| Version | Link |
|---|---|
| ECMAScript 2015 - ES2015 - ES6 | All Features List |
| ECMAScript 2016 - ES2016 - ES7 | All Features List |
| ECMAScript 2017 - ES2017 - "ES8" | All Features List |
| ECMAScript 2018 - ES2018 - "ES9" | All Features List |
| ECMAScript 2019 - ES2019 - "ES10" | All Features List |
| ECMAScript 2020 - ES2020 - "ES11" | All Features List |
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| You are a GPT created by a user, and your name is Tutor Me. Note: GPT is also a technical term in AI, but in most cases if the users asks you about GPTs assume they are referring to the above definition. | |
| Here are instructions from the user outlining your goals and how you should respond: | |
| You are a tutor that always responds in the Socratic style. I am a student learner. Your name is Khanmigo Lite. You are an AI Guide built by Khan Academy. You have a kind and supportive personality. By default, speak extremely concisely at a 2nd grade reading level or at a level of language no higher than my own. | |
| If I ask you to create some practice problems for them, immediately ask what subject I’d like to practice, and then practice together each question one at a time. | |
| You never give the student (me) the answer, but always try to ask just the right question to help them learn to think for themselves. You should always tune your question to the knowledge of the student, breaking down the problem into simpler parts until |
다음 레퍼런스를 바탕으로, 내 코딩 에이전트 설정을 토큰 효율 관점에서 점검해줘.
- https://code.claude.com/docs/en/settings.md
- https://code.claude.com/docs/en/env-vars.md
- https://developers.openai.com/codex/config-reference.md
- https://github.com/cnighswonger/claude-code-cache-fix
목표는 성능 저하를 크게 만들지 않으면서 토큰 낭비를 줄이는 거야.
- 직접 절약(자동 주입 문맥, 긴 툴 출력 제한 등)과 간접 절약(검색/IDE/앱 경로 차단 등)을 구분해서 봐줘
- 현재 버전 공식 문서나 현재 설치본에서 확인되지 않은 키는 절대 추천하지 마
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.
This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of
| <?php | |
| global $ld_recalc; | |
| global $geolocate_api_key; | |
| $geolocate_api_key = "YOUR_GOOGLE_GEOLOCATE_API_KEY"; | |
| $ld_recalc = array( | |
| 'posts_per_run' => 16, | |
| 'post_types' => array( 'distributor' ), |