kylemcdonald

jbranchaud

Platforms that support running Playwright + Chromium

• AWS Lambda -- Running End-to-End Tests with Playwright on AWS Lambda
• Cloudflare Workers -- Automate an isolated browser instance with just a few lines of code
• Google Cloud -- Creating an automatic monitoring application with Node.js, Playwright, and Google Sheets on Google Cloud Platform
• Digital Ocean -- How To Run End-to-End Tests Using Playwright and Docker
• BrowserStack -- Run your first Playwright test on BrowserStack
• GitHub Actions -- [Playwright CLI with GitHub Actions CI](https://github.com/mic

kconner

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

snmishra

FROM node:18-slim as builder
ARG PLAYWRIGHT_VERSION
ARG PLAYWRIGHT_BROWSERS_PATH=/ms-playwright
RUN npx playwright@${PLAYWRIGHT_VERSION} install chromium
FROM node:18-slim as runner
ARG PLAYWRIGHT_VERSION
ARG PLAYWRIGHT_BROWSERS_PATH=/ms-playwright
COPY --from=builder /ms-playwright /ms-playwright
RUN npx playwright@${PLAYWRIGHT_VERSION} install-deps chromium
USER node

ericflo

import os
import json
import random
import textwrap
import re
import math

import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset, IterableDataset

tonybaloney

# Delete all forks that haven't been updated since 2020
gh auth refresh -h github.com -s delete_repo
gh search repos \
  --owner tonybaloney \
  --updated="<2020-01-01" \
  --include-forks=only \
  --limit 100 \
  --json url \
--jq ".[] .url" \ | xargs -I {} gh repo delete {} --confirm

rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

veekaybee

Using Whisper to transcribe audio

This episode of Recsperts was transcribed with Whisper from OpenAI, an open-source neural net trained on almost 700 hours of audio. The model includes an encoder-decoder architecture by tokenizing audio into 30-second chunks, normalizing audio samples to the log-Mel scale, and passing the data into an encoder. A decoder is trained to predict the captioned text matching the encoder, and the model includes transcription, as well as timestamp-aligned transcription, and multilingual translation.

The transcription process outputs a single string file, so it's up to the end-user to parse out individual speakers, or run the model [through a sec

dryan

-i https://pypi.org/simple
anyio==3.6.2; python_full_version >= '3.6.2'
certifi==2022.12.7; python_version >= '3.6'
click==8.1.3; python_version >= '3.7'
colorama==0.4.6
commonmark==0.9.1
h11==0.14.0; python_version >= '3.7'
httpcore==0.16.3; python_version >= '3.7'
httpx==0.23.1
idna==3.4

veekaybee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture