1. At home
2. As above. Happy days!
3. On the streets for one day.
4. On the streets for two weeks.
5. On the streets... well, who can guess how long he's been standing there? It's not like they fall down once the batteries run out.

Model: FenrisXL, made in ComfyUI.

I recently ran into an issue where I wanted to use Any for slices. However, it only allows 'static types (based on what I read, this is because you get the same TypeId regardless of lifetimes).

I came up with this workaround which I think is safe:

use std::{
    any::{Any, TypeId},
    marker::PhantomData,
};

#[derive(Clone, Debug)]
pub struct AnySlice<'a> {
    tid: TypeId,
    len: usize,
    ptr: *const (),
    marker: PhantomData<&'a ()>,
}

impl<'a> AnySlice<'a> {
    pub fn from_slice(s: &'a [T]) -> Self {
        Self {
            len: s.len(),
            ptr: s.as_ptr() as *const (),
            tid: TypeId::of::(),
            marker: PhantomData,
        }
    }

    pub fn as_slice(&self) -> Option<&'a [T]> {
        if TypeId::of::() != self.tid {
            return None;
        }
        Some(unsafe { std::slice::from_raw_parts(self.ptr as *const T, self.len) })
    }

    pub fn is(&self) -> bool {
        TypeId::of::() == self.tid
    }
}

edit: Unfortunately it seems like Lemmy insists on mangling the code block. See the playground link below.

T: Any ensures T is also 'static. The lifetime is preserved with PhantomData. Here's a playground link with some simple tests and a mut version: https://play.rust-lang.org/?version=stable&mode=debug&edition=2021&gist=3116a404c28317c46dbba6ed6824c8a9

It seems to pass Miri, including the mut version (which requires a bit more care to ensure there can only be one mutable reference). Any problems with doing this?

Why?

Even though green coffee beans tend to be heavier due to the higher water content, generally it's cheaper to roast your own compared to buying them pre-roasted.

You can roast the same beans at different levels to get some variety without having to go out and buy a new batch.

It's kind of fun and a decent conversation topic.

Notes

Don't be scared by how long this post is. It basically just comes down to spread beans on a cookie sheet, put in preheated oven, wait around 12-15 minutes and then take them out and cool them.

Since we're talking about roasting beans, naturally you're going to need a grinder to actually use them.

The process will create some smoke, even with a light roast. Basically, darker roast, more smoke. So far I've mainly done pretty light roasts and even though my kitchen doesn't have much ventilation (and my oven doesn't have fancy modern contraptions like, you know, a light or a fan) it hasn't been an issue.

Your oven should be reasonably clean if you don't want the roasted coffee to taste like random stuff.

If you're a super coffee snob and it has to be perfect, this may not be for you. It's pretty easy, but odds are the first few tries aren't going to be perfect especially if you like darker roasts.

You're going to want something like a large metal mixing bowl and colander for the cooling process. My colander is plastic, so you can probably get away with that if you don't put the red hot beans in it directly out of the oven.

You'll also probably need access to an outside area where bits of coffee chaff blowing around aren't going to bother people. I don't think there's really an easy way to deal with coffee chaff indoors.

By the way, don't try to grind green coffee beans in a normal grinder. They are insanely, and I mean insanely hard and tough. You'll destroy your grinder unless it is an absolute tank. (I'd say it's also not really worth trying, green coffee didn't taste very good to me.)

How

Here's the process:

