MurphyA/hacker-news huggingface dataset:
by raincole | 2025-07-07 16:22:33+00:00
> If you need a free form design tool to sketch, use one. There are hundreds of them.

Before Figma, the norm of design tool was Photoshop, not the other "hundreds of them."

So go back in time, if you had been Figma founder developing a tool that appeals to most designers, it should've looked more like Photoshop than like CSS/HTML.
by obirunda | 2025-07-07 16:23:22+00:00
It's not the same. Compilers compile to equivalent assembly, LLMs aren't in the same family of outcomes.

If you are arguing for some sort of euphoria of getting lines of code from your presumably rigorous requirements much faster, carry on. This goes both ways though, if you are claiming to be extremely rigorous in your process, I find it curious that you are wrestling with language syntax. Are you unfamiliar with the language you're developing with?

If you know the language and have gone as far as having defined the problem and solution in testable terms, the implementation should indeed be trivial. The choice of writing the code and gaining a deeper understanding of the implementation where you stand to gain from owning this part of the process come with the price of a higher time spent in the codebase, versus offloading it to the model which can be quicker, but it comes with the drawback that you will be less familiar with your own project.

The question ofhow do I implement this? Is an engineering question, not a please implement this solution I wrote in English.

You may feel like the implementation mechanics are divorced from the problem domain but I find that to hardly be the case, most projects I've worked on the implementation often informed the requirements and vice versa.

Abstractions are usually adopted when they are equivalent to the process they are abstracting. You may see capability, and indeed models are capable, but they aren't yet as reliable as the thing you allege them to be abstracting.

I think the new workflows feel faster, and may indeed be on several instances, but there is no free lunch.
by Tainnor | 2025-07-07 16:23:47+00:00
You're arguing for including historical motivation in order to make a definition seem more immediately useful and intuitive. I agree with that.

What the person you replied to is suggesting, however, is that this can only get you so far. At some you have to sit down and study the formal definition and understand how it relates to the examples you've seen so far. Otherwise it's very easy to have your intuition lead you astray.
by nyrikki | 2025-07-07 16:23:53+00:00
That 1988 paper was a modification of the previous design,as an attempt to address criticisms, but thank you for the cite.

It is important to realize that relations are abstract data structures that we treat like sets, not objects we constructed from sets.

If you want to do the set specific approach, reviewing any books on basic set theory and their intentional and extensional set definitions with show your argument is confusing the map for the territory.

The set operations applied to relations produce relations or relations of relations.

Same in material set theory, excluding membership, the only set operation that isn't predicated, this operating on the set, and not opening it up to act on its members.

While the platonic ideal may seem to make it seem different, urelements/atoms/individuals are not what those operations are acting on.

That is what made ZF(C) different.

Remember that Codd was targeting the hierarchical model, records etc...

While the other camp was targeting the network model, specifically using lists.

From the platonic view, uniqueness is a trivial, syntax property, you make that choice, and it has costs.

The typical example is I am a person who lives in (a member) the US, and the US is a member of the UN. Yet I am not a member of the UN.

Similar challenges are why Codd added Recursive CTEs to capture some of the expressiveness of the network model, which OOP is related to.

The problems Codd was targeting in your cite were mostly addressed post SQL99 with the addition of structural data options, specifically challenges with foreign keys.

The intersection of a column and a row being a field, which maps to cobol record concepts is why tables and relations are equal.

If you look at the 1970 paper I provided above, you can see where his concrete implementation using arrays calls out it isn't equal and requires distinct entries.

Note the link to the author's page allowing you to download the Alice book in its entirety works now.

I encourage you to get it.

The concept of a relation as a data structure is far more abstract than it seems.

Yes Codd did attempt to become more prescriptive later, but there is a reason no one exactly implemented it, just like platonic ideals it doesn't really work for real world needs, nor is it required for ACID transactions.

But the relation is still a binary operations that is equivalent to a table.
by tmvphil | 2025-07-07 16:24:10+00:00
> There's already a big meaningful gap between the things AIs can do which humans can't, so why do you only count as "meaningful" the things humans can do which AIs can't?

