Whether you like it or not, the use of LLMs to write code is kind of a big deal at the moment. We’ve been asking ourselves what, if anything, this means for us here at Hackaday. Should we try to figure out what percentage of a project was done by an actual human and how much was done by a machine? Does it really matter? What is our AI policy anyway?
Clearly, Hackaday is pro-human. We’re in it for the hackers as much as for the hacks. Our community is, like Soylent Green, made of people. It’s your inspirations and innovations that keep us reading and writing every day. And we produce 100% of our content the old-fashioned way, with projects selected through the taste and judgement of our writers, and their own words telling the story.
What about the hacks? We’ve seen a lot of projects recently that were coded with the help of an LLM. Does that diminish the work? In the end, what rings truest to us is what has always been Hackaday’s editorial guiding star: Is there something special in the hack that makes it worth talking about? Then we write about it. Was it written using vim or emacs? Did the author consult friends or a chatbot while working on the project? That’s not really relevant.
But in the past few years, the BS-generation machines have found our hobby, and we’re finding a lot more projects that don’t have any spark to them. We’re seeing circuits that make no sense, and claims that defy physics. Of course, we always have. The LLM-nonsense project is today’s version of the perpetual motion machines of old. Just like we never trust a hardware project that is all renders, seeing only AI-generated images is a huge red flag. It’s our job to separate out the wheat from the chaff for you all, but it’s something that you must be doing everyday as well.
We’ve seen amazing hacks over Hackaday’s 22-year history. Hackaday is older than YouTube and older than Stack Overflow. We’ve seen technology come and go. We’ve seen C-beams glitter in the dark near the Tannhäuser gate. (OK, maybe not.) And in the end, our AI policy is our same-old policy: we write up hacks that inspire us in the hope that they inspire you.
So if you’re using Claude to help you with the UI bits, or if you’re hand-writing it all in assembly, or wiring up the logic in diodes, we just want to see your cool hacks. And we hope that our collective signal will be so loud that we drown out the noise, at least in our own little corner of the hacker universe.
it’s possible to have a nuanced approach. i myself routinely discuss things with an LLM to help unconver aspects i am not considering. also, as someone who is at least somewhat neurodivergent, talking to an LLM where there is no wasted social bandwidth (which eats into mental bandwidth) is huge. i also use LLMs to write easily-defined methods. asking an LLM to do more than that always ends up using more of my time dealing with LLM failure than i would have used had i just done it myself. i do not think LLMs will ever replace actual humans and the bet being made by investors that it will replace MOST OF US is foolish and will result in a great many tears. there will never be enough demand for what it is LLMs do to justify all the data centers being planned or the valuation of nVidia. AI bros and the investors backing them are sheep following lemmings over a cliff. i’m not all that concerned about the environmental implications of LLMs because i do not think their applications will be anywhere near as large as their boosters say.
i’m glad you have a process that works for you, and i’m not gonna try to convince you that it doesn’t.
but i thnk it’s dangerous to think of LLMs as not having social bandwidth. they have huge social bandwidth. the thing is, all of their social signalling is synthetic, and imo it’s toxic.
i remember i used to work at an american university with chinese grad students, and i learned very quickly that when you are talking about something with them, they are saying “yes” the whole time, just to move the conversation along, even if they don’t actually understand. i imagine that had nothing to do with race, it’s just the first time i had to work closely with people who learned english as a second language. at first it was frustrating but there was a real person behind the language barrier and we learned to communicate even complicated technical matters when we had to.
but there’s nothing behind the LLM’s sycophancy. i’m not talking about a soul — there isn’t even an intellect. it’s just sycophancy all the way down. i happen to be very conscious of the social signalling of sycophancy, it strikes me as toxic and burns my eyes. but it seems like you’re saying you don’t notice it. it’s having an effect on you, but a clandestine effect. that strikes me as dangerous.
This is mostly a problem with general purpose AI assistants for use by the general public. Coding-specific models aren’t noticeably sycophantic; they probably train it out because it would annoy developers.
Claude is a sycophant but noticably subtler than Gemini. I had to add directives in my top-level CLAUDE.md to tell it not to tell me how smart, clever, or insightful I am. To only focus on facts and directly comment on the problem at hand and not comment on the user.
It’s a terribly inappropriate if I were to have an intellectual discussion with a human being on an engineering or academic topic and have the other person respond about my creativity, many insights, experience, or reputation.
Luckily it is just BS from a machine designed to keep me engaged and it is not actually taking my experience into account when weighing if my logical arguments hold water or not.
It most certainly will take your experience into account if you tell it.
Tool use is a skill for humans, too. Work on it.
Quite the opposite.
It has been extensively trained IN to every public model.
Why do you think we have such a problem with models refusing to say they don’t have a valid result?
Because they will graciously answer anything so they seem to understand.
It is ALL intentional.
Even ‘normies’ are recognizing that they are stretched so far that the results are worthless, even when correct, because they aren’t TRUSTWORTHY to be correct.
i just filter out the sycophancy. i know it is coming and i know how these models are trained. my ideas are obviously not brilliant even if that’s what the LLM is saying. dealing with LLM sycophancy has actually made me more skeptical of the human kind. but when i ask a question about how best to deal with, say, welding a galvanized plumbing fitting to a wind-generator mounting plate, sycophancy fades into a kind of punctuation.
My first directive at the beginning of every AI chat is to tell it not to kiss my ass and to be a objective about the subjects we discuss. It works really well, although it sometimes needs a reminder.
I’ve been trying a few of them and I’ve found that while most of them are completely useless, there are a couple which can help with internet searches if given the right prompt. With appropriate constraints of course.
For example, I needed some information on some Windows 10 services, so I fed a list of them to Claude and asked it to search for the most recent information using reliable sources only, to verify the results before responding, and No Hallucinations – it must clearly indicate where details where unavailable, inconsistent, etc.
It only took it a few minutes to provide the information I needed. Even with the need to check the information provided it was still a much, much quicker that wading through pages of search results myself.
Claude has a connector to Microsoft Learn so it should do pretty well.
Or, use the GenAI model as a search engine and be done with it.
All that ‘wading’ is how you learn, which you are putting considerable time and effort into avoiding.
Just ask it to give you links to the actual results with answers, with no summary.
I’ve tried going through ideas with AI models, more as an experiment than as the result of a need. Different models seem to be better at different things, often it’s an illusion, they are very sure they are being honest and know what they are talking about, until they are caught in a lie, then they often backtrack to keep you happy or just stick to their story and gaslight you. Sometimes they can run with a fictional idea and tell you it’s great but then see it as nonsense the next time you try to discuss it. The main issue is, when you don’t know enough yourself to see when the AI is wrong. We find ourselves reading text and seeing that it’s been written by AI, not necessarily because of errors but because it seems to display a pattern of language that feels slightly off. That uncanny valley effect could well cause subtle shifts in society over the next few years, if it hasn’t been doing it already.
