MurphyA/hacker-news huggingface dataset:
by i80and | 2025-05-28 12:06:01+00:00
People have been talking about "education and retraining" to make up for jobs lost to technology since I was young. It's a nice idea, but I'm curious if that actually happens at scale rather than in isolated pockets. Retraining is hard, expensive, and risky.

My general experience is that people tend to fall backwards into either lower-quality work or permanent semi-unemployment, but I could be mistaken about that.
by Molitor5901 | 2025-05-28 12:06:07+00:00
It is only a matter of time before the bulk of truck transport is autonomous. Some can say it won't happen, or it won't happen anytime soon, but that is to be extraordinarily short sighted into what is the goal of the shippers: to remove the human. Truck driving is not an industry I would recommend anyone getting into, but truck maintenance, yes.
by frenchmajesty | 2025-05-28 12:06:38+00:00
I disagree with the author here. His point can be summarized as "LLMs produce bad code, LLMs lead to the teams's average skills decreasing and they can't maintain large codebases over time".

Yet nowhere he addresses the #1 flaws to his position: rate of improvement of the technology, and its promise to deliver on saved money and gained speed.

In all the companies I've seen engineering leadership hardly really gives a shit about things OP says are important. They just care that customers are happy, the system is stable, and its malleable enough to allow for pivots when need be.

Good discussions & documentation about architecture before starting the work that gets peer-reviewed + A non-stupid engineer putting on good guardrails around the LLM's output + the extensive unit test suites in CD/CI + peer reviews on the PRs = all downsides near eliminated while all upside gained.

This is how we work at my company today (health startup). Google and Meta also boast publicly +30% of new lines of code are AI-generated in their companies today. That the state of *today*; assume in 5 years these AIs are 10x better... I simply cannot foresee a world where LLM-assisted coding is not the de-facto way be a software engineer.
by bsaul | 2025-05-28 12:06:51+00:00
"[AI] is not capable of working at a conceptual level".

I wonder where did author got that feeling. What recent LLMs proved time and time again is that they are definitely able to work at conceptual level (by correctly translating concepts from one language to another depending on the context for example). Saying it doesn't "understand" the concepts as humans do is a different thing. It wouldn't "understand" pain, because it has no experience of it. But humans constantly talk about thing they've never personally experienced (and indeed maybe they shouldn't, but that's another topic).
by mjburgess | 2025-05-28 12:07:13+00:00
The problem with people "who have spent time in machine learning or reinforcement learning" is that they've spent no time, literally none, understanding what a concept is.

There is no such thing as a higher dimensional concept, nor can they be projected into a weight space, because they aren't quantities.

The concept, say, "Dog" composes with the concept, "Happy" to form "Happy Dog". The extension(Dog) is all possible dogs, the extension(Happy) is all happy objects, the extensions here compose. The intension of "Dog" depends on its context, eg., "get that damned dog!" has a different intension than, "I wish I looked less like a dog!". And the intensions here do not compose like the extensions.

Take the act of counterfactual reality-oriented simulation, called "imagination" narrowly, call that `I`. And "curry" this operator with a simulation premise, "(as if) on mars", so we have I' = I(Mars)(...).

Now, what is the content of I'(Dog), I(Happy Dog), I(Get that damned dog), I(get that damned happy dog) ? and so on

The contents of `I` is nowhere modelled by "projection" because this does not model composition, and is not relevantly discrete and bounded by logical connectives.

These are trivial issues which arise the moment you're aware that few writing computer science papers have ever studied the meaning of the words they use with abandon.
by LeonB | 2025-05-28 12:07:18+00:00
If the King — without the support of the general populace or of the parliament, but acting in his own interests, like a king of old — dissolved the Parliament, he could and very swiftly would be ousted, legally.

The line of succession would be followed until an individual was found who was willing to support the democracy. This was proven in 1685.

They swear allegiance to the King (or Queen) — but it’s understood that a new King or Queen can be swapped in. The extremely stilted and socially restrained manner in which Queen Elizabeth (for example) behaves is because they entirely know that their family does not hold the nation hostage, it’s quite the opposite.
by api | 2025-05-28 12:07:24+00:00
I'm interested. I don't think it should just be an older coders club though. It should be for anyone of any age that's into the craft of fine, well built software, and who wants to learn from people who have been into it a long time.

