skysight-inc/HackerNews huggingface dataset:
You are not alone.

At the core AI/ML is giving you answers that have a high probability of being good answers. But in the end this probability is based on avarages. And the moment you are coding stuff that is not avarage AI does not work anymore because it can not reason about the question and 'answer'.

You can also see this in AI generated images. They look great but the avarage component makes them all look the same and a kind of blurry.

For me the biggest danger of AI is that people put too much trust in it.

It can be a great tool, but you should not trust it to be the truth.
I had to read it twice ;)
Fighting bias is a good thing, you'd have to be pretty...er...biased to believe otherwise. Bias is fundamentally a distortion or deviation from objective reality.

This, on the other hand, is just fucking stupid political showboating that's hurting their SV white knight cause. It's just differently flavored bias
The project's governance says the maintainers can vote to make a decision. Why can't they vote to leave the CNCF?
If an adult was found to be giving access to pornographic magazines and tapes to their child, and especially someone else's child, and even placing them on the child's desk in their bedroom, I think the police or CPS would want to get involved.

While I understand a computer is just as tool, it does seem weird that we just gloss over the fact that websites just hand porn to children and don't worry about it, but if you did it on the street you would get run out of town at best and arrested at worst.
Well, hopefully we’ll still have toast and jam, so there’s that.

This suggests a sort of cultural event horizon beyond which AI cannot see. Five years from now, the way actual people talk and think about a new topic will be drowned in AI content, creating a gulf between reality as people actually see it and the AI universe. Unfortunately for many people their reality is already slated to become AI-mediated.
news?

https://www.taigamotors.com/en/snowmobiles/
Or perhaps people are poo pooing a useful tool and they asked it something like "read these transcriptions from our many hour long workshops about this new project and write an agenda for a kick off meeting, summarise the points we've already decided and follow up with a list of outstanding questions".

Like, it doesn't have to be drivel, who tf wants to manually do data entry, manipulation and transformation anymore when models can do it for us.
I've stopped trying to use GPTs for complex tasks like what you describe, but I find them to be invaluable for getting a lot of grunt work done on my hobby projects.

As a concrete example: GitHub Copilot has been absolutely life-changing for working on hobby programming language projects. Building a parser by hand consists of writing many small, repetitive functions that use a tiny library of helper functions to recursively process tokens. A lot of people end up leaning on parser generators, but I've never found one that isn't both bloated and bad at error handling.

This is where GitHub Copilot comes in—I write the grammar out in a markdown file that I keep open, build the AST data structure, then write the first few rules to give Copilot a bit of context on how I want to use the helper functions I built. From there I can just name functions and run Copilot and it fills in the rest of the parser.

This is just one example of the kind of task that I find GPTs to be very good at—tasks that necessarily have a lot of repetition but don't have a lot of opportunities for abstraction. Another one that is perhaps more common is unit testing—after giving Copilot one example to go off of, it can generate subsequent unit tests just from the name of the test.

Is it essential? No. But it sure saves a lot of typing, and is actually less likely than I am to make a silly mistake in these repetitive cases.
They don't show any of the topology, UVs, texture maps, skeletons of the extracted meshes, which makes me skeptical about what they're actually doing to animate them, and whether they've gone through the process of making multiple animations for a moderately complex mesh generated from this tool or just have a few toy examples.

It's still very impressive research, at least.
Is a content moderation policy the same thing as "security"? Do we get to apply the best practices of the one to the other because they overlap to a smaller or larger degree?
Once CoPilot can understand entire workspace and have deep understanding of your project, it will likely seem a lot smarter than it seems now.

All I use it for is to avoid repetitive stuff as it's exceptionally good at guessing my next step.

The autocomplete bits feel wrong most of the time, and as fast as API updates happen it's mostly a wash in terms of productivity.
Nice one! I had/have a similar idea, tried to build something, but you know how side projects go… you have a good idea, you register the domain, you get new project fever and try to prepare an MVP but unfortunately something happens and you stop.

