MurphyA/hacker-news huggingface dataset:
by vFunct | 2025-06-03 02:23:09+00:00
Been vibe coding for the past couple of months on a large project. My mind is truly blown. Every day it's just shocking. And it's so prolific. Half a million lines of code in a couple of months by one dev. Seriously.

Note that it's not going to solve everything. It's still not very precise in its output. Definitely lots of errors and bad design at the top end. But it's a LOT better than without vibe coding.

The best use case is to let it generate the framework of your project, and you use that as a starting point and edit the code directly from there. Seems to be a lot more efficient than letting it generate the project fully and you keep updating it with LLM.
by nostrebored | 2025-06-03 02:23:12+00:00
And not everyone working in things that aren’t “hacked together MVPs” has your experience. You can read any number of reports about code generated at FAANG, incident response tooling that gets to RCA faster, etc.

There are obviously still things it can’t do. But the gap between “I haven’t been able to get a tool to work” and “you’re wrong about the tool being useful” is large.
by Uehreka | 2025-06-03 02:23:49+00:00
> Not everyone is just cranking out hacked together MVPs for startups

Now here’s the fun part: In a really restrictive enterprise environment where you’ve got unit tests with 85% code coverage requirements, linters and static typing, these AI programming assistants actually perform even better than they do when given a more “greenfield” MVP-ish assignment with lots of room for misinterpretation. The constant “slamming into guardrails” keeps them from hallucinating and causes them to correct themselves when they do.

The more annoying boxes your job makes you tick, the more parts of the process that make you go “ugh, right, that”, the more AI programming assistants can help you.
by cafp12 | 2025-06-03 02:24:10+00:00
I am no expert, but I have to imagine it would be quite hard to learn electronic repair without learning some basic circuits.

I've learned a decent amount with some electronics adjacent hobbies, like 3d printing, diy sim racing stuff, mechanical keyboards. Mainly just copying things other people build. Enough that recently I was able to diagnose a broken transformer in an electronic theater chair power supply.

This channel is awesome:

https://www.youtube.com/@greatscottlab/videos
by cpard | 2025-06-03 02:24:10+00:00
There are a couple different reasons that make this acquisition interesting.

First is their long pursuit of HTAP and the failures around unistore.

Snowflake wanted to get into transactional workloads for a long time and for good reasons.

I wonder what will happen to Unistore after this acquisition.

The other interesting part is ETL/ELT, CDC and the whole business of replicating transactional databases into OLAP.

What crunchy built with duckdb and iceberg is a potential solution to this problem. A problem that has been painful to solve for a long long time.

Being able to replicate your transactional database into your data lake or data warehouse without having to deal with Debezium and all the rest of the stuff, is going to make many data teams happy.
by atonse | 2025-06-03 02:24:18+00:00
Louis Rossmann’s electronics guide was the first time these concepts truly clicked for me: https://youtube.com/playlist?list=PLkVbIsAWN2ltOWmriIdOc5Cti...

Apart from that, just fix broken stuff. Practice like any other skill, like others have said.

Like decide what skills to learn based on what’s broken.

Need to solder something together? Buy the soldering iron. Need to figure out which capacitor you need to replace with? Learn how to identify capacitors and navigate digikey.com
by SamBam | 2025-06-03 02:24:45+00:00
But there are appropriate contexts.

I don't know your line of work, but presumably there are contexts where you wouldn't say "fuck," like to your CEO, or your top client, or your kid's teacher, or something, right?

So people just have different opinions on where the line is, and that line has shifted to include more contexts. That's simply what people are noticing.
by ivape | 2025-06-03 02:25:10+00:00
The primary skill a developer hones is the ability to know when something is already solved for you. This is where innate laziness is a gift. Why should I write a fetch function if api.fetch exists? This is a natural game time automatic decision, like a baseball player running for first base soon as the ball hits the bat. The natural thing that should be happening to every developer is the natural realization that the LLM is the ultimate api. I have literally sat here going “well why build anything, this damn thing does this and that for free”. For example, why build a todo app? Any of the LLMs can mostly do this. This is not self awareness, but game conditioning and fitness. You should automatically be feeling the sensation just like someone playing a sport for the millionth time.

