Sumiya

Sumiya

@sumiya.page

Husband. Dad. I like tinkering. See my projects here: https://sumiya.page/projects.html Opinions mine, not my employer’s. I occasionally blog here: https://sumiya.page

48 followers46 following123 posts

Early morning calm. Haystack Rock, Pacific City, Oregon. Caught this view a few minutes before the first rays of sun hit the waves on a fortunately clear morning. Beautiful #PNW.

Bending Spoons acquiring Airtable shows that software is now just as any other business. Have cost > revenue? And dont see long term viability? And need to return money to your lenders? You either declare bankruptcy or get bought out. Unless you are GameStock and get a memestock frenzy.

Went there after 2 years and noticed an addition to the graffiti wall in the tunnel between Hillsboro Public Library and Hillsboro Airport. I think DIARYOFABROKENMAN23 is onto something here.

AI is doing to software what modern framing did to brick houses (esp. in US). Sure, brick houses still get built by specialized craftsmen for a premium. But the vast majority of construction shifted to standardized, cheap, and fast. Software development is hitting its drywall era.

Sumiya reposted

Replying to @carnage4life.bsky.social

What it means for tech workers: A.) What the internet did to journalism jobs is what AI will do to tech jobs. The 2010s were glory days that may never return. B.) Get good at AI or switch fields.

On a Pro subscription for Claude, and last night I was able to send just a single message. “Continue this conversation”. And then it went off and kept working until it exhausted my session limit. That was around 10 PM. It is about 5 AM now and I sent “Continue now.”. Let’s see what happens next.

Checking back after work. The next prompt was "Continue now.". It used 40% of the allowed limit. I am beginning to think I am doing something wrong.

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None of these are permanent advantages. They are simply skills that will hold their value longer than syntax knowledge or pattern matching. 26/

When I feel doubt about whether I’m just relying on a tool or actually building capability, I try to treat it as a signal. It’s an indicator that I’m working at the right layer of the stack: the one that requires intuition, context, and skin in the game. 27-<end>

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This knowledge is almost never written down anywhere useful. It lives in relationships, in institutional history, and in reading a room. 24/

I do not think it is permanently beyond what AI can eventually work with, but right now it is the layer that feels most dependent on being a human inside a specific human context. 25/

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Context matters! The last important thing I want to say is that a lot of engineering judgment in an enterprise is actually about understanding the environment a technical decision has to survive in. 22/

Why does this stakeholder want what they say they want? What is the organizational dynamic actually driving this requirement? When will a technically sound recommendation get quietly ignored, and why? 23/

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AI makes the path of least resistance so much smoother that it becomes dangerously easy to stay in your comfort zone. I practice working in unfamiliar territory deliberately: not to become a generalist, but to practice the specific skill of reasoning without reliable priors. 20/

I want to build the muscle of thinking through a problem before the AI can hand me the solution, because that friction is exactly where the learning happens. 21/

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Be a beginner! The thing I find most uncomfortable, and arguably most vital, is placing myself in contexts where my experience is a weak guide. 18/

When I work in a domain I know well, I pattern-match fast. That is mostly an asset, but it fails quietly when a situation looks familiar but is structurally distinct. 19/

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But I want to be careful about overclaiming here. AI has been trained on all of that literature, too. The edge, if there is one, is in the "situated recognition" of when a particular analogy actually applies to a specific problem I am standing inside. 16/

Whether that recognition is something only experience produces, or something better models will eventually replicate, I do not know. 17/

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Think across domains - AI won't do that yet unless prompted! I treat my curiosity as a research strategy. Steve Jobs famously said you cannot connect the dots looking forward; you can only connect them looking backward. 12/

By tinkering in unfamiliar domains - finance, robotics, etc - I’m simply casting a wider net for those connections. It is quite possible that my architectural intuitions have come from thinking about how completely unrelated systems handle similar structural problems. 13/

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The human advantage here is accountability. AI can be a partner in brainstorming potential failure points, but it doesn’t have skin in the game. It can suggest five ways a system might fail, but it doesn’t have to live with the fallout of that decision. 10/

Framing the problem is about weighing the organizational cost of a pivot against the technical debt of a path. That requires a human who understands what the system needs to survive. 11/

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A machine is not accountable - use that to your advantage. AI is brilliant at "given this problem, here is a solution." It is less reliable at "is this actually the problem we should be solving?" 8/

I spent years defaulting to implementation before questioning the framing. It took conscious practice to break that instinct, but I suspect the urge is universal: the spec arrives, and the brain immediately starts sprinting toward code. 9/

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Post-mortems exist, but they describe what failed, not what it felt like to make the decision from inside it, with incomplete information and organizational pressure in every direction. Most of these insights never reach the public, and even those that do are often sanitized. 6/

I am aware this argument has limits. AI excels at learning from static results, but humans are better at learning from process history. While models will eventually get better at reasoning about counterfactuals, for now, the texture of "why we didn’t do X" remains a human advantage. 7/

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Learn from failures! AI models learn from documented outcomes. Code that shipped. Architectures that scaled. Decisions that turned out right. This makes them powerful at recognizing patterns from known solutions. 4/

The blind spot, as I think about it, is in what was avoided. The judgment call that prevented a catastrophic rewrite. The architectural decision that was seriously considered and quietly rejected. These rarely get written down in enough detail to be learnable. 5/

The question I kept asking myself was which AI tools to use and how to use them well. That matters, but it is probably the wrong place to spend most of my attention. 2/

The more interesting question is what AI will remain weakest at, and whether I am actually building those capabilities in myself, or just assuming I have them. 3/

It’s fun building stuff for myself. It still takes effort but I can push in unknown territories with AI. Built an iOS app for myself for the first time ever. Over the past few evenings and weekends, I built OctaneLog to help me record my story - an iOS app that turns my daily commutes … 1/

… and road trips into vivid narratives, automatically capturing and interpreting the world rushing past my windshield while I keep my eyes where they belong: on the road. And I don’t know Swift. I’ve never built an iOS app before. Not available on the App Store but you can build and install … 2/

Today in things kids say. The other day I was trying to work with my daughter on something and I ended up saying: “This is how I used to do this when I was a kid.” 1/3

And with all her innocence and the seven years of wisdom she has, she waited a couple of seconds, took a deep breath, gave me her c’mon now look, looked deep into my eyes, and calmly said to me - “but Daddy, I am not you. I can do it my way”. 2/3

I had no comeback! In one fell swoop she made me realize I was sort of being presumptuous but she also made me feel really proud. Kids have unique personalities but going by my sample size of one, I reckon seven is when they begin to assert their individuality. 3/3