You really need to put effort into it in order to create something. As others have said, everyone has ideas, it’s the execution that matters ;-)
GPT-4 was addictive for me. I subscribed to replace online language classes and it was an excellent instructor. After passing the exam I unsubscribed and I'm living fine with 3.5. My screen time definitely lowered :) I bought $5 of GPT-4 API credits to use when 3.5 really can't do the job, but it rarely happens. Asking MS Copilot is also another great way to use GPT-4 for free. On the job I mostly use GH Copilot for code completion and it's great as it provides suggestions that are in line with the code style of my team. On serious tasks all chat bots allucinate and I also feel I'm spending as much time correcting them as if I studied the topic from scratch, because I (want to?) believe what they say but end up wasting a lot of time fixing their suggestions. I'm also thinking about SQL, today it suggested me an `UPDATE table JOIN another_table SET column` and I was surprised I could use JOINs in update statements, but the bot was so sure it was the right keyword. I tried to understand where _else_ the syntax error could be, until I turned back to postgres official documentation and verified there's no JOIN, only FROM, just like I remembered.
It’s incredible for my use case.

I have been involved in software and implementing technical things since the late 90s and from time to time have been pretty good at a few things here and there but I am profoundly rusty in all languages I sort of know and useless in ones I don’t.

But I’m technical. I understand at sort of a core level how things work, jargon, and like the key elements of data structures and object oriented code and a MVC model and whatever else. Like I’ve read the right books.

Without ChatGPT I am close to useless. I’m better off writing a user story and hiring someone, anyone. Yes I can code in rails and know SQL and am actually pretty handy on the command line but like it would take me an entire day and tons of googling to get basic things working.

Then they launched GPT and I can now launch useful working projects that solve business problems quickly. I can patch together an API integration on a Sunday afternoon to populate a table I already have in a few minutes. I can take a website I’m overseeing and add a quick feature.

It’s literally life changing. I already have all the business logic in my head, and I know enough to see what GPT is spitting out and if it’s wrong and know how to ask the right questions.

Unlike the OP I have no plans to do anything complex. But for my use cases it’s turned me from a project manager into a quick and competent developer and that’s literally miraculous from where I’m standing.
It keeps happening.
Oh heck yes, let's get all Baudrillard's Sign Order Stages. Are we here yet?

> Stage 4 is a pure simulacrum, in which the simulacrum has no relationship to any reality whatsoever. Here, signs merely reflect other signs and any claim to reality on the part of images or signs is only of the order of other such claims.

From the article:

> What teenagers today are offered instead is a hyperactive landscape of so-called aesthetics — thousands of them, including everything from the infamous cottagecore to, these days, prep. These are more like cultural atmospheres, performed mainly online, with names and looks and hashtags, an easy visual pablum. They come and go and blend and break apart like clouds in the wind, many within weeks of appearing. They have much content but little context — a lot to look at but a very thin relationship to any "real life" anything, like behaviors or gathering places. On one end, even a distinctly in-the-world subculture (like, say, grunge) can be reduced to a vibe packet of anodyne references (cigarettes, grimy things); on the other, a mere mood tone can be elevated to something offered as lifestyle (there are girls who enjoy the color red and a certain Euro effortlessness, and they are called Tomato Girls, while others who prefer white are called Vanilla Girls). If two dozen things on a Pinterest page feel as if they go together, chances are someone, even just as a lark or experiment, is calling it an aesthetic.

Maybe not Full Stage 4 simulacra/hyperreal but it feels like the meaning is left, the signs are starting to blur & become indistinct, are indeed becoming little more than pointers to other signs. Hyperreal mmay be upon us.

