TGTGInsighttelegram intelligenceLIVE / telegram public index
← GitHub Trends

TGINSIGHT SIMILAR POSTS

Find similar content

Source channel @githubtrending · Post #14913 · Jul 3

#typescript#boilerplate#boilerplate_code#jamstack#javascript#js_boilerplate#netlify_template#next_js#next_theme#nextjs#nextjs_starter#nextjs_template#react#react_boilerplate#reactjs#starter_kit#starter_project#starter_template#tailwind_css#tailwindcss#typescript You can quickly start a modern web project using a ready-made Next.js boilerplate that includes the latest Next.js 15 features, Tailwind CSS 4, and TypeScript. It offers built-in user authentication, multi-language support, type-safe database tools, error monitoring, AI code reviews, and security features like bot protection. The setup is easy with local and remote database options, automatic testing, and deployment guides. This saves you time and effort by providing a flexible, production-ready foundation with best practices, letting you focus on building your app instead of configuring tools and infrastructure. It also supports smooth development with live reload and VSCode integration. https://github.com/ixartz/Next-js-Boilerplate

Results

2 similar posts found

Search: #array

当前筛选 #array清除筛选
djangoproject

@djangoproject · Post #316 · 04/28/2017, 06:09 AM

https://github.com/blissnd/easyxls Convert any #spreadsheet into a Python internal #dict/#array data structure, for easy processing. Can also handle pivot tables. For pivot table usage, header_row_start & header_col_start need to be set equal to the top left corner of the pivot table => header_row_start=8, header_col_start='c' in the included example. Column IDs must always be lowercase chars in quotes, e.g. 'a'.

djangoproject

@djangoproject · Post #129 · 08/31/2016, 03:36 PM

https://pypi.python.org/pypi/numpy #NumPy is a general-purpose #array-processing package designed to efficiently manipulate large #multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional #arrays. NumPy is built on the #Numeric code base and adds features introduced by #numarray as well as an extended #C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose #data-base applications.