I also like /r/tinycode for its spirit. My #1 saying in coding is "simplicity is harder than complexity." It's gone downhill like the rest of Reddit though.

I'm also not totally anti-AI. I use it a little bit. I just think if you aren't a good developer you aren't competent to use it properly. It's like saying autocomplete will make a bad developer good. I think it's like super autocomplete. Also found it useful for "here's a big block of code, explain it" -- you have to check what it tells you of course, but it gives you a passable first pass analysis, like asking a junior dev to do that.
by shireboy | 2025-05-28 12:07:45+00:00
I’ve wanted this for a while. I worked on a bank app where the home rolled solution was atrocious. Line of business apps don’t make sense in Microsoft store. But really where I land is to greatly prefer web apps deployed to IaaS because deployment is easier and compatibility is usually a known quantity. Debugging installer or desktop app issues on remote servers and desktops is a hassle I like to avoid if I can
by bgwalter | 2025-05-28 12:08:36+00:00
Stereotypical anti-luddite quotes are not thinking. Automation in construction has not led to lower prices, on the contrary, prices are exploding. Automation in car manufacturing has not led to lower prices, cars are still expensive. Automation in health care didn't prevent prices from exploding.

If a monopoly emerges, by definition control and prices are centralized. And I really don't want broligarchs that follow the agenda 2025 to control the food chain.
by srazzaque | 2025-05-28 12:09:27+00:00
Get the LLM to read and summarise N pages at a time, and store the outputs. Then, you concatenate those outputs into one "super summary" and use _that_ as context.

Theres some fidelity loss but it works for text, because there's quite often so much redundancy.

However, I'm not sure this technique could work on code.
by Marius_Manola | 2025-05-28 12:09:37+00:00
There's basically so much educational content out there, that you could argue there's no need for AI to "generate" lessons, when it could just restructure existing ones.

Some make it the case that you need "personalised education" for everything, but it's not at all clear that's the solution. For deeply technical things, you have to explain them from the ground up, and that can't be personalised in n ways...
by silvestrov | 2025-05-28 12:09:49+00:00
This will require companies to be able to survive for some time using the old code and to recognise the importance of cleaning up.

Has this ever happened besides the Y2K fixes?

Wouldn't it be much more likely that the companies will simply go under? Or that they will make a team that writes a completely new version of the code, somewhat like Mac OS X was a replacement of MacOS 9 and not a cleanup.
by nyrikki | 2025-05-28 12:09:55+00:00
Honest question, what type of CSV to SQL code are you doing whre correctness doesn't count?

Perhaps my few decades in the industry have been in areas whree it is always the details, correctness, and fitness for purpose that tends to make those problems hard, not the work itself.

I do see a use case for throw away spikes, or part of a red-green-refactor, etc.. but if accuracy and correctness aren't critical, data cleanup is easy even without an LLM.
by bittermandel | 2025-05-28 12:10:33+00:00
I think this take is quite shallow and lacks insight into how one would actually build a somewhat complex technical platform.

We are not using a microservice pattern at all. I am not sure where you get that from. If anything we have several "macro services".

Our final setup is quite complex as we are building a literal cloud provider, but in practice we have a Go API, a Docker registry, a Temporal Worker and a Kubernetes controller. Whats complicated is everything else around it. We run our platform on bare-metal and thus have auxiliary services like a full-blown Kubernetes cluster, Ory Hydra + Kratos, SpiceDB, Cilium, Temporal Cluster + Workers and some other small things. We need to be able to test this locally to feel safe to release to production. And in turn our production environment is almost identical to our local environments.

None of that would be possible unless we've done something similar to what we have built today. Most companies cannot run their entire stack on their laptop, more unlikely that they could run a full cloud provider.
by foxes | 2025-05-28 12:10:37+00:00
My most recent ai related peeve is people releasing some ai tool and claim that it's "democratising x". "democratising art", "democratising education" -- yeah giving all your data to and relying on a centralised model run by a fascist mega corp is so totally democratic. Maybe can't blame them -- that is what they actually mean based on "democracy" in the US etc though.
by tantalor | 2025-05-28 12:10:42+00:00
I think intent matters.

