With AI you need to think bigger
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When I realized this was possible, I wanted to set up a project that would allow me to use the Pi as a bridge from my document scanner (has the ability to scan to a USB port) to a SMB share on my network that acts as the ingest point to a Paperless-NGX instance.
Scanner -> USB "drive" > Some of my code running on the Pi > The SMB Share > Paperless.
I described my scenario in a reasonable degree of detail to Claude and asked it to write the code to glue all of this together. What it produced didn't work, but was close enough that I only needed to tweak a few things.
While none of this was particularly complex, it's a bit obscure, and would have easily taken a few days of tinkering the way I have for most of my life. Instead it took a few hours, and I finished a project.
I, too, have started to think differently about the projects I take on. Projects that were previously relegated to "I should do that some day when I actually have time to dive deeper" now feel a lot more realistic.
What will truly change the game for me is when it's reasonable to run GPT-4o level models locally.
This guide was a huge help: https://github.com/thagrol/Guides/blob/main/mass-storage-gad...
We see so many stories about how terrible AI coding is. We need more practical stories of how it can help.
I’m teaching kids in Bayview how to code using AI tools. I’m trying to figure out the best way to do it without losing anything in between.
With my pilot students I’ve found the ones I gave cursor are outperforming the ones who aren’t using AI.
Not just with deliverables, but with fundamental knowledge(what is a function?).
Small sample size so I don’t want to make proclamations… but I think a generation learning how to code with these tools is going to be unstoppable.
Are you testing this knowledge in a situation where they don't have access to AI tools?
If not, then I seriously wonder if this claim means anything
Control group might be using AI tools(I tell them not to but who knows) but the experiment group has received instructions and are encouraged to use the tools.
I made a PoC of a 2FA authenticator (think Yubikey) that automatically signs you in. I use it for testing scenarios when I have to log out and back in many times, it flies through what would otherwise be a manual 2FA screen with pin entry, or navigating 2FA popups to select passkey and touching your fingerprint reader.
Obviously not very secure, but very useful!
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?
They have some examples that emulate an USB keyboard and mouse, and the app shows how to configure the Gadget API to turn the phone into whatever USB device you want.
The repo is unfortunately inactive, but the underlying feature is exposed through a stable Linux kernel API (via ConfigFS), so everything will continue working as long as Android leaves the feature enabled.
You do need to be root, however, since you will essentially be writing a USB device. Then all you have to do is open `/dev/hidg0`, and when you read from this file you will be reading USB HID packets. Write your response and it is sent on the cable.
I always wondered if we could convert an old andoid phone or tablet into a USB/wireless graphics-tablet for drawing input -- or as a live annotator for screen presentations where I can mirror the PC slideshow on the tablet and use the tablet to make annotations during a lecture say.
If there are any such projects already -- I would e very keen to take a look.
Did you?
If you wanted to expand on it, or debug it when it fails, do you really understand the solution completely? (Perhaps you do.)
Don't get me wrong, I've done the same in the last few years and I've completed several fun projects this way.
But I only use AI on things I know I don't care about personally. If I use too much AI on things I actually want to know, I feel my abilities deteriorating.
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.
With all of this said, I can see how this could be problematic with less experience. For this scanner project, it was like having the ability to hand off some tasks to a junior engineer. But having juniors around doesn't mean senior devs will atrophy.
It will ultimately come down to how people use these tools, and the mindset they bring to their work.
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 a lot of the reason linux isn't used for lots of things is that you have to basically be a sysadmin to set up some things.
for example, setting up a local LLM.
I wonder what you would call getting a "remote" AI to help set up a local AI?
Something like but not exactly emancipation or emigration or ...
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.
After years of this I decided to give an AI a shot at the code. It produced something plausible looking and I was excited. Was it that easy?
The code didn't work. But the approach made me more motivated to look into it and I found a solution.
So although the AI gave me crap code it still inspired the answer, so I'm calling that a win.
Simply making things feel approachable can be enough.
Then it was a little messy, so I asked it to refactor it.
Of course, not everything lends itself to this: often I already know exactly the code I want and it's easier to just type it than corral the AI.