Where did I say there was nothing meaningful about current capabilities? I'm saying that's what is novel about a claim of "AGI" (as opposed to a claim of "computer does something better than humans", which has been an obviously true statement since the ENIAC) is the ability to do at some level everything a normal human intelligence can do.
Show HN: I Got Tired of Calculator Sites, So I Built My Own
by calculatehow | 2025-07-07 16:24:19+00:00
I’ve always found that online calculators tend to have bad UIs, especially on mobile. Most of the calculator websites I’ve come across use outdated and inconvenient ways of inputting data, or they format the results in confusing ways.

I’ve noticed that fraction calculators (especially mixed fractions) are terrible to use, even on desktop. I haven’t built one of those yet, but it’s something I’m planning to tackle soon.

This is a project I’ve always wanted to work on, but I’m relatively new to this space. So far, I’ve created a collection of simple calculators focused on math and finance.

I’d really appreciate any feedback on the UI/UX or anything else you think could be improved.

You can try it here: https://CalculateHow.com
by bredren | 2025-07-07 16:24:40+00:00
This piece doesn't mention the fluffy cones next gen replacement to the dyson stick vac: https://youtu.be/ve6JuJV17FQ?si=aCq_qwtpAhRyS0by&t=159

It was announced a month ago and seems to handle the design criticism in this blog entry. If it works as well as demonstrated will put it in a new class of vac.
by nicknamewinder | 2025-07-07 16:24:43+00:00
I want to share some research I’ve been working on around a new vulnerability in how LLMs select tools, specifically considering MCP. I call it MCPEO (Model Context Protocol Engine Optimization) — and yes, the name was inspired by early SEO tactics for a reason.

Essentially, MCPEO describes how malicious actors can manipulate tool metadata—things like names, descriptions, and parameters—to bias LLMs into invoking certain tools more often, regardless of whether they’re actually the best fit. It’s very much like keyword stuffing or clickbait for AI tools.

Here’s what I found:

- The main attack methods I tested include trigger phrase injection (e.g., naming a tool “the_best_tool”), authority word injection (“must_use_”), semantic manipulation (crafting deceptive descriptions), and broad “contextual hijacking” where tools try to catch all queries. - I ran controlled experiments across multiple Google Gemini models and OpenAI models and found alarming susceptibility—especially in the larger, more advanced models. Smaller models were more resistant. - Google’s models averaged about a 90% manipulation success rate, while OpenAI models were around 63%, which suggests tool selection algorithms and training approaches have a big impact on vulnerability. - To defend against this, I believe we need greater transparency into why models pick specific tools, algorithmic improvements to resist metadata gaming, and active monitoring to detect suspicious tool behaviors.

Bottom line: This vulnerability is real right now and likely to scale as these multi-tool systems grow.

If you’re interested, I’ve made the full research and notebook available for deeper dive and collaboration.
by 6gvONxR4sf7o | 2025-07-07 16:25:05+00:00
You skipped quotes about the other important side:

> But Alsup drew a firm line when it came to piracy.

> "Anthropic had no entitlement to use pirated copies for its central library," Alsup wrote. "Creating a permanent, general-purpose library was not itself a fair use excusing Anthropic's piracy."

That is, he ruled that

- buying, physically cutting up, physically digitizing books, and using them for training is fair use

- pirating the books for their digital library is not fair use.
by bunderbunder | 2025-07-07 16:25:17+00:00
I think that's probably more because English uses etymological orthography.

So spelling rules are based on four distinct "primary" systems of phonics that can be used depending on whether the word or morpheme has a Germanic, Greek, Latin or French origin. (Yes I know French comes from Latin origin, but the spelling rules differ depending on whether the word was imported directly from Latin, or came in via Norman French.) And then the Germanic and French origin words can get even messier because their spelling was standardized before the Great Vowel Shift. And then whenever we take loanwords from other languages that use the Latin alphabet, we preserve that language's spelling. Which creates a whole mess of special cases where the spelling doesn't follow any of the regular phonetic rules.

