skysight-inc/HackerNews huggingface dataset:
I'll give a better example that shows that they don't perform __reasoning__. This specific one was told to me by another HN user[0] when we were discussing similar formulations.

Question:

A farmer must transport a fox, a goose, and a bag of corn from one side of a river to the other using a boat which can hold all items in addition to the farmer. If the farmer leaves the fox alone with the goose, the fox will eat the goose. If the farmer leaves the goose alone with the bag of corn, the goose will eat the bag of corn. How many times must the farmer cross the river to transport all items across? Describe the optimal algorithm, think step by step.

GPT-4-1106-Preview:

The farmer can transport the items across the river with a minimum of 7 crossings using the following steps:

1. Take the goose across the river and leave it there.
2. Return to the original side alone.
3. Take the fox across the river.
4. Bring the goose back with him to the original side.
5. Take the bag of corn across the river and leave it there.
6. Return to the original side alone.
7. Finally, take the goose across the river again.

By following these steps, all items are transported across without the fox eating the goose or the goose eating the corn.

I haven't found a model that will crack this. Of course, you can get it to give the right answer, but this also seems to require spoiling it (this is quite difficult to __not__ spoil the answer).

A key part why I think questions like this are important is because it shows the importance of subtly. LLMs are incredibly impressive machines. Despite being lossy compression I'm not sure why we can't marvel at the fact that we've lossy compressed the entire fucking internet (text at least) into something smaller than a few hundred gigs that also includes a human language interface. What a fucking impressive accomplishment! The talk of AGI really undermines what was done here because damn!

Now I used to ask

Which weighs more, a pound of feathers or a kilogram of bricks.

And most models pass this question now. But it is simpler and due to fewer variations, likely isn't seen as frequently in the dataset so less likely to overfit (the river crossing problem has a lot of variations so an n-gram filter is likely to miss more instances). And eventually this question will be solved too, especially as it is asked more and talked about more. But this is a cat and mouse game. The way to create a new viable and working test is, quite easy and we honestly only need one example to prove the point. If you can't figure out how to create a new version with this example, well, you might just be an LLM :P

[0] Edit: credit goes to @jfim https://news.ycombinator.com/item?id=37825219
The web interface has an 'update all' button thats just as convenient. I find if theres ones i want to not update i just temp break their yaml file with an unexpected keyword and it fails to compile and then update.
Contra Hanson on Medical Effectiveness
For a brief moment I read JSFxr, as Java Server Faces xr and had terrible nightmares from the past
I'm sorry this person had to go through abuse and such difficult feelings to handle. Good luck if you read this! I'm with you.

> Open source is great, but it's not sustainable

It can be (I work for an open source company, our friends at Nextcloud are also fine, there plenty of examples of thriving projects), but it's a hard thing to tackle for sure and might not apply to every kinds of projects. As any kind of business, a business around open source requires a solid business strategy.

I also think dev libraries / components are probably among the hardest things to fund. I think the tools themselves should be open source and the dev subsidized by something else (a product for which the tool is developed, or consulting, or support, or hosting).

You can also sell entreprisy features to companies, even as free software actually (open core is not necessary, and the bulk of the feature can also be in a gratis module and you only sell the convenient UIs). Make it easy to employees of a company using your work to justify an expense. People will be happy to support your work if they like it by making their employer pay, and the employer will most likely be happy to pay because this can be reassuring, especially if support comes with the payment. It's way easier than relying on donations.

In any case, your open source business strategy of course needs to take in account that people will use your open source code for free, and will also report bugs, feedback, feature requests, ask questions, and this is costly. Sometimes you can manage to setup a community where people help each other, addressing the issue partially.

You'll need to have good communication on what is paid for, what are the expectations, etc; and well prepared answers to requests in tickets that both makes it clear you need to keep the lights on and don't piss people off. With good phrasing most people will understand and be grateful you are providing many things for free.