Let's say you are making an AI-controlled radiation therapy machine. You prompt and train and eval the system very carefully, and you are quite sure it won't overdose any patients. Well, that's not really good enough, it can still screw up. But did you do anything wrong? Not really, you followed best practices and didn't make any mistakes. The LLM just sometimes kills people. You didn't intend that at all.

I make this point because this is already how these systems work today. But instead of giving you a lethal dose of radiation, it uses slurs or promotes genocide or something else. The builders of those bots didn't intend that, and in all likelihood tried very hard to prevent it. It's not very fair to blame them.
by nssnsjsjsjs | 2025-05-28 12:10:48+00:00
3d printing will slowly edge its way into more manufacturing. The humble stepping motor really is eating the world. 3dp is one manifestation of it!

Back to AI though.

I just checked the customer support page of a hyped AI app generator and its what you expect: "doesn't work on complex project" "wastes all my tokens" and "how to get a refund"

These things are over promising and a future miracle is required to justify valuations. Maybe the miracle will come maybe not.
by bluGill | 2025-05-28 12:10:59+00:00
not all the world is a web site or even internet connetted. not all the world has no safety concerns.

if you work in medical or aviation areas every release legally needs extensive - months - testing before you can release. If there are issuse found in that testing you start over. Not all tests can be automated.

i work in agraculture. the entire month of July there will be nobody in the world using a planter or any of the software on it. there is no point in a release then. the lack of users means we cannot use automated rollback if the change somehow fails for customers - we could but it would be months of changes rolled back whe Brasil starts planting season.
by westurner | 2025-05-28 12:11:14+00:00
Can [OpenTPU] TPUs be fabricated out of graphene, with nanoimprinting or a more efficient approach?

From https://news.ycombinator.com/item?id=42314333 :

>> From "A carbon-nanotube-based tensor processing unit" (2024) https://www.nature.com/articles/s41928-024-01211-2 :

>>> Using system-level simulations, we estimate that an 8 bit TPU made with nanotube transistors at a 180 nm technology node could reach a main frequency of 850 MHz and an energy efficiency of 1 tera-operations per second per watt.
by bryancoxwell | 2025-05-28 12:11:37+00:00
I honestly don’t, although maybe that hype cycle was before my time.

But this seems an unfair comparison. For one, I think 3D printing made me better, not worse, at engineering (back when mechanical engineering was my jam), as it allowed me to prototype and make mistakes faster and cheaper. While it hasn’t replaced all manufacturing (or even come close), it plays an important role in design without atrophying the skills of the user.
Show HN: TorusKit – Web Animations Right in HTML
by andrewlech | 2025-05-28 12:11:41+00:00
Hi HN, TorusKit is a JavaScript library that lets you create simple or complex web (CSS) animations right in the HTML. No need to create any JS or CSS files. I was working on this couple of years to be as simple to use as possible. It uses [data-tor] attributes with a simple (low-code like) syntax:

<div data-tor="animate:[[translateX(2rem)], [scale(1.5)] <1s, infinite, alternate>]">Hello</div>

It’s kind of like AOS (Animate On Scroll), but more flexible and not just limited to scroll, also simpler than GSAP for someone who doesn't like the code complexity.

It supports things like: – Animations and chaining – Scroll-based animations (parallax) – Mouse-follow effects – Stagger + group animations – And much more

I’m still fixing bugs and edge cases, but it’s functional and stable enough for small/medium sites. It works best with WordPress, Bootstrap-based, or other static pages. The Svelte support is coming soon!

Site: https://toruskit.com Docs: https://docs.toruskit.com

I’d really appreciate any feedback. Thanks!
by zaphar | 2025-05-28 12:11:45+00:00
The LLM trend lines may indicate that further research, experimentation, and investment in LLMs is warranted. But it is a mistake to assume that trend lines predict the future. Yet this is the primary evidence that is raised in support of LLMs in response to critics.

