MurphyA/hacker-news huggingface dataset:
by godelski | 2025-05-06 20:55:43+00:00
Not gonna lie, that was a bit creepy. We're deep in a day old thread that you have no other comments in. Do you scrape HN looking for mentions of ripgrep?

Forgive me if I'm a bit surprised!

I still stand that silent errors are significantly worse than loud ones

| it's worse to not get files you're expecting vs get more files than you're expecting. In the later case there's a pretty clear indication you need to filter while in the former there's no signal that anything is wrong. This is objectively a worse case. > The other crazy thing here is... you don't have to use ripgrep!

If it wasn't clear, I don't ;)

I don't think grep ignoring .gitignore files is "a bug". Like you said, defaults matter. Like I said, build artifacts are one of the most common things for me to grep.

Where we strongly disagree is that I believe aliases should be used to add functionality, where you believe that it should be used to remove functionality. I don't want to start another fight (so not linking the last). We're never going to see eye-to-eye on this issue so there's no reason to rehash it.
by rmason | 2025-05-06 20:55:47+00:00
One of the things they do not tell you in college entrepreneur classes is the downside. I well remember the 2000 Internet crash. An entrepreneur I looked up to and who had been somewhat of a mentor to me lost everything. I do mean everything; his company, house and marriage.

He ended up living in rental locker and showering at the gym. He'd had a chance in 1999 to sell out and net tens of millions of dollars but his VC's wouldn't let him do it. Very few people have the mental fortitude to bounce back from that and start another company. I'm betting Dan is one of those people.
by vaibhavdubey97 | 2025-05-06 20:55:51+00:00
Sorry I think I explained poorly. Plexe does build deep learning models automatically. When it gets a dataset and a problem description, it automatically evaluates various model architectures (NNs being one of them).

Plexe experiments with multiple approaches - from traditional algorithms like gradient boosting to deep neural networks. It runs the training jobs and compares performance metrics across different architectures to identify which solution best fits your specific data and problem constraints.
by johnea | 2025-05-06 20:56:50+00:00
I agree with that. Both in terms of your statement regarding the understandings of economists working in their field, and in a sense of trying to meet in the middle here, because I don't think we're that far apart.

My main objection is not against what specialists do when working within their field of expertise, it's that the main impacts of economics on society at large is via the:

> politicians or non-economists “playing with dad’s gun”.

And like the outcomes of the literal interpretation of that analogy, the results are often deadly, and usually for innocent bystanders.

Wealth often presents actions as if they are shown to be mathematically correct, via "economic theories", when they are primarily used as justification of exploiting others for more gain for the people that already have the most.

If economics is going to be beneficially applied outside of experts working in their field, then it needs to be corralled by bounds that consider the impacts on society as a whole in the real world, and unfortunately that's not what's happening.
by jay_kyburz | 2025-05-06 20:57:31+00:00
here you go.. found this in another thread about the signal fiasco. Just one small example.

https://www.reuters.com/article/us-apple-fbi-icloud-exclusiv...

Apple puts on a good face, and I will admit I think there are probably better than the rest. But they sill put you in a walled garden that can be a little difficult to step out of.
by Cyphase | 2025-05-06 20:58:11+00:00
A couple of potential answers:

* It's not about computers and them (Liz and Werner). It's about Cosmo.

* Computer dating is about algorithms and pattern matching. Cosmo didn't have any suspicion of Liz and Werner going on a date; even if he saw a type-mismatch, humans are complex and multi-faceted. But when he learned a computer program ostensibly made the match, his alarm bells went off.
by pixl97 | 2025-05-06 20:58:20+00:00
Eh, I think part of the problem here is the speed of load switching. From the article it looks like the loads could generate dozens to hundreds of demand spikes per minute. With most battery operated loads that I've ever messed with we're not switching loads like that. It's typically 'oh a fault, switch to battery' then some time later you check the power circuit to see if it's up and switch back.

This looks a whole lot more like high frequency load smoothing. Really it seems to me like a continuation of a motherboard. Even if you have a battery backup on your PC you still have capacitors on the board for voltage fluctuations.
by duped | 2025-05-06 20:58:24+00:00
afaik it's not possible to configure buffer size and sample rate from within a user application without exclusive mode, which actually does matter for the non-exclusive use case. iirc it was even worse where applications' streams would be resampled transparently and buffered, which is absolutely not what you want.