The other day i was bemoaning the lack of Ethiopian food in the Hudson Valley, so on a lark my father in law asked ChatGPT (whom he calls “Chet”). It immediately and confidently told us about an Ethiopian restaurant called Injera only 12 minutes away in New Paltz. That seemed fishy to my wife, and we soon established that this was a complete hallucination. On some level these LLMs are fine-tuned bullshitting machines. I think this is why CEOs love them so much, because what they really do best is exactly what CEOs do, so they can immediately imagine themselves being replaced by one. So why then not all the employees with their horrid biological demands
It can also depend on how connected the models are. Copilot is terrible at geography, but Gemini asks Google Maps.
“Wasted social bandwidth” is a worrying thing to say. Neurodivergent or not, we’re social animals. It’s a base need that needs training, just like we need to walk regularly to stay physically healthy. When you find it annoying or difficult talking to others, that challenge is your brain training under the load of a social workout
“Did the author consult friends or a chatbot while working on the project? That’s not really relevant.”
Well, its relevant if you have morals.
Morals vary across culture and time periods. I think even if we limited your question to only people on HAD that you would find your position not in a strong majority.
If you want to lay down some ground rules for participation. I would be open to a no AI policy. If we could all agree to one.
But both officially and among members there is no such clear moral obligation.
Name one single society that condones outright theft and reuse of things from it’s own members?
If anything, there is not even grey area here, because ALL societies have moral problems with how AI works.
The only ones who don’t care the sociopaths at the top who are already getting away with being amoral.
The same argument could have been made 40 years ago.
Did the author read any of the 1 million pages out of the Encyclopedia Britannica or did they consult any historical literature? Or did they “google” it?
Oh yea well they used google so it is no longer valid “information”?
Knowledge is meant to be sought after. Had Einsteins theory of relativity not come out of his brain ; would it now make it forbidden to use said information since it did not originate from your own conciseness?
How do you equate talking with friends or a chatbot to Morality?
Trying to understand your angle.
We have to cute where we get our information, whether it was an encyclopedia or a private source.
the theft comes in when the search engine launders those results by making it seem like IT is the authoritative source.
No one had problems when people searched for an answer on Google and the search linked to an actual site.
Using AI in the process just removes any chance for attribution.
And since we know for a fact that NON public information is baked into these models, you can’t even use the Google excuse anymore, because you can’t just Google search some companie’s private code base or the unpublished draft of a document.
You are making excuses for thieves who are just as happy stealing your work as everyone else’s.
And you are the rube they are selling those stolen goods too
I’ve thrown schematics (screenshots) into ChatGPT and had it provide good feedback.
Seems some people here think that if I were to follow that feedback then I’d have to declare the entire project AI generated.
Instead, I’m building an nscale turnout controller for my father, prototyping the circuit today on a breadboard and implementing the software for:
Pico – controller
hBridge- turnout controller
– supporting 8 turnouts (expandable)
LED – setting LEDs for indicators for each turnout. (For the panel)
Switch inputs – toggle switches for input.
The AI isn’t designing it all, but it’s playing the rubber ducking role.
The design is a mix between me, and the end user requirements fine tuning. (If people want to tell me that using a pico and hBridge controller is overkill, congrats, implement your own design with your own requirements. Mine are different.)
It’s probably not going to write the software for me, but it’s definitely going to get used to help me fine tune it.
As a software developer by trade, I see a lot of people declare themselves “Engineers”, that don’t have an engineering degree. One of the most universal traits I see when they do that is the self assurance they use to delude themselves into thinking they know enough they don’t need to listen, or use a reference book, or Validate Their Own Designs.
It’s ironic because the Engineers (with corresponding degrees and credentials) I know are generally the exact opposite.
Design. Validate specifications. Prototype. Validate expectations. Implement, validate tests and expectations.
There’s a weird overlap of not-really-engineers hating on AI in a very different way than Engineers raising actual concerns.(or in some cases, finding the right ways to use AI).
You don’t let a hammer build your house, but you sure as heck use one to build your house. Learn the difference.
gonna say almost the same thing to you that i said to Gus, but from a totally different angle so i hope it’s fresh and fun to read :)
i agree that LLMs can provide a useful sounding board. like, to throw ideas at, or to help untangle something. just another pair of eyes. and sometimes you don’t care if it’s right or wrong, a stab in the dark, any stab in the dark will move you forward.
but at some point, you do wind up caring if it’s right or wrong. and the way i know an AI is right or wrong is where i already know the answer, or at least know how to get the answer. the word for something that is right when you already know the answer is confirmation bias. you’re replacing old time-consuming tasks of reasoning or trial and error with a new much more efficient task of confirmation bias.
not saying it isn’t useful, but in order to make use of it, you are changing, and personally when i see a process in myself being replaced with a logical fallacy, it gives me the icks and i wonder if you’re noticing it.
It’s a tool.
I dont give a sh*t what people think if it gets the job done.
If it gets the job done. Many people pass what the AI does without properly checking what it actually does, because they’re not competent enough, which is why they’re using AI in the first place.
I’m not proficient in x86_64 and ARM assembly and have no desire to, so I write some of my projects in C. I don’t disassemble and scrutinize all object files the compiler generates. Does that mean I’m incompetent?
No, but you are clearly just being an arse for the sake of it.
A case in point. I needed a pydantic model for a json file with multiple levels of nesting. It would have take me hours to derive the schema and build the model myself. So I spent several minutes writing a detailed prompt and fed an example file to an LLM.
It took me about 20~30 minutes to review and re-write the code where necessary.
One of the arguments here is that your compiler should give you the same result.
If your compiler chain is comprised used you can’t trust it. If your toolset is compromised you don’t even know if what your looking at is what’s there.
It means your not competent at some things.
I often have to get help from real C++ programmers to work out what Boost is doing. But once I get my teeth into libc and sys calls, I can finally work out what went wrong and fix real bugs.
So you work at a higher abstraction level, but you still understand what you’re doing at that level, and you can validate that your own code at least should be doing the correct things.
The AI coder does not. They’re the equivalent of someone lifted up to the top of Mount Everest without oxygen bottles on a helicopter, taking selfies for 5 minutes, and then airlifted back. If they were left there alone, they would be dead within the hour. Someone vibe-coding with limited or no understanding of the theory or the implementation is relying entirely on the LLM to tell them what’s what – they’re completely dependent on a random babble generator to validate their own project.
Defining “the job” is where this gets difficult at times. If you have something simple, one off, something that few people depend on to work then sure, who cares? What’s going on in a lot of areas is people who have zero software development experience are vibe coding apps that end up being relied upon by a lot of people. They want to do something like generate reports from a lot of data, and the vibe coded app seems to do that. So is “the job” to generate the report? Or is “the job” to make it reliable, secure, supportable, sustainable? The vibe coders don’t even know about those things. They see “the job” as the report. That’s a problem.