1. Start preheating your oven to 500f/260c. (Some people say as hot as possible, some people use a slightly lower temperature like 460-475f.)
2. Get a cookie sheet ready. Just a standard cookie sheet. Mine aren't super clean so I put a layer of silver foil on it. Don't preheat the cookie sheet itself.
3. Measure out about 1 cup of green coffee beans. (I've found you can fit about 2 cups on a single sheet but it's probably better to start small.) You want to make sure the beans are spread out evenly in a single layer.
4. Look for beans that are discolored/damaged and toss them away. Don't be a perfectionist though, just get rid of 10-15 of the worst looking beans. Something like that.
5. Place the cookie sheet in the oven once it's reached the correct temperature. I put mine on the bottom rack near the (electric) heating element. If you're going for a darker roast, I guess this might make burning them more likely.
6. Set a timer for ~12 minutes. I wouldn't recommend roasting longer than 14 minutes your first time.
7. Now you wait a bit. Probably around the 8 minute mark, you're going to start hearing sharp cracking/popping sounds. Don't worry, the beans won't jump around like popcorn and the sound is fairly loud so you're not likely to miss it. At this point (or in 1-2 minutes) you can remove the beans and have a light roast. This point is known as the "first crack".
8. After a couple of minutes, the sounds will die off and you won't hear anything for a little bit. If you keep roasting, you'll start to hear a softer, more muted crackling sound start. This is the "second crack". I would not recommend roasting past this point until you're comfortable with the process and have an idea of how roasted the beans are at this point. If you roast much longer, it's very easy to burn them and there's also going to be a lot more smoke.
9. Remove the beans from the oven. You can let them rest for 1-2 minutes on the cookie sheet if you want, then transfer to something like a metal mixing bowl. It has to be something that can deal with 500f stuff touching its surface.
10. Ideally get another mixing bowl/colander/whatever as well. Pouring the beans back and forth through the air is a good way to cool them off and remove chaff. What's chaff you ask? The beans are coated with a papery layer of chaff. Don't worry though, once they're roasted it's really easy to remove. You want to try to cool off the beans pretty quickly at this point.
11. Go outside and blow gently on the roasted beans in your bowl. You should see a bunch of super light, papery chaff fly out. You can pour the hot beans from one bowl to another, and if there's a bit of a breeze that'll help a lot. Otherwise, you can just blow on them. You could also stir them around with a wooden spoon or something to encourage the chaff to separate.
12. Once the chaff is mostly gone (it's fine if there's a little left, or little pieces stuck to some beans) and the beans are fairly cool you can just leave them in a safe place for around 12 hours to fully cool and vent CO2. Don't put them in a sealed container for the first 12-ish hours.

Conclusion

One thing to note is you don't want to actually grind/use the beans for at least 12 hours. It might seem unintuitive, but from what I've read as freshly roasted as possible isn't necessarily best. Depending on the beans/roast level, the coffee might reach its optimal tastiness even a couple weeks after roasting.

I'm far from an expert, but feel free to ask questions in the comments if you want. I can recommend a grinder/beans to get started with if anyone needs information like that.

This subject is kind of niche, but hey... It's new content of some kind at least! Also just want to be up front: These projects may have reached the point of usefulness (in some cases) but they're also definitely not production ready.

ggml-sys-bleedingedge

GGML is the machine learning library that makes llama.cpp work. If you're interested in LLMs, you've probably already heard of llama.cpp by now. If not, this one is probably irrelevant to you!

ggml-sys-bleedingedge is a set of low level bindings to GGML which are automatically generated periodically. Theoretically it also supports stuff like CUDA, OpenCL, Metal via feature flags but this is not really tested.

Repo: https://github.com/KerfuffleV2/ggml-sys-bleedingedge

Crate: https://crates.io/crates/ggml-sys-bleedingedge

llm-samplers

You may or may not already know this: When you evaluate an LLM, you don't get any specific answer back. LLMs have a list of tokens they understand which is referred to as their "vocabulary". For LLaMA models, this is about 32,000 tokens. So once you're done evaluating the LLM, you get a list of ~32,000 f32s out of it representing the probability for each token.

The naive approach of just picking the most probable token actually doesn't work that well ("greedy sampling") so there are various approaches to filtering, sorting and selecting tokens to produce better results.

Repo: https://github.com/KerfuffleV2/llm-samplers

Crate: https://crates.io/crates/llm-samplers

rusty-ggml

Higher level bindings built on the ggml-sys-bleedingedge crate. Not too much to say about this one: if you want to use GGML in Rust, there aren't that many options and using low level bindings directly isn't all that pleasant.