It's not at all clear that the upside gained outweighs the cost in your hypothetical scenario nor is should it be taken as a given that the trend line will continue as is for long enough to create enough upside to outweigh that cost.
by furyofantares | 2025-05-28 12:12:09+00:00
I'm quite big on using LLMs in coding, and aside from the inflammatory graphic at the top and the comment about loss of joy (I haven't lost my joy at all personally), I agree with the entire rest of the post completely. (For context I've been programming for 30 years now and still love it.)

I'm still trying to figure out how to produce a codebase using LLMs and end up as a expert in the system at the end of it while still coming out ahead. My hope is I can be more of an expert a bit faster than before, not less of an expert a lot faster.

It feels within reach to me as long as there's frequent periods of coming to a complete understanding of the code that's been produced and reshaping it to reduce complexity. As well as strong technical guidance as input to the LLM to begin with.

I think there's still a lot of learning to do about how to use these things. For example, I've tried LLM-heavy things in lua, C, and C#. The LLM was great at producing stuff that works (at first) in lua, but lua was miserable and the code ended up a huge mess that I can't be bothered to become an expert in. The LLM was really tripped up on C and I didn't make it that far, I didn't want to watch it fight the compiler so hard. C# has been great, the LLM is reasonably effective and I have an easy time consuming and reshaping the LLM code.

I've always liked static type systems but I like them even more now, in part because they help the LLM produce better code, but mostly because they make it a lot easier to keep up to speed on code that was just produced, or to simplify it.
by westurner | 2025-05-28 12:12:18+00:00
What about QPUs though?

Can QPUs (Quantum Processing Units) built on with electrons in superconducting graphene ever be faster than photons in integrated nanophotonics?

There are integrated parametric single-photon emitters and detectors.

Is there a lower cost integrated nanophotonic coherent light source for [quantum] computing than a thin metal wire?

"Electrons turn piece of wire into laser-like light source" (2022) https://news.ycombinator.com/item?id=33493885
by FirmwareBurner | 2025-05-28 12:12:27+00:00
Automation has led to lower prices of commodity widgets like for example 4K TVs.

It doesn't result in lower prices of items and services necessary for survival in modern society which are also subject to regulatory capture like real estate, healthcare and education.

In the case of the latter, all advance in technology and automation move to the pockets of shareholders but the cost to the consumer stays the same or even goes up once oligopolies form.
by RamtinJ95 | 2025-05-28 12:12:29+00:00
I love duckDB and this looks just absolutely brilliant!

One question for me is, lets say i want to start using this today and at work we are running snowflake. I get that each analytics person would have to run duckdb + this extension on their local machines and point to the blob store and the database that is running datalake extension, for now that would be say a VM running duckdb. When I run the actual query where does the computation happen? And what if I want a lot of computation?

Is the solution currently to host a huge duckdb VM that everyone ssh's into and run their queries or how does that part work?
by walterbell | 2025-05-28 12:12:41+00:00
> Assume in [N] years these AIs are 10x better

Why has OpenAI been acquiring "application" layer companies for large financial sums, instead of improving their own tools to build application layer codebases?

> 30% of new lines of code is AI-generated

"Watching AI drive Microsoft employees insane", 500 comments, https://news.ycombinator.com/item?id=44050152
by wrasee | 2025-05-28 12:12:42+00:00
I think that there is a reasonable argument to be made that, among other reasons, choosing this kind of structure means that precisely this kind of argument can be made to solicit donations to help fund certain parts of the picture yet therefore but indirectly allow more of other income to remain available for allocation elsewhere.

Hence the allocation of that “other income” is still very much a related concern, but with just enough indirection that Mozilla can evade scrutiny of it when it comes to executive pay.
by Artgor | 2025-05-28 12:13:22+00:00
You know, sometimes I feel that all this discourse about AI for coding reflects the difference between software engineers and data scientists / machine learning engineers.

Both often work with unclear requirements, and sometimes may face floating bugs which are hard to fix, but in most cases, SWE create software that is expected to always behave in a certain way. It is reproducible, can pass tests, and the tooling is more established.

