Field Notes

Prediciton: In 10 years, when AI is coding 99.9% of all software out there, I think there will be a market for "Artisanal Code".

You know, like how there's Starbucks vs the local mom-and-pop coffee shop, or IKEA vs hand-crafted furniture, where the differentiator is that it's small and "handcrafted."

The messaging would be 𝟭𝟬𝟬% 𝗛𝘂𝗺𝗮𝗻-𝗖𝗿𝗮𝗳𝘁𝗲𝗱 𝗖𝗼𝗱𝗲, which takes 10x longer, and you can feel the struggle in every git commit. Built on the developers' homebrew DIY full-stack framework.

Is it time to start a Kickstarter campaign for typewriter keyboards to complete the set?

I've burned the midnight oil and worked over the weekend many times to get something done for my job in tech.

The result isn't less work after completing the task. It's usually the opposite. I get rewarded with more work.

That is just the nature of working at my job in tech; there are always problems to solve, which is great because I love solving problems.

But it can also mean the finish line on when you can "rest" constantly moves.

Over time, I've come to understand that not everything needs to be done immediately.

It's a journey in managing workload, not an instant solution. Everything urgent is not always important.

I'm not against burning the midnight oil or working over the weekends. There are times when it is necessary.

But I'm against doing that for unimportant or non-urgent things.

1. Not Saying No Enough

This isn't just saying no to meetings; it is saying no to stakeholders' requests, timelines, or customer feature requests. This can be hard, especially if you want to please everyone but its critical in order to prioritize the product vision and strategy.

2. Starting with solutions instead of problems

Another common mistake is to become too feature-focused. For example, wanting to incorporate AI into the product and directly focusing on getting buy-in without clearly defining the problem and why AI would solve it.

3. Relying Solely on Data

Data is essential for making decisions but is not the only factor. Junior PMs must move from data-driven to data-informed decisions, which factor in market trends, business objectives, intuition, and context.

4. Choosing complex solutions over simpler ones

It may be tempting to create complex solutions to problems so that you can incorporate a specific technology. This can result in longer development time and increased costs. Keep it simple and lean.

Anyone who thinks clearly should be able to write clearly—about any subject at all.

-- William Zinsser

A quote from "Writing to Learn"

Dr. Fei-Fei Li often highlights a massive gap in AI: we can easily teach a computer to identify a person, a boat, and water.

But it takes incredible processing power to realize those objects combined represent a rowing team.

As humans, we don’t just "take a picture" with our eyes. We use heuristics and past experiences to interpret:

Physics: How the oars displace the water.

Intent: The intense synchronization of the athletes.

Narrative: The stakes of a high-speed race.

We often take our mental processing power for granted. But the real magic isn’t in identifying the objects, it’s in making sense of the scene.