My hackles briefly came up with what seemed a dig at screens; I was afraid it was going to chalk up all the problems of culture to social media. But it avoided that & fell really really close to my feelings, with the hazy lack of resolution that seems due for a weird wild world spinning on:

> It’s the culture available to them that is failing, by no longer being able to connect any of these categories with lived experience or social meaning. Kids, in all their blowzy creativity — the same creativity that invented movements from Romanticism to hippiedom to hip-hop — need more, deserve more.

The local world around us have effervesced into far off high flying supply chains, and we are surrounded by whiz-bang fancy tech we absolutely positively can no longer disassemble & understand & figure out (there's always reams of code and trying to see it is a Felony Anti-Circumvention because the devices terms of service can say that now).

Rebuilding local meaning seems so hard. This is such a challenge.
Then we get back to square one: better no rules at all than secret rules.

This would also be less of a problem if we didn't have a few companies that are economically more powerful than many small countries running everything. At least then I could vote with my feet to go somewhere the rules aren't private.
To me this sounds like an opinion that would be common in the US, mostly because of where the trust and fears seem to be (private companies versus government).

I think everybody (private companies, government, individuals) will try to influence and will affect your personal life. What I am worried about is who has the most efficient way to influence a lot the average person - because that entity can control on long term a lot more.

My impression is that in the European Union - due partially to a complex system - is harder for any particular actor to do much on its own (even the example with Denmark secret service asking NSA for data about citizens - I guess it is harder for them to do that rather than just get directly the data).

So what I am afraid is focused and efficient entities having the data, hence I am more afraid of private companies (which are focused and sometimes efficient) rather than governments.
PerfectMemory is only available on Windows at the moment.
I thought that they teached people to hold it right
The killer app of ai is robots. Like, literally killer but also farmer, cleaner, builder etc
Google's Gemma to run locally on Nvidia GPUs
These tests are also being used to test alternative implementations, such as uutils coreutils[0], in Rust.

0. https://github.com/uutils/coreutils
See also: https://youtu.be/2jeCleMzj1A?si=578UFr6KIl4-IfjS.
Absurd. Systemd was adopted by redhat because it is their baby. And because redhat controls gnome, gnome created a hard depend upon systemd.

Which is why the debian vote went te way it did.

This is history. Systemd was not adopted due to merit.
How many times did that person run the prompt before selecting a result to share?

First try:

https://g.co/gemini/share/e7731082e485

It also generates "caucasian British woman" upon request, but the link-generator broke, so I can't share the result.
If it does, shouldn't there be 60% asians?
What is so strange about it? Nobody directly elects the UK Prime Minister. It's the Westminster System, and it's been around since the 13th century.
One the one-hand, "no". On the other, that's an interesting observation. There are known Θ(N^2) problems, which a linear transformer substitute couldn't possibly solve (in a single pass), and which current transformers might be able to. It's an open question how many of the current LLM capabilities rely on quadratic compute, but linear transformers might be a pipe dream for some current use cases.

Generally though, I don't think that's quite the same. Collapsing all O(N^2) problems down to O(N) would collapse the hierarchy and prove P=NP, but it's blatantly impossible and would prove a lot of falsehoods too.
Hm, what commands are there that don't support filenames?

`tr` is the only one I can think of, excluding ones that really can only operate on already-open file descriptors (the `read` builtin, `flock` in certain modes, ...)
That seems unusual, post some examples?
have you tried codeium? it's pretty good for a free tool, it also does RAG against your whole project
This is untrue. Nash equillibriums in general, and especially in poker, can be exploited in situations where one player is playing a negative EV strategy which happens at loose games. Morton's theorem can be used to derive examples where a player playing optimally under the assumption that all players are acting independently loses out to another player playing suboptimally under that same assumption due to the presense of yet a third player playing a negative EV strategy. These kinds of scenarios are known as implicit collusion, basically the two players who are implicitly colluding together have an advantage over the single player who is making optimal decisions.

