MurphyA/hacker-news huggingface dataset:
by puttycat | 2025-06-02 22:22:42+00:00
> An LLM can be instructed to just figure all that shit out. Often, it will drop you precisely at that golden moment where shit almost works, and development means tweaking code and immediately seeing things work better.

Well, except that in order to fix that 1% you'd need to read and understand whatever the LLM did and then look for that 1%. I get the shills just thinking about this, whether the original programmer was human or not. I'd rather just write everything myself to begin with.
by kentonv | 2025-06-02 22:22:48+00:00
Yes!

We're about to enter a world where everyone has their own custom software for their specific use cases. Each of these is relatively simple, yet they may replace something complex. Excel is complex because it needs to handle everyone's use cases, but for any one particular spreadsheet, you could pretty easily vibe-code a replacement that does that one spreadsheet's job better than Excel can.

I've also found that vibe-coding a presentation as a React app is better than using Power Point.
by nixpulvis | 2025-06-02 22:22:52+00:00
I keep talking to people who've had a good bit of success using gemini or cluade to build quick prototype front ends for some applications. I think theres some questions in my head of how well the process scales when you want to keep adding features, but according to them it's not been hard getting it to make the needed changes.

My issue with it is that it gates software development behind paid services with various levels of context supported. Absolutely not the dream I have of how more software should be open source and everyone should be empowered to make the changes they need.
by jacobgkau | 2025-06-02 22:22:56+00:00
I use words that convey very clearly what I mean, such as "don't invent a function that doesn't exist in your next response" when asking what function a value is coming from. It says it understands, then proceeds to do what I specifically asked it not to do anyway.

The fact that you're responding to someone who found AI non-useful with "you must be using words that are the opposite of what you really mean" makes your rebuttal come off as a little biased. Do you really think the chances of "they're playing opposite day" are higher than the chances of the tool not working well?
by tsimionescu | 2025-06-02 22:23:15+00:00
You're missing the point. The main thing the AI does is to generate code based on a natural-language description of a problem. The liners and tests and on exist to guide this process.

The initial AI-based work flows were "input a prompt into ChatGPT's web UI, copy the output into your editor of choice, run your normal build processes; if it works, great, if not, copy the output back to ChatGPT, get new code, rinse and repeat".

The "agent" stuff is trying to automate this loop. So as a human, you still write more or less the same prompt, but now the agent code automates that loop of generating code with an LLM and running regular tools on it and sending those tools' output back to the LLM until they succeed for you. So, instead of getting code that may not even be in the right programming language as you do from an LLM, you get code that is 100% guaranteed to run and passes your unit tests and any style constraints you may have imposed in your code base, all without extra manual interaction (or you get some kind of error if the problem is too hard for the LLM).
by slg | 2025-06-02 22:23:21+00:00
>It won't catch other types of logic error, but I would classify those as bugs, not hallucinations.

Let's go a step further, the LLM can produce bug free code too if we just call the bugs "glitches".

You are making a purely arbitrary decision on how to classify an LLM's mistakes based on how easy it is to catch them, regardless of their severity or cause. But simply categorizing the mistakes in a different bucket doesn't make them any less of a problem.
by sanderjd | 2025-06-02 22:23:24+00:00
Yep, this is the thing I worry about as well.

I find these tools incredibly useful. But I constantly edit their output and frequently ask for changes to other peoples' code during review, some of which is AI generated.

But all of that editing and reviewing is informed by decades of writing code without these tools, and I don't know how I would have gotten the reps in without all that experience.

So I find myself bullish on this for myself and the experienced people I work with, but worried about training the next generation.
by abdullin | 2025-06-02 22:23:40+00:00
I'd say that the core principles stayed the same for more than a year by now.

What is changing - constraints are relaxing, making things easier than they were before. E.g. where you needed a complex RAG to accomplish some task, now Gemini Pro 2.5 can just swallow 200k-500k of cacheable tokens in prompt and get the job done with a similar or better accuracy.
by ofjcihen | 2025-06-02 22:23:41+00:00
Honestly it’s really unfortunate that LLMs seem to have picked up the same hype men that attached themselves to blockchains etc.