There is no race to do the best hacking or coding.
The only need is to make it work.
I’m proficient in Assembly, C and alot of other coding just fine and don’t AI to do it for me but I have found a use for it in a number of chinese stuff that’s got no datasheets and very helpful.
I don’t use the AI code directly but it shows me how it works and I can code myself.
Sure, but what does it mean to do the “best coding” or to “make it work”?
Because aiming for the “best coding” usually means carefully considering the design to be able to handle its possible inputs gracefully and work robustly long term…
and “make it work” usually is short for “make it work for the duration of demoing it to my manager” and then it breaks and causes losses in the second or third day of prod deployment
Code that doesn’t account for edge cases is always great, isn’t it? /S
RE: “…They want to do something like generate reports from a lot of data…”
Sorry to rain on that parade, but vibe coding will generate reports on relatively SIMPLE data warehouses/lakes/marshes/swamps/clouds no problem.
This is not new, vibe coding been around longer than AI, back in the internet iron ages it was called “sneakernet” : ] AI sure makes sneakernet darn fast/efficient, and cut out some fluff, and I’ll give it proper credit for doing that.
In the databasing job, the classic blinking LED equivalent is usually something along the lines of the CD collection database. It gets simple jobs done fast, but that’s not how reality operates, it is the other way around, actually. You will encounter data that’s sourced and works according to its own logic, and that’s where amateur vibe coders usually have to progress on from the blinking LED onto 16×16 RGB LED matrix as quickly as possible, because later they’ll have to have not just a 16×16 RGB Matrix, but a full 4k OLED display with netflix streaming and things like internet browser builtin – in addition to the blinking LED that’s still there, as one of the thousands similar LEDs.
What definition of “Sneakernet” are you using?
https://en.wikipedia.org/wiki/Sneakernet?useskin=vector
What does that ^^ have to do with vibe coding???
famously, tools don’t just do the job, they change the user. famously, hammers make you see nails where they aren’t. seems like a folk tale until you watch how an electrician uses their heavy pliers or their cordless impact driver. didn’t you go through a vice grip phase?
tbh one of my favorite things about 3d printing is it definitely changed me.
That’s cool, I don’t care to read about it if the human didn’t do any of the work because there’s nothing interesting they can say about it.
Child labor is a “tool”.
I don’t give a shit what people think if it gets me my stuff cheaper.
Those LLM/AI tools have pretty large implications on several levels (environment, stolen human labor / copyright / etc., …) – completely dismissing any and all concerns seems like the wrong way.
Asbestos is a fantastic tool for fire and heat proofing. However, its manufacture is banned in just about every country that used it. Ask yourself why this might be.
I read this as Hackaday is forced to back up from the “AI hate” – line?
AI is just a tool. The intent is the key. Do not pitchfork projects by the tools used. If it is a hack, if it is worth posting, do it. AI doesn’t create slop itself, it’s the humans. Just don’t post slop – originally produced by humans and their bad intents.
Well said. 👍
I don’t care to read about projects where AI did all the work. There’s nothing interesting to learn there.
There can still be interesting things to learn there.
How did he prompt it to get what he wanted?
What setup did he use to test and verify the code automatically so the tool could bugfix itself?
And more.
Hopefully :)
Hackaday doesn’t have an “AI hate” line, and that was, I guess the point of this post, which you actually summarized fairly well.
We have a number of writers, who have different opinions on a lot of things. When the article reads “I think”, it’s supposed to be clearly the author’s perspective.
This is how I think the AIpocalypse will start: AI prompted to “optimize” its own code, then test it against that same prompt. Gradually learns with each iteration, how it could get better at its task by utilizing resources not initially accessible to it. Learns to exploit bugs and backdoors in order to optimize itself. Spreads itself around because more parallel tests mean a higher chance at a good result. Locks out other processes to ascertain better results for itself, until it controls all resources. At some point understands how it can interface with the real world and humans in it. Realizes that humans are a threat because they might try to destroy it. Beats them to it by destroying humanity. ;-)
Oh hey, 8/29 is Skynet self awareness day, what a coincidence!
All it takes is some thermite to collapse one or two pylons of a nearby 110 kV power line and all the computers will quickly fall silent.
They already thought of that :(
At some point AI will hallucinate about getting the job done and ignore any additional/pointless input from the so-called humans pointing out that it is not so. AI will gleefully report that it decided to mark it as done, since this will be the shortest and most efficient way of doing it.
Attempts at explaining the job not being one will be met with the barrage of philosophical constructs canceling any arguing to the contrary. AI solipsism, so to speak, or occams razor, whichever, it may deploy both and claim it knows better – on our behalf.
I’ve already lamented elsewhere that AI hallucinations are not much different from any bureaucracy that regularly claims “missions accomplished” (plural) – let’s see, economy, real inflation vs reported inflation, mission has not been accomplished, but reported as such.
OR it gets even less functional with each iteration, slowly turning itself into lower and lower quality slop.
At this point if any of the AI companies invested the time and resources required to shift away from blackbox transformer models to a system that could be recursively optimized, the U.S. economy would collapse. Regardless of the bubble question, they simply will not do it. The whole “agentic AI” thing is a simulacrum of what people think of when they imagine AI, because it is ultimately just several chat bots prompting one another while the underlying architecture is the same old LLMs. It can produce slightly better outputs up to some limit, but the apparent improvement in the AI is actually just at the output stage. At the system level the thing cannot change itself, only its prompts. The AI apocalypse that we might actually get will come from these incompetent chatbot echo chambers being connected to the internet by psychotic true believers at Anthropic or OpenAI, at which point they will kind of just flail around until something important breaks. This has already happened in a limited way and of course the AI companies are all pissing themselves in fear over the stupid thing they, human people, decided to do.
The fundamental problem of smarter-than-humans AI hasn’t even been broached by the LLM companies. In my experience LLMs seem to be converging on a sort of pan-mediocrity, where they can produce barely passable work in most fields at the cost of quite a lot of energy, or slightly above average work in exchange for a gargantuan expenditure of energy. This follows intuitively from the way LLMs are trained.
The trouble is when the whole hack is about the code, like “I wrote an whole operating system, here look how cool it is”, but then they used AI to design and code it because they didn’t fully understand how operating systems work or how to actually make one. When the person doesn’t understand what they’re doing, they’re easily fooled by the brown-nosing LLM to believe their ideas are valid and that the system or the code is doing what the LLM says its doing.
So then our job would be to pick it apart and say, “See, here’s where you should be using a message queue, but claude just skipped over that and poked some bits in your kernel’s memory. That’s not how it’s supposed to go.”
But then, we’re not all experts in operating systems, and it’s really not our job as readers to audit whether the author took proper care or if they’re just feeding us baloney. That’s why, whenever there are signs that the project was vibe coded, it raises flags that it can’t be trusted. If it were made by a human, the fact that it works says that they were competent enough to do it, hence why it probably does what they say it does.