Developers thinking AI is a fad is like a left fielder thinking about if they should stick their hand out to catch the ball or not. You can’t think about this.

Anyway, I find myself in stasis trying to actually see what to build given the reality of the power of this api. The South Park episode of “The Simpsons did it” is an apt episode. We are very much at the point where the continuous question will be “Well the LLM can do it”.
by komali2 | 2025-06-03 02:25:27+00:00
The self hosted and vim / nvim / emacs integrated llm / agentic / MCP situation is being implemented real time as we speak. There's no good canonical solution right now, especially for self hosting. If you are willing to make API calls to openapi or Microsoft, your options expand dramatically.

I think most people are using off the shelf paid solutions like cursor, windsurf, warp terminal, Claude code.

I'd love to get a set up with a locally hosted full version of deepseek.
by gr2020 | 2025-06-03 02:27:02+00:00
I haven't used zed specifically, but were you using your own API key for Claude? If so, you were probably running into the Anthropic API rate limits [1]. You can either a) deposit more funds to move your way up the tier list, or instead access Claude via something like OpenRouter, which will give you much higher limits.

[1] https://docs.anthropic.com/en/api/rate-limits
by sbierwagen | 2025-06-03 02:27:08+00:00
Over the last year and change I've generated 70 thousand images on civitai.com, a website that hosts neural net image generation models. (For porn, obviously)

Twenty out of a hundred gens are malformed horrors. Seventy out of a hundred are boring or cack-handed. Nine are interesting and worth exploring. And one out of a hundred I would say the algorithm accidentally produces real art.

Roll that one out of a hundred too many times when you're just starting out, and, well, you end up like me.
by Manoj99Q | 2025-06-03 02:27:17+00:00
Location:Chicago, USA Remote:Preferred, but open to hybrid and on-site as well Willing to relocate: Yes Technologies:JavaScript/TypeScript (React,Next,Express,Node),C# (ASP.NET),Python(Flask, Langhcain, Langgraph, Tensorflow),Unity,AR/VR,Docker,Git,n8n,GCP,AWS Résumé/CV: Email: manoj.gembali.j9@gmail.com Portfolio:https://portfolio.gamebali.com/

I’m a recent graduate and spend most days hopping between React/Next.js, Node/Flask backends, and Unity projects that mix AI with mixed reality. My toolbelt leans JavaScript/TypeScript and Python, plus C# for anything 3-D. I’m comfortable shipping to AWS or GCP, containerizing with Docker/K8s, and dabbling in LangChain/LangGraph whenever LLMs can save time. I’m open to relocating to a good tech hub if it means tackling something inventive.
by kentonv | 2025-06-03 02:27:55+00:00
I think the data supports that there were about 5 distinct days when I did a large amount of work on this library, and a sprinkling of minor commits through the rest of the month. Glen's commits, while numerous, were also fairly minor, mostly logistical details around releases.

This library is not the only thing I was working on, nor even the main thing. As the lead engineer of Cloudflare Workers I have quite a few other things demanding my time.
by Velorivox | 2025-06-03 02:28:04+00:00
For me this moment came when Google calendar first let you enter fuzzy text to get calendar events added, this was around 2011, I think. In any case, for the end user this can be made to happen even when the computer cannot actually handle fuzzy inputs (which is of course, how an LLM works).

The big change with LLMs seems to be that everyone now has an opinion on what programming/AI is and can do. I remember people behaving like that around stocks not that long ago…
by chrismorgan | 2025-06-03 02:28:32+00:00
Determinism is only one part of it: predictability and the ability to model what it’s doing is perhaps more important.

The physics engine in the game Trackmania is deterministic: this means that you can replay the same inputs and get the same output; but it doesn’t mean the output always makes sense: if you drive into a wall in a particular way, you can trigger what’s called an uberbug, where your car gets flung in a somewhat random direction at implausibly high speed. (This sort of thing can lead to fun tool-assisted speedruns that are utterly unviable for humans.)