I don't use windows for audio anymore so I can't comment on this in win11, but it used to be that WASAPI suffered unless you set your PC in "performance" mode in your power settings whereas ASIO was unaffected.

And yes, latency matters! For live performance you're looking for < 2.5ms of one-way latency to get a roundtrip of under 5ms. After that point it starts being perceptible to players. This is not a performance floor so much as a scheduling one, and ime windows audio scheduling under dsound/wasapi was always shakey.
by epolanski | 2025-05-06 20:59:13+00:00
> no amount of prompting will get current models to approach abstraction and architecture the way a person does

Which person it is? Because 90% of the people in our trade are bad, like, real bad.

I get that people on HN are in that elitist niche of those who care more, focus on career more, etc so they don't even realize the existence of armies of low quality body rental consultancies and small shops out there working on Magento or Liferay or even worse crap.
by ZeroGravitas | 2025-05-06 20:59:16+00:00
On the one hand, I'm geekily interested in how tech can monitor and improve these devices.

But on the other hand the info here (e.g. 1% are undersized and 10% are oversized) seems to demonstrate that just rolling out more heat pumps is the important thing and everything else is fiddling around the edges.

Ways to improve financing and the install process and customer journey are the real low hanging fruit.
by RachelF | 2025-05-06 20:59:20+00:00
> How so or why so, considering their brain size relative to their body, is not well understood.

One factor is neuron size. Bird neurons are around 0.4 the length of mammalian neurons, so you can pack in around 8 times the neurons in a given volume.

A large parrot has the same number of neurons as a medium sized dog.
by drlobster | 2025-05-06 20:59:23+00:00
I think what I'm after is being able to put these in pipeline.

I.e. if I already have some data cleaning/normalisation, some dimensional reduction and then some fitting, being able to drop the Agent in place with an appropriate description and task.

Cleaning: Feed it a data frame and have it figure out what needs imputing etc.

The rest: Could either be separate tasks or one big task for the Agent..
by impresburger | 2025-05-06 20:59:36+00:00
No, not by default. In fact, the default installation of plexe doesn't include deep learning libraries.

Plexe _can_ build deep learning models using `torch` and `transformers`, and often the experimentation process will include some NN-based solutions as well, but that's just one of the ML frameworks available to the agent. It can also build models using xgboost, scikit-learn, and several others.

You can also explicitly tell Plexe not to use neural nets, if that's a requirement.
by hinkley | 2025-05-06 20:59:46+00:00
Large customers pay not by wattage but by… I’m spacing on the word but essentially how much their power draw fucks up the sine waves for voltage and current in the power grid.

I imagine common power rail systems in hyperscaler equipment helps a bit with this, but for sure switching PSUS chop up the input voltage and smooth it out. And that leads to very strange power draws.
by wtallis | 2025-05-06 20:59:58+00:00
You're badly mischaracterizing tests like ACID2. The test definition includes a long list of things that invalidate the test, including things like changing the zoom level. So it's wrong to construe that test as requiring pixel-perfect rendering when it explicitly doesn't cover exactly the kind of user agent controls I'm talking about.

Your comments about LaTeX do not seem to contradict anything I said.
by abletonlive | 2025-05-06 21:00:10+00:00
> I use it mostly for Golang and Rust

I'm starting to suspect this is the issue. Neither of these languages are in the top 5 languages so there is probably less to train on. It'd be interesting to see if this improves over time or if the gap between the languages become even more intense as it becomes favorable to use a language simply because LLMs are so much better at it.

There are a lot of interesting discussions to be had here:

- if the efficiency gains are real and llms don't improve in lesser used languages, one should expect that we might observe that companies that chose to use obscure languages and tech stacks die out as they become a lot less competitive against stacks that are more compatible with llms

- if the efficiency gains are real this might disincentivize new language adoption and creation unless the folks training models somehow address this

- languages like python with higher output acceptance rates are probably going to become even more compatible with llms at a faster rate if we extrapolate that positive reinforcement is probably more valuable than negative reinforcement for llms
by Nevermark | 2025-05-06 21:00:44+00:00
I think that is what makes it a humor goldmine.

Clippy was useless.