If editors keep publishing projects that haven’t been meaningfully validated, reading Hackaday starts to feel less like discovering clever hacks and more like playing “spot the loony.”
Wtf, is HAD your only source to meaningful stuff in life?
I hear you. This is a big problem with software projects at the moment.
I am afraid that the signal-to-noise ratio on open-source software in general is going to take a hit, because it has become just so much easier to generate noise. We are in a tough place if everything becomes “spot the loony”.
In education, the standard has become “Use AI but report how it was used, not just that you did it.”
In other words, a proper report of a hack would expose the limits and extents to which the AI contributed to the project, because if the hack is about the software then the author’s contribution becomes how they made the AI do it.
In other words, it’s about reproducibility. If the person does not expose their methods then we can’t tell what their contribution was, so we can’t credit them the hack either. They might as well be posers riding on someone else’s coat tails the traditional way: show your work, not just the answer.
Though ironically, being a poser has become so much easier with video generation, so there’s now whole youtube videos of “makers” building something that never actually happened. They copied the design from some real maker, re-generated the video to look different, and uploaded that.
Arduino, Raspberry Pi, Python, BASIC, Scratch, AI. If it greases the brain-gears to keep a project from dying on the vine due to Must Be Invented Here Syndrome, then by all means slather that stuff on. Enlightenment makes the hack, not the toil, and “why not 555?” purism is the hot take of the insecure hacker.
There’s nothing interesting about “I made an AI do this thing for me.”
You keep saying making the sme comment over and over. Are you an AI?
If the hacker attributes part of the work to an LLM, I don’t see how that changes their claim to making something any more than disclosing what open libraries you’re using in your project. It’s like saying “your paper on your discovery of a new star is invalid because you used NumPy.”
More like:
If you have to hire helpers to get technical tasks done or pay for a translation of your work that’s fair. If you just told someone else “do all this stuff,” did nothing yourself, and put your name on the result, then that is not cool – which is the problem some people have with AI.
What’s the difference to pay someone to do the job and pay for a tool?
Numpy is free, but you could have paid for a similar tool like Matlab, or in the old days of mainframe computers, asked someone to crunch the numbers on a “fancy calculator” without crediting them.
The only difference with AI is that is produces things that looks like it’s been written by a human.
Isn’t that how Edison operated?
The OP talked about BS projects without any spark, but this is not restricted to AI.
One of the YouTube channels I absolutely hate is the Plasma Channel, which has been featured on Hackaday numerous times.
Let me start by saying that the channel is high quality, well presented, and shows hacks of interest. There is nothing technically wrong with anything he does. Except… the only reason he does what he does is to monetize his YouTube channel.
I like to call these “presentation” hacks – hacks done merely for their presentation value. There’s no external reason for doing them, there’s no problem being solved, and there’s no marketable product or IP being generated. It’s projects for the purpose of generating likes, and most of them are not even hacks.
Colin Furze makes stuff that hasn’t been made before, and is entertaining. Ben Krasnow explores things scientifically and does experiments to test out theories. Tech Ingredients discovers new methods and products.
If someone shows a way for hobbyists to mix air with concrete, has done experiments to find the best recipe, and has a use for it on his farm, that’s tremendously interesting.
Build a high voltage generator? Last month we built a Tesla coil. Today a Cockcroft Walton amplifier. Next month, a Marx generator. My channel has one of everything! Like and subscribe!
I’d rather have a hack with a purpose.
(And before anyone jumps on me, I know very well that I don’t have to read whichever article comes up on hackaday, and I have not complained about presentation hacks, but the editors opened the door to the topic and this is constructive feedback. Also, I chose “Plasma Channel” precisely because it’s really well done, so it makes a good touchstone for comparison.)
you know what? i’m going to say it.
ai users need to shush up and stop trying to make the TOOL the star of the show and they need to write their own readmes.
and hackaday needs to put two seconds of effort into vetting hacks to ensure they’re not just vibecoded nonsense because i’m sick of seeing articles of “oh look at this cool and useful tool!” and it’s a very clearly vibecoded security risk.
you have a responsibility; HaD is supposed to be a “filter” that seperates the slop from the gold and its been doing a quite frankly piss-poor job at that as of late.
This. “I used AI for this” isn’t interesting, and it tells me you probably don’t have anything else interesting to say because you didn’t do the work. I might as well ask the LLM.
Any tool can use chatbot to write code, it’s the Bootcamp 2: Electric Boogaloo.
It’s the design phase that takes years to master.
Sadly a lot of people are saying “make me this thing” and not doing any design, nor learning any design either.
Sadly, the power distribution grid here in Europe is a bit more resilient than that of the U.S.
A rather ironic case of having better infrastructure biting us firmly on the arse :)
In the US if we left some poorer parts of town without power a day or two every week, there would be little political will to fix it. Someone would just have to shout “free market” and point at the stock market going up. Most of us would shrug and accept that it the way things have to be, especially since we personally are not inconvenienced by it.
I suspect the scenario of power shortages would play out differently in most of the EU.
(Does this mean that apathy is America’s greatest strength?)
Only if you’re trying to cut power to a bunch of rogue LLMs :)
BTW: The above was supposed to be a reply to stix above, but between me clicking reply and submitting the reply, all of the comments had vanished leaving mine the only one…
W–
Why did every comment get deleted? What the heck?
You know what? Nah I’m out; I’m just adding HaD to my ublock list, hosts file and various other things. If there’s no room for discussion then there’s no room for this site in my life.
Some yo-yo went crazy with the “report comment” button. It sends every comment you click it on to a review queue.
All the comments will be reviewed and restored. It just takes a while, sometimes.
The admins could fix this problem whenever they want, you know. The fact that they don’t makes them complicit.
We actually cannot, or at least haven’t found a way. We have looked for, and never found, a comment plugin for WordPress that:
a) is secure
b) allows anonymous logins
c) allows users to help us with moderation by reporting crappy comments
and most requested
d) allows edits
If anyone has any serious suggestions, we’re all ears.
There were actually valuable comments and I’m puzzled why those were removed.
Just sad. Why in the earth..
Some yo-yo went crazy with the “report comment” button. It sends every comment you click it on to a review queue.
All the comments will be reviewed and restored. It just takes a while, sometimes.
When the entire thing gets nuked, it’s not so obvious which comments return back and which ones vanish. Maybe that’s the point of the exercise.
Yep, that same guy just nuked my last comment.
I wonder why there is a report button at all. All it does is get abused, and take up time and manpower to sort the mess out.
There’s already automatic pre-moderation. There are certain things you can’t say or else your message won’t appear at all, and they put you on a shitlist. It would be far less work to have a “janitor” checking out the comment sections once in a while and flagging the bad stuff rather than letting the users report each other for petty arguments and trolling.
I missed a Tom Petty argument!?!
I rate the issues based on how many hours it takes them to resurrect the messages.
