plan to save technical debt and architectural drift.

Whether you’re using a plastic stencil from the 70s / 80s or the latest LLM, the secret to great software remains the same: Understand the requirements and logic before you touch the keys.

How much time do you spend “ruling out” your logic before you ask AI to build?

#SoftwareEngineering #GenerativeAI #Flowchart #Programming

Vibe Coding vs Paper Coding

Forget fancy AI or modern vibe coding tools. My journey started with something much more physical: “Painful Paper Coding.” My very first program was born on a stack of yellow punch cards.

Long before the cloud, we had the giant mainframe. To get these huge machines to do anything, I had to follow a strange old ritual…

  • Step one: Buy a stack of blank cards. They were cheap – about 25 cents for 50 tickets to total frustration.
  • Or, I could “borrow” a few cards from a friend or a rival lab when no one was looking 😎
  • Next, find a free keypunch machine. I had to type out my code line by slow, painful line.
  • The concluding step involved delivering my stack of cards to the data center’s small window, where the “high priests” (the operators) would process them through the massive computer (IBM S/360).

After waiting 15 minutes or so, I’d get a big printout, find one tiny typo, and have to start the whole nightmare all over again.

Still, I loved every minute of it.

I loved the noisy machines and the massive computer. I even enjoyed the careful planning and flowcharting I had to do before punching a single card.

I enjoyed the challenge of writing efficient code to save money and time, and I loved the feeling of being in total control of my code.

Even with today’s smart tools and easy coding, I still miss that feeling.

𝗧𝗵𝗲 𝗱𝗮𝘆𝘀 𝗮𝗿𝗲 𝗹𝗼𝗻𝗴, 𝗯𝘂𝘁 𝘁𝗵𝗲 𝘆𝗲𝗮𝗿𝘀 𝗮𝗿𝗲 𝘀𝗵𝗼𝗿𝘁

Ten years might feel like an eternity in our daily routines, but in the timeline of #𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, it’s a blink of an eye that changed everything.

Just ten years ago this week, the world tuned in to a historic confrontation: #𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱’𝘀 #𝗔𝗹𝗽𝗵𝗮𝗚𝗼 𝘃𝘀. 𝗚𝗼 𝗚𝗿𝗮𝗻𝗱𝗺𝗮𝘀𝘁𝗲𝗿 𝗟𝗲𝗲 𝗦𝗲𝗱𝗼𝗹. It was a watershed moment that redefined our understanding of machine intelligence.

The match wasn’t just about a computer winning a game; it was about the profound “creativity” and “resilience” displayed by both sides:

  • 𝗠𝗼𝘃𝗲 𝟯𝟳 (𝗚𝗮𝗺𝗲 𝟮): AlphaGo placed a stone in a location no human expert would have ever considered. It was a move so “inhuman” it shocked the commentators, yet it ultimately proved to be a stroke of strategic genius.

  • 𝗧𝗵𝗲 “𝗗𝗶𝘃𝗶𝗻𝗲 𝗠𝗼𝘃𝗲” (𝗚𝗮𝗺𝗲 𝟰): Just when it seemed the machine was invincible, Lee Sedol played Move 78 – a brilliant, unexpected wedge that confused the algorithm and secured a victory for humanity. It was a stunning display of human spirit and the ability to find a path where none seemed to exist.

Since that week in 2016, the pace of AI advancement hasn’t just continued—it has accelerated exponentially. We are no longer just watching AI play games; we are working alongside it to solve complex global challenges.

If you haven’t seen it yet, I highly recommend watching 𝘁𝗵𝗲 𝗔𝗹𝗽𝗵𝗮𝗚𝗼 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲. It is a gripping, emotional look at this turning point in history.

The last decade proved that AI can surprise us, but the “Divine Move” reminded us of the unique power of human ingenuity. Now is the time for us to work together, leveraging these tools to make the 𝗯𝗲𝘀𝘁 𝗺𝗼𝘃𝗲 𝗼𝗳 𝗼𝘂𝗿 𝗹𝗶𝘃𝗲𝘀.

How has your perspective on AI changed since that 2016 match? Let’s discuss in the comments.

#AI #Tech #Strategy #Leadership #DigitalTransformation

Success is often built on what happens at 2:00 AM

We often celebrate the launch ceremonies and the awards, but we rarely talk about the chaos right before the ribbon-cutting.

Back in 2001, my team was preparing to launch Hong Kong’s first comprehensive e-government platform. But at 2:00 AM on launch day, we discovered a show-stopping issue: a missing SSL Certificate on a key server.

There was no “download from the cloud” back then. I had to physically go get it.

I took a taxi to the Certification Authority in Kwun Tong, told the driver to keep the meter running, and rushed into the data center. An hour later, I emerged with a floppy disk in a brown paper bag-the missing piece of the puzzle.

As I jumped back into the cab and told the driver to floor it, he looked in the rearview mirror and asked, “Mission accomplished?”

“Accomplished,” I said.

He smiled and drove us to the finish line. He probably thought we were spies, but the reality was just as high-stakes for us. The system went live, and the project went on to win the Stockholm Challenge Award.

Reflecting on this 25 years later, the technology has changed, but the lesson hasn’t: Delivery isn’t just about code; it’s about doing whatever it takes to get the job done.