代码文件编码都是UTF-8
generic skill
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generic skill
Don't add cmments to evey line. Add just the most important comments to explain some cases or complex logic.
generic skill
1. Sıralamaları her zaman en doğru ve optimum yapılacaklara göre sıralayın.
まず、ユーザーから受け取った指示を確認します:
This extension is utilizing the web tinker package by spatie: https://github.com/spatie/laravel-web-tinker.
.llmrules
2. 你当前正在开发一款 vuepress2.0 的主题,以及一些插件生态。
- NEVER delete files, folders, or directories
site_url: https://swe-bench.com
coverage:
<a href="http://swe-bench.github.io">
- repo: https://github.com/astral-sh/ruff-pre-commit
All notable changes to the PyPI package for SWE-bench ([`swebench`](https://pypi.org/project/swebench/)) will be documented in this file.
__pycache__/
Documentation puts useful information inside other people’s heads. Follow these tips to write better documentation.
DALL·E-3 is the latest version of our DALL-E text-to-image generation models. As the current state of the art in text-to-image generation, DALL·E is capable of generating high-quality images across a wide variety of domains. If you're interested in more technical details of how DALL·E-3 was built, y
The [OpenAI API embeddings endpoint](https://beta.openai.com/docs/guides/embeddings) can be used to measure relatedness or similarity between pieces of text.
When GPT-3 fails on a task, what should you do?
People are writing great tools and papers for improving outputs from GPT. Here are some cool ones we've seen:
The [`gpt-oss` models](https://openai.com/open-models) were trained on the harmony response format for defining conversation structures, generating reasoning output and structuring function calls. If you are not using `gpt-oss` directly but through an API or a provider like Ollama, you will not have
[Large language models][Large language models Blog Post] are functions that map text to text. Given an input string of text, a large language model predicts the text that should come next.
ROOST and OpenAI have prepared a guide that explains how to write policy prompts that maximize [gpt-oss-safeguard's](https://github.com/openai/gpt-oss-safeguard) reasoning power, choose the right policy length for deep analysis, and integrate oss-safeguard's reasoning outputs into production Trust &
Codex and the `gpt-5.2-codex` model (recommended) can be used to implement complex tasks that take significant time to research, design, and implement. The approach described here is one way to prompt the model to implement these tasks and to steer it towards successful completion of a project.