MurphyA/hacker-news huggingface dataset:
by mns | 2025-07-18 07:31:33+00:00
> But now I can ask legal advice from AI, which levels that playing field. Everybody who has a computer or smartphone and internet-access can consult an AI lawyer or doctor. AI can be my Harvard. I can start a business and basically rely on AI for handling all the paperwork and basic business decisions, and also most recurring business tasks. At least that's the direction we are going I believe.

We had a discussion in a group chat with some friends about some random sports stuff and one of my friends used ChatGPT to ask for some fact about a random thing. It was completely wrong, but sounded so real. All you had to do was to go on wikipedia or on a website of the sports entity we were discussing to see the real fact. Now considering that it just hallucinated some random facts that are on Wikipedia and on the website of an entity, what are the chances that the legal advice you will get will be real and not some random hallucination?
by yreg | 2025-07-18 07:32:00+00:00
Two days ago there were two redditors who had the same happen to them - banned for allegedly breaching 3.2(f). One from Australia the other from NZ.

https://old.reddit.com/r/iOSProgramming/s/oUVIuVWeJe

Hearing tales like these makes me super nervous. I don't think there's anything I can do to protect my app/account.
by raffraffraff | 2025-07-18 07:32:39+00:00
I've found that if you're a novice coder you don't know what to ask for.

Your decades of experience are probably a bit like mine: you sense the cause of a problem in an almost psychic way, based on knowledge of the existing codebase, the person who wrote the last update, the "smell" of the problem. I've walked into a major incident, looked at a single alert on a dashboard, and almost with a smile on my face identified the root cause immediately. It's decades of knowledge that allow to know what to ask for.

Same with vibe coding: I've been having tremendous fun with it but occasionally a totally weird failure will occur, something that "couldn't happen" if you wrote it yourself. Like, you realise that the AI didn't refactor your code when you added the last feature, so it added "some tiny thing" to three separate functions. Then over several refactors it only updated one of those "tiny things", so now that specific feature only breaks in very specific cases.

Or let's say you want to get it to write something that AI seems to have problems with. Example, assemble and manage a NAS array using mdadm. I've been messing with that recently and Google Gemini has lost the whole array twice, and utterly failed to figure out how to rename an array device name. It's a hoot. Just to see if it would ever figure it out I kept going. Pages and pages of over-and-back, repeating the same mistakes, missing the obvious. Maybe it's been trained on 10 years of Muppets online giving terrible advice on how to manage mdadm?
by stavros | 2025-07-18 07:32:45+00:00
You need to run the cost/benefit analysis here: if I had avoided Claude Code, all that would have happened is I would have written much less code.

What's the cost between never using Claude, and using it and getting these lower limits? In the end, I'm left with no Claude in both situations, which leaves me better off for having used it (I wrote more code when Claude worked).
by sigmoid10 | 2025-07-18 07:35:28+00:00
You are describing the state of LLMs from 2 years ago. Which basically means they were just pre-trained on the internet and then fine tuned to follow a particular instruction format. Current models still use this as a first step, but are then trained a lot using reinforcement learning, which has given them much better skills at reasoning and logic than human tainted data ever could. See how Grok 4 for example still eagerly dismisses all those right wing hoaxes, despite being massively tuned to favour right wingers by its creators carefully selecting pre-training data.
by csa | 2025-07-18 07:35:29+00:00
> Among non-technical friends, after the initial wow factor, even limited expectations were not met.

Indeed. I think that current AI tech needs quite a bit of scaffolding in order for the full benefits to be felt by non-tech people.

> Then it was tainted by the fact that everyone is promoting it as a human replacement technology

Yeah. This is a bad move. AI is a human force multiplier (exponentializer?).

> which is then a tangible threat to their existence

This will almost certainly be a very real threat to AI adoption in various orgs over the next few years.