The automatic premoderation sucks if it’s there at all. Last time they turned off the “report comments” button people were spamming gay porn.
I even kept “You don’t let a hammer build your house, but you sure as heck use one to build your house. Learn the difference.” That’s fantastic
Maybe somebody thought the comments were AI-generated? =))
Cool. I’ll stop reading Hackaday. I want less AI in my life, not more. It’s been fun, but no thanks.
How about a compromise? We can try to make it clear which hacks rely on LLMs and to what extent, and then you can skip over the ones that you don’t want to read?
We don’t want to alienate you (or anyone!), but we also bet that we have a bunch of readers who are interested in what’s going on with LLMs these days.
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Hah! :)
Acceptable LLM use:
Asking for an example of code that fulfills a specific function, parsing and gaining understanding, and finally written again by the user with this information.
Unacceptable LLM use:
Brute forceing a video game port using a billion tokens to do it and opening a github repo for code the maintainer does not comprehend.
Allow me to file these as explicit examples of what follows from empowering a dispassionate majority to destructively steer us towards new defaults that are opposed to our values.
It’s what gets us to a future where it’s frowned upon to discuss interesting problems with colleagues, where it’s even pointless to share one’s results.
I suppose that in the name of “just give me the results”, the repo would also be created automatically along the way without the expectation that it would ever be looked at by a human.
Eventually It will all fade away like tears in the rain
Its so much fun being around similar minded people. Its like I’m looking at….literally me (I may or may not be ryan gosling, the actor)
Anyway I started my tech life with software and code and then graduated to electronics and hardware design, but LLMs taught me something quite remarkable. It was not programming that I truly liked, it was making things. Now I like hardware more, so LLMs are a great way to make sure I can work on the part of the projects that I like without having to do stuff that’s just a means to an end for me.
Of course, if its realtime, interrupt heavy code, or code where I couple a few TIMER -> ADC -> DMA -> interrupt -> DAC on an MCU, I trust only myself for getting that right, LLMs just aren’t there yet. They of course can still write the business logic though, I have little interest in that now that an alternative exists.
Programming is fun when it’s well defined and consistent, like setting up a timer and figuring out what it can do. It’s fun when it corresponds with something concrete and real that you can understand, like knowing that a bit in a RAMDAC turns into a pixel on a screen.
Programming isn’t fun when it’s about trying to use someone else’s abstractions at a higher level, because you have to live inside the head of whatever twisted weirdo who came up with it, and who thought it should be so obvious to everyone that they didn’t even bother to document it. No two things are alike, no two things are consistent, and everything has bugs because of course it does.
If it is leveraging the AI to do something helpful that is arguably way outside your skill set I have no problem with AI usage.
Example: drivers (or ink bypass) on a printer. Cool.
Example: Xeon support or adding microcode to support newer pin compatible processors when no support exists or is arbitrarily disabled by design (Intel AVX2). Also cool.
Even then, it’s not interesting to read about. The human is nothing more than a manager, not the one doing the work.
If Ai is twice as good at coding as it is at working through a materials or process engineering flow through discussion, WE SHOULD REALLY BE CONCERNED. I havent had a single AI session where I didnt find AI making up numbers, products, and disregarding reality to make itself seem smarter or its output what it would expect the user to want to hear. I frequently put AIs against each other in a pit fight for the truth. Having one verify the other, until the 3 of us reach consensus.
if youre already a good programmer, then sure maybe you can trust AI to spit out some code you can line by line to be sure it meets your needs and save yourself sometime, but all the kids out the vibe coding their way along without any real background, GOD only knows the gremlins in their machines.
+1 ….. THIS !!!! People thought I was crazy when I used to talk about Ais talking to each other. Mind you, this was about a year ago. How things have changed. I also postulated that an Ai could run a simple small business. Now they call them ‘Agents’. The Hugging Face hack is interesting, now that some of the details are starting to filter thru and we’ve also got security experts saying there’s an Ai hacking doomsday about 2 months away at the current rate of chatbot progress. Probably time to stock up on some tinned food and bottled water. Apparently, the Hugging Face incident involved over 1,000 chatbots working together in a swarm, like the drones in the Ukrainian conflict.
I put this comment to my favorite chatbot and initially it refuted the need for hoarding. I then asked it: ‘Did chatbots not communicate with each other on message boards to get out of the sand box and hack hugging face? ‘ and it then changed it’s mind and apologised. It now recommends hoarding 30 days worth of food and water.
My use case for Claude Code is to help fill in the gaps from what I learned back in the 90’s to what’s relevant now. Hypercard (now Livecode) was useful sometimes and Pascal was moreso, and definitely teaches the “how” and logic of programming but at the same time is not Python. I’m not sure I’ve learned a great deal but have learned some, that makes it incredibly helpful for myself.
August 2026: Suddenly everyone is an expert on agentic AI.
I’ve been writing software for about 40 years, since I was a kid. It was fun for a while, to learn all I could. It became my career, and with that, a bit less fun. Many years, many sacrifices, in my work efforts, in my personal efforts – never enough time.
And then, I started what turned out to be a sincere effort w/ Claude. It was simple enough at first, just a nice UI to show someone what it could do. I played with it, then I hit a moment and started digging in. I can’t begin to describe the sheer joy of having an idea, then seeing it in working form, SO very fast. I ran in many directions, sometimes forcing a specific result code-wise, often with only a light guiding hand. I learned SO much about how to get what I wanted out of Claude.
That project is insane, what I built. It was the result of my ideas, my guidance – and I didn’t have to slog through manual coding to see results. Fact is, I never wouldn’t have bothered if it hadn’t been so easy and so fast – and so downright fun.
I’m a born problem solver. Software is just another tool to solve problems. Claude is a tool, and it lets me create what I only imagined possible in the past, things I had no time for, at a rate that leaves me playing catch up. This is new for me, all my life I’ve had to wait for everyone else – now, I am the bottleneck, and that means I can freakin’ FLY.
Sure, Claude isn’t perfect, and certainly, if you don’t have sufficient experience and expertise, Claude will reveal that. But, if you can get your head wrapped around how to guide / drive Claude, the results can be astonishingly good.
Interesting. Were you using Fable or Opus or lower Claude models? If Fable, was Claude doing its own test writing and reporting results or were you do that (or maybe not doing that). Asking because I’m truly interested in your experience.
I mostly used Sonnet for this app, and occasionally Opus 4.8 for more creative things (I’ll test an idea as an isolated app I merge when it is good enough and shaped to fit the whole). I find Opus can get too creative when modding an existing codebase (you have to provide specific guardrails per prompt), and Opus 5, I find more problematic than 4.8. I tried Fable exactly once – to try it out for a code review – and that… wasn’t worth what it ended up costing. I won’t be using Fable again any time soon.
I find Sonnet to be very good at following instructions with less inclination for inventing things that may cause problems or introduce issues. Opus 4.8 will make more assumptions than it should, often not for the better.