MLE work with models that are stochastic in nature. The usual tests aren't about models producing a certain output - they are about metrics, that, for example, the models produce the correct output in 90% cases (evaluation). The tooling isn't as developed as for SWE - it changes more often.

So, for MLE, working with AI that isn't always reliable, is a norm. They are accustomed to thinking in terms of probabilities, distributions, and acceptable levels of error. Applying this mindset to a coding assistant that might produce incorrect or unexpected code feels more natural. They might evaluate it like a model: "It gets the code right 80% of the time, saving me effort, and I can catch the 20%."
Vakgpt: Open-Source Chat Wrapper for Rapid AI Prototyping
by krishna-vakx | 2025-05-28 12:13:45+00:00
We thought this might be helpful to the community.

VAKGPT, an open-source chat wrapper designed to help developers and founders quickly build and test AI-powered chat applications.

What is VAKGPT? VAKGPT is a modular, scalable chat wrapper that allows you to plug in your preferred language models and get started instantly. It's designed to simplify the creation of proof-of-concepts and deployment of conversational interfaces with minimal friction.

Key Features Model Flexibility: Supports various models, including:

deepseek-chat

meta-llama/Llama-3.3-70B-Instruct

meta-llama/Llama-3.2-90B-Vision-Instruct

meta-llama/Meta-Llama-3.1-405B-Instruct

Modular Architecture: Frontend and backend run as separate services—use them independently or together.

Scalable Design: Built to support growing user bases and high-volume interactions.

Open Source: Freely available under the MIT license for the community to use, extend, and contribute to.

Tech Stack Frontend: Next.js for fast, modern web experiences.

Backend: Python and LangChain for robust AI integration.

Database: MongoDB for scalable data storage.

Live Demo Check out the hosted version at https://vakgpt.com.

GitHub: Check out GitHub repo at https://github.com/kk9393/VAKGPT.

Contributions Welcome We invite developers, researchers, and enthusiasts to contribute to VAKGPT. Join our community to help shape the future of conversational AI.
by capitainenemo | 2025-05-28 12:13:45+00:00
True. At that point you're close to the intent of teaspoons/tablespoons/cups with easier to remember units. tsp = 5ml tblsp = 15ml cup = 240ml

with all the handy halvings in between + higher granularity of ml.. 'course I'm usually approximating on a gramme scale anyway.

But for larger stuff I round up to 250.
by mjburgess | 2025-05-28 12:14:05+00:00
They work in a token space whose metrical structure is given by proxies for concepts. So at a point in this space I can "walk towards" points which cluster around the token "dog".

This is a weak model of some features of concepts, eg., association: "dog" is associated with "cat", etc. But it, e.g., does not model composition, nor intension, nor the role of the term in counterfactuals. (See my comment elsewhere in this comments section on this issue).

However you can always brute force your way to apparent performance in some apparently conceptual skill if the kinds of questions you ask are similar to the trainign data. So eg., if someone has asked, "if dogs played on mars, would they be happy?" etc. or similar-enough-families-of-questions... then that allows you to have a "dog" cluster around "literal facts" and a "dog" cluster around some subset of preknown counterfactuals.

If you want to see the difference between this and genuine mental capabilities, note that there are an infinite combination of concepts of abitary depth, which can be framed in an infinite number of counterfactauls, and so on. And a child armed with only those basic components, and the capacity for imagination, can evaluate this infinite variety.

This is why we see LLMs being used most by narrow fields (esp. software engineers) where the kinds of "conceptual work" that they need has been extremely well documented and is sufficiently stable to provide some utiltiy.
by bittermandel | 2025-05-28 12:14:43+00:00
I don't think you are wrong at all. As we are all founders with an OK salary and this is our area of expertise, so we're able to take advantage of our previous experiences and reap the benefits. We're probably uniquely positioned here.

I had massive issues at my previous employer with Bazel. They did not try to make Bazel work for non-SREs, which as you can imagine didn't work very well. So it's definitely not a silver bullet!

We should probably write a blog post about our setup!