All it takes is a neo-Luddite in a gatekeeper position, and high-value AI use cases will get booted to the curb.
by throw1235435 | 2025-07-18 07:35:33+00:00
I think the point of the poster is that there's no need for them because as he puts it replace "the majority of engineers". Tech is about to be decimated - software as a field is about to shrink quite substantially. To be honest the way things are going it is possible IMO (although would severely disadvantage me) - 3 years ago given the amount of skill required and lifetime learning in it I would of never thought this. Why do you need juniors - well you may need them but with the oversatuation it will take maybe 20 years (a generation to retire) for the current crop of seniors to get exhausted given AI will decimate them too according to the OP. That's definitely a far enough away time for it to be someone else's problem - by then AI might be significantly better anyway; we could even have superintelligence and SWE jobs might be the least of our worries.

Great for the owners of tech companies and non-tech companies using tech (many non-technical, capital based who now scale with less cost base); not so great for people with tech skills who invested for their future. Which is the goal of these AI companies. Your pain/unemployment/cost is their profit/economic surplus.

To the OP its "exciting times" for capital - probably for many people however it seems like an impending nightmare is coming especially if they are relying on intellectual work to survive/provide/etc. Most CC articles on HN trend I've seen trend to the top; which shows it is definitely instigating either greed or fear in people. I would argue that "code" seems to be the most successful AI product to date - most others are just writing assistants, meme generators, risky operators, etc at least for me. Its code that seems to instigate this persistent fear.

On a side note, at least for me, AI has personally dissuaded me from learning/investing more in this career as a senior engineer with almost 20 years experience. I hope I'm wrong.
by rcarmo | 2025-07-18 07:37:52+00:00
Hey Nick! (See my other comment someplace, which is very closely aligned to your point)

The bit I want to surface in this thread is that there is actually a lot that can be done with sub-flows and custom functions alone (even without added third-party modules) - I built my own library of AI/“agentic” subflows with raw HTTP request nodes and a simple convention for passing LLM context in payloads so I’m a bit biased here, but overall I find Node-RED superior in the amount of control I have over how data is handled.

(Also, I am running Node-RED in bun rather than NodeJS and it is way more efficient, although that means I can’t rely on some third-party nodes, but that is another story)
by iforgotpassword | 2025-07-18 07:38:07+00:00
Most important reasons imo:

1) even though reality has proven us wrong time and time again, we can just not look at the dependency too closely and just act as if it's written and maintained by competent, caring people and is of highest quality. No worries!

2) in case shit hits the fan, let's assume worst case and there is a vuln in the dep and you get hacked... It's somebody else's fault! \o/
by darkoob12 | 2025-07-18 07:38:42+00:00
You shouldn't believe Big Tech on their PR statements.

They are decades behind in AI. I have been following AI research for a long time. You can find best papers published by Microsoft, Google, Facebook in past 15 years but not Apple. I don't know why but they didn't care about AI at all.

I would say this is PR to justify their AI state.
by hammyhavoc | 2025-07-18 07:39:00+00:00
You're describing the process working—DNA gave investigators a lead, which they followed up with legally obtained, direct evidence tying DeAngelo to the crime. That’s not guilt by association; that’s good policing. Framing that as something sinister ignores the outcome: a serial rapist and murderer finally faced justice. That’s not dystopia—it’s what society should be doing—or would you rather a rapist, kidnapper and murderer walked freely?
by mynameismon | 2025-07-18 07:40:10+00:00
From my understanding of cryptography, most schemes are created with the assumption that _any_ function that does not have access to the secret key will have a probabilistically small chance of decoding the correct message (O(exp(-key_length)) usually). As LLMs are also a function, it is extremely unlikely for cryptographic protocols to be broken _unless_ LLMs can allow for new types of attacks all together.
by the_duke | 2025-07-18 07:40:11+00:00
Just on the coding side, tools like Claude Code/Codex can be incredibly powerful, but a lot of things need to be in place for it to work well:

* A "best practices" repository: clean code architecture and separation of concerns, well tested, very well-documented

* You need to know the code base very well to efficiently judge if what the AI wrote is sensible

* You need to take the time to write a thorough task description, like you would for a junior dev, with hints for what code files to look at , the goals, implementation hints, different parts of the code to analyse first, etc

* You need to clean up code and correct bad results manually to keep the code maintaineable

This amounts to a very different workflow that is a lot less fun and engaging for most developers. (write tasks, review, correct mistakes)

In domains like CRUD apps / frontend, where the complexity of changes is usually low, and there are great patterns to learn from for the LLM, they can provide a massive productivity boost if used right.

