3,906 karma · joined August 13, 2008
- https://www.ludigraphix.org/
- https://itunes.apple.com/fr/app/ludigraphix/id1376937727
Here are some (old) image builds with it:
https://www.flickr.com/photos/_rexxar_/
It's not performative.
At the moment, it sort of work for simple one-file project with no dependencies if you don't mind there is no garbage collector. (it try to compile recursively library imports but linking logic is not implemented)
They had a deficit last year, so they can probably avoid to pay tax this year by balancing last year loss with this year profit.
> Someone didn't get the memo that for LLMs, tokens are units of thinking.
Where do you get this memo ? Seems completely wrong to me. More computation does not translate to more "thinking" if you compute the wrong things (ie things that contribute significantly to the final sentence meaning).If you go on particular laws, you can see the previous versions and how it changed. Example: https://www.legifrance.gouv.fr/codes/section_lc/LEGITEXT0000...
Click on "version" then "comparer" buttons and you will see a diff.
The code of the article: https://github.com/ieviev/mini-gzip/blob/main/src/main.rs
Top level declarations of the C code:
#define MAXBITS 15
#define MAXLCODES 286
#define MAXDCODES 30
#define MAXCODES
#define FIXLCODES 288
struct state
local int bits(struct state *s, int need)
local int stored(struct state *s)
struct huffman
local int decode(...)
local int construct(...)
local int codes(...)
local int fixed(...)
local int dynamic(...)
int puff(...)
Top level declarations of the Rust code: const MAXBITS: usize = 15;
const MAXLCODES: usize = 286;
const MAXDCODES: usize = 30;
const FIXLCODES: usize = 288;
const MAXDIST: usize = 32768;
struct State<'a>
struct Huffman<'a>
fn decode
fn construct
fn codes
fn fixed
fn dynamic
fn stored
pub fn inflateThey are not counter-example. You use the other "in-" prefix that take an adjective and give the opposite adjective, not the one that create a verb from a noun.
Some of the main categories (page 8 of the pdf):
- Construction: -11.0k
- Manufacturing: -12.0k
- Transportation and warehousing: -11.3k
- Private education and health services: -34.0k
- Information -11.0k
- Leisure and hospitality -27.0k
It seems to go down in lots of different sectors.https://web.archive.org/web/20030526120130/http://www.ensta....
There are four courses:
- Expert Systems
- Machine Learning
- Artificial Evolution
- Cognition and Reasoning