For my purpose, I use bun, though npm, yarn and pnpm (for node) are also supported
bun create astro@latest
You'll be pleasently surprised by a helpful control center
How to quantize 70B model so it will fit on 2x4090 GPUs: | |
I tried EXL2, AutoAWQ, and SqueezeLLM and they all failed for different reasons (issues opened). | |
HQQ worked: | |
I rented a 4x GPU 1TB RAM ($19/hr) instance on runpod with 1024GB container and 1024GB workspace disk space. | |
I think you only need 2x GPU with 80GB VRAM and 512GB+ system RAM so probably overpaid. | |
Note you need to fill in the form to get access to the 70B Meta weights. |
—– BEGIN LICENSE —– | |
Mifeng User | |
Single User License | |
EA7E-1184812 | |
C0DAA9CD 6BE825B5 FF935692 1750523A | |
EDF59D3F A3BD6C96 F8D33866 3F1CCCEA | |
1C25BE4D 25B1C4CC 5110C20E 5246CC42 | |
D232C83B C99CCC42 0E32890C B6CBF018 | |
B1D4C178 2F9DDB16 ABAA74E5 95304BEF | |
9D0CCFA9 8AF8F8E2 1E0A955E 4771A576 |
0x8545
: Original 84
-> 85
0x08FF19
: Original 75
-> EB
0x1932C7
: Original 75
-> 74
(remove UNREGISTERED in title bar, so no need to use a license)I've been writing Rust full-time with a small team for over a year now. Throughout, I've lamented the lack of clear best practices around defining error types. One day, I'd love to write up my journey and enumerate the various strategies I've both seen and tried. Today is not that day.
Today, I want to reply to a blog post that almost perfectly summarised my current practice.
Go read it; I'll wait!
[ | |
"Amaranth", | |
"Amber", | |
"Amethyst", | |
"Apricot", | |
"Aquamarine", | |
"Azure", | |
"Beige", | |
"Black", | |
"Blue", |
// Charles Proxy License | |
// Registration code for any version of Charles, who would want to use a cracked version? | |
// Charles 4.5.5 is currently the latest version and is available. | |
Registered Name: https://zhile.io | |
License Key: 48891cf209c6d32bf4 | |
Author: Neo Peng |
This is not working complete code.
This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.
It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.
To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.
Stay focused, keep building.
This is a tutorial on how to write a fuzzer for a non-trivial real-world library, namely Artem Amirkhanov's CDT. It is a library for computing Constrained Delaunay Triangulations (CDTs, hence the name of the library). We will be working from the 9d99b32ae56b26cd2781678dc4405c98b8679a9f commit, since that is what I originally wrote the fuzzer for, and that way, we will be able to rediscover the same bugs I found back then.
If you want to follow along, clone the library using
$ git clone https://github.com/artem-ogre/CDT
$ cd CDT