But this results in a style of work that is a lot less engaging for most developers.
by crinkly | 2025-07-18 07:40:17+00:00
When you say progress, I wonder at what cost. The apparatus for original thought isn't used and pushing through a problem and deriving the satisfaction of doing that doesn't happen. That leads to the wrong motivational cues. The brain is lazy and likes to take the easy route. Eventually you become merely a machine operator, which is something a lot of my colleagues are starting to do. And it's very worrying.

The biased behaviour towards just asking for a solution both devalues you and leads to any innovative outcomes disappearing before they had a chance to exist.

I've ended up being the company ideas man not because I'm good but everyone else stopped thinking.
by eisbaw | 2025-07-18 07:40:41+00:00
Noise? Iterating in PRs - if humans are involved.

That said, Terragon here (which is akin to Codex and Jules) are often "too autonomous" for my taste. Human in the loop is made more difficult -- commenting may not be enough: I can't edit the code from those because the workspace is so ephemeral and/or remote.
by nialse | 2025-07-18 07:41:22+00:00
The study makes subclasses of autism based on parent reported behavioral phenotypes in questionnaires. They find that the subclasses have different genetic characteristics. Importantly they do not initially map genetic characteristics to the subclasses, thus there is no prediction of autism (subclasses) from genetics. However it lends additional credibility to the notion that autism is rather the autisms, a set of disorders with similar behavioral profiles but different causes.
by rcarmo | 2025-07-18 07:41:52+00:00
You can navigate a graph just fine if you set up flows to iterate through it, just like state machines (there are a few patterns to do that in Node-RED with branching and iteration). It’s perfectly possible.

However, there is something you mention that I think is important - dashboard 2 is still missing critical functionality—for instance, I cannot set it up as the “/“ (homepage) URL in an instance (there’s a lingering bug for that for around a year now) and it is still far from being as well integrated as the original.
by shakna | 2025-07-18 07:42:31+00:00
You're the outlier - and the explanation is simple. Where you are, divorce and family court filings are part of the public record. A request isn't necessary, because the background checking org already can see the full documentation. They care to, because who you are in debt to, can influence you in future. And it takes about 3sec to confirm this, rather than sitting there saying "no, you".

Your employer enjoys your data without your say so [0].

EU, UK [1], Au [2], NZ... They have to ask. So they do.

[0] https://www.dshs.texas.gov/vital-statistics/marriage-divorce...

[1] https://www.equifax.co.uk/Products/learning-centre/marriage-...

[2] https://www.auscheck.gov.au/what-we-do/background-checks
by Kwpolska | 2025-07-18 07:43:32+00:00
If dependency == library, it is hard in C or Python, but it isn’t in Rust or Java.

You could easily run a Rust program on bare Linux, but using Docker might simplify the version management story.

But even if you use a language with a sane dependency management story, you probably depend on PostgreSQL, and running it in Docker can be easier than installing it on the system (since distros really like to mess with the PostgreSQL configuration).
by imiric | 2025-07-18 07:43:48+00:00
> The people that are happy with it and praising the avenues offered by LLM/AI solutions are creating codebases that fulfill their requirements, whatever those might be.

Ah, but "whatever those might be" is the crucial bit.

I don't entirely disagree with what you're saying. There will always be a segment of power users who are able to leverage their knowledge about these tools to extract more value out of them than people who don't use them to their full potential. That is true for any tool, not just in software.

What you're ignoring are two other possibilities:

1. The expectation of users can be wildly different. Someone who has never programmed before, but can now create and ship a smartphone app, will see these tools as magical. Whatever issues they have will either go unnoticed, or won't matter considering the big picture. Surely their impression of AI tooling will be nothing short of positive. They might be experts at using LLMs, but not at programming.