It helps to have it generate code sometimes to just explore ideas and refine the prompt. If its obviously wrong, thats ok, sometimes I needed to see the wrong answer to get to the right one faster. If its not obviously wrong, then its a good enough starting point we can iterate to the answer.
Its great for ideating in that way. It does produce some legendary BS though.
A novice with no idea could blunder through but get lost quickly.
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.
It’s as bad as untangling the last guy’s typescript spaghetti. He quit, so I can’t ask him about it either.
Most of the time, programmers don’t record all of the assumptions and design decisions in code or documentation.
most of the time there’s an old ticket in the work tracker.
I’d bet those tickets contain at least as much info as any prompt.
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.
What model? What wrapper? There's just a huge amount of options on the market right now, and they drastically differ in quality.
Personally, I've been using Claude Code for about a week (since it's been released) and I've been floored with how good it is. I even developed an experimental self-developing system with it.
This is exactly my experience using AI for code and prose. The details are like 80% slop, but it has the right overall skeleton/structure. And rewriting the details of something with a decent starting structure is way easier than generating the whole thing from scratch by hand.
It’s downsides, such as hallucinations and lack of reasoning (yeah) aren’t very problematic here. Once you’re familiar enough you can switch to better tools and know what to look for.
You can now do literally anything. Literally.
Going to take a while for everyone to figure this out but they will given time.
> 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
Maybe you are running into the problem I did early. I told it what I wanted. Now I tell it what I want done. I use Claude Code and have it do its things one at a time and for each, I tell it the goal and then the steps I want it to take. I treat it as if it was a high-level programming language. Since I was more procedural with it, I get pretty good results.
I hope that helps.
For every problem that stops you, ask the LLM. With enough context it’ll give you at least a mediocre way to get around your problem.
It’s still a lot of hard work. But the only person that can stop yourself is you. (Which it looks like you’ve done.)
List the reasons you’ve stopped below and I’ll give you prompts to get around them.
Theres no way it will "fall short".
You just have to improve your prompt. In the worst case scenario you can say "please list out all the different research angles I should proceed from here and which of these might most likely yield a useful result for me"
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.
AI leads to more useless dives down into the internets.
As a teenager, I remember being annoyed that the newspapers had positive articles on the rejuvenating properties of nonsense like cupping and reiki. At least a few of my friends' parents had healing crystals.
People have always believed in whatever nonsense they want to believe.
(Shrug) It was pretty much true. But it's like what Linus says in an old Peanuts cartoon: https://www.gocomics.com/peanuts/1969/07/20
Edit: and for that matter I also would not trust a brain surgeon who had only read about brain surgery in medical texts.
Weirdly you’ll get a lot of useful experience as you analyze yourself through 80 years.
About language (point (1)), I get a lot of "hypnotism for salesmen to non technical managers and roundabout comments" (e.g. "which wire should I cut, I have a red one and a blue one" // "It is mission critical to cut the right wire; in order to decide which wire to cut, we must first get acquainted with the idea that cutting the wrong wire will make the device explode..." // "Yes, which one?" // "Cutting the wrong one can have critical consequences...")
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.
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.
I made a challenge to various lawyers and the Stanford Codex (no one took the bait yet) to find critical mistakes in the "reasoning" of our Legal AI. One former attorney general told us that he likes how it balances the intent of the law. Sample output (scroll and click on stats and the donuts on the second slide):
Samples: https://labs.sunami.ai/feed
I built the AI using an inference-time=scaling approach that I evolved over a year's time, and it is based on Llama for now, but could be replace with any major foundational model.
Presentation: https://prezi.com/view/g2CZCqnn56NAKKbyO3P5/ 8-minute long video: https://www.youtube.com/watch?v=3rib4gU1HW8&t=233s
info sunami ai
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.
With an extra 23 months of experience under my belt since then I'm comfortable to say that the effect has stayed steady for me over time, and even increased a bit.
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
AI is going to be a great multiplier but if the base is 0, you can multiply it by whatever you want.
I feel ChatGPT-like products are like outsourcing to cheaper countries, it might work for some but for anyone else, now they have to hire more expensive people to fix/redo the work done by the cheaper labor. This seems to be exactly the same but using AI.
