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this post was submitted on 31 Aug 2025
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Showerthoughts
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A "Showerthought" is a simple term used to describe the thoughts that pop into your head while you're doing everyday things like taking a shower, driving, or just daydreaming. The most popular seem to be lighthearted clever little truths, hidden in daily life.
Here are some examples to inspire your own showerthoughts:
- Both “200” and “160” are 2 minutes in microwave math
- When you’re a kid, you don’t realize you’re also watching your mom and dad grow up.
- More dreams have been destroyed by alarm clocks than anything else
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That's about being willing to eat the nre for one-off special purpose parts that have geometries not attainable by casting or machining.
3d printing is not the default fabrication method now that we're getting good at it. It just shines in certain applications.
AI is often pitched as being able to do anything, eventually. We even try to use passive fail safes over active ones, a century after electricity became commonly available. Because that will always be a better solution by the nature of the options. AI is the same way. It is a distillation of human English language that is written. Why you would think that that could eventually replace all software developers, or any other field that produces text as its output makes any sense is beyond me. I can't see how that could be true.
Getting a little theoretical here
With the current state of the technology, 3d printing lags behind some traditional manufacturing techniques like machining and in terms of speed, cost, quality, available materials, etc. except for some relatively niche cases.
However, that gap is closing a bit every day, it may or may not ever catch up completely or surpass the old technique in those aspects
But if it does ever get close, I could very much see 3d printing being a preferred method
Subtractive manufacturing like machining, by design, creates a lot of waste, all of the chips and off cuts that are removed from the stock are either discarded or require additional energy and/or materials to recycle.
And things like injection molding require custom molds that wear out over time, and can be expensive to design and manufacture
And in either case, you're largely locked into making one thing on an assembly line at a time, and to switch over to a different product you're probably going to need to switch out a lot of the molds and tooling, recalibrate everything, etc. which can be time consuming.
With 3d printing, you could theoretically use only the amount of material that's actually in the finished product (if you design it that it doesn't require any external supports ) you don't need any custom tooling or mold, just generic, interchangeable nozzles (for FDM, LCD screens or lasers or whatever the equivalent is for other printing technologies) and you could switch production from one item to another by just hitting print on a different file.
Again, we're not there, may never be there, but it's a cool thing to think about
Exactly. It's not true. Any company that fires all of its developers and sets up some poor intern to prompt-engineer updates to their codebase is going to fail spectacularly.
Source: I'm a software developer and use LLMs regularly. There are certain tasks they are very good at, but anyone who commits unexamined code generated by an LLM gets exactly what they deserve.
Ok, im a hardware dev. They've tried to make us do software style project management every time there's a new fad (agile last time). It usually doesn't fit.
What do you find them useful for in your role? Like a coding partner, you can ask questions? Or linting? Im at a loss in my role. I need to know the proprietary code base to write a single line of value. We aren't allowing anyone to train an ai on our code. Thats a huge security problem if anyone does.
So there are a few very specific tasks that LLMs are good at from the perspective of a software developer:
And that's... pretty much it. I've experimented with building applications with "prompt engineering," and to be blunt, I think the concept is fundamentally flawed. The problem is that once the application exceeds the LLM's context window size, which is necessarily small, you're going to see it make a lot more mistakes than it already does, because - just as an example - by the time you're having it write the frontend for a new API endpoint, it's already forgotten how that endpoint works.
As the application approaches production size in features and functions, the number of lines of code becomes an insurmountable bottleneck for Copilot. It simply can't maintain a comprehensive understanding of what's already there.
They are getting faster, having larger context windows, and becoming more accurate. It is only a matter of time until AI simply copy-cats 99.9% of the things humans do.
Actually, there's growing evidence that beyond a certain point, more context drastically reduces their performance and accuracy.
I'm of the opinion that LLMs will need a drastic rethink before they can reach the point you describe.
We have 100M context AI, we just need better attention mechamisms.