LLMs are very useful. I use them as a better way to search the web, generate some code that I know I can debug but don’t want to write and as a way to conversationally interact with data.

The problem is the hype machine has set expectations so high and refused criticism to the point where LLMs can’t possibly measure up. This creates the divide we see here.
by genewitch | 2025-06-02 22:23:42+00:00
I think I wanted to say elsewhere in the thread, but probably did not, that my first Kindle was $35, brand new from Amazon. No keyboard but it did have hardware buttons for page turn at least. It lasted until I ran it into the back of my heel backing away from my desk in an office chair, and amazon gave me $15 for it toward a new kindle, which were on sale the week I broke mine because a new kindle was being released, so my new kindle cost $45, but was originally over $120 new, if not $150. Paperwhite.
by keybored | 2025-06-02 22:23:45+00:00
Thankfully the uncrazy person is going to get us on that sane VC AI wavelength.

> If you’re making requests on a ChatGPT page and then pasting the resulting (broken) code into your editor, you’re not doing what the AI boosters are doing. No wonder you’re talking past each other.

They’re playing 3D chess while you’re stuck at checkers.

I do things suboptimally while learning the ropes or just doing things casually. That doesn’t mean that I judge the approach itself by my sloppy workflow. I’m able to make inferences about what a serious/experienced person would do. And it wouldn’t involve pasting things through three windows like I would do.

So of course I don’t judge AI by “ask chatty and paste the response”.

Yes indeed: “deploying agents” is what I would imagine the Ask Chatty And Paste workflow taken to Perfection to look like.

> LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious. LLMs drastically reduce the number of things you’ll ever need to Google. They look things up themselves. Most importantly, they don’t get tired; they’re immune to inertia.

Most Rube Goldberg machines are very tedious and consist of fifty-too-many parts. But we can automate most of that for you—

I could not have ever imagined a more Flintstones meets Science Fiction clash than AI According To Software Engineers. You’re using AI to generate code. And no one cares how much. It’s just so tedious in any case.

A wortwhile approach would have been to aspire to make or generate technology artifacts that could be hidden behind a black box surface with a legible interface in front. Is the code tedious? Then make the AI come up with something that is well-designed, where the obvious things you want is given freely, where minor customizations are just minor tweaks, and larger deviations require only proportionally larger changes. Uh, how about no? How about generating 20KLOC line “starter” some-framework project with all the 20KLOC “tedious” bits hanging out, then we can iterate from there. The AI made a Git log and everything so it’s ya know audited.

But maybe I’m being unfair. Maybe we are moving towards something not quite as stupid as Deploy ChatGPT 50X? Or maybe it’s effectively going to behind a black box. Because ya know the AI will deal with it all by itself?

> Are you a vibe coding Youtuber? Can you not read code? If so: astute point. Otherwise: what the fuck is wrong with you?

> You’ve always been responsible for what you merge to main. You were five years go. And you are tomorrow, whether or not you use an LLM.

No!, and what the fuck is wrong with you? We are Flintstone technologists and I’ll be damned if I can’t get my AI brain chip-injected, genetically enhanced for speed horsey cyborg for my modern horse-drawn carriage patent.
by andrepd | 2025-06-02 22:24:17+00:00
I've not yet been in a position where reading + cleaning up the LLMs bad code was faster and/or produced better code than if I wrote it by hand. I've tried. Every time someone comes up and says "yeah of course you're not using GPT4.7-turbo-plus-pro" I go and give a spin on the newfangled thing. Nope, hasn't happened yet.

I admit my line of work may not be exactly generic crud work, but then again if it's not useful for anything just one step above implementing a user login for a website or something, then is it really gonna take over the world and put me out of a job in 6 months?
by nitwit005 | 2025-06-02 22:24:17+00:00
> Does an intern cost $20/month? Because that’s what Cursor.ai costs.