The abstractions part you mention, there’s the key. Good abstractions make predictable. Turn the steering wheel to the left, head left. There are still odd occasions when I will mispredict what some code in a language like Rust, Python or JavaScript will do, but they’re rare. By contrast, LLMs are very unpredictable, and you will fundamentally never be able to mentally model what it achieves.
by colechristensen | 2025-06-03 02:28:45+00:00
Roughly in order

Beginner

* Get a Miniware TS101 USB C soldering iron, some solder, solder wick, flux

* Get a mediocre multimeter

* Do a little soldering assembly kit to learn how to solder, there are some good NASA manuals and videos to learn techniques and you won't need to do as good a job as they do

* Learn how to identify and replace broken capacitors

* Learn how to replace power and USB connectors

* ^ (a huge proportion of things that are fixable at all are going to be those two)

Intermediate

* Learn about static (ESD) safety, get the equipment and supplies to prevent frying things yourself

* Get an arduino or other low cost prototyping board and learn to program it and do little projects

* Find some broken consumer electronics with guides on https://www.ifixit.com/Guide and try to fix them, these things are often less about "electronics" and more about glue

* Read the parts of Zen and the Art of Motorcycle Maintenance about maintaining and fixing things

* Read https://www.eevblog.com and watch https://www.youtube.com/eevblog for information and inspiration

Advanced

* Get an oscilloscope and bench power supply, learn how to probe and analyze signals on boards

* Figure out yourself what next steps to pursue, you should be able to at this point, or at least to ask more specific questions
by PeterHolzwarth | 2025-06-03 02:29:01+00:00
Wow, I'm surprised this post hasn't gotten hit by the HN flame-war detector: We have threads downvoted into forced-collapsed that are headed by completely innocuous comments. It really is strange how people here are not able to accept different viewpoints in a discussion: I've three times now returned to the comments to just blanket-upvote everything that's starting to turn grey.
by BoorishBears | 2025-06-03 02:29:09+00:00
This honestly feels like a diversion from the actual point which you proved: for some class of issues with LLMs, the underlying problem is learning how to use the tool effectively.

If you really need me to educate you on the meaning of opposite...

"contrary to one another or to a thing specified"

or

"diametrically different (as in nature or character)"

Are two relevant definitions here.

Saying something will 100% happen, and saying something will sometimes happen are diametrically opposed statements and contrary to each other. A concept can (and often will) have multiple opposites.

-

But again, I'm not even holding them to that literal of a meaning.

If you told me even half the time you use an LLM the result is that it solves a completely different but simpler version of what you asked, my advice would still be to brush up on how to work with LLMs before diving in.

I'm really not sure why that's such a point of contention.
by hydrogen7800 | 2025-06-03 02:29:37+00:00
I sometimes wonder how the nature of North America was perceived by the early european settlers. With the body of american myth, including the wild west, etc, you could get the impression that the plains were more vast, the mountains taller, the rivers rougher and the animals bigger than anywhere else in the world. Compared to continental Europe, I suppose this could be true. But were the plains of central Asia, the Himalayas, the animals of Africa unknown to these people? Or maybe they were know, and the experiences of the other placed I mentioned were similarly described.
by paulgerhardt | 2025-06-03 02:29:52+00:00
If you want to git gud?

Find some popular piece of electronics with a fatal design flaw that’s trivially fixed with some basic rework that you can flip for $25-$1200. Do this a hundred times. Branch out.

This changes year to year but could be fixing joycons, reflowing bga’s, upgrading soldered memory on newer laptops, doing case swaps on consoles or smartphones, and as others have pointed out, lots of automotive, vintage collectible computer, or hifi work.

A plugin hakko soldering iron, a hot air reflow station, Amtech STIRRI-V3-TF flux, some copper braid, and Kester SAC305 lead free solder will get you 95% of the way there.

The financial component means you churn through hundreds of devices rather than dozens or less if you were to do it as a hobby.
by tedious-coder | 2025-06-03 02:30:00+00:00
But what do you make of the parent’s second paragraph? This is the way I feel as well - I would rather not spend my time asking AI to do something right that I could just do myself.