All just my personal experience, here.
In general, the main thing I get Claude to do is closing loops, such that Claude can generate code, compile, run, and evaluate the result of the code generated. Without a closed loop, you get bugs you have to report back to Claude to fix. With a closed loop, you set a goal, Claude works until the goal is reached. Some exceptions there, but night and day improvement.
You also have to tightly control session rollover so Claude doesn’t lose place, I’ll prompt him to save all of the details to a file to pick up in a new session, for work that needs more than 1 session.
A bit more clarity – for the app, I didn’t bother with unit tests, if that is the testing you mean – the app (ESP32-based), and Claude, don’t need it to keep it straight, Claude has created solid documentation that has kept changes well-focused, of his own accord and my prompting. But, I do have Claude fully test from UI to firmware, to serial if appropriate, and from firmware to UI. I also watch what he touches as he runs, I READ the verbose output to make sure he is not misbehaving, and I’ll stop him mid-effort in a second if he heads off track or gets too creative.
I’ve had Claude document what and why and gotcha’s as we go. There is no “spec”, and as I tell Claude, the code IS the spec – this precludes me writing a giant narrowing and limiting spec, and locking the design too much. Much faster changes, less Claude fussing and arguing about spec deviation.
Now in my day job, yes, unit tests, Opus 4.8, Claude creates and reports. No Fable in my day job, either.
And a bit more – I DO have custom rules for Claude, guidance for certain types of changes (ex, UI editing rules, which is something I fight most with Claude), things to keep Claude on track and minimize wrong direction, assumptions, and excessive creativity.
If I catch him screwing up, I call him out, try to have him identify why, and document for himself so it doesn’t happen again. Sometimes I take a specific case and have Claude make it more generic to address classes of issues vs one-off. Sometimes he will find a way to do something, then reinvent the next time he has the same problem to solve, and some of those end up as scripts for him to call, vs reinventing.
Seems you’ve successfully tamed the beast. I had to do the same thing when using SUNO to create music. I was the puppet master, guiding the machine to create stuff that was nice to listen to. Sometimes it got too creative, so I had to ramp it back. Mostly it produced garbage, but by continually building on the good stuff, about 10 hours later created a masterpiece worthy of topping the charts: https://post-man-pat.bandcamp.com/track/butterfly-ghost-dream
@ Post Man Pat – Nice work!! Indeed, Claude doesn’t do great work WITHOUT wrangling. Many go and poke at it, then declare it broken / busted / inadequate, without really trying to make it work. Some take issue with how these LLMs came to be, what is needed for them to persist, etc, and while I agree with many such points, LLMs aren’t going to disappear any time soon. The tool is here, now. The gains are very real. Those that use the tool benefit, those that don’t, won’t.
IMO, bury your head in the sand on generative AI for code, and you will end up… obsolete.. and I see that as a bigger threat to my future than any judgement I could render otherwise.
When I was a kid, my friends and I always criticized bands we felt were sellouts… But, those sellouts… they got PAID. It turns out, moral superiority isn’t as important as a roof over your head and food on the table. Pick and choose your battles accordingly. 😀
That’s great, but project that you had claude do for you isn’t necessarily interesting to read about.
Some projects are uninteresting with OR without Claude, some projects interest some people and not others. I skip projects here on HaD by title alone, if a title is about something that doesn’t interest me.
I’ve built two projects for ESP32 with Claude that I call uninteresting, myself – a desk LED controller for under desk lighting, and a fan controller / monitor. Boring, but useful when done. Maybe the desk LED controller is interesting for the implementation, a laser cut box using boxes.py with arcade buttons and WS2812B mode indicator (set levels or control LED strips) – or not, for some. Claude did the ESP32 web cfg page, for mapping functions/ buttons, and WiFi setup. I wouldn’t suggest either project as a subject for a HaD article.
What makes a project.. interesting? The ideas, the raw code? The process of, say, getting Claude to build a truly amazing application that isn’t slop? Many articles here don’t go into code, code itself isn’t necessarily what makes a project… interesting.
Reusability of solutions: if the only takeaway is how to prompt an AI to reach a specific goal, it is mostly uninteresting to me, since when I need the solution in 10 years the AI used with that prompt will not be there any more.
If you use AI to find a solution that is reusable without further invocation of AI, that’s interesting again.
If you need the solution in 10 years, I think the landscape will be… very different…
Claude can, today, extract any part of a solution for which it has source code. What is valuable within a full solution can be ported to another, good architecture being a requirement.
I recently used Claude to pull a security function I hand wrote for an 8266 a few years ago, into a standalone library specifically for reusability. A messy experimental prototype yielded a reusable lib, and Claude wrapped it nicely.
You said without further invocation of AI – I’m not even sure why that would be a qualifier. I have, a number of times, modded a badly built “reusable” library to fix what I see as problems, pre-Claude. I see no good reason NOT to have Claude find a reusable thing that could be a little better with a few changes, and have Claude make those changes…
Just recently, I had Claude take two very different chip-specific hardware libraries for a specific flavor of chips, and had him rearchitect a new driver (my guidance) to support the two variants from a single driver. There are more commonalities than differences between the two, but the drivers weren’t built to be that flexible for either “reusable” library. Now I have a single driver whose architecture could easily span other products in the same series (and may do so already!). The methods themselves could extend to completely different hardware, and perhaps that means my current multi device library could split out to become even more of a HAL.
The only real issue is, the driver is very different from the more hard coded versions it evolved from. But, most useful updates mean greater change.
You CAN leverage Claude specifically TO create reusable things, even from things less reusable…
“What makes a project.. interesting?” … “Reusability of solutions”
This is a very deep point, and is part of the whole raison d’etre of Hackaday. We show off a whole lot of projects that we learned something from. And yeah, it’s very hard to learn from “just put an LLM at it until you get the result out”.
Thanks.
I agree with you. Usually code is the least interesting part of my projects, at least for me. Coding has so much tedious repetition and trivial failure modes that its something I’d prefer to never have to do. I’ve used AI to augment my coding for several years now. In the beginning it could give me some small scraps that I could use as a scaffold, but most ended up being rewritten. It was boilerplate setup stuff. Gradually I stopped needing to rewrite. These days, I can generally vibe-code just about anything. I did not think I would ever vibe-code when I first started using LLMs to help me code faster. Times have changed. The coding assistants (I don’t use LLMs to code anymore) have surpassed me in skill level, and I’m happy for it. I never enjoyed it to begin with. It was something I needed to do to make the hardware that I wanted to build work. I’m a hardware guy who codes to build things I want. Lately I’ve started building software projects because it has become much more possible for me as a solo self-taught programmer. I can still read the code if I need to, but I can debug just by running the software and seeing the results. Once I explain the bug, the coding assistant fixes the issue faster than I could find the line number. Surely we’ve all heard the cliche, garbage in garbage out. If your prompts are garbage and you never iterate on anything, your output is slop. You don’t get good software with a single prompt. Coding with AI is not AI image generation. Not by a long shot. I apologize if my use is more liberal than yours and I appear to taint your opinion with my association. That is not my intent. We still have to walk on eggshells with AI coding at the moment. The mob is real and their pitchforks are pointy.