But attaching a Clippy to a language model? Still nominally useless, but mindfully so!

It would be self-deprecating (un-deprecated???) humor for Microsoft, which would take the edge off of the often pushy and tone-deaf corporate look they continually and crassly paint themselves into by default.

And actually potentially useful as a branding touchstone: a visual and interface link across otherwise seemingly disparate model interfaces. Clearly delineating and bridging MS AI tools from all the other mixes of tools we are accumulating.

They could lean into the “clip” in Clippy with a side app for saving and organizing clippings and logs of notable interactions with any MS model, akin to a notes app. With features for compressing convos into compact topic cheat sheets (with retained sources & convos), lists and other helpful info gathering and leveraging tasks.

An ongoing accumulated compressed common core of context for both (hu)man and machine, er … Clippy.
by johnfn | 2025-05-06 21:01:13+00:00
In most reasonably-sized websites, Tailwind will decrease overall bundle size when compared to other ways of writing CSS. Which is less code, 100 instances of "margin-left: 8px" or 100 instances of "ml-2" (and a single definition for ml-2)? Tailwind will dead-code eliminate all rules you're not using.

In typical production environments tailwind is only around 10kb[1].

[1]: https://v3.tailwindcss.com/docs/optimizing-for-production
by Zambyte | 2025-05-06 21:01:28+00:00
And why do you think Zig (and Odin, but I'm not really familiar with that one) is not comparable in complexity to C? If you start with C, replace the preprocessor language with the host language, replace undefined behavior with illegal behavior (panics in debug builds), add different pointer types for different types of pointers (single object pointers, many object pointers, fat many object pointers (slices), nullable pointers), and make a few syntactic changes (types go after the names of values in declarations, pointer dereference is a postfix operator, add defer to move expressions like deallocation to the end of the scope) and write a new standard library, you pretty much have Zig.
by byearthithatius | 2025-05-06 21:01:38+00:00
I was so excited for this, but sadly it doesn't work at all, not even UI feedback for the error:(

