With AI you need to think bigger

This answers a question I didn't realize I had. I had already been thinking about some kind of utility gadget made up of a Pi Zero with a tiny screen and battery, but an Android phone solves a lot of problems in one go.

Interesting !

I have a bunch of older android phones that could be repurposed for some tinkering.

The touchscreen display and input would open a lot more interactive possibilities.

Is there a community or gallery of ideas and projects that leverage this?

> Did you?

Yes.

> do you really understand the solution completely?

Yes; fully. I'd describe what I delegated to the AI as "busy work". I still spent time thinking through the overall design before asking the AI for output.

> But I only use AI on things I know I don't care about personally.

Roughly speaking, I'd put my personal projects in two different categories:

1. Things that try to solve some problem in my life

2. Projects for the sake of intellectual stimulation and learning

The primary goal of this scanner project was/is to de-clutter my apartment and get rid of paper. For something like this, I prioritize getting it done over intellectual pursuits. Another option I considered was just buying a newer scanner with built-in scan-to-SMB functionality.

Using AI allowed me to split the difference. I got it done quickly, but I still learned about some things along the way that are already forming into future unrelated project ideas.

> If I use too much AI on things I actually want to know, I feel my abilities deteriorating.

I think this likely comes down to how it's used. For this particular project, I came away knowing quite a bit more about everything involved, and the AI assistance was a learning multiplier.

But to be clear, I also fully took over the code after the initial few iterations of LLM output. My goal wasn't to make the LLM build everything for me, but to bootstrap things to a point I could easily build from.

I could see using AI for category #2 projects in a more limited fashion, but likely more as a tutor/advisor.

I think this effect of losing your abilities is somewhat overblown.

Especially when AI has saved me on actually explaining specific lines of code that would have been difficult to look up with a search engine or reference documentation and know what I was looking for.

At some point understanding is understanding, and there is no intellectual "reward" for banging your head against the wall.

Regex is the perfect example. Yes, I understand it, but it takes me a long time to parse through it manually and I use it infrequently enough that it turns into a big timewaster. It's very helpful for me to just ask AI to come up with the string and for me to verify it.

And if I were the type of person who didn't understand the result of what I was looking at, I could literally ask that very same AI to break it down and explain it.

I've been thinking about writing up a blog post about it. Might have to do a Show HN when time allows.

This guide was a huge help: https://github.com/thagrol/Guides/blob/main/mass-storage-gad...

I was also writing a SANE-to-Paperless bridge to run on an RPi recently, but ran into issues getting it to detect my ix500. Would love to see the code!

I set up ollama on our work ‘AI server’ (well, grunty headless workstation running Ubuntu) and then got Dolphin-Mixtral to help me figure out why it wasn’t using the GPUs. :)

I ended up having to figure it out myself (a previous install attempt meant the running instance wasn’t the one I’d compiled with GPU support) but it was an interesting exercise.

I had some results last week that I felt were really good - on a very tricky problem, using AI (Claude 3.7) helped me churn through 4 or 5 approaches that didn't work, and eventually, working in tandem, "we" found an approach that would. Then the AI helped write a basic implementation of the good approach which I was able to use as a reference for my own "real" implementation.

No, the AI would not have solved the problem without me in the loop, but it sped up the cycle of iteration and made something that might have taken me 2 weeks take just a few days.

It would be pretty tough to convince me that's not spectacularly useful.

I've tried it, and ended up with the completely wrong approach, which didn't take that long to figure out, but still wasted a good half hour. Would have been horrible if i didn't know what I was doing though.

> some of the tedious boiler-plate code is taken care of.

For me that is the bit which stands out, I'm switching languages to TypeScript and JSX right now.

Getting copilot (+ claude) to do things is much easier when I know exactly what I want, but not here and not in this framework (PHP is more my speed). There's a bunch of stuff you're supposed to know as boilerplate and there's no time to learn it all.

I am not learning a thing though, other than how to steer the AI. I don't even know what SCSS is, but I can get by.

The UI hires are in the pipeline & they should throwaway everything I build, but right now it feels like I'm making something they should imitate in functionality/style better than a document, but not in cleanliness.

What language are you writing? I mostly write go these days, and have often wondered if it is uniquely good in that language given its constraints.

I love throwing questions at it where previously it would have been daunting because you don't even know the right questions to ask, and the amount of research you'd need to do to even ask the proper question is super high.

Its great for ideating in that way. It does produce some legendary BS though.

I prefer open source models.

