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I have to admit the merging of worlds here is wild. I never expected to read Mike Tomlin, long time coach of the Pittsburgh Steelers on HackerNews yet here we are. It was not on my Bingo card. Refreshing, indeed.

This comment gave me an uptick in hunger, instantaneously.

This article reminds me of a time when I was pushing my kid on a swing in the backyard after work. It was all I was doing. No background processes running mentally. With all the obligations and people waiting on me at work and deliverables due yesterday, I had managed to turn it off. It occurred to me that simply pushing my kid on a swing and thinking of nothing else was probably the most important thing I could ever do in that single moment. That was a freeing change of perspective for me.

I have flip-flopped more than ever in the last 365 days about prioritizing good code vs good product, in the AI age. This helps clarifies why.

I have come to the conclusion that we just do not know yet. There is a part of me that believes there is a point somewhere on the grand scale where the code quality genuinely does not matter if the outcome is reliably and deterministically achieved. (As an image, I like to think of Wall—E literally compressing garbage into a cube shape.)

This would ignore maintenance costs (time and effort inclusive.) Those matter to an established user base (people do not love change in my experience, even if it solves the problem better.)

On the other hand, maybe software is meant to be highly personal and not widely general. For instance, I have had more fun in the past two years than the entire 15 years of coding before it, simply building small custom-fitted tools for exactly what I need. I aimed to please an audience of one. I have also done this for others. Code quality has not mattered all that much, if at all. It will be interesting to see where things go.

Is it just me or could this apply to commentary as well? Sometimes, I set out to comment with all my thoughts and their intricacies related to the subject, but sometimes the simplest one contributes far more to the conversation. In my experience, simplicity enables others to more freely participate and contribute.

I highly relate to this. Code works or it doesn’t. My writing feels a lot more like self expression. I agree that’s harder to “let go” to an agent.

> If the original function isn’t working as expected, I suspect that the agent-created test will test the functionality as it exists, not as was intended, regardless of what it calls the test case.

I have experienced this on many occasions. It ultimately adds up to a sneakily false sense of code stability.

Love the simplicity. These sort of “less is more” solutions should become more prevalent as average code volume continues to rapidly increase

> It was at the same time depressing (I’m dumb), liberating (I don’t have to pretend I’m not dumb anymore) and exciting (I have a chance to be not dumb anymore).

> Learning is hard work, and if you don’t respect the process, it won’t happen.

These two ideas resonate well with me. My experience in pursuit of steady and sustainable growth in any area of interest has had these in common. You have articulated them well enough for me to realize that. I appreciate that.

I am also at a similar point in life that sits at the intersection of building consistent habits that support goals and balancing priorities like family life. “It’s a marathon, not a sprint” has never been more applicable.

I initially found Gemini Pro 2.5 to work well for coding. Over time, I found Claude to be more consistently productive. Gemini Pro 2.5 became my go-to for use cases benefitting from larger context windows. Claude seemed to be the safer daily driver (if I needed to get something done.)

All that being said, Gemini has been consistently dependable when I had asks that involved large amounts of code and data. Claude and the OpenAI models struggled with some tasks that Gemini responsively satisfied seemingly without "breaking a sweat."

Lately, it's been GPT-5 for brainstorming/planning, Claude for hammering out some code, Gemini when there is huge data/code requirements. I'm curious if the widened Sonnet 4 context window will change things.

This made me chuckle. Excellent comparison. I share your sentiment; it’s more exciting than distracting.

You have seemed to pinpoint where I believe a lot of opportunity lies during this era (however long it lasts.) Custom integration of these models into specific workflows of existing companies can make a significant difference in what’s possible for said companies, the smaller more local ones especially. If people can leverage even a small percentage of what these models are capable of, that may be all they need for their use case. In that case, they wouldn’t even need to learn to use these tools, but (much like electricity) they will just plug in or flip on the switch and be in business (no pun intended.)

One of my favorite use cases includes simple tasks like generating effective mock/masked data from real data. Then passing the mock data worry-free to the big three (or wherever.)

There’s also a huge opportunity space for serving clients with very sensitive data. Health, legal, and government come to mind immediately. These local models are only going to get more capable of handling their use cases. They already are, really.

I agree. It’s not improbable for there to be _more_ needs to meet in the future, in my opinion.