Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
![Screenshot 2023-12-18 at 10 40 27 PM](https://private-user-images.githubusercontent.com/3837836/291468646-4c30ad72-76ee-4939-a5fb-16b570d38cf2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.OEmVVHRkcbqLvFJEofgZvIIihQ5-MMIbh8GyUOoOXSw)
<artifacts_info> | |
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity. | |
# Good artifacts are... | |
- Substantial content (>15 lines) | |
- Content that the user is likely to modify, iterate on, or take ownership of | |
- Self-contained, complex content that can be understood on its own, without context from the conversation | |
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations) | |
- Content likely to be referenced or reused multiple times |
!pip install pythainlp | |
from pythainlp import word_tokenize | |
from pythainlp.tokenize import DEFAULT_DICT_TRIE as trie | |
# default behavior | |
print(word_tokenize('ฝนตกทั่วฟ้า')) # ['ฝนตก', 'ทั่ว', 'ฟ้า'] | |
# modify behavior | |
trie.remove('ฝนตก') | |
trie.add('ทั่วฟ้า') | |
word_tokenize('ฝนตกทั่วฟ้า') # ['ฝน', 'ตก', 'ทั่วฟ้า'] |
Using this amazing project tmate.io you can SSH tunnel to your colab notebook machine. You can with few lines of code download the binary, run an instance in the background and output the generated SSH address.
!wget -nc https://github.com/tmate-io/tmate/releases/download/2.4.0/tmate-2.4.0-static-linux-i386.tar.xz &> /dev/null
!tar --skip-old-files -xvf tmate-2.4.0-static-linux-i386.tar.xz &> /dev/null
!rm -f nohup.out; bash -ic 'nohup ./tmate-2.4.0-static-linux-i386/tmate -S /tmp/tmate.sock new-session -d & disown -a' >/dev/null 2>&1
!./tmate-2.4.0-static-linux-i386/tmate -S /tmp/tmate.sock wait tmate-ready
Use the gsutil cors command to configure CORS on a bucket:
gsutil cors set cors-json-file.json gs://example
Where cors-json-file.json contains:
#!/usr/bin/env python3 | |
""" | |
License: MIT License | |
Copyright (c) 2023 Miel Donkers | |
Very simple HTTP server in python for logging requests | |
Usage:: | |
./server.py [<port>] | |
""" | |
from http.server import BaseHTTPRequestHandler, HTTPServer |
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