I’m not trying to invalidate experiences itt, cause I have a similar one. But it feels futile as we are stuck with our pre-AI bloated and convoluted ways of doing things^W^W making lots of money and securing jobs by writing crap nobody understands why, and there’s no way to undo this or to teach AI to generalize.
I think this novelty is just blindness to how bad things are in the areas you know little about. For example, you may think it solves the job when you ask it to create a button and a route. And it does. But the job wasn’t to create a route, load and validate data and render it on screen in a few pages and files. The job was to take a query and to have it on screen in a couple of lines. Yes it helps writing pages of our nonsense, but it’s still nonsense. It works, but feels like we have fooled ourselves twice now. It also feels like people will soon create AI playbooks for structuring and layering their output, cause ability to code review it will deteriorate in just a few years with less seniors and much more barely-coders who get into it now.
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.
I have been using Claude Code and Aider and I do think they provide incredibly exciting potential. I can spin up new projects with mind boggling results. And, I can start projects in domains where I previously had almost no experience. It is truly exciting.
AND...
The thing I worry most about is that now non-technical managers can go into Claude and say "build me XYZ!" and the AI will build a passable first version. But, any experienced software person knows that the true cost of software is in the maintenance. That's 90% of the cost. Reducing that starting point to zero cost only reduces the total cost of software by 10%, but people are making it seem like you no longer need a software engineer that understands complex systems. Or, maybe that is just my fears vocalized.
I have seen LLMs dig into old and complex codebases and fix things that I was not expecting them to handle. I tend to write a lot of tests in my code, so I can see that the tests pass and the code compiles. The barbarians have come over the walls, truly.
But, IMHO, there is still a space where we cannot ask any of the AI coding tools to review a spec and say "Will you get this done in 2 months?" I don't think we are there, yet. I don't see that the context window is big enough to jam entire codebases inside it, and I don't yet see that these tools can anticipate a project growing into hundreds of files and managing the interdepedencies between them. They are good at writing tests, and I am impressed by that, so there is a pathway. I'm excited to understand more about how aider creates a map of the repository and effectively compresses the information in ways similar to how I keep track of high level ideas.
But, it still feels very early and there are gaps. And, I think we are in for a rude awakening when people start thinking this pushes the cost and complexity of software projects to zero. I love the potential for AI coding, but it feels like it is dangerously marketing and sales driven right now and full of hype.
Boggle a skeptical mind
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.
But, my takeaway is that I could have fixed this exact problem in five minutes if I had written the JS code. But, I have not looked at anything other than glancing at the diffs that fly by in the session.
The day before this I was quote $1000/yr/user for software that could do this for us.
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).
Good Explainer: "The Disturbing Reality of AI Coding" https://www.youtube.com/watch?v=QnOc_kKKuac
I think a lot of arguments about LLM coding ability stem from people using them for the former or the latter and having very different experiences.
I tried Vibe Coding for the first time on Friday and was blown away. It was awesome. I met some people (all programmers) at a party on Friday and excitedly told them about it. One of them tried, he loved.
Then yesterday I read a LinkedIn (Lunatic?) post about "Vibe Design", where PMs will just tell the computer what to do using some sort of visual language, where you create UI elements and drop them on a canvas, and AI makes your vision come true, etc, etc...
And my first thought was: "Wait a minute, I've seen this movie before. Back on late 90s / early 2000s it was the 4th generation programming language, and Visual Basic would allow anyone to code any system"...
And while it's true Visual Basic did allow a BUNCH of people to make money building and selling systems to video rental shops and hair saloons, programmers never went away.
I welcome anyone building more software. The tools will only get better. And programmers will adapt them, and it will make us better too, and we will still be needed.
Run /map. It's nothing fancy, really.
If you know what you are doing the tools can improve output a lot - but while you might get on for a little bit without that experience guiding you, eventually AI will code you into a corner if it's not guided right.
I saw mention of kids learning to code with AI and I have to say, that's great, but only if they are just doing it for fun.
Anyone who is thinking of a career in generating code for a living should first and foremost focus on understanding the principles. The best way to do that is still writing your own programs by hand.
I can have the LLMs build me custom bash scripts or make me my own Obsidian plugins.
They're all little cogs in my own workflow. None of these individual components are complex, but putting all of them together would have taken me ages previously.