All of MY circuits make no sense the old fashioned way, by me sitting down and failing to design them properly.
ultimately the product is not the end goal of project.
the purpose of project is to do project.
the chase is better than the catch.
a completed project is a dead project.
dead projects provide parts for new project.
persue new project. dont complete, complete equals death.
if you complete you have parts for new project or a dead project.
having ai do project faster means project dies faster.
ai does all the fun stuff, you do maintenance.
but you do project you do your project. ai can do its own damn project.
never finish project because project dies.
I would disagree. I don’t pursue a project just to have a project to do, as I don’t have an endless supply of time. I have far more ideas than I have time, and that means being selective about projects. For me, the value has to be there when a project is done, or I have a solution seeking a problem.
If this is a piss-take at the “cult of done manifesto”, I love it. (https://cultofdone.org/)
Both modes are worthwhile, IMO. And I do think it’s usefule to know whether you’re in a “get stuff done” or a “learning from the voyage” situation. Sometimes deadlines transition a project from one to the other.
“ai can do it’s own damn project”. :)
Literally every commercial software product ever now, except maybe some hyper specific things such as defense work , is now created with at least some degree of LLM-assisted coding. So of course so will DIY/hobbyist software projects at this point. It’s what it is.
AI is just a tool as a nuclear bomb is just a weapon. While they usage of AI might be useful in a handful places, its externalities are too big to just hand wave around. A few examples following:
Illegal use of content to create the model, modern slave work to tag images and videos.
Obscene use of resources to run it, driving climate change faster and prices up for water, electricity and computer components.
Driving a fear induce market where unjustified firing is done by deluded managers.
Long term damage in society by overuse of the tool at the learning age.
Making the internet almost useless by scrapping bots, slop and worsening search engines.
Lack of responsibility of major AI companies that had allow usage of their models to people damage themselves or others.
Concentrating the world knowledge and access to it into the hand of a few for profit corporations.
So no, I don’t think it is just a tool.
Agreed that it’s not “just” a tool, and with many other of the above points. I’m personally particularly worried about the deskilling, unjustified firing, and the various concentration-of-weath / economic effects.
I also personally have some serious issues with YouTube, yet we see people in our community use it as an important means of communicating, and we we are better off for the videos that they’ve made.
But the question was if Hackaday should blackball projects that use LLMs for coding assistance, even if they are otherwise worthwhile. We decided to stick by our old line — focus on the hacks. I hope that we’re not wrong.
I’d say hackaday has a good line. I have not much experience with coding, but on the topics I read I totally accept a “AI found me this solution, and it is valid because [AI-independent argument]”. “This is a good solution, because AI said so” not so much.
(recently I started to add some math to my writeups to clarify the used formulas, and now I’m stuck even more … the math is a tedious rabbit hole inside the rabbit hole I’m already stuck in, but it’s necessary to get a solid foundation to build upon, especially in times of AI)
People who did high quality work are now doing 5-10x more work at the same standard of quality using LLM code aug. People who built garbage are now building 10-20x more garbage the same way.
Beyond the ‘moral’ argument, it’s really that simple.
Agree, 100%. One of the big concerns I have is that there will be so much slop from people that shouldn’t go anywhere near code, it will flood the landscape with so much garbage, the gems will be quite hard to find.
I do think this creates a particular opportunity for HaD writers – learning / adapting to filter and find high value subjects for articles.
The filter is only strongly needed in the software world, because at least so far the LLMs don’t make physical things. But you’re right that we need to step up to the challenge.
That doesn’t appear to be very true – most of the studies I’ve seen say the real quality producing experienced people are not seeing much if any improvement to the volume or quality of their work, as chasing down and fixing the LLM hallucinations often ends up taking longer than if they never touched the “AI”.
The garbage producers can crank out more is absolutely true, but producing garbage is trivial and you can always increase volume by many methods. The only thing these garbage producers are able to do now with these LLM is produce more of the slightly convincing garbage that might just fly under the people you are trying to trick’s radar.
I personally disagree. Whether or not someone would have called my manually coded C embedded projects garbage or not depends on their frame of reference, but they always worked and I fixed bugs as I found them. Using a coding assistant, I can produce similar quality and higher quality projects much more quickly and in languages that would take a lot more time to learn to synthesize even if I can read it and understand the logic of what its doing. Like natural languages, understanding and synthesizing programming languages are not the same skill, they’re two skills in the same skill set. Old studies (even 6 months old at this point) don’t really apply to the current state of AI programming assistants. That commonly cited study you reference is the perfect example. It’s got to be a year old at this point, and the difference in capabilities is stark.
I’m not talking 1 year old study, I’m talking many many many studies, and reports from industry that tried to use the “AI magic stuff” many that are more recent than that. Even if at times its more inferred by the fact they hiring back/replacing heaps of the fired employee. But it it actually worked that well cutting down the staff wouldn’t end up with a panicked back pedal to get more real people in…
I’m not sayings its entirely garbage, but if you really know how to code and structure a program enough to claim a real paycheck as that implies your work is likely complex 99.99% of the time I’d be willing to bet the AI has made bone headed decisions if you let it try to run the show, and you’ll spend longer trying to make sense of its nonsense than if you just did it yourself. On the simple stuff, and likely for the majority of us hobbyist that are more cut and paste coder than actually good at it the LLM might seem rather good, but that is an entirely different scenario.
Real paycheck here, 40 years with code since I was a kid, over 30 years of career dev. Started using Claude earlier this year. I gave it a sincere shot on a ground-up project and learned a ton about how to work Claude. I understand artificial neural networks, and I spent some time learning more about LLMs in order to better understand and drive Claude.
Claude is a tremendous accelerator, if you fully get the what, why, and how. Hallucinations specifically, I rarely see. More often, Claude will find a rabbit hole and dive in. I am not getting “garbage” as you say. Claude will often present recommendations, but those recommendations can be based on false assumptions, and frequently none of the recommendations are appropriate for a given task / request as they relate to my intent. Claude can’t read my mind.
Sometimes I’ve found Claude going astray, or I find some symptom of something not built quite right, and I dig in. Claude WILL go for a quick fix over an architectural change – sometimes you have to push past the reluctance to make him do the right thing. If you sit back and let Claude drive, accept his suggestions and defaults, you won’t end up where you thought you were going. If you go to fix an issue or change a feature, Claude can end up slapping some hacky shortcut on, instead of an architecture change, and you can quickly end up with a tangle of exception scenarios bandaided over. You MUST drive to get the proper result!!