> Part of being a senior developer is making less-able coders productive, be they fleshly or algebraic. Using agents well is both a both a skill and an engineering project all its own, of prompts, indices, and (especially) tooling. LLMs only produce shitty code if you let them.

A junior developer often has negative value to a team, because they're sapping the time of more senior developers who have to help train them, review code, fix mistakes, etc. It can take a long while to break even.

The raw cost of Cursor's subscription is surely dwarfed by your own efforts, given that description. The actual calculous here should be the cost to corral Cursor, against the value of the code it generated.
by albertzeyer | 2025-06-02 22:24:27+00:00
Do you have any comparisons in terms of WER? I doubt that GPT-4o-transcribe is better than the best models from that leaderboard (https://huggingface.co/spaces/hf-audio/open_asr_leaderboard). A quick search on this got me here: https://www.reddit.com/r/OpenAI/comments/1jvdqty/gpt4otransc... https://scribewave.com/blog/openai-launches-gpt-4o-transcrib...

It is stated that GPT-4o-transcribe is better than Whisper-large. That might be true, but what version of Whisper-large actually exactly? Looking at the leaderboard, there are a lot of Whisper variants. But anyway, the best Whisper variant, CrisperWhisper, is currently only at rank 5. (I assume GPT-4o-transcribe was not compared to that but to some other Whisper model.)

It is stated that Scribe v1 from elevenlabs is better than GPT-4o-transcribe. In the leaderboard, Scribe v1 is also only at rank 6.
by simonw | 2025-06-02 22:24:39+00:00
If you think in code, try prompting them in code.

I quite often prompt with code in a different language, or pseudo-code describing roughly what I am trying to achieve, or a Python function signature without the function body.

Or I will paste in a bunch of code I have already written with a comment somewhere that says "TODO: retrieve the information from the GitHub API" and have the model finish it for me.
by hugs | 2025-06-02 22:24:58+00:00
1) Get a 3D printer (or access to one). Or visit your local art supply store (for structural basswood) and hardware store (for nuts and bolts).

2) Get an electronics starter kit. (Adafruit or SparkFun are good places to start looking.)

3) Buy/borrow the book "Practical Electronics for Inventors". You don't need to read it cover to cover, but it's very useful. (Realistically, though, an LLM is probably good enough to explain basic concepts and/or help you pick parts these days.)

4) Build a toy robot with parts you printed (or purchased) and parts from the electronics kit.

5) Once you've built the toy robot, make something bigger! (or the existing one better!)

6) Optional: Find someone to buy your toy robot so you can have the funds to design and build your next one.
by johnea | 2025-06-02 22:25:09+00:00
One could always argue that the lie is in the ear of the receiver 8-/

I would argue, that if the output of the LLM is to be interpreted as natural speech, and the output makes an authoritative statement, which is factually incorrect, but stated as if it were true, this is a lie.

The problem is that the tech is presented as if it did have the internal state, that you accurately describe it not having.

The lie in this example, is when it is prompted to describe the process by which it reached a result, and that description has no resemblance to the actual process by which it reached the result.

This isn't a misrepresentation of some external facts, but a complete fabrication, that does not represent how it reached that result, at all.

However many users will accept this information, since it only involves internal aspects of the tool itself.

The fact that the LLM doesn't have this introspective information, is part of exactly why LLMs are NOT intelligence, artificial or otherwise.

And yet they are being presented as such, also, a lie...
by malfist | 2025-06-02 22:25:22+00:00
Once? Lol.

I present a simple problem with well defined parameters that LLMs can use to search product ingredient lists (that are standardized). This is the type of problems LLMs are supposed to be good at and it failed in every possible way.

