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.

Reading Over AI Summaries

𝗪𝗵𝘆 𝗜’𝗺 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗗𝗲𝗲𝗽 𝗥𝗲𝗮𝗱𝗶𝗻𝗴 𝗢𝘃𝗲𝗿 “𝗔𝗜 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝗲𝘀”

We are all drowning in tasks, and time for actual reading is shrinking by the second. With the rise of AI, it’s tempting to just skim an AI-generated summary and call it a day.

But I’ve realized the real value of a book isn’t just the “data” – it’s the 𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗽𝗮𝗰𝗲 it creates. It’s about understanding the context, the nuance, and the “why” behind the “what.”

Over the Easter break, I finally finished “𝗖𝗼-𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: 𝗟𝗶𝘃𝗶𝗻𝗴 𝗮𝗻𝗱 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜” by 𝗘𝘁𝗵𝗮𝗻 𝗠𝗼𝗹𝗹𝗶𝗰𝗸.

What makes this book stand out? Mollick isn’t a pure “tech guy.” He approaches AI from a user’s perspective, making his insights incredibly practical and grounded.

𝗧𝗵𝗲 𝟰 𝗚𝗼𝗹𝗱𝗲𝗻 𝗥𝘂𝗹𝗲𝘀 𝗼𝗳 𝗔𝗜

If you want to master AI, Mollick suggests these four principles:

• Always Invite AI to the Table: Use it for everything to see where it shines (and where it fails).
• Be the “Human in the Loop”: AI is a co-pilot; you are still the captain responsible for the final output.
• Treat AI Like a Person: (A very smart, slightly weird intern). Give it context, feedback, and clear instructions.
• Assume This Is the Worst AI You Will Ever Use: The technology is the “weakest” it will ever be right now. Imagine what’s coming next.

𝗢𝗻𝗲 𝗧𝗼𝗼𝗹, 𝗠𝗮𝗻𝘆 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝘀

The book dives deep into how AI shifts roles depending on your needs. It can be your:

• Creative Partner for brainstorming.
• Coworker for heavy lifting.
• Tutor for learning new skills.
• Coach for personal growth.

𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: AI shouldn’t just replace our thinking; it should augment it. If you have the chance, put down the summary and pick up the book. Your brain will thank you for the “empty space.”

Link to the book: https://amzn.to/4tvk1j7

#AI #CoIntelligence #EthanMollick #ContinuousLearning #FutureOfWork #DeepWork

Human In the Loop, Or Not?

“Human in the loop” (HITL) is the most overused phrase in AI today. It’s also the most misunderstood.

In many workflows, the human has become a mechanical bottleneck – a “rubber stamp” meant to click ‘Approve’ or ‘Next’ without truly engaging. This isn’t just a waste of talent; it’s a recipe for mediocrity.

In 2026, we don’t just need a human in the loop. We need an Expert Human in the Loop.

The difference?
• HITL (Mechanical): Checking for typos or formatting. Approving output because it “looks right.”
• EHITL (Expert): Challenging the AI’s logic. Applying domain-specific nuance. Spotting the subtle hallucinations that only a pro with 10+ years of experience can see.

AI can give us the 80% in seconds. But that final 20% – the part that actually moves the needle – requires us to apply our expertise to the AI, not just after it.

Don’t just check the AI’s homework. Teach it how to think.

#AI #FutureOfWork #Expertise #HumanCentricAI #WorkflowInnovation #DigitalTransformation

Everything Is Different

“Isn’t it funny how day by day nothing changes, but when you look back, everything is different?” – C.S. Lewis.

Looking back at the trajectory of AI from 2024 to 2026, the transformation is staggering. We’ve moved from simple chatbots to sophisticated Multi-Agent systems. We are no longer just “using AI”; we are orchestrating it.

Welcome to the ‘Agent Manager’ era, where the most valuable skill is no longer just ‘prompting’ – it’s ‘intent engineering.’

To thrive, we must adapt:

• From Chatbots to Agents: GPT-5.4 and Claude 4.6 now execute complex workflows autonomously.
• The Multi-Agent Norm: Orchestration (via OpenClaw) mirrors high-functioning human teams.
• Human Edge: Our value lies in judgment, curation, and defining clear goals.

How are you evolving for the Agent Manager era?

#AI #FutureOfWork #AgenticWorkflows #TechTrends2026 #Innovation #DigitalTransformation