I bit the bullet last week and tried to force myself to use a solution built end to end by AI. By the time I’d finished asking it to make changes (about 25 in total), I would’ve had a much nicer time doing it myself.

The thing in question was admittedly partially specified. It was a yaml-based testing tool for running some scenarios involving load tests before and after injecting some faults in the application. I gave it the yaml schema up front, and it did a sensible job as a first pass. But then I was in the position of reading what it wrote, seeing some implicit requirements I’d not specified, and asking for those.

Had I written it myself from the start, those implicit requirements would’ve been more natural to think about in the progression of iterating on the tool. But in this workflow, I just couldn’t get in a flow state - the process felt very unnatural, not unlike how it would’ve been to ask a junior to do it and taking 25 rounds of code review. And that has always been a miserable task, difficult to force oneself to stay engaged with. By the end I was much happier making manual tweaks and wish I’d have written it myself from the start.
by k_sze | 2025-06-03 02:30:07+00:00
It's odd. It seems like this is not the first Japanese team to have developped artificial blood. I did a quick search and it seems there was another team at least as early as 2019 (https://web.archive.org/web/20201111233217/http://www.asahi....)

So what's different this time?

(Upon further examination, the 2019 team at the National Defense Medical College also had Dr Hiromi Sakai. So why is this news now?)
by abadar | 2025-06-03 02:30:38+00:00
I learned basic electronics repair from modding and building arcade controllers, aka Fightsticks. That eventually evolved into Game Boy repair, and iPod retrofitting, and electronics restoration. It's kind of like cooking; find something you want to make, then follow the directions, and then make something else, and eventually, you'll come across a problem that you have the foundation to solve.

The most basic core of practical electronics repair is that there are things that should be conductive and things that shouldn't be conductive and you need to figure out how to do either.
by xpe | 2025-06-03 02:31:11+00:00
Some people make these kinds of claims for ethical reasons, I get it. But be careful to not confuse one’s ethics with the current state of capability, which changes rapidly. Most people have a tendency to rationalize, and we have to constantly battle it.

Without knowing the commenter above, I’ll say this: don’t assume an individual boycott is necessarily effective. If one is motivated by ethics, I think it is morally required to find effective ways to engage to shape and nudge the future. It is important to know what you’re fighting for (and against). IP protection? Human dignity through work? Agency to effect one’s life? Other aspects? All are important.
by naasking | 2025-06-03 02:31:36+00:00
> Currently, the #1 spot sits at a score of 0.09, not 0.9. A far cry from being useful.

The best intellisense and code completion tools would solve 0.00. Those were the only tools we were using just a couple of years ago. 0.09 is a tremendous jump and the improvements will accelerate!
by windexh8er | 2025-06-03 02:31:36+00:00
> Meanwhile the rest of the world learned how to use it.

Very few people "learned how to use" Google, and in fact - many still use it rather ineffectively. This is not the same paradigm shift.

"Learning" ChatGPT is not a technology most will learn how to use effectively. Just like Google they will ask it to find them an answer. But the world of LLMs is far broader with more implications. I don't find the comparison of search and LLM at an equal weight in terms of consequences.

The TL;DR of this is ultimately: understanding how to use an LLM, at it's most basic level, will not put you in the drivers seat in exactly the same way that knowing about Google also didn't really change anything for anyone (unless you were an ad executive years later). And in a world of Google or no-Google, hindsight would leave me asking for a no-Google world. What will we say about LLMs?
by JumpCrisscross | 2025-06-03 02:31:46+00:00
> What have you written with ai that ha made you or your business money

I use R a little more than I should, given the simplicity of my work. Claude writes better R quicker than I can. I double check what it's doing. But it's easier to double check it used twang correctly than spend five trying to remember how to use the weird package that does propensity scoring [1].

I'm sure data analysis will still sort of be a thing. But it's just not as useful anymore in the form of a human being for most commercial applications at sub-enterprise scale.