If you hired master woodworker and he didn't know what wood was, you'd hardly trust him with hard things, much less simple ones
by omot | 2025-06-02 22:25:31+00:00
I think of programming languages as an interface between humans and computers. If anything, the industry expanded because of this abstraction. Not everyone has to learn assembly to build cool shit. To me AI is the next step in this abstraction where you don't need to learn programming languages to potentially build cool projects. The hard part of software engineering is scale anyways. My bet is that this will expand the industry in unprecedented ways. Will there be contraction of traditional programming jobs? Absolutely. The growth in tech jobs over the last 20 years weren't more assembly programmers. They were abstraction experts. I'm sure the next wave will be even bigger, professional prompting will explode in size.
by gdubs | 2025-06-02 22:25:31+00:00
It sounds like you think I don't find value in using machines in their precise way, but that's not a correct assumption. I love code! I love the algorithms and data structures of data science. I also love driving 5-speed transmissions and shooting on analog film – but it isn't always what's needed in a particular context or for a particular problem. There are lots of areas where a 'good enough solution done quickly' is way more valuable than a 100% correct and predictable solution.
by mtklein | 2025-06-02 22:26:02+00:00
I completely agree that technology in the last couple years has genuinely been fulfilling the promise established in my childhood sci-fi.

The other day, alone in a city I'd never been to before, I snapped a photo of a bistro's daily specials hand-written on a blackboard in Chinese, copied the text right out of the photo, translated it into English, learned how to pronounce the menu item I wanted, and ordered some dinner.

Two years ago this story would have been: notice the special board, realize I don't quite understand all the characters well enough to choose or order, and turn wistfully to the menu to hopefully find something familiar instead. Or skip the bistro and grab a pre-packaged sandwich at a convenience store.
by mgraczyk | 2025-06-02 22:27:04+00:00
Hmm how did you get that from what I listed?

Every codebase I listed was over 10 years old and had millions of lines of code. Instagram is probably the world's largest and most used python codebase, and the camera software I worked on was 13 years old and had millions of lines of c++ and Java. I haven't worked on many self contained things in my career.

LLMs can help with these things if you know how to use them.
by 0cf8612b2e1e | 2025-06-02 22:27:18+00:00
The artificial blood is created by extracting hemoglobin — a protein containing iron that facilitates the transportation of oxygen in red blood cells — from expired donor blood. It is then encased in a protective shell to create stable, virus-free artificial red blood cells. As these artificial cells have no blood type, there is no need for compatibility testing.

Blood-derived synthetic. Still cool, but continues to require a pool of donors.
by TheOtherHobbes | 2025-06-02 22:27:55+00:00
By the same logic we should allow anyone with an LLM to design ships, bridges, and airliners.

Clearly, it would be very unwise to buy a bridge designed by an LLM.

It's part of a more general problem - the engineering expectations for software development are much lower than for other professions. If your AAA game crashes, people get annoyed but no one dies. If your air traffic control system fails, you - and a large number of other poeple - are going to have a bad day.

The industry that has a kind of glib unseriousness about engineering quality - not theoretical quality, based on rules of thumb like DRY or faddy practices, but measurable reliability metrics.

The concept of reliability metrics doesn't even figure in the LLM conversation.

That's a very bizarre place to be.
by rv888 | 2025-06-02 22:28:09+00:00
Location: Los Angeles, CA

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Technologies: A wide range but lately: Django, FastAPI Python, AI, OpenAI / ChatGPT, Roku Brightscript, Linux, React, and various cloud services like AWS, Google Cloud Platform, and Digital Ocean. Streaming and broadcasting using Ant media server.

Résumé/CV: https://www.linkedin.com/in/ryanvinson/

Email: info@ryanvinson.com

Who I am:

Fractional CTO and Principal Engineer with 25+ years of layered experience across a multitude of technology solutions. I've often been called a Swiss army knife of technology solutions. I've consulted executives on the best approaches, interviewed and hired their staff, lead and managed teams of developers for quick turnaround projects as well as more in depth ongoing products. Language experience with Mandarin Chinese (advanced) and Khmer (basic).