Ignoring the fact that there is no known GTO solution for more than 2 players, GTO only applies under the assumption that everyone is acting independently for some fairly formal and strict notion of independence. This requirement is broken in many real world games with more than 2 players potentially playing loose and wild, where they may not actually be colluding or coordinating with one another, but they behave as if they were.
I get a tremendous amount of value from ChatGPT, like you said for things where I would previously have to google -> stack overflow it's incredibly useful. It works as a insanely good search/autocomplete and that is worth a ton. I love being able to sketch a function with an example input/output and have it return something correct, or at least close 95% of the time. As an experienced dev it's easy for me to look at it and get to 100%.

It's also so helpful to be able to just ask questions of the documentation on popular projects, whether it be some nuance of the node APIs or a C websockets library, it saves me countless hours of searching and reading through documentation. Just being able to describe what I want and have it suggest some functions to paste into the actual documentation search bar is invaluable.

Similarly I find it's really helpful when trying to prototype things, the other day I needed to drop an image into a canvas. I don't remember off top exactly how to get a blob out of an .ondrop (or whatever the actual handler is) and I could find it with a couple minutes of google and MDN/SO, but if I ask ChatGPT "write me a minimal example for loading a dropped image into a canvas" I get the exact thing I want in 10 seconds and I can just copy paste the relevant stuff into MDN if I need to understand how the actual API works.

I think you're just using it wrong, and moreover I think it's MUCH MUCH more useful as an experienced engineer than as a beginner. I think I get way more mileage out of it than some of my more junior friends/colleagues because I have a better grasp on what questions to ask, and I can spot it being incorrect more readily. It feels BAD to be honest, like it's further stratifying the space by giving me a tool that puts a huge multiplier on my experience allowing me to work much faster than before and leaving those who are less experienced even further behind. I fear that those entering the space now, working with ChatGPT will learn less of the fundamentals that allow me to leverage it so effectively, and their growth will be slowed.

That's not to say it can't be an incredibly powerful learning tool for someone dedicated to that goal, but I have some fear that it will result in less learning "through osmosis" because junior devs won't be forced into as much of the same problem solving I had to do to be good enough, and perhaps this will allow them to coast longer in mediocrity?
Atuin is simply fantastic. Ellie keeps adding interesting new features. Just an all around fantastically useful tool. Bravo.
the boston dynamics dog was retrofitted with multimodal LLM a few months ago and its snarky as hell

https://youtu.be/djzOBZUFzTw?si=NL4eFyMTAe1FcNhC

timestamp: 5:05
I think puppeteer is the way to go!
Repeat after me: security by obscurity is weak.

Clearly people can work out what some of the rules are, so why not just publish them. If you need to alter them when people figure out how to get around them, well, you already had to anyways.
> Ringgold joined Boeing in 2011 at the company’s North Charleston, S.C., production facility, where she rose to become a senior quality manager.

Wasn't the South Carolina site the one that had all the 787 QC issues?
Geoglify: Open-Source Maritime Data Analysis with Nuxt, DeckGL, Node, MongoDB
Hey Hacker News!

Excited to share Geoglify, an open-source project revolutionizing maritime data analysis. Built with Nuxt.js, Vue.js, Node.js, and MongoDB. Real-time ship tracking, GeoJSON layers, and more.

Join us on GitHub.

Let's explore the seas together!
I understood it perfectly.
Postgres seriously needs a columnstore backed table instead of just a rowstore.

MSSQL has this and it is magic.
SingleStore has it, and it is wonderful.

I'm willing to give a bounty of $1000 to whoever adds that into main postgres tree.

Snowflake is great as a warehouse. it's latency is shit when it comes to fast lookups and aggregates. If you can tolerate >1s api calls, that is fine. It takes forever to insert a few rows in a large table.

If you want a proper live DB, snowflake is a rich man's poor database.
A browser is an extreme case, one of the most difficult types of software and full of stupid minutia and legacy crap. Nobody want to volunteer for that.