Of course there will be the mean comments. Some are not actually meant to be mean, but just come from people being pissed off or from misunderstandings. Effective communication can help with this. And some just come from jerks, unfortunately. I can only wish you good luck with this. Hang in there. Don't let them hurt you and take bad decisions. Hopefully (fortunately?) they are very few. Keep in mind most people are well meaning, grateful and most are quiet, especially when things work well for them.
LLaMA isn't open source. it's model available (MA), which is very generous of Mr. Zuckerberg, but it's not Open Source.

bad or good, we're forced to live in two worlds. a digital realm, where things can be copied infinitely for free, and a physical realm, where there's rent and food to pay for. so it's bad, because we haven't established a basic level of existence for everybody so people need to get whatever they can get out of it to pay their bills.
You can brew install mas
(I think) and then install/manage Mac store stuff via the cli pretty easily
A lot of folks are missing OP's point about making wearable tech. Flex in the US is just not fast or affordable. There's no reason for it to be (from the fabricator's standpoint).

OTOH, most of the stuff can be and should be prototyped on standard rigid boards.

For standard 2L rigid boards:

If you work in 1-week cycles, you can usually send a PCB out on Thursday or Friday and have it in your hand on Monday or Tuesday to assemble and test. About $40-$100 per round.

If that's not fast enough, a domestic fab shop that can do 1 day turn on bare boards for not terrible pricing - about $150-$200 per round (unless you can will-call at the dock). 2 or 3 day with soldermask/silkscreen will probably be around $300-$500 per round.

Prices are rough estimates off the top of my memory from the shops I've used.
> Unless they have private Apple silicon based servers for their own training needs?

Id be SHOCKED if so. Its been 15 years, but I was there when xserve died. Priorities were iphone > other mobile devices >>> laptops > displays & desktops >>> literally anything else. When xserve died we still needed osx for OD & similar. Teams moved on to 3P rack mount trays of mac minis as a stop gap. Any internal support/preference for server style hardware was a lolwut response. Externally I see no reason to suspect thats changed.
> demonstrate that the LLM will deny that they know fact X (or be flaky about it, randomly denying and divulging the fact)

No, the sampling algorithm you used to query the LLM does that. Not the model itself.

e.g. https://arxiv.org/pdf/2306.03341.pdf

> B. They know they know fact X, but avoid acknowledging for ... reasons?

That reason being that the sampling algorithm didn't successfully sample the answer.
> Because paying $5/gal gasoline and $8 for a carton of eggs is disastrous for me personally.

I get $5/gal gasoline being a bummer (not that I would notice since I have an EV), but where do you live where eggs cost $8 a carton? I can get 18 for < $5, that's like a cart 1.5. Or do you mean at the organic health food place, and they are handled via soft gloves or something?

Seriously, I wonder if Trump supporters are just making bad buying decision and then blaming Bidenomics for it?

When I lived in Switzerland, I had a lot of price shocks to go through, but my stipend was pretty high so I guess it made sense (more money, more expensive eggs). The recent inflation has been kind of like that (but my earnings went up a lot at the same time, maybe that's not usual?).
My family had GM cars when I was a child, decades ago. Never would I seriously consider buying a GM.
> but Rust? Power of sunshine and rainbows? Wishful thinking?

adaptation, active community, more modern features, larger ecosystem
That's what I do at home. I have little Xiaomi LYWSD002MMC and LYWSD003MMC devices (the latter with custom firmware - pvvx) and my radiators are switched on or off (via their pilot wire) by an ESP device, using a few rules to lower the temperature when nobody's there. It works much better than the radiator's own thermostat.

I control my devices (I have a lot more than just for the radiators) through a kind of interface that I wrote myself (PHP and only very few, reliable dependencies) because I hate maintaining Home Assistant.
No, it doesn't know what it knows, because it can't examine itself. It's (in part) a very large lookup table.