Now I can just drop all of them into the conversation and ask it for a new script that works with them to do X.
Here's an example where I built a custom screenshot hosting tool for my blog:
When it comes to some personal projects, I've written a slew of them since AI coding tools got quite good. I'm primarily a backend developer, and while I've done quite a bit of frontend dev, I'm relatively slow at it, and I'm especially slow as CSS. AI has completely removed this bottleneck for me. Often times if I'm coding up a frontend, I'll literally just say "Ok, now make it pretty with a modern-looking UI", and it does a decent job, and anything I need to fix is an understandable change that I can do quickly. So now I'll whip up nice little "personal tool" apps in literally like 30 mins, where in the past I would have spent 30 mins just trying to figure out some CSS voodoo about why it's so hard to get a button centered.
But in my job, where I also use AI relatively frequently, it is great for helping to learn new things, but when I've tried to use it for things like large scale, repo-wide refactorings it's usually been a bit of a PITA - reviewing all of the code and fixing it's somewhat subtle mistakes often feels like it's slower than doing it myself.
It that sense, I think it's reasonable to consider AI like a competent junior developer (albeit at "junior developer" level across every technology ever invented). I can give a junior developer a "targeted" task, and they usually do a good job, even if I have to fix something here or there. But when I give them larger tasks that cross many components or areas of concern, that's often where they struggle mightily as well.
This weekend, I called my colleague and asked him to call them back and see if they’re still trying to migrate. AI definitely has changed my calculus around what I can take on.
Thinking bigger is a practice to hone.
Being able to write code that compiled into assembly, instead of directly writing assembly, meant you could do more. Which soon meant you had to do more, because now everyone was expecting it.
The internet meant you could take advantage of open source to build more complex software. Now, you have to.
Cloud meant you could orchestrate complicated apps. Now you can't not know how it works.
LLMs will be the same. At the moment people are still mostly playing with it, but pretty soon it will be "hey why are you writing our REST API consumer by hand? LLM can do that for you!"
And they won't be wrong, if you can get the lower level components of a system done easily by LLM, you need to be looking at a higher level.
I re-created this system using an RPi5 compute module and a $20 camera sensor plugged into it. Within two hours I wrote my first machine learning [application], using the AI to assist me and got the camera on a RPi board to read levels of wine in wine bottles on my test rig. The original project took me six weeks solid!
Not everyone wants to be a "prompt engineer", or let their skills rust and be replaced with a dependency on a proprietary service. Not to mention the potentially detrimental cognitive effects of relegating all your thinking to LLMs in the long term.
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.
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.
The former puts you in command of more machinery, but the tools are dependable. The latter requires you to stay sharp at your current level, else you won’t be able to spot the problems.
Although… I would argue that in the former case you should learn assembly at least once, so that your computer doesn’t seem like a magic box.
Isnt this what a compiler is really doing? JIT optimizes code based on heuristics, it a code path is considered hot. Sure, we might be able to annotate it, but by and large you let the tools figure it out so that we can focus on other things.
1. Lower learning curve, for new or revived skills (weaving - gather and summarize)
2. Lower incremental cost: almost always faster at choosing the next step (scouting)
3. Faster time-to-maturity: when building test suites, faster both at first-pass coverage and at gap-filling (zoo-keeping)
But the huge, gaping pitfall -- now coming down the tracks even faster -- is the same as without AI: once you build a complex system, committing to its invariants, you quickly find it difficult to change in the direction you want, because each choice triggers a tangle of incommensurable choices. Sometimes heroic re-factorings (beyond AI now) can extend life a bit, but usually the only people who can avoid abrupt stasis are those with deep experience and the insight into how to change things (incrementally). I'd guess with AI such pitfalls become more common and more difficult to escape.
So: yes, think bigger for projects with relatively definitive and complete requirements, but take care with foundational code you expect to grow.
.. and even automating the testing to check results match, coming up with edge cases etc.
... and if thats true, then they could be useful for _optimizing_ by porting to other apis / algos, checking same-ness of behavior, then comparing performance.