> and terrible where the problem has not been tackled much by the public

Very much so (I should have added this as a downside in the original comment). Before I even ask a question I ask myself "does it have training data on this?". Also, having a bad answer is only one failure mode. More commonly, I find that it drifts towards the "center of gravity", i.e. the mainstream or most popular school of thought, which is like talking to someone with a strong status-quo bias. However, before you've familiarized yourself with a new domain, the "current state of things" is a pretty good bargain to learn fast, at least for my brain.

> My experience is instead that LLMs (those I used) can be helpful there where solutions are quite well known

Yes, that's a necessary condition. If there isn't some well known solution, LLMs won't give you anything useful.

The point though, is that the solution was not well known to the GP. That's where LLMs shine, they "understand" what you are trying to say, and give you the answer you need, even when you don't know the applicable jargon.

Agreed. LLMs pull you towards the average knowledge, and they suck when you're trying to find a creative solution that challenges the status quo.

I'm old enough to remember when they first said that about the Internet. We were going to enter a new enlightened age of information, giving everyone access to the sum total of human knowlege, no need to get a fancy degree, universities will be obsolete, expertise will be democratized.... See how that turned out.

  > You can now do literally anything. Literally.

In practice, not so much. Not in my experience. I have a drive littered with failed AI projects.

And by that I mean projects I have diligently tried to work with the AI (ChatGP, mostly in my case) to get something accomplished, and after hours over days of work, the projects don’t work. I shelve them and treat them like cryogenic heads. “Sometime in the future I’ll try again.”

It’s most successful with “stuff I don’t want to RTFM over”. How to git. How to curl. A working example for a library more specific to my needs.

But higher than that, no, I’ve not had success with it.

It’s also nice as a general purpose wizard code generator. But that’s just rote work.

YMMV

I don't know. I wouldn't trust a brain surgeon who has up til now only been messing around on LLMs.

Edit: and for that matter I also would not trust a brain surgeon who had only read about brain surgery in medical texts.

I spent a couple weekends trying to reimplement microsoft's inferencing for phi4 multimodal in rust. I had zero experience messing with ONNX before. Claude produced a believably good first pass but it ended up being too much work in the end and I've put it down for the moment.

I spent a lot of time fixing Claude's misunderstanding of the `ort` library, mainly because of Claude's knowledge cutoff. In the end, the draft just wasn't complete enough to get working without diving in really deep. I also kind of learned that ONNX probably isn't the best way to approach these things anymore. Most of the mindshare is around the python code and torch apis.

"One former attorney general told us that he likes how it balances the intent of the law."

In a common law system you generally want actionable legal advice based on predictions on how a judge would rule in a case not "balances the intent of the law" whatever the heck that means.

.

I know you’re being disparaging by using language like “bake into their identity” but everyone is “something” about “something”.

I’m “indifferent” about “roller coasters” and “passionate” about “board games”.

To answer the question (but I’m not OP), I’m skeptical about LLMs. “These words are often near each other” vastly exceeds my expectation at being fairly convincing that the machine “knows” something, but it’s dangerously confident when it’s hilariously incorrect.

Whatever we call the next technological leap where there’s actual knowledge (not just “word statistics” I’ll be less skeptical about.

Your framing is extrapolative, mendacious and is adding what could charitably be called your interpersonal problems to a statement which is perfectly neutral, intended as an admission against general inclination to lend credibility to the observation that follows.

Someone uncharitable would say things about your cognitive abilities and character that are likely true but not useful.

They didn’t say that they were invested in it.

> invested

Very probably not somebody who blindly picked a position, easily somebody who is quite wary of the downsides of the current state of the technology, as expressed already explicitly in the post:

> It’s downsides, such as hallucinations and lack of reasoning

Probably all the hype and bs.

Claude 3.7 basically one-shot a multiplayer game's server-authority rollback netcode with client-side prediction and interpolation.

I spent months of my life in my 20s trying to build a non-janky implementation of that and failed which was really demoralizing.

Over the last couple weekends I got farther than I was able to get in weeks or months. And when I get stumped, I have the confidence of being able to rubber-duck my way through it with an LLM if it can't outright fix it itself.

Though I also often wonder how much time I have left professionally in software. I try not to think about that. :D

Frustratingly I feel we've made very little progress towards a fix for that problem in nearly 2.5 years!

I want to expand on your sentiment about our pre-AI mindset. Programming has made it easy to do things of essentially no value, while getting lots of money for it. Programming is additive and creative; we can always go further in modelling the world and creating chunks of it to use. But I don't see the value in the newest CRUD fullstack application or website. I don't see the intellectual stimulation or even a reasonable amount of user benefit. Programming allows us to produce a lot, but we should be scrutinizing what that lot is. "AI" that enhances what we've been doing will just continue this dull industry. Greed and a nebulous sense of progress are the primary drivers, but they're empty behind it all. Isn't progress supposed to be about good change? We should be focusing on passion projects and/or genuinely helping, or better yet elevating, users (that is to say, everyone).