OTOH, someone who has been programming for decades, and strives for a certain level of quality in their work, will find the experience much different. They will be able to see the flaws and limitations of these tools, and addressing them will take time and effort that they could've better spent elsewhere. As we've known since the introduction of LLMs, domain experts are the only ones who can experience these problems.

So the experience of both sides is valid, and should have equal weight in conversations. Unlike you, I do trust the opinion of domain experts over those of user experts, but that's a personal bias.

2. There are actual flaws and limitations in AI tooling. The assumption that all negative experiences are from users who are "holding it wrong", while all positive ones are from expert users, is wrong. It steers the conversation away from issues with the tech that should be discussed and addressed. And considering the industry is strongly propelled by hype and marketing right now, we need conversations grounded in reality to push back against it.
by orwin | 2025-07-18 07:44:29+00:00
I interrupted this fascinating read to tell that "actually", quantum computers are great at multi-dimensional calculation if you find the correct algorithms. It's probably the only thing they will ever be great at. You want to show that finding the algorithm is not possible with our current knowledge.

anyway, making the computer do the calculation is one thing, getting it to spew the correct data is another.... But still, the article (which seems great at the moment) brushes it of a bit too quickly.
by TeMPOraL | 2025-07-18 07:45:20+00:00
TL;DR: their subscriptions have an extra built-in margin closer to 70%, because the entry price, target audience and clever choice of rate limiting period, all keep utilization low.

----

In this case I'd imagine it's more of an assumption that almost all such subscriptions will have less than 33% utilization, and excepting few outliers, even the heaviest users won't exceed 60-70% utilization on average.

"33% or less" is, of course, people using a CC subscription only at work. Take an idealized case - using CC only during regular working hours, no overtime: then, even if you use it to the limit all the time, you only use it for 1⁄3 of the day (8h), and 5 days a week - the expected utilization in this scenario is 8/24 × 5/7 = 24%. So you're paying a $200 subscription, but actually getting at most $50 of usage out of it.

Now, throw in a rate limit that refreshes in periods of 5 hours - a value I believe was carefully chosen to achieve this very effect - and the maximum utilization possible (maxing out limit, waiting for refresh, and maxing out again, in under 8 hours wall-clock time), is still 10 hours equivalent, so 10/24 × 5/7 = 30%. If you just plan to use CC to the first limit and then take meetings for the rest of the day, your max utilization drops to 15%.

Of course people do overtime, use same subscription for personal stuff after work and on weekends, or just run a business, etc. -- but they also need to eat and sleep, so interactively, you'd still expect them to stay below 75% (83% if counting in 5-hour blocks) total utilization.

Sharing subscriptions doesn't affect these calculations much - two people maxing out a single subscription is, from the provider side, strictly not greater than two subscriptions with 50% utilization. The math will stop working once a significant fraction of users figure out non-interactive workflows, that run CC 24/7. We'll get there eventually, but we're not there yet. Until then, Anthropic is happy we're all paying $200/month but only getting $50 or less of service out of it.
Show HN: UML is dead – so I'm building the tool to revive it
by SamiCostox | 2025-07-18 07:45:29+00:00
Hi HN,

UML diagrams were meant to standardise system design, but they’ve become outdated — slow to build, hard to change, and rarely used in practice. Most tools feel clunky, and diagrams often go stale fast.

I’m building RapidChart, a tool to bring diagramming back to life — fast, flexible, and developer-first.

What makes it different:

Cursor-style interface for rapid diagram creation

Fully customisable and exportable

AI-assisted (works with GPT-4, Claude, etc.)

100% free

This began as part of my research at Imperial College London, and I’d really value your feedback.

Here’s the link: https://www.rapidcharts.ai

Thanks!
by hulitu | 2025-07-18 07:46:10+00:00
> But whenever someone answered the call and built a Smartphone with QWERTY keyboard, the product failed commercially, simply because also to people claiming they want such a phone, at the point of sale they were less attractive than their slimmer, lighter, all-screen counterparts.

No. They were a) rare and b) much more expensive. AFAIK only Blackberry had a phone with QWERTY keyboard.