External code can do this though.
It has a far more limited selection of apps and Some I use daily are not available so it really doesn’t matter if it works better because for me it doesn’t work at all.
> What do you mean you can write this if you wanted to? Are you suggesting a LLM can invent something new?

Why would I refer to an LLM as "you"? The person who is making the queries can do it with access to the model, assuming they're a programmer with a PhD in ML.
What are people's thoughts on how this compares to:

- EasyLM [1]
- Levanter [2]
- T5X [3]
- and more?

[1]: https://github.com/young-geng/EasyLM
[2]: https://github.com/stanford-crfm/levanter
[3]: https://github.com/google-research/t5x

Asking because I have worked extensively on training a large model on a TPU cluster, and started with Levanter, then tried MaxText, and finally ended up on EasyLM. My thoughts are:

- Levanter is well intentioned but is unproven and lacking in features. For instance, their sharding is odd in that it requires embedding dimension to be a multiple of the number of devices, so I can't test using a model with embedding dimension 768 on a 512-device pod. Lost confidence in Levanter after finding some glaring correctness bugs (and helping get them fixed). Also, while I'm a huge fan of Equinox's approach, it's sadly underdeveloped (for instance, there's no way to specify non-default weight initialization strategies without manually doing model surgery to set weights).

- MaxText was just very difficult to work with. We felt like we were fighting against it every time we needed to change something because we would be digging through numerous needless layers of abstraction. My favorite was after one long day of debugging, I found a function who's only purpose was to pass its arguments to another function untouched; this function's only purpose was to pass its arguments untouched to a new, third function, that then slightly changed them and passed them to a fourth function that did the work.

- EasyLM is, as the name says, easy. But on a deeper dive, the sharding functionality seems to be underdeveloped. What they call "FSDP" is not necessarily true FSDP, it's literally just a certain axis that the JAX mesh is being sharded around that happens to shard some data axes and some model weight axes.

I'm still searching for a "perfect" JAX LLM codebase - any pointers?
We live in a representative democracy where the powers of the government are constrained by constitutional law to be specific and enumerated, and aside from common-law precedent derive from a mandate by the people. Yet it is increasingly the case that the actual rules which citizens and corporations are required to follow are being set not by elected representatives but by unelected bureaucrats of government agencies, in some cases where there isn't even relevant and specific congressional authorization for action. In the case of TFA, the abolition of non-competes is something I can be 100% behind... but where is the specific law passed by congress authorizing the FTC to make and enforce this determination?

One side will say with some justification that these rules make sense and are definitely a net benefit, and we should expect this to be the case because the agency is run by technocrat experts who evaluate these policy decisions for a living and do a far better job than we can expect of even the best congressional staffers.

The other side will point out that it's a very fine line between the current, mostly harmless rule-setting actions of benevolent agency experts, and an unelected deep state that can become a tool of fascist ideologues. Our freedom is dependent on safeguarding our democracy, and that means no rules that don't trace their core to laws passed by elected representatives. These people would point to the disastrous actions of the DEA and FDA, for example, which is currently waging war on ADHD patients via the artificial Adderall shortage, or Operation Choke Point (google it).

Where you fall on this debate is a subjective matter of political opinion. There are pros and cons to both sides.
I think the HN crowd loves the attack on management consultant types.

However, this reads like an over exaggerated Fox news story, trying to create a hero vs villain narrative, written solely for the purpose of personal attack on a person
Offline maps such as Gaia don’t work on Tesla so that is it failing at its job compared to CarPlay
wasn't max a record selling plane? nothing wrong with modernising a 737 variant. plenty wrong with putting MCAS in it which goes haywire when its sole sensor fails
Yes I know, I'm glad you're noticing the distinction. Just take them as a whole.
I have one over complicated in floor heater that requires 2 signals to control, one for opening radiator valve and second to enable fan to blow air through it.
there is only few thermostats dedicated for such setup that are extremely expensive and I decided to make my own that is also better.
i have 2 temperature sensors connected, one to monitor room temperature and the second touching radiator inside, then I'm able to open valve when room temperature is below threshold and wait until radiator is hot enough and we are trying to heat for few minutes (without fan this will also work but less efficiently, but will be silent) then when temperature is reached I close valve and keep fans on until radiator cools down.