The UI showed literally no change. So I checked and the console shows:

``` Try: 14 Not Found 681-7df1b139fa2dc9f0.js:14:3379 Try: 15 Not Found 681-7df1b139fa2dc9f0.js:14:3379 Try: 16 Not Found 681-7df1b139fa2dc9f0.js:14:3379 Try: 17 Not Found 681-7df1b139fa2dc9f0.js:14:3379 Try: 18 Not Found 681-7df1b139fa2dc9f0.js:14:3379 Try: 19 Not Found 681-7df1b139fa2dc9f0.js:14:3379 Try: 20 Not Found 681-7df1b139fa2dc9f0.js:14:3379 Gave up after 10 seconds. 681-7df1b139fa2dc9f0.js:14:3379 filteredSuggestions Array(3) [ {…}, {…}, {…} ] 681-7df1b139fa2dc9f0.js:14:3379 ```

Also your table doesn't fit in the viewport so I can't see the results.

Firefox Ubuntu.
by chubot | 2025-05-06 21:01:55+00:00
Hm I wasn't aware of this project, it seems to have started in 2022

Other than OSH, it seems to be the only shell that aims for POSIX/bash compatibility, out of dozens of alternative shells: https://github.com/oils-for-unix/oils/wiki/Alternative-Shell...

As far as I know, OSH is the most POSIX- and bash-compatible shell:

Nine Reasons to Use OSH - https://oils.pub/osh.html

If I have time, I will run this through our spec tests: https://oils.pub/release/0.29.0/test/spec.wwz/osh-py/index.h...

---

About this part: There are a number of other POSIX-ish shells implemented in a non-C/C++ implementation language

OSH is implemented in an unusual style -- we wrote an "executable spec" in typed Python, and then the spec is translated to C++.

That speeds it up anywhere from 2x-50x, so it's faster than bash on many workloads

e.g. a "fibonacci" is faster than bash, as a test of the interpreter. And it makes 5% fewer syscalls than bash or dash on Python's configure (although somehow this doesn't translate into wall time, which I want to figure out)

It's also memory safe, e.g. if there is no free() in your code, then there is no double-free, etc.

---

As mentioned on the OSH landing page, YSH is also part of the Oils project, and you can upgrade with

shopt --set ysh:upgrade

If you want JSON and so forth, e.g.

ysh$ json read < x.json ysh$ = _reply (Dict) {shell: "ysh", fun: true}

YSH aims to be the "ultimate glue language" - https://oils.pub/ysh.html
by AznHisoka | 2025-05-06 21:02:11+00:00
I searched for 'data providers that start with the letter R that sell job postings data', and it's been 15 minutes and it barely verified the first row.

But if it filtered it first to "start with the letter R", it would only have to look at perhaps 5% of the results it's trying to verify!

So it's doing needless verification of results that will be thrown out by another filter that should've been applied first!
by cma | 2025-05-06 21:02:14+00:00
They likely knew continued training on code would have some amount of catastrophic forgetting on other stuff. They didn't throw away the old weights so probably not sunk cost fallacy going on, but since it is relatively new and they found out X% of API token spend was on coding agents (where X is huge), compared to what token spend distribution looked like on prior Geminis that couldn't code well, they probably didn't want the complexity and worse batching of having another model for it if the impacts weren't too large and decided they didn't weight coding enough initially and it is worth the tradeoffs.
by casey2 | 2025-05-06 21:02:36+00:00
Open AI needed to spend $3B pivoting away from bigdata based AI. But instead they went for the most shorted sighted move possible of snapping up the "trendiest" company nobody has ever heard outside the Ycombinator echo chamber.

Typical VI-fallacy BS. If LLMs were actually good they would replace IDEs completely not be integrated.
by epolanski | 2025-05-06 21:02:40+00:00
This so much.

To me it seems so strange that few good language designers and ml folks didn't group together to work on this.

It's clear that there is a space for some LLM meta language that could be designed to compile to bytecode, binary, JS, etc.

It also doesn't need to be textual like we code, but some form of AST llama can manipulate with ease.
by antirez | 2025-05-06 21:03:01+00:00
You can have two arguments that are semantically as distinct and important as quantity and price and be both integers, and if you swap them is a big issue anyway. And you would be forced, if you like this kind of programming, to create distinct types anyway. But I never trust this kind of "toy" defensive programming. The value comes from testing very well the code, from a rigorous quality focus.
by e_i_pi_2 | 2025-05-06 21:03:49+00:00
Even worse - it's sold to the lowest bidder so they can sell it back to you for profit. This is a perfect example of why GDP isn't a good measure for how well a country is doing - if the kids do it themselves they may learn a bunch and enjoy it and make their lives better, but it doesn't create jobs or profit, so the incentives are against it.
by morpheuskafka | 2025-05-06 21:03:56+00:00
As an example, US has Uber and Lyft and Mexico has Uber and Didi. When someone from Mexico goes to US, they probably wont have Lyft, and someone from the US won’t have Didi. So Uber gets most travellers’ business automatically.

Likewise even though Uber in Japan is (almost) all taxis and not actual Uber drivers, most global tourists have Uber and not something like Go that’s specific to Japan. So they are profiting off almost all the taxi rides from Western visitors.
by gorniv | 2025-05-06 21:04:02+00:00
It doesn’t work for Apple private relay users, but it does work for those who signed in with Apple without using “Hide My Email.”

The good news is that Apple’s private relay addresses are unique per user, so if someone contacts us, we can match and update their account to a regular email. We’ve added a banner to our site to help guide affected users through this.