Buttons are much more expensive than a touchscreen (see cars for examples).
by fuzzfactor | 2025-07-18 07:46:13+00:00
These are major breakthroughs.

Looks to me like this couple is making progress over the long term that few individuals would be able to match.

And now getting it right after 40 years by concentrating on things that you just can't expect to be within reach after only 30 years or so.

Some of the most admirable type of research that most people are ever going to see.
by tsimionescu | 2025-07-18 07:47:03+00:00
It's simple conceptually: you find an encryption method Enc that guarantees `Sum(Enc(x), Enc(y)) = Enc(Sum(x, y))`. That's ultimately all there is to it. Then, you give the server enc_x and enc_y, the server computes the sum, and returns to you enc_sum. You then decrypt the value you got and that's x+y.

Since lots of functions behave in this way in relation to sums and products, you "just" need to find ones that are hard to reverse so they can be used for encryption as well.

Unfortunately this turns out to not work so simply. In reality, they needed to find different functions FHESum and FHEMultiply, that are actually much harder to compute (1000x more CPU than the equivalent "plaintext" function is a low estimate of the overhead) but that guarantee the above.
by FirmwareBurner | 2025-07-18 07:47:13+00:00
Because Apple are one of the gatekeepers now, that's why they're getting regulated everywhere. It's not like how you can move from a Ford to a Honda, a lot of people have their entire digital lives with notes, chats, photos, videos and thousands of dollars of purchases tied to Apple's account, plus the network effect where the whole family has iPhone because of iMessage. Moving to another phone brands, they'd loose all that.

That's Apple's moat and they're fighting to the death for it because they don't have any other cash cow to milk.

Non Apple user btw.
by kodarna | 2025-07-18 07:47:43+00:00
Laypeople don't install operating systems. As long as they get help to set up Linux with working drivers and configuration, the rest of Linux is actually easier because it doesn't change default browsers, add unwanted applications, pop-ups with scary text etc unlike Windows.

I've had a great experience putting my computer illiterate mother on Linux Mint. I've never had to help her once with the operating system or programs (she only uses Firefox and an IPTV application) whereas Windows was a mess at times.
by bluecalm | 2025-07-18 07:48:05+00:00
Now take that good one and compare them to a guy like Thibault Duplessis (Lichess author), look at his GitHub and commit history for Lichess and you will quickly realize that even with your 0.1x starting point 1000x engineers are out there.

The guy single handedly created the best online chess site out there outperforming whole commercial teams in every area (performance, interface, features, whatever you choose). His work ethic is legendary and he pays himself below entry level salary (for US standards).
by William_BB | 2025-07-18 07:48:59+00:00
The reason is probably complexity and the task at hand.

In my experience, LLMs are great at small tasks (bash or python scripts); good at simple CRUD stuff (js, ts, html, css, python); good at prototyping; good at documentation; okay at writing unit tests; okay at adding simple features in more complex databases;

Anything more complex and I find it pretty much unusable, even with Claude 4. More complex C++ codebases; more niche libraries; ML, CV, more mathsy domains that require reasoning.
by jahewson | 2025-07-18 07:49:59+00:00
You’re in luck, around half the page linked to above is devoted to explaining that:

> The idea for tsx came about during a time when the Node.js ecosystem was getting fragmented due to the release of ES Modules (ESM). As packages migrated to ESM, projects struggled to reconcile their CommonJS apps with ESM dependencies.

>Back then, ts-node was the go-to tool for running TypeScript in Node.js, but it lacked ESM support and was quite complicated to use. We noticed several open-source tools using esbuild to run TypeScript in Node.js and decided to bring these efforts together into one simple, cohesive project.
by sugarpimpdorsey | 2025-07-18 07:50:50+00:00
It makes more sense if you view it for what it is: Honest Satya's Certificate Authority.

Microsoft showed they can semi-competently run a PKI. The end.

Now had the Linux folks stepped up to the plate early on, instead of childishly acting like Secure Boot was the computing antichrist, the story might be different. But they didn't. We only have shim because some people at Red Hat had the common sense to play ball.