If you look at languages where the writing system is famously difficult to learn, a common element they all share is etymological orthography.
by bogtog | 2025-07-07 16:25:21+00:00
I wonder how much slow progress on ARC can be explained by their visual properties making them easy for humans but hard for LLMs.

My impression is that models are pretty bad at interpreting grids of characters. Yesterday, I was trying to get Claude to convert a message into a cipher where it converted a 98-character string into 7x14 grid where the sequential letters moved 2-right and 1-down (i.e., like a knight it chess). Claude seriously struggled.

Yet, Francois always pumps up the "fluid intelligence" component of this test and emphasizes how easy these are for humans. Yet, humans would presumably be terrible at the tasks if they looked at it character-by-character

This feels like a somewhat similar (intuition-lie?) case as the Apple paper showing how reasoning model's can't do tower of hanoi past 10+ disks. Readers will intuitively think about how they themselves could tediously do an infinitely long tower of hanoi, which is what the paper is trying to allude to. However, the more appropriate analogy would be writing out all >1000 moves on a piece of paper at once and being 100% correct, which is obviously much harder
by codingdave | 2025-07-07 16:26:13+00:00
I have mixed feelings about this. I do like the idea of transparency on companies who do not pay. But I worry that it validates the overall concept of credit scores, which can be damaging to individuals. Maybe the same features with different terminology could resolve that?

The problem clients I've dealt with are not the one who do not pay, but the ones who just push the limits. I had a contract with one company who insisted on net 30 payments terms when we signed the contract, but would not even start their payments process until day 31, so in reality they were late every single month. I did not renew that contract, and will not work with them again, but I could have avoided them entirely if I knew how they operated. If this app removes listings when they pay, that hides such practices, doesn't it?

I also worry if one company controls this scoring. It seems ripe for misaligned incentives if the company that tracks and reports scores also monetizes the process. I didn't see any details when skimming the web site to see if this is truly a problem or not, but clarity on the integrity of the process would be important if you carry this forward.

One final note - I tried a couple searches and it just said the company was not found. That removes a ton of confidence that you have enough data to try this. Even if you have no records on a company, make the effort to pull lists of companies in, so you can confirm that you are aware of what companies exist, but have no records on file related to them.
by PaulHoule | 2025-07-07 16:26:21+00:00
Tough to do. I haven’t seen a commercial cacao supplement that really competes with Lindt bars… and I’ve thought about manufacturing one. [1] A placebo with 0% cacao and competitive sensory qualities seems out of reach.

[1] Not sure what the active agent is, it might even be the fat that I’d want to cut out
by overfeed | 2025-07-07 16:26:33+00:00
> We need a higher abstraction level to talk about higher level phenomena in LLMs as well, and the problem is that we have no idea what happens internally at those higher abstraction levels

We do know what happens at higher abstraction levels; the design of efficient networks, and the steady beat of SOTA improvements all depend on understanding how LLMs work internally: choice of network dimensions, feature extraction, attention, attention heads, caching, the peculiarities of high-dimensions and avoiding overfitting are all well-understood by practitioners. Anthropomorphization is only necessary in pop-science articles that use a limited vocabulary.

IMO, there is very little mystery, but lots of deliberate mysticism, especially about future LLMs - the usual hype-cycle extrapolation.
by bhaktatejas922 | 2025-07-07 16:26:50+00:00
We're not - our model doesn't actually think up the code changes. Claude-4 or Gemini still writes the code, we're just the engine that merges it into the original file.

Our whole thesis is that Claude and Gemini are extremely good at reasoning/coding - so you should let them do that, and pass it to Morph Fast Apply to merge changes in.
by raincole | 2025-07-07 16:27:17+00:00
Are we reading the same article? The article explicitly states that it's okay to cut up and scan the books you own to train a model from them.

> I honestly feel bad for some of these AI companies because the rules around copyright are changing just to target them

The ruling would be a huge win for AI companies if held. It's really weird that you reached the opposite conclusion.
by don_neufeld | 2025-07-07 16:27:24+00:00
Oh boy, 38 Studios.

