I think only 2 of these are "copy-able":

1. Zero-width space: ‎
2. Zero-width non-joiner: ‌‌‌

SELECT
WHERE
CRUD
Change Configuration
Aggregation & Grouping
Pattern matching

This prompt maintains itself. Each firing updates one recurring task's cron expression, then schedules the next firing at the following US daylight-saving transition. Do not edit it except to set the configuration values below.

• TRIGGER_ID — trig_xxxxxxxxxxxxxxxxxxxxxx (the Claude Cowork cloud scheduled task to keep on time)
• EST_CRON — 0 12 * * *
• EDT_CRON — 0 11 * * *

Picking the right architecture = Picking the right battles + Managing trade-offs

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

No, I'm not talking about Treebeard. Rather, I'm talking about characters that can be used to represent tree structures, especially directories and files in directory structures.

In graph theory, a tree graph a tree is an undirected graph in which any two vertices (nodes like directories and files) are connected by exactly one (1..1) path, or equivalently a connnected acyclic undirected graph. The connections are edges (aka links or lines).

The examples below will represent the following tree (listed in alphabetical order):

root/

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.

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.

• Яндекс
  • Программы стажировок
    • Разработка (Python, C++, Java, Go, Kotlin, Scala, C, Flutter, фронтенд, DevOps, Android, iOS) *Машинное обучение
    • Аналитика данных
    • Информационная безопасность
    • Тестирование
• Менеджмент

This is a collection of books that I've researched, scanned the TOCs of, and am currently working through.  The books are selected based on quality of content, reviews, and reccommendations of various 'best of' lists.

The goal of this collection is to promote mastery of generally applicable programming concepts.

Most topics are covered with Python as the primary language due to its conciseness, which is ideal for learning & practicing new concepts with minimal syntactic boilerplate.

JavaScript & Kotlin are listed in the Tooling section; as they allow extension of VS Code and the IntelliJ suite of IDEs, which cover most development needs.

>>> docker exec -it CONTAINERID /bin/sh
/app # telnet
/bin/sh: telnet: not found

/app # apk update
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
v3.7.0-243-gf26e75a186 [http://dl-cdn.alpinelinux.org/alpine/v3.7/main]
v3.7.0-229-g087f28e29d [http://dl-cdn.alpinelinux.org/alpine/v3.7/community]