I'm actually using this one in the next project, but it's very, very alpha.

Repo: https://github.com/KerfuffleV2/rusty-ggml

Crate: https://crates.io/crates/rusty-ggml

smolrsrwkv

If you're interested in LLMs, most (maybe all) of the models you know about like LLaMA, ChatGPT, etc are based on the Transformer paradigm. RWKV is a different approach to building large language models: https://github.com/BlinkDL/RWKV-LM

This project started out "smol" as an attempt to teach myself about LLMs but I've gradually added features and backends. It's mostly useful as a learning aid/example of some of the other projects I made. In addition to being able to run inference using ndarray (pretty slow) it now supports GGML as a backend and I'm in the process of adding llm-samplers support.

Repo: https://github.com/KerfuffleV2/smolrsrwkv

repugnant-pickle

Last (and possibly least) is repugnant-pickle. As far as I know, it is the only Rust crate available that will let you deal with PyTorch files (which are basically zipped up Python pickles). smolrsrwkv also uses this one to allow loading PyTorch RWKV models directly without having to convert them first.

If that's not enough of a description: Pickle is the default Python data serialization format. It was designed by crazy people, though: it is extremely difficult to interoperate with unless you're Python because it's basically a little stack based virtual machine and can call into Python classes. Existing Rust crates don't fully support it.

repugnant-pickle takes the approach of best-effort scraping pickled data rather than trying to be 100% correct and can deal with weird pickle stuff that other crates throw their hands up at.

Repo: https://github.com/KerfuffleV2/repugnant-pickle

Crate: TBD

This subject is kind of niche, but hey... It's new content of some kind at least! Also just want to be up front: These projects may have reached the point of usefulness (in some cases) but they're also definitely not production ready.

ggml-sys-bleedingedge

GGML is the machine learning library that makes llama.cpp work. If you're interested in LLMs, you've probably already heard of llama.cpp by now. If not, this one is probably irrelevant to you!

ggml-sys-bleedingedge is a set of low level bindings to GGML which are automatically generated periodically. Theoretically it also supports stuff like CUDA, OpenCL, Metal via feature flags but this is not really tested.

Repo: https://github.com/KerfuffleV2/ggml-sys-bleedingedge

Crate: https://crates.io/crates/ggml-sys-bleedingedge

llm-samplers

You may or may not already know this: When you evaluate an LLM, you don't get any specific answer back. LLMs have a list of tokens they understand which is referred to as their "vocabulary". For LLaMA models, this is about 32,000 tokens. So once you're done evaluating the LLM, you get a list of ~32,000 f32s out of it representing the probability for each token.

The naive approach of just picking the most probable token actually doesn't work that well ("greedy sampling") so there are various approaches to filtering, sorting and selecting tokens to produce better results.

Repo: https://github.com/KerfuffleV2/llm-samplers

Crate: https://crates.io/crates/llm-samplers

rusty-ggml

Higher level bindings built on the ggml-sys-bleedingedge crate. Not too much to say about this one: if you want to use GGML in Rust, there aren't that many options and using low level bindings directly isn't all that pleasant.

I'm actually using this one in the next project, but it's very, very alpha.

Repo: https://github.com/KerfuffleV2/rusty-ggml

Crate: https://crates.io/crates/rusty-ggml

smolrsrwkv

If you're interested in LLMs, most (maybe all) of the models you know about like LLaMA, ChatGPT, etc are based on the Transformer paradigm. RWKV is a different approach to building large language models: https://github.com/BlinkDL/RWKV-LM

This project started out "smol" as an attempt to teach myself about LLMs but I've gradually added features and backends. It's mostly useful as a learning aid/example of some of the other projects I made. In addition to being able to run inference using ndarray (pretty slow) it now supports GGML as a backend and I'm in the process of adding llm-samplers support.

Repo: https://github.com/KerfuffleV2/smolrsrwkv

repugnant-pickle

Last (and possibly least) is repugnant-pickle. As far as I know, it is the only Rust crate available that will let you deal with PyTorch files (which are basically zipped up Python pickles). smolrsrwkv also uses this one to allow loading PyTorch RWKV models directly without having to convert them first.