The whole "vibe coding" doesn't grab me - as I feel the bottleneck is with my creativity and understanding rather than generating viable code - using a productive expressive language like javascript or lisp and working on a small code base helps that.
eg. I would like to be able to take an algo and port it to run on GPU for example... without having to ingest much arcane api quirks. JAX looks like a nice target, but Ive held off for now.
I recently ported skatevideosite.com from svelte/python to ruby on rails. I leaned on AI heavily to translate the svelte files to ERB templates and it did a wonderful job.
My experiene generally with these systems is that they are good with handling things you _give_ it, but less so when coming up with stuff from scratch.
For example I've tried to use it to build out additional features and the results are subpar.
Agree, yeah "vibe coding" is super cringe haha
If you look at things like LLM party for comfyui, it's the same basic concept.
Or at least make a digital back up of Earth.
Or at least represent an LLM as a green field with objects, where humans are the only agents:
you stand near a monkey, see chewing mouth nearby, go there (your prompt now is “monkey chews”), close by you see an arrow pointing at a banana, father away an arrow points at an apple, very far away at the horizon an arrow points at a tire (monkeys rarely chew tires).
So things close by are more likely tokens, things far away are less likely, you see all of them at once (maybe you’re on top of a hill to see farther). This way we can make a form of static place AI, where humans are the only agents
Once we get a feel for what the AI can and cannot do, bigger bets will occur.
Currently, companies seem tight - team counts are held low, and hiring is frozen. Very much a position of "let's see how much AI efficiency gains us".
But eventually, the money sitting on the sidelines will look attractive again - attractive for taking bigger, more ambitious bets.
More hiring has to take place.
Would love to hear folks' experience around "managing" all this new code.
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I have separate system prompts for taboo-teaching, excessive-pedanticism, excessive-toxicity, excessive-praise, et cetera.
My general rules is anything & everything humans would never ever do, but that would somehow allow me to explore altered states of consciousness, ways of thinking, my mind, the world, better. Something to make me smarter from the experience of the chat.
that's so cool. all those grand ideas that felt so far away are right here ready to grasp and realize.
You don't want to use AI for complex things that you might not fully understand. These are the cases where you'll miss the hallucinations and get fucked
I envy this optimistic. I am not the opposite (im a sr engineer with more than 15 years of experience), but I am scared about my future. I invested too much time in learning concepts, theory, getting a Master degree, and in a few years all of my knowledge can be useless in the market.
Having an LLM next to you means there is never a stupid question, I ask the AI the same stupid questions repeatedly until I get it, that is not really possible with a smart human, even if they have the patience, you are often afraid to look dumb in their eyes.
Forking to different technologies and languages is one thing (I've been there, I started with PHP and I haven't touch it for almost a decade now), but being replaced by a new tech is something different. I don't see how I could pivot to still be useful.
This coin has two sides. If a CTO can live without you, you can live without an expensive buffer between you and your clients. He’s now just a guy like you, and adds little value compared to everyone else.
It's like saying since all of us know how to write, we all can sell books.
And to torture the analogy further since Im already in this rabbit hole, masseuses and babysitters probably have to put in the same amount of effort in their work.
But seeing the progress and adoption, I wonder what will happen when that valuable skill (how to think about a big system, etc) will also be replicated by AI. and then, poof.
But, if it does, I will go the way of all those buggy whip makers and find something else to do. (And it will probably be doing something with AI.)
But if the advancement moves too slowly, we will have some serious pipeline problems filling senior engineer positions, caused by the destruction that AI (combined with the end of ZIRP) has caused to job prospects for entry level software engineers.
An undergrad using the hottest tech right of the bat? Cooked.
It's like giving the world 128gb of ram and 64bits in 1970, we would have just maxed it out by 1972.
What if I tell you there is an undergrad that just flunked a class and is depressed and cries about it? Considers changing their major? This is pre-AI. We have a chance that undergrads will never feel that way again. Not intimidated by anything.
There's shitty ways to pass and good ways to pass
People using this phrase should probably stop, it's become extremely tiresome as a cliche
but I could be a Feynman radian out on those vectors in leet space.
I have plans for many things I didn’t have the energy for in the past.
That's still a valid worry. At best, you can do larger projects on your own than before.
> or that a project will use a technology or programming language I don’t know.
Was that ever a worry? I've always considered it an opportunity for self improvement.