What I am looking for:

I'm looking for projects that need a part time Fractional CTO or principal engineer. I currently have 1 or 2 days available each week.
by martythemaniak | 2025-06-02 22:28:12+00:00
At 0 temperature an LLM is a Map<String,String> - a string input (key) will give you the same string output (value) every time. Hypothesis: there exists a key whose value is a complete, working, fully-tested application which meets your requirements 100% and fulfills your business need. This key is the smallest, most complete description of what your application does. It is written in natural language and represents a significantly compressed version of your application code.

My part-time obsession over the last few months has been trying to demonstrate this and come up with a method for finding these magic keys (I even tried to get the LLMs to search for me, lol). What I really want is to give the latest thinking models (200k input, 100k output) a 5-6 page design doc (4k words, 5k tokens) and have them produce a complete 5kloc (50k tokens) microservice, which would show a 10x compression. It's hard, but I haven't seen any reason to think it wouldn't work.

For better or worse, I think this will be close to what IC jobs will be like in few years. Fundamentally, our jobs are to try work with other functions to agree to some system that needs to exist, then we talk to the computers to actually implement this. If we switch kotlin+compiler for design doc+llm, it still going to be somewhat the same, but far more productive. Agents and such are somewhat of a stop-gap measure, you don't want people giving tasks to machines, you want to accurately describe some idea and then let the computers make it work. You can change your description and they can also figure out their own tasks to evolve the implementation.
by kelipso | 2025-06-02 22:28:42+00:00
It’s called critically analyzing your sources. It’s done by tons of people including scientists and journalists. It does matter what intelligence agencies say and release, you need to know how to critically analyze what they say and release in order to intelligently distinguish between the truth and the lies.

Saying “their beliefs are unfalsifiable” is just a boring accusation. Like looking at the worst scientists and accusing all scientists that their research is bs.
by makeitdouble | 2025-06-02 22:28:46+00:00
> Am I the only one who remembers when that was the stuff of science fiction?

Would you go to a foreign country and sign a work contract based on the LLM translation ?

Would you answer a police procedure based on the speech recognition alone ?

That to me was the promise of the science fiction. Going to another planet and doing inter-species negotiations based on machine translation. We're definitely not there IMHO, and I wouldn't be surprised if we don't quite get there in our lifetime.

Otherwise if we're lowering the bar, speech to text has been here for decades, albeit clunky and power hungry. So improvements have been made, but watching old movies is a way too low stake situation IMHO.
by rorylaitila | 2025-06-02 22:28:57+00:00
I used to really burn out before I ever got any traction. Basing motivation on any external reward is just too hard, too much of the time.

I work now on multiple business projects that interest me. Short term my paying clients, medium term tools I build for myself and clients that I can also sell, long term hobby that might turn into something (adretro.com is my fun).

Before the VC carnival money era, this is probably the default way to build.
by busymom0 | 2025-06-02 22:29:09+00:00
I think LLM hype is more deserved and different from that of blockchain.

There's still a significant barrier to entry to get involved with blockchain and most people don't even know what it is.

LLMs on the other hand have very low barrier to at least use- one can just go to google, ChatGPT etc and use it and see its effectiveness. There's a reason why in the last year, a significant portion of school students are now using LLMs to cheat. Blockchains still don't have that kind of utilization.
by troupo | 2025-06-02 22:29:20+00:00
> It seems as though an LLM would truly have no issues integrating societal concepts, obscure references, pop culture, and more, and be able to compare it across culture to find a most-perfect translation.

Somehow LLMs can't do that for structured code with well defined semantics, but sure, they will be able to extract "obscure references" from speech/text
by keybored | 2025-06-02 22:29:43+00:00
> The time has come for llm users to put up or shut up - if it’s so great, stop telling us and show and use the code it generated on its own.

I’m open to that happening. I mean them showing me. I’m less open to the Nth “aww shucks, the very few doubters that are left at this point are about to get a rude awakening” FOMO concern trolling. I mean I guess it’s nice for me that you are so concerned about my well-being, soon to be suffering-being?

Now, AI can do a lot of things. Don’t get me wrong. It has probably written a million variations on the above sentiment.