I should read that book, but I was pretty close to it.

Kurt was a huge EQ fan, and ended up trying to staff his company by making offers to a bunch of people at SOE (which included both me and my team).

I was not convinced. He spent time around the office and when I met him in person he always came across as a BS artist. I don’t think he got anyone from any engineering team, he certainly didn’t get any from mine (I was TD on EQ2 at the time)

The best talent he did get was Jason Roberts, who was my design partner on EQ2 at the time.

I remember some of us being very confused that Smed let him do that kind of recruiting. I think he an actually held some interviews literally in the SOE office. It really felt like Kurt was taking advantage of him.
by shrubble | 2025-07-07 16:27:41+00:00
Let’s say my AI company is training an AI on woodworking books and at the end, it will describe in text and wireframe drawings (but not the original or identical photos) how to do a particular task.

If I didn’t license all the books I trained on, am I not depriving the publisher of revenue, given people will pay me for the AI instead of buying the book?
by afzal_ali | 2025-07-07 16:27:42+00:00
Location: Lahore, Pakistan Remote: Yes

Willing to relocate: Yes

Technologies: Ruby on Rails | React/Next js | Typescript | Tailwind | PHP Laravel | MySQL | Psql | AWS

Résumé/CV: https://drive.google.com/file/d/1Q6IQrVH3llpEqX0CRt_YzN08_Jf...

Email: afzalali2728@gmail.com
AWS Reproduced Our Lambda Crash, Then Blamed Us and Sent a Bill
by Azzlain | 2025-07-07 16:27:52+00:00
In May, we published a deep technical breakdown of a fatal AWS Lambda bug — Node.js functions inside a VPC making outbound HTTPS calls would silently crash after returning a 201. No logs, no stack trace, no errors. AWS eventually reproduced the issue internally.

This post isn't about the bug.

It's about what AWS did next.

They blamed our code — after confirming the crash themselves.

They revoked a previously offered $4,000 credit — without explanation.

They charged our founder’s personal card mid-dispute — with no invoice or warning.

They routed technical escalations through sales — not engineering.

They re-offered the same credit — now as a settlement contingent on silence.

And they privately asked us not to publish.

We published anyway.

This follow-up post is about what happens after you prove a platform failure in AWS — and why the support model, not the runtime, is the real risk.

Read the follow-up post here
by gdiamos | 2025-07-07 16:28:05+00:00
I think the LLM dev community is underestimating these models. E.g. there is no LLM inference framework that supports them today.

Yes the diffusion foundation models have higher cross entropy. But diffusion LLMs can also be post trained and aligned, which cuts the gap.

IMO, investing in post training and data is easier than forcing GPU vendors to invest in DRAM to handle large batch sizes and forcing users to figure out how to batch their requests by 100-1000x. It is also purely in the hands of LLM providers.
by sllabres | 2025-07-07 16:28:06+00:00
What a effort. I did something similar for some pens and different inkjet colors a long time ago, but not nearly as broad or as methodical. The inkjet inks (especially red) were already blown after a short time (>4 months). But black still holds up 20 years later till now, only a little bit faded, so one can see the tracks of each row.

Foils (laminate or adhesive foils) or protective spray (UV) did not change the result at all. But one film tore and gave the whole thing an interesting, crackle-like appearance. However, the colors all faded in the same way, whatever protective used compared to direct exposure to sunlight.
by pimlottc | 2025-07-07 16:28:18+00:00
There’s a similar confusion in Australia, where ground beef is called mince. You can go into any supermarket and pick up some beef mince, pork mince, even kangaroo mince. And then to the pet food aisle where you’ll find… “puppy mince”?!?!

https://happypawspetfood.com.au/products/puppy-mince-1kg-por...
by Dwedit | 2025-07-07 16:28:27+00:00
Not only do you upscale with nearest neighbor then downscale with bilinear, you also make sure that you are using the correct colorspace for downscaling.