One-time sign-in via email still works for everyone else, and we’re looking into ways to let users securely update their email via support if they can no longer log in.

We’re waiting on a response from Apple and will post updates as we get them. Thank you!
by impresburger | 2025-05-06 21:04:14+00:00
Interesting! We don't currently support this explicitly.

You could wrap the Plexe-built model in a scikit-learn Estimator like I mentioned, and you can specify the desired input/output schema of the model when you start building it, so it will fit into your Pipeline.

This is an interesting requirement for us to think about though. Maybe we'll build proper support for the "I want to use this in a Pipeline" use case :)
by skrtskrt | 2025-05-06 21:05:21+00:00
there are answers in the thread you linked that show how easy and clean the error handling can be.

it can look just like a more-efficient `except` clauses with all the safety, clarity, and convenience that enums provide.

Here's an example:

* Implementing an error type with enums: https://git.deuxfleurs.fr/Deuxfleurs/garage/src/branch/main/... * Which derives from a more general error type with even more helpful enums: https://git.deuxfleurs.fr/Deuxfleurs/garage/src/branch/main/... * then some straightforward handling of the error: https://git.deuxfleurs.fr/Deuxfleurs/garage/src/branch/main/...
by sokoloff | 2025-05-06 21:05:59+00:00
Fixing peeling paint correctly is a really substantial undertaking, especially as compared to picking up litter.

Removing the loose paint, figuring out if there’s a moisture problem, rot/rust, or other physical repairs needed, buying appropriate paint (maybe matching the color), then sanding, cleaning, priming, sanding, cleaning, and painting.

Slapping a bit of new paint over a problem is not the answer unless you want to make a recurring problem out of it.
by Ygg2 | 2025-05-06 21:06:08+00:00
> which is not some proof from god or anything that you seem to be seeking

You know what, you or anyone else can start with actual proofs. I don't want your lived experience and memes as proof, I need messages, posts from people (bonus points for sudo-rs members) that show how zealous they are about Rust and rewriting all the things in it.

I'm a Java dev, that just dabbles in Rust. I've seen observed many claims about Rust zealotry with no actual proof. Whenever I ask for them, I get a blank stare and dodging.

Hence, my comparison with the book from Ghost in the Shell. It, too, was a meme, an actual memory virus. Everyone read it, and no one could find the proof it existed.
by dbacar | 2025-05-06 21:06:20+00:00
Majority of people on earth don't solve hard, unique, but well-defined problems, do we? I dont expect AGI to to solve one of Hilbert's list of problems (yet). Your definition of AGI is a bit too imposing. Saying that I believe you would get answers from an LLM better than most of the answers you would get from an average human. IMHO the trend is obvious and we will see if it stalls or keeps the pace.
by ahmeneeroe-v2 | 2025-05-06 21:06:31+00:00
No idea, but look at the homicide rate or Wyoming (590k, 2.6/100k) vs that of any similarly-populated US city: Baltimore (576k, 58/100k), Albuquerque (562k, 21/100k), Fresno (544k, 13/100k).
by willbryk | 2025-05-06 21:06:36+00:00
Because Google makes money from ads, they're not actually optimized to build the best general search engine in the world, they're optimized to build the search engine that makes the most from ads, which is correlated with being a good search engine but not perfectly aligned. Our business model (paying directly for the search) incentivizes us to try to return the highest quality results, without any bias toward making money from ads. It also enables us to do things like pour a ton of compute/resources into a query to get the best possible results we can find, because someone would pay us a lot for that, and that's hard to do under an ads-based model.
by janalsncm | 2025-05-06 21:07:22+00:00
I am curious about what the load curves look like in these clusters. If the “networking gap” is long enough you might just be able to have a secondary workload that trains intermittently.

Slightly related, you can actually hear this effect depending on your GPU. It’s called coil whine. When your GPU is doing calculations, it draws more power and whines. Depending on your training setup, you can hear when it’s working. In other words, you want it whining all the time.
by omneity | 2025-05-06 21:07:38+00:00
No idea about SOTA but naively it doesn't seem like a very difficult problem:

- Ensure all TLA+ specs produced have the same inputs/outputs (domains, mostly a prompting problem and can solved with retries)

- That all TLA+ produce the same outputs for the same inputs (making them functionally equivalent in practice, might be computationally intensive)

Of course that assumes your input domains are countable but it's probably okay to sample from large ranges for a certain "level" of equivalence.

EDIT: Not sure how that will work with non-determinism though.
by kurthr | 2025-05-06 21:07:47+00:00
For a simple example try twisting a ductile aluminum bolt (or clay), twisting a brittle piece of chalk (or concrete), or twisting a composite (twig). They all fail differently (and the first two at 45deg to each other. Mohr's circle is interesting, and fatigue failure more interesting still.

https://youtu.be/1YTKedLQOa0?t=533
by rustyminnow | 2025-05-06 21:08:09+00:00
I submitted a patch for an annoying terraform provider once. It took about a week to fix and almost 3 years for them to merge it upstream. I got to learn Go and gained a much more solid understanding of how terraform works. I gained more from undertaking the project than from the actual fix.

> Consider the way you think it should be done is not the only "right" way and you'll open more doors for yourself.

Absolutely.