> And it’s poisoned with opinions from the internet.

This is the scary part. What current AI's are very effectively doing is surfacing the best solution (from a pre-existing blog/SO answer) that I might have been able to Google 10 years ago when search was "better" and there was less SEO slop on the internet - and pre-extract the relevant code for me (which is no minor thing).

But I repeatedly have been in situations where I ask for a feature and it brings in a new library and a bunch of extra code and only 2 weeks later as I get more familiar with that library do I realize that the "extra" code I didn't understand at first is part of a Hello World blog post on that framework and I suddenly understand that I have enabled interfaces and features on my business app that were meant for a toy example.

Meaning, give you an example?

This morning I created a new project. I provided a postgres database URL to a remote service (with a non-standard connection string, includes a parameter "?sslmode=require"). Then, I said:

  * "Write me a fastapi project to connect to a postgres database using a database url."
  * "Retrieve the schema from the remote database." It used psql to connect, retrieves the schema. That was unexpected, it figured out not only a coding task, but an external tool to connect to a database and did it without anything more than me providing the DATABASE_URL. Actually, I should say, I told it to look inside the .env file, and it did that. I had that URL wrong initially, so I told it to reload once I corrected it. It never got confused by my disorganization. 
  * It automatically added sqlalchemy models and uses pydantic once it figured out the schema.
  * "Create a webpage that lets me review one table."
  * "Rewrite to use tailwindcss." It adds the correct tailwindcss CDN imports.
  * It automatically adds a modal dialog when I click on one of the records.
  * It categorized fields in the database into groupings inside the modal, groupings that do indeed make sense.

But, this saved me a lot of time and it works.

I used claude to create a piece of software that can render gerber files (essentially vector files used in electronics manufacturing), overlay another gerber file on top of it with exact alignment, and then provide a GUI for manually highlighting components. The program then calculates the centroid, the rotation, and prompts for a component designator. This all then gets stored in a properly formatted Place file, which is used for automating assembly.

The day before this I was quote $1000/yr/user for software that could do this for us.

I used Claude + aider to create a webhook forwarding service (receives webhooks, forwards them to multiple destinations, and handles retries, errors, etc). Included a web interface for creating new endpoints and monitoring errors, and made it fully dockerized and ready to be deployed into a kubernetes cluster.

Took me like 3 hours and a few dollars in api credits to build something that would have taken me multiple days on my own. Since it's just an internal tool for our dev environments that already does what we need, I don't care that much about maintainability (especially if it takes 3 hours to build from scratch). That said, the code is reasonably usable (there was one small thing it got stuck on at the end that I assisted with).

I wanted a whiteboard app - simple, infinite, zoomable, scrollable svg-based area with custom shapes and elements. It just needed prompts like "add zoom", "add a rect shape", "add control points for resize", "add text shape", "make position attributes able to reference another objects properties, + basic arithmetic" (for example to make line connect 2 objects, or size of one mirror size of another, etc.). It made all of these with so little effort from me. I would never undertake such a project without an LLM.

Ok, I meant to say, I know about /map, but there is so much to explore here with an open source coding tool and LSP. It feels like you could build some very powerful ways to represent connections using a lot of cool tools that are in use inside our editors. More structured and more relevant that the poor representations I can keep inside my crappy head.

I recall hearing a lot of assembly engineers not wanting to let their skills rust either. They didn't want to be a "4th gen engineer" and have their skills replaced by proprietary compilers.

Same with folks who were used to ftp directly into prod and used folders instead of source control.

Look, I get it, it's frustrating to be really good at current tech and feel like the rug is getting pulled. I've been through a few cycles of all new shiny tools. It's always been better for me to embrace the new with a cheerful attitude. Being grumpy just makes people sour and leave the industry in a few years.

I agree that not everyone wants to be. I think OPs point though is the market will make “not being a prompt engineer” a niche like being a COBOL programmer in 2025.

I’m not sure I entirely agree but I do think the paradigm is shifting enough that I feel bad for my coworkers who intentionally don’t use AI. I can see a new skill developing in myself that augments my ability to perform and they are still taking ages doing the same old thing. Frankly, now is the sweet spot because the expectation hasn’t raised enough to meet the output so you can either squeeze time to tackle that tech debt or find time to kick up your feet until the industry catches up.