Machine learning is fun and ultimately it doesn't require a lot of code. If people have the compute, open source maintainers will have the interest to exploit it due to the high coolness-to-work-required ratio.
Anecdotally people are saying it may actually have gotten significantly worse recently. So it’s possible that the results you might have got from GPT-4 a month ago are no longer anything like as accurate or useful.
I'm very surprised to see that the ignition control is on the slow speed can bus...
E.g. Menwith Hill
Yes, since content moderation is a form of authorization policy.
>really good auto complete engines

What do you think we are?

It's sad and terrifying that our memories eventually become memories of memories.
C-Infinity is a bulky, quixotic solution to VR's nausea problem
Go haters are in full display with this one. LOL

"go run" is yet another wonderful feature of an awesome language.
"turning a big dial taht says 'Racism' on it and constantly looking back at the audience for approval like a contestant on the price is right" - dril
Thanks! I think it's this one if anyone else is interested: https://www.technologyreview.com/2023/06/23/1074321/chip-pat...
This is kind of the attitude that folks at companies had last decade (e.g. early 2010s) which prevented the adoption of deep neural networks until people could no longer overlook their efficacy.

If the NN doesn't get it right, it's absolutely not the end of the world. First of all, triaging the failures usually turns up something interesting / some connecting thread. And as for fixing issues, your options are typically:

1. Retrain with more examples to help it get it right (easier these days since you can take advantage of synthetic data / self- or semi-supervised approaches). Specifically it will help to generate or find examples that approximate the failures.

2. Shape your loss to penalize undesirable behavior / tendencies you've identified in triage

3. Add inputs or outputs that help your net solve the problem better (e.g. side inputs that may help the net not overfit to its available data)

These "inscrutable black boxes," while hard to edit directly, are not impossible to evaluate which means you can just measure to see if they're better than more explicit systems. And most real world systems are patchworks of ML + heuristics to give some desired level of performance.

Furthermore there is a big field of folks dedicated to elucidating what your net is learning (e.g. saliency). The tools are pretty cool -- for example in computer vision you can visualize whether your net is overfitting to background data in your training examples.

A valid critique of all of this is simply that it's expensive. Very expensive to build teams to warehouse and clean huge amounts of data, then build unbiased / fair model evaluations, then build models, then integrate them into real world stuff, usually to be used in conjunction with non-learned algos.

Another valid critique is that many companies have started using models they can't retrain or fix (e.g. off the shelf LLMs). This is dangerous territory since you lose so many of the options I outlined above.
Thank you. Even as a child, maybe because Im an immigrant and learned English in grade school, I was annoyed by the term homophobic. I remember saying a "joke" back around middle school days that I'm not afraid of gay people, I just don't like them.

As an adult now, I don't really care, but just like Ive grown to dislike people that sleep around casually (even though I was one of them from 18-35), I also don't really like the idea of gay hookup culture. Since that seems a lot more popular in the male gay community (I don't know about women), I also generalize that I don't like the popular gay culture either. If liberals/leftists can talk constantly how they don't like trump supporters or conservatives, and even talk down their intelligence levels, I don't see why I'm not allowed to not like someone who believes and does things I don't like.

Calling people like me homophobic doesn't really resonate to me and basically makes me ignore anything beyond that.
The build system should come with it. Even if it requires me to follow conventions, it’s magnitudes better than rolling my own. I can add to it if I need to.

The worst offenders are C and C++ projects. Make? CMake? You’re on your own. During development, it’s so good to be able to just runtime run source like in go and bun.
It can help with complicated tasks. When prompting via the chat interface, it comes more naturally to prompt with a full description of the problem.

With too much assumed context, it only does a good job of spitting out the answer to a common problem, or implementing a mostly correct version of the commonly written task similar to the one requested.

When you use copilot, are you shaping your use to its workflows? Adding preceding comments to describe the high-level goal, the high-level approach, and other considerations for how the code soon to follow interacts with the rest of the codebase?
Neural Wavelet-Domain Diffusion for 3D Shape Generation
Check PDF linked there for images. Really impressive results.