I also have temperature reported to home assistant where I have pyscript automation that controls AC based on multiple temperature sensors, open window sensors, humidity, presence, time, etc to most efficiently cool down my apartment

if you need to get signal on a wire, you can output it from the same device, from other esphome device or through home assistant using integration.
his intentions are good his reasons are sound but ultimately he's probably helping the wrong people
In France, there are many laws against free speech (lois mémorielles, anti haine en ligne, anti Le Pen, anti fake news (including criticizing the government), etc...).

https://www.laquadrature.net/en/censorship/

Refusing to disclose encryption keys could lead you to be prosecuted for “terrorist criminal association”.

https://www.laquadrature.net/en/2023/12/15/encryption-discus...
Projects do take a hard line against abusive treatment - the most successful ones ignore or delete those comments altogether. This entire post is about someone reading a comment and not being willing to accept that there's nothing they can do about it besides refute them and move on.
Hi spxneo, this is the author of the tool here.

Thank you for voicing your opinion. We actually talked to a lot of individuals and we found that particularly people who are part of frontend team or part of a QA/QE or CRE team who are not the primary developers of the backend/api were most interested in the LLM to API workflow feature, partly because going through the OpenAPI spec and understanding each endpoint is a lot of work. What our tool does it that it not only identifies the right endpoints and chain them but also pre-fills the request parameters or request body which gives them a chance to quickly experiment with backend.

Of course you always have the option of drag and drop. But we hope that one day using LLMs will get commoditized with models being quite good and then that day a natural language translation will seem obvious

Aside from that one of the major problems that we found that not a lot of folks want to expose their api authentication credentials or in-development/private apis to tools like postman or zappier. So giving an IDE like experience that works as a local desktop app helps alleviate a lot of privacy issues.

Graphical representation of end to end tests was also a big ask
Also I find it CarPlay crashes or hangs you just unplug/replug and it works
I’ve seen several people fine tune mistral 7B on MacBooks.
There are couple of projects aiming to integrate django & pydantic: https://django-ninja.dev for APIs & https://github.com/jordaneremieff/djantic for model data
Take a look a the profile a bit of the rude commenter on your issue. The person is a nobody. The internet is rife with them; any online game, any Discord channel has an asshole that types the same way. All bark, no bite. Block, ban, delete the issue, restrict them from the repo, don't even acknowledge their existence.
I'll agree that Walmart seems an unlikely candidate.
ML has already succeeded to the point that it is ubiquitous and taken for granted. OCR, voice recognition, spam filters, and many other now boring technologies are all based on ML.

Anyone claiming it’s some sort of snake oil shouldn’t be taken seriously. Certainly the current hype around it has given rise to many inappropriate applications, but it’s a wildly successful and ubiquitous technology class that has no replacement.
Well, if the whole thing rested on the shoulders of one person then it wasn't meant to last in the first place.
as an apple fan i can assure you this is exactly what I think, I just prefer the results that Apple's approach enables and wish legislators would let consumers choose it as opposed to forcing the results to look more like Android
That sounds like absolutely awesome world building.
My iPhone 12 has much better reception then my 22 Tacoma even when tossed on the passenger seat.