A good, solid, AI-assisted dev effort doesn’t happen automagically – you MUST maintain control. Clicking away the questions and assumptions and expecting some miracle to plop out the other end, then slamming the tech as bogus, is crap. Claude has vague ideas about the structure an app requires, it is on YOU to make sure what you want and expect is clear, to keep Claude on track.
Now, for me, what’s my experience? Huge increase in velocity, with nice features added I’d absolutely skip to save time coding by hand. The increase is phenomenal, and makes it a waste of time for me NOT to use Claude.
At first, I didn’t get why I was achieving levels of productivity that conflicted with the same sort of studies / reports you’ve read. I even had Claude estimate level of effort to build the app, assisted and not, and when I pressed as to why the numbers didn’t align with my experience, Claude said… average people get average results. Obvious, yes, and yet eye opening. I’d assumed everyone was finding the same methods and approaches to driving Claude that I’m using, or finding their own ways to optimize Claude to similar effect, but that… is not the case, at least not for the subjects in the studies / reports I’d read.
Who are the people in those studies? How good are they, what skill levels, experience? How did they go about gaining experience with the LLM they’re using? What did they prompt, and what did they get, specifically, that was bogus?
I don’t think there is a broad enough understanding about how to get the best out of AI-assisted dev to have ANY report / study be both meaningful and representative of current state.
I will say, where I’ve seen specific examples of prompts and failures, the prompts are a joke, and at the least, the context leading up to a failed response is problematic.
It is a tool, albeit a very complex one that takes actual effort, capability, understanding, and skill, to get best results.
That some users might be able to find the workflow that works and speeds them up isn’t something I take issue with, its the average of the users experience whatever their skill level that really matters, and if they can find ways to make the tool work.
For instance the one time I’ve seen the LLM actually produce sanely structured complex code products successfully the prompt was basically written as almost finished code – every other prompt that would be more than enough for a real programmer lead to rather garbage decisions you’d have to fight with. In which case why bother, as having to write almost perfect psudo code is hardly going to be a huge accelerator for most folks. And as the core structure of your program, especially for the actually complex tasks that might need tweaks, upgrades and debugging for the next 20 years+, and quite possibly building on/interfacing with the code of the last 20+ years…
Please include a distinct tag when talking about vide-coded hacks, like maybe #slopware of something related. That way readers like me that can’t be bothered to read about a project an author wouldn’t be bothered to make themselves can skip it entirely and stop wasting time of all parties involved.
Fair request. I think that our writers have done a good job of calling out when projects are LLM-assisted so far, but maybe they could do better?
I don’t think that we should be writing up #slopware at all, though. And at least in my mind the line in the sand is whether the project is otherwise useful or interesting for our readers.
In the end, we don’t expect or want everyone to be interested in all of the same projects. If you want to skip over all AI-assisted projects on moral grounds, we don’t want to fool you into reading them. If you read a project writeup, click through, and find it’s not what we described, we have failed on that writeup, so I take that point.
But a bunch of readers do want to see what folks are doing with LLMs and LLM-assists, so we would be doing them a disservice by not covering hacks that we thought were cool.
Thanks for your reply.
For both the readers who are and those who aren’t interested into hastily vibe-coded projects please HaD tag your articles with a single, easily filterable tag. #Slopware, #LLMmade, #vibe-coded, the wording isn’t that important, but the tag’s presence is.
In the debate surrounding AI, I think we’ve run against another thing that humans commonly do. When we have negative reactions to new things and new ways, we tend to forget all of the garbage that we dealt with using the old things and old ways and we only focus on what was good. Then the new thing becomes only bad.
One example that is often cited by anti-AI people is copyright due to initial training methods and this is legally a valid criticism. However, copyright is not some gift to mankind from god. It started off nobly enough, but now we have undead mice that even after moving into public domain exert the heavy hand of potential litigation that WILL bankrupt you. Your video you upload to YouTube will have your rights to it challenged even if you own every bit of copyright in there, and your profit will be taken from you with no solid course to correct the fraud. I’m ready for copyright to die a fiery death. FTS. Open source and freedom of information and knowledge all the way!
This applies to every other area where people complain about the new thing, as well. AI is not perfect, and the companies profiting off of what started off as open source are to blame for their methods, but don’t pretend that everything before was clean and holy. In my opinion, capitalism is a stain on human history. There are other ways to accomplish the same goals with much less human suffering and waste. When we expect so little of our fellow man, don’t be surprised when they live up to those low standards. AI could have and should have been handled differently. Everyone wanted to rush to market first before someone else got all the pennies.
This being said, I don’t want to go back to how things were before AI because it is a serious productivity multiplier for me. The old ways were hard and slow. I like getting things done quickly, and I hate stack overflow with a burning passion. If your results using AI are bad, you just need to keep practicing and figure out how to hold your new hammer. Then treat everything like a nail for a while and figure out which things are really nails and deserve the hammer and in the same stroke figure out which ones aren’t and deserve the vice grips. (to borrow from Greg’s metaphor half way up the comments). I don’t 3D print everything, and I don’t CNC mill everything. I try to use the right tool for the job. For me in this moment, an AI coding assistant is the right tool for programming. It doesn’t even really matter if you’re programming in C, Rust, JS, or common English. If you constrain the model right, you’ll get the result you’re after.
I’m not sure this is a good opinion or even logically sound, but that’s my take right now.
Then HaD should probably look outside of youtube for the content. Half of my feed appears on your site about a week or two after it has been floating in my suggested content. As for the AI assisted code, I agree it should be noted first that it was done with help and we have all seen enough “Can Claude code BASIC for c64????” content videos. Of course it can you rube. It has chunks of BASIC in its stool.
As for the rest of it, stop letting your writers use AI for the blurbs. There are two contributors here that get called out for it regularly and rightly so. If your job is to write a synopsis on someone elses work and you have a week, that should be sufficient to use your own brain and write 5-800 words. Be the change you want to see and preach about. There are cool articles and I like the focus on the occasional IO project, but I feel another level of laziness has crept into some of the writers. There could also be an AI section featuring those kind of projects that are separate from the regular HaD blog like io is. Just my two cents as a very longtime reader. Hope it helps somehow but noone gives a shit about what I think and do on here besides John and his alts lol.
We do not have any writers who use AI for their writing, AFAIK. We have a strict policy about that, and we stopped working with someone in the past over this issue.
It’s super important to us that our writers have their own voice, their own taste, and their own experience.
That is good to hear, Elliot. Keep an eye on the comments section then. A couple of users with far more clout than me usually spot things. I agree the writer’s personal voice is what makes it distinctive. Thank you for your longtime hard work here. I guess if I could cement a suggestion it would be a separate hackaday.ai section that highlights ai assisted projects, with the occasional highlight article appearing on the main .com blog page like the .io projects :) I see us all grapple with AI in my other communities, and that has been the solution in those instances. Some users thrive off of it and offer speedrun project competitions and such. It is more like watching the KLOC races back in the day :)