[1] https://cran.r-project.org/web/packages/twang/index.html
by forgotoldacc | 2025-06-03 02:32:03+00:00
There's the old quote from Babbage:

> On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

This has been an obviously absurd question for two centuries now. Turns out the people asking that question were just visionaries ahead of their time.

It is kind of impressive how I'll ask for some code in the dumbest, vaguest, sometimes even wrong way, but so long as I have the proper context built up, I can get something pretty close to what I actually wanted. Though I still have problems where I can ask as precisely as possible and get things not even close to what I'm looking for.
by BigCatStuff | 2025-06-03 02:32:16+00:00
I agree that the diagnosis and visual inspection is the most important skill in fixing random items.

In the channels that I suggested, all of them go into the repair not knowing what the fault actually is. They take the viewer through the whole diagnosis, and they (with the exception of Electronics Repair School) are not electronics technicians.

Once a person has seen enough different ways of diagnosing items (by watching videos or hands on trying), then faults in other items become easier to find.
by nickpsecurity | 2025-06-03 02:32:31+00:00
They mostly copy and mix patterns in the training data. Lots of repetition with variations on them is helpful for their generalization. Languages like Python and Go have a ton of code in Github, etc like that. I saw that using Python with GPT 3.5/4.

If it's a rarer language, the math doesn't do as good of a job on piles of random code. There's just not enough for it to learn from. I cant speak for Rust since I dont know the numbers but imagine it's much less than Python or Go.

I have seen some evidence, though, that harder languages are harder for them to code in. GPT 3.5 used to struggle with C++ for something that it could easily produce in Python. It could actually produce things in C more easily than C++. It makes sense, though, because there's both more context needed for correctness and more behavioral patterns to write it.

My solution, which I only prototyped in GPT due to leaving AI, was to use AI's to write code in languages like Python which non-AI tools transpiled to high-performance code in C++ or Rust. Think the Python to C++ compiler or maybe Nikita. Later, with hallucinations mitigated enough, add LLM's to those transpilers.

As a side benefit, it let's you sell a product accelerating or increasing predictability of applications in that language. That's a non-AI investment. There's a few companies doing that, too. So, one could sell to the AI crowd, the "language X in business" crowd, or both.
by boshalfoshal | 2025-06-03 02:33:37+00:00
And as it stands, AI is nowhere close to (1) and (2), but is pretty close to making all of (3) redundant.

This could be because most work is actually frivilous (very possible), but its also easy for them to sell those since ostensibly (1) and (2) actually require a lot of out of distribution reasoning, thinking, and real agentic research (which current models probably aren't capable of).

(3) just makes the most money now with the current technology. Curing cancer with LLMs, though altruistic, is more unrealistic and has no clear path to immediate profitability because of that.

These "AGI" companies aren't doing this out of the goodness of their hearts with humanity in mind, its pretty clearly meant to be a "final company standing" type race where everyone at the {winning AI Company} is super rich and powerful in whatever new world paradigm shows up afterwards.
by simplesimon890 | 2025-06-03 02:35:24+00:00
I live in the path of the drones mentioned in the article and it's an incredibly frustrating experience to be outside and have them fly over the property. they are noisy, intrusive, and increasingly more frequent ( maybe a pass nearby every half hour on a busy day )

Living in an urban environment always will entail some unwanted sounds, dogs barking, passing cars, the occasional helicopter or whatever, but to have a drone passing over your neighborhood to deliver someone coffee or a parcel feels like exploiting every possible avenue to make money, regardless of how disruptive it is to the local population.

However bad they are now, it will be 10x the number of drones in a few years. It's a depressing thought.... but hey, at least someone gets their shitty coffee and adds a few euro to the profit of some company so it'll all be worth it in the end.
by ryandrake | 2025-06-03 02:35:33+00:00
Since the "state of the art" seems to change every week, what's a good way to try out the current "state of the art", without spending $600? I'd love to give it a shot and be proven wrong, but I'm not going to spend 1/2 a mortgage payment on a trial.

EDIT: Looks like the "Cursor" thing has a free trial. Might start there.