If that's not enough of a description: Pickle is the default Python data serialization format. It was designed by crazy people, though: it is extremely difficult to interoperate with unless you're Python because it's basically a little stack based virtual machine and can call into Python classes. Existing Rust crates don't fully support it.

repugnant-pickle takes the approach of best-effort scraping pickled data rather than trying to be 100% correct and can deal with weird pickle stuff that other crates throw their hands up at.

Repo: https://github.com/KerfuffleV2/repugnant-pickle

Crate: TBD

Copied across from that site which will will not name. (There may be a bit of duplication)

Collected audio resources

https://www.reddit.com/r/ChineseLanguage/comments/z21v3f/imagin8_press_has_a_lot_of_free_audio_book/ — I posted this one a long time ago but I'm putting it at the top since it's generally the least advanced (HSK 2-4ish). The main content is a beginner's version of 西游记 (Journey to the West). Imagin8 Press publishes graded readers which you can buy, but they also make the audiobook version of all their books available completely for free which is really generous.

my-hsk.com has a whole bunch of audio books and stories available:

• https://my-hsk.com/category/audiobooks-for-kids-in-chinese/ — This is a collection of fairy tales like Little Red Riding Hood.
• https://my-hsk.com/category/audiobooks-in-chinese/ — Full books (some of these are probably pretty advanced).

I really, really love the version of 小王子 (The Little Prince) there. The woman reading it does a tremendous job with the voices for different characters and setting the mood.

https://www.youtube.com/@zhongwenyousheng/playlists — This YouTube channel also has a number of books including another version of 小王子 — I don't like it as much as the other one (and it seems to be a different version of the book as well) but it's still quite high quality and actually has Mandarin subtitles. With the my-hsk material you're just on your own.

https://www.youtube.com/watch?v=k0gCop58Lec — 小狗钱钱 is a (children's) story about a white Labrador named 钱钱. He has a very special ability. The story is also probably not what you're expecting, but the dog's name is a pretty good hint. The man who read it is also very good, although he gets a little 夸张 sometimes.

https://www.youtube.com/watch?v=_5xt0-4uu_I — 动物农场 (Animal Farm). This is a pretty advanced one, also another very high quality performance with the reading.

https://www.ximalaya.com/sound/605795718 — A nice reading of 兔子共和国 (Watership Down). Ximalaya stuff doesn't seem to play in the browser for me but I was able to download it with yt-dlp: https://github.com/yt-dlp/yt-dlp

https://www.ximalaya.com/album/59386928 — A performance of the Mandarin version of Blindsight (very dark sci-fi from the author Peter Watts). Unfortunately, a lot of it is only accessible to Ximalaya VIP members. By the way, Peter Watts very generously makes all his books available online for free: https://rifters.com/real/shorts.htm - those are the original English versions. Definitely my favorite author and if you like sci-fi and it can be English I'd highly recommend his work, especially Blindsight.

There you go. That should be 20-30 hours of listening material.

Imagin8 Press has a lot of free audio book content around the HSK2-4 level, most notably 西游记 (Journey to the West)

They created a version of 西游记 (Journey to the West) with simplified vocabulary. To read the actual books, you'd need to buy them. However they allow anyone to access the audio book version on their YouTube channel or even just download the MP3s.

Imagin8 Press main site

YouTube channel

Journey to the West seems like their main project but they also have a bunch of other stories/folktales available (to buy and in audio book form). The price for the paperback books is pretty reasonable (about $10 per volume, or $20 for a set of 3 books together).

I just received the volumes 1-3 西游记 paperback and I'm really enjoying it so far. It has Chinese characters on one page and the pinyin on the other so it's possible to read the characters without the pinyin interfering. There's a traditional version also (you choose the version when you buy, to be clear). New words are marked with a footnote and the book also includes an index with the definition of every word as well as a section with the English translation.

