augbog

Frontend Technical Interview Prep

EDIT: Well this has been linked now so just an FYI this is still TBD. Feel free to comment if you have suggestions for improvements. Also here is an unrolled Twitter thread of a lot of the tips I talk about on here.

I've been doing frontend for a while now and one thing that really gripes me is the interview. I think the breadth of knowledge of a "Frontend Engineer" has been so poorly defined that people really just expected you to know everything. Many companies have made this a hybrid role. The Web is massive and there are many MANY things to know. Some of these things are just facts that you learn and others are things you really have to understand.

Every time I interview, I go over the same stuff. I wanted to create a gist of the TL;DR things that would jog my memory and hopefully yours too.

Lots of these things are real things I've been asked that caught me off guard. It's nice to have something you ca

leo-aa88

Guide to Connecting to a Bluetooth Device using bluetoothctl

This guide walks you through the process of connecting to a Bluetooth device on a Linux system using the bluetoothctl command-line tool. It covers initial setup, scanning, pairing, and connecting procedures, along with troubleshooting tips.

Prerequisites

• A Linux distribution with Bluetooth support (e.g., Arch Linux).
• The bluez package installed, which provides bluetoothctl and related utilities.
• A working Bluetooth adapter installed on your system.
• Sudo privileges to execute commands that require root access.

arbaes

#!/usr/bin/env bash
# Atomic Arch / atomic-lockfile AUR campaign check
# Sources:
# - https://lists.archlinux.org/archives/list/aur-general@lists.archlinux.org/thread/FGXPCB3ZVCJIV7FX323SBAX2JHYB7ZS4/
# - https://www.sonatype.com/blog/atomic-arch-npm-campaign-adds-malicious-dependency
# - https://ioctl.fail/preliminary-analysis-of-aur-malware/
set -uo pipefail

# Known IOC strings: the malicious npm dep names this campaign rotates through,
# plus the payload's path inside the npm package ("preinstall": "./src/hooks/deps").

damonzwicker

Composition Note: ERC-8004 + ERC-8263 + OCP

A reference guide for implementers building on the AI agent verification stack

Co-authored by:

• Vincent Wu — ERC-8263 (Onchain Proof Layer for AI Agents)
• Damon Zwicker — Observation Commitment Protocol (OCP)
• Tiago Merlini — ERC-8004 / WYRIWE / execution-attestation profile

---

wesleyit

##   CRONTAB HINTS AND TIPS
##   
##   
##   Entry              Description     Equivalent To
##   @yearly (or @annually)     Run once a year at midnight in the morning of January 1         0 0 1 1 *
##   @monthly   Run once a month at midnight in the morning of the first of the month   0 0 1 * *
##   @weekly    Run once a week at midnight in the morning of Sunday    0 0 * * 0
##   @daily             Run once a day at midnight      0 0 * * *
##   @hourly    Run once an hour at the beginning of the hour   0 * * * *
##   @reboot    Run at startup  @reboot

0xdevalias

Bypassing Cloudflare, Akamai, etc

Some notes/resources for bypassing anti-bot/scraping features on Cloudflare, Akamai, etc.

Table of Contents

• Notes
• See Also
• My Other Related Deepdive Gist's and Projects

if0rest

Очередная инструкция на случай амнезии.

---

YT-DLP - БАЗИРОВАННАЯ тулза для скачивания контента с медиахостингов, и многие проги ей завидуют хранят под капотом именно её ядро. Оседлав этого жеребца, смотришь на других наездников, как на бедолаг с раздолбанным очком. Приступим.

1. yt-dlp работает в связке с ffmpeg, поэтому скачиваем свежие релизы первого и второго и распаковываем исполняемые файлы в одну папку.

2. Поскольку yt-dlp работает через CLI, чтобы вызывать его из любого места добавим путь к программе в переменную среды Windows PATH.

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

jweinst1

from alpaca.data.historical import StockHistoricalDataClient
from alpaca.data.requests import StockSnapshotRequest
from alpaca.data.enums import DataFeed
from alpaca.data.historical import OptionHistoricalDataClient
from alpaca.data.requests import OptionChainRequest, StockBarsRequest
from alpaca.data.enums import OptionsFeed
from alpaca.trading.enums import ContractType, AssetClass
from alpaca.trading.enums import QueryOrderStatus, OrderSide, OrderClass, TimeInForce, OrderStatus, OrderType
from alpaca.trading.client import TradingClient
from alpaca.trading.requests import GetOrdersRequest, LimitOrderRequest, TakeProfitRequest, StopLimitOrderRequest, GetOptionContractsRequest

interfect

#!/usr/bin/env bash
# castanet.sh: Script to connect a chromecast to a WiFi network.
#
# Allows you to put your Chromecast on WiFi and do Chromecast initial setup
# without using the Google Home app at all, just using a normal Linux computer.
#
# You do need your Chromecast to be on Ethernet, or (untested) to join its setup WiFi
# network with your PC, and you also need to find out its IP yourself with e.g.
# Wireshark.