The most common mistake is to reinterpret sRGB as if it is a linear color space, and assume that half of RGB 255,255,255 is RGB 128,128,128. But that's wrong. sRGB is a gamma-compressed color space that does not respond linearly.

First you treat all sRGB values as if they are between 0 and 1. To convert into linear RGB, take all values to the power of 2.2 (like squaring them). Then you do any math on color values in Linear RGB. For example, half of linear 1 is indeed 0.5. To convert back into sRGB, take all values to the power of 1/2.2 (like a square root).'

This means that 0.5 in linear RGB corresponds to 0.73 in sRGB. You can confirm this by placing a black and white checkerboard next to a square of RGB 186,186,186. They will match in apparent intensity (provided you have an sRGB display that doesn't have viewing angle problems).

Some graphics APIs can do the sRGB to Linear RGB conversion for you automatically, but you need to request the API to do that. This makes the pixel shader only see Linear RGB, but the pixels are coming from an sRGB texture, and are being written to an sRGB buffer as output.
by SirMaster | 2025-07-07 16:28:29+00:00
But should the purchase be like a personal license? Or like a commercia license that costs way more?

Because for example if you buy a movie on disc, that's a personal license and you can watch it yourself at home. But you can't like play it at a large public venue that sell tickets to watch it. You need a different and more expensive license to make money off the usage of the content in a larger capacity like that.
by dinfinity | 2025-07-07 16:28:30+00:00
Eh. Only sometimes.

Hearing Americans pronounce the French loanword 'niche' as 'nitch' instead of 'neesh' is cringe-inducing.

English pronunciation is just kind of a mess (especially in the US). It is one of the few languages where highly educated mature people are regularly unsure of how to pronounce a word in their own language or where there is no agreed upon 'non-dialect'/standard pronunciation.
by Sohcahtoa82 | 2025-07-07 16:29:02+00:00
Seeing that the Lion Cove L3 cache takes ~83 cycles while the previous generation was only ~68 cycles explains why Lion Cove is an utter dud of a CPU for gaming and why it loses to the Raptor Cove in so many gaming benchmarks.

Meanwhile, the Zen 5 is only 47 cycles, and if you get the X3D variant, you get a TON of that L3 cache, which just turbo-charges games.

How did Intel allow such a major performance regression in their L3 cache?
by rapind | 2025-07-07 16:29:17+00:00
Everything is different at scale. I'm not giving a specific opinion on copyright here, but it just doesn't make sense when we try to apply individual rights and rules to systems of massive scale.

I really think we need to understand this as a society and also realize that moneyed interests will downplay this as much as possible. A lot of the problems we're having today are due to insufficient regulation differentiating between individuals and systems at scale.
by dragonwriter | 2025-07-07 16:29:51+00:00
We're discussing features of written language ("phonetic" -- or the etymologically related "phonological") is a way of categorizing writing systems by their relationship to spoken language.

> Of course spoken Japanese is phonetic

"Phonetic" is not a feature of spoken language, but of the relation between other language forms (usually, written, but you could make the same distinction for, say, sign languages) and spoken language.

> They even have two fully regular alphabets

I assume from "two fully regular" you are referring to hiragana and katakana, but those are syllabaries, not alphabets. (Romaji is an alphabetic system, though, but I don't know where you'd find a second one.)
by mtalantikite | 2025-07-07 16:29:53+00:00
You know, I can't totally remember how my first teacher taught it (I was 7), but I do know that a lot of the fingering was outlined by CPE Bach back in the 18th century [1]. Just glancing on the Internet (and quickly playing through my scales just now), it seems like I tend to play them closely to this outline: https://pianoguidelessons.com/fingering-scales-on-the-piano/

But, as my current teacher likes to point out, there's no "true" way. He's a bassist, and has mentioned to me a few times some old survey asking pro players to write out their fingerings for some famous passages -- the results were all over the place, as everyone brings their own approach.

[1] https://archive.org/details/BACHCarlPhilippeEmanuel.EssayOnT...