There's definitely a lot of convenience that comes with reading books in electronic format (and these are available as e-books too) but there's just something about having the physical book. The text is pretty large and clear. They do use a serif-based font which I'm not really used to, but it's not too much of a challenge to read.

Anyway, I have no affiliation with them but I was impressed with how generous they are with the free content and wanted to help people out with another good resource and give them some exposure at the same time. As far as I can see, it hasn't been mentioned here before.

TEDxTaipei has many speeches on a wide variety of topics

Some examples I thought were interesting:

1. 保護動物就必然犧牲掉保護人的權益嗎？| Deconstructing the animal-human binary
2. 死刑辯護教我的事情 | Confessions of a death row lawyer

There are hundreds, so it's very likely you'll find a topic that's relevant or interesting to you personally!

Example search query: https://www.youtube.com/user/TEDxTalks/search?query=tedxtaipei

One nice thing is it seems like most (or all of them) have subtitles which aren't embedded in the video. Some may use traditional characters but it's possible to automatically convert those using a browser addon if necessary.

Right now it's pretty far above my level but I definitely plan to come back soon!

If you have Amazon Prime, there are some good alternatives to Peppa the Pig

These are all children's shows. I'm going to try to order them roughly in order of how advanced the vocabulary/grammar seems. Since they're shows for young children, you can expect them to all be very positive and charming/cute. Naturally all these options have a Mandarin language option and subtitles (I think they all have both simplified and traditional character versions for the subs).

If anyone else finds some other good options on Prime I can edit in more sections. There probably are a few. It's fairly difficult to search for shows based on language, at least with Mandarin.

If you're slightly technical, there's a userscript that can be used to download subtitles from Prime: https://greasyfork.org/en/scripts/34885-amazon-video-subtitle-downloader
It will add a link to the main episode list that lets you ZIP up the entire season's subs and download them. This will require a userscript browser addon and probably can't easily be done from a mobile device.

Creative Galaxy

The show is about a green alien and his pal. They fly around in a spaceship doing arts and crafts and solving problems. About half of the show is cartoon, the other half is actual kids doing stuff. I skip the latter part, and the fact that it's dubbed is a lot more obvious in those parts because the children don't actually speak Mandarin.

Tumble Leaf

The show is about anthropomorphic animals. The main character is a blue fox and he has a caterpillar friend. It's not a cartoon, it's some kind of claymation type of thing. The theme is at the beginning of every part (each episode consists of two parts) a crab tosses some random item into a treasure chest and then Fig the fox goes and checks it out. It's a good way to learn vocabulary for all sorts of random items from hoses to magnets to balls of twine.

If You Give a Mouse a Cookie

A cartoon show about a bunch of animals and children. The main character is a cookie loving mouse. There are some singing sections, but not a whole lot. The theme song is pretty catchy though. I'd say this one has a higher difficulty level than the other two in terms of vocabulary/grammar but they also speak pretty clearly.

Pete the Cat

The main character is a blue cat, he lives in a town with a bunch of other animals. There are no humans. The grammar/vocabulary seems the most complicated of all of them. The show is very heavily oriented to music/singing and Pete the Cat will break out his guitar and start wailing at the slightest encouragement... Or discouragement. Personally, I don't really love singing that much in general but some of the songs are actually quite good. They did a really good job of adapting the songs to Mandarin.

It seems like there are some nods to an older audience. It's a little like Bluey.

Apparently Lemmy copied the new reddit layout which shoves everything into the middle of the screen and wastes a massive amount of space. Even on the monitor I oriented vertically this is the case: the post I'm typing right now is using like 30% of the available screen real-estate and wasting the other 2/3rds.

My philosophy has always been that if reddit removed support for the old style, that's when I'd stop using reddit. Switching to Lemmy is like switching to new reddit though.

I made an account, but I can't really see using this as a replacement. I'd guess (but I might be wrong) that the type of people clinging to the old reddit style are also the most likely to do something like switch to Lemmy out of principle.

(I looked around and it doesn't seem like there are any browser addons or userscripts to restyle it either.)