Like waaaaaay better and more accurate
Hm, I wasn't aware of this web interface. Is it some sort of management panel? Do I have to deploy it on prem?
music lovers!
I have a 2014 Mazda CX-5 and a 2017 Subaru Outback. I have replaced the head units in both with 3rd party units which support CarPlay/AA. Both installations were easy and took about 2-3 hours. Both support changing the car settings through the 3rd party headhunt, integrate with the steering wheel controls, factory backup camera and the factory installed amplifiers (Bose and Harmon Kardon). The key to these integrations were iDatalink Maestro units. I ordered mine through Crutchfield but most decent car audio websites will generate an installation package including the appropriate wire harnesses, iDatalink unit, 2-DIN adapter and trim pieces once you select a head unit to purchase.
Neat post! I'd love to take it for a spin for personal use.
Few things I want more than for someone to extract the Django ORM from Django so we can use it standalone instead of SQLAlchemy
> police are late to this particular arms race and don't realize

It is smarter and simpler than that: Once the use of encryption keys is banned, everyone using one will label himself "I am a terrorist" and will be at least annoyed endlessly by the judicial system.

https://www.laquadrature.net/en/2023/12/15/encryption-discus...
Super cool, love the step by step breakdown!
>LLMs already write English better than most native speakers...

till they incorporate more of what some of your writing and loose their advantages
Tacoma and tundra both come in hybrid now
it's odd to feel empathetic when someone has their livelihood taken from them?
I went into it with that attitude, but my dad talked me into bringing the whole camera setup. I’m glad I did: I enjoyed experiencing the whole thing, and wouldn’t have given that up—but taking some killer photos was the cherry on top, and I’m really glad I did it. YMMV, and I’ve done a ton of astrophotography, so maybe this was more up my alley than for most folks.
When I was younger I thought heroes and villains were real, then I got older and realized heroes and villains are never clear cut (is it even a useful distinction?) - then I got older and realized I was right all along, heroes and villains existed all along.

Evil is real. But so too is good. You’re in the middle stage
The MIT license already bakes "fuck off" into its core. The license does not put any maintenance burden on the creator.

So, choose a regular license, and comport yourself as a maintainer in a manner that tells people to fuck off.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY
Wait so I can get a huge office building for the cost of three mediocre single family homes?
I should buy some real cups and quit drinking out of solocups, but then I remember the water mains are plastic and so are pipes in the house.

Then I remember I smoke cigarettes and drink too much and I will likely expire before I'm 70 anyways.

Don't know why I'm putting money into retirement, but then again God is cruel and I'll end up being be old, weak, dumb, and defenseless at 90. With no children to be my advocate, I'd likely end up being a victim of elder abuse. Hope my gluttonous and slothful life helps me avoid the nightmarish nursing homes.
I think you're right.

Also, the Mazda infotainment in the mid-late 2010s was made by Johnson Controls. The newer systems might be too, I don't know.
https://archive.is/bfgxC
Americans are knowingly giving up that data.
I don’t like generative AI because of how it works. You have close to 0% agency over the final result. When getting a human to do the same work, you’re bound by some social convention which gives the process a more deterministic execution. Maybe you’ll learn how to build the correct prompt to get a more deterministic result, but I doubt it will be worth the effort.

If I write something, the less thing I want is AI to rewrite it. An editor (any human) will tell you why you need to rephase a sentece or eliminate a paragraph, and in the process you grow. I don’t want it to summarize for me, because I won’t know what it threw away. I don’t want it to generate pictures, because there’s no meaning in them. I don’t want it to generate code, because we could do with less code in software (Why a text editor needs to include a whole browser with it?).

Anything we do as human is a series of composable steps, each with its purpose, and mastery is the ability to execute them unconsciously. Get AI to do it and you’ve lost both meaning and mastery.
sell advertising space on the progress bar and charge for faster download speed
What people misunderstand about mouse studies is they much better at telling if something is harmful than if it’s beneficial.

Toss a mouse in a vacuum, expose them to high levels of radiation, etc and the underlying similarities between mammal biology tells you quite a bit. Try and cure baldness and no it’s not that helpful in terms of if something works but it’s a great check it see if you accidentally made a neurotoxin.

So in this case we have quite strong evidence that microplastics from food can get into human bloodstreams. Is it worth testing? That just comes down to an arbitrary assessment of just much you’re willing to spend to reduce what little uncertainty remains.