Adam Daniels

Adam Daniels

1597820995

Tailwind CSS v1.7.0

Another new Tailwind release is here! This time with support for gradients, background-clip, experimental support for using @apply with variant utilities, and tons more. Let’s dig in!

New features

Gradients

The big one for this release — Tailwind now ships with built-in support for background gradients!

Gradients are designed with a highly composable API that lets you specify up to three color stops in one of 8 directions by default:

<div class="bg-gradient-to-r from-orange-400 via-red-500 to-pink-500">
  <!-- ... -->
</div>

This is made possible by a new backgroundImage core plugin (which you can use for any background images you like!) and a new gradientColorStops core plugin.

The default configuration for these plugins looks like this:

// tailwind.config.js
module.exports = {
  theme: {
    backgroundImage: {
      'gradient-to-t': 'linear-gradient(to top, var(--gradient-color-stops))',
      'gradient-to-tr': 'linear-gradient(to top right, var(--gradient-color-stops))',
      'gradient-to-r': 'linear-gradient(to right, var(--gradient-color-stops))',
      'gradient-to-br': 'linear-gradient(to bottom right, var(--gradient-color-stops))',
      'gradient-to-b': 'linear-gradient(to bottom, var(--gradient-color-stops))',
      'gradient-to-bl': 'linear-gradient(to bottom left, var(--gradient-color-stops))',
      'gradient-to-l': 'linear-gradient(to left, var(--gradient-color-stops))',
      'gradient-to-tl': 'linear-gradient(to top left, var(--gradient-color-stops))',
    },
    gradientColorStops: (theme) => theme('colors'),
  },
  variants: {
    backgroundImage: ['responsive'],
    gradientColorStops: ['responsive', 'hover', 'focus'],
  },
}

Learn more the original pull request.

New background-clip utilities

We’ve also added a new backgroundClip core plugin that you can use to control how background are rendered within an element.

It includes 4 new utilities:

ClassCSSbg-clip-borderbackground-clip: border-boxbg-clip-paddingbackground-clip: padding-boxbg-clip-contentbackground-clip: content-boxbg-clip-textbackground-clip: text

Combined with the new gradient features, you can use this to do cool gradient text stuff like this:

<h1 class="text-6xl font-bold">
  <span class="bg-clip-text text-transparent bg-gradient-to-r from-teal-400 to-blue-500">
    Greetings from Tailwind v1.7.
  </span>
</h1>

Only responsive variants are enabled for the backgroundClip plugin by default:

// tailwind.config.js
module.exports = {
  variants: {
    backgroundClip: ['responsive'],
  },
}

New gap utility aliases

For some dumb reason I named the column-gap and row-gap utilities col-gap-{n} and row-gap-{n} respectively, which isn’t terrible but it’s not consistent with how other things in Tailwind are named.

I was finding myself getting them wrong all the time — is row-gap the gaps in a row, or the gap between rows?

Tailwind v1.7 introduces new gap-x-{n} and gap-y-{n} utilities that do the exact same thing but have names that don’t suck. They make way more sense than the actual CSS names now that gap for flexbox is starting to roll out too, since flexbox has no “columns”.

These utilities will replace the old ones in v2.0, but for now they both exist together.

We recommend migrating to the new names now, and disabling the old names using this feature flag:

// tailwind.config.js
module.exports = {
  future: {
    removeDeprecatedGapUtilities: true,
  },
  // ...
}

Tailwind will issue a warning in the console to remind you that you are including deprecated classes in your build until you enable this flag.

New contents display utility

We’ve added a new contents class for the recent display: contents CSS feature.

<div class="flex">
  <div><!-- ... --></div>
  <!-- This container will act as a phantom container, and its children will be treated as part of the parent flex container -->
  <div class="contents">
    <div><!-- ... --></div>
    <div><!-- ... --></div>
  </div>
  <div><!-- ... --></div>
</div>

Learn more about it in this great article by Rachel Andrew.

Default letter-spacing per font-size

You can now configure a default letter-spacing value for each font-size in your tailwind.config.js theme, using a tuple syntax:

// tailwind.config.js
module.exports = {
  theme: {
    fontSize: {
      2xl: ['24px', {
        letterSpacing: '-0.01em',
      }],
      // Or with a default line-height as well
      3xl: ['32px', {
        letterSpacing: '-0.02em',
        lineHeight: '40px',
      }],
    }
  }
}

This new syntax is supported in addition to the simpler [{fontSize}, {lineHeight}] syntax that was recently introduced.

Divide border styles

We’ve added utilities for setting the border style on the divide utilities:

<div class="divide-y divide-dashed">
  <div><!-- ... --></div>
  <div><!-- ... --></div>
  <div><!-- ... --></div>
  <div><!-- ... --></div>
</div>

These utilities include responsive variants by default:

// tailwind.config.js
module.exports = {
  variants: {
    divideStyle: ['responsive'],
  },
}

Access entire config object from plugins

The config function passed to the plugin API now returns the entire config option when invoked with no arguments:

tailwind.plugin(function ({ config, addUtilities, /* ... */ })) {
  // Returns entire config object
  config()
})

Define colors as closures

You can now define your colors as callbacks, which receive a bag of parameters you can use to generate your color value.

This is particularly useful when trying to make your custom colors work with the backgroundOpacity, textOpacity, etc. utilities

// tailwind.config.js
module.exports = {
  theme: {
    colors: {
      primary: ({ opacityVariable }) => `rgba(var(--color-primary), var(${variable}, 1))`,
    },
  },
}

Currently the only thing passed through is an opacityVariable property, which contains the name of the current opacity variable (--background-opacity, --text-opacity, etc.) depending on which plugin is using the color.

Deprecations

Tailwind v1.7 introduces a new feature flagging and deprecation system designed to make upgrades as painless as possible.

Any time we deprecate functionality or introduce new (stable) breaking changes, they will be available in Tailwind v1.x under a future property in your tailwind.config.js file.

Whenever there are deprecations or breaking changes available, Tailwind will warn you in the console on every build until you adopt the new changes and enable the flag in your config file:

risk - There are upcoming breaking changes: removeDeprecatedGapUtilities
risk - We highly recommend opting-in to these changes now to simplify upgrading Tailwind in the future.
risk - https://tailwindcss.com/docs/upcoming-changes

You can opt-in to a breaking change by setting that flag to true in your tailwind.config.js file:

// tailwind.config.js
module.exports = {
  future: {
    removeDeprecatedGapUtilities: true,
  },
}

If you’d prefer not to opt-in but would like to silence the warning, explicitly set the flag to false:

// tailwind.config.js
module.exports = {
  future: {
    removeDeprecatedGapUtilities: false,
  },
}

We do not recommend this, as it will make upgrading to Tailwind v2.0 more difficult.

Deprecated gap utilities

As mentioned previously, Tailwind v1.7.0 introduces new gap-x-{n} and gap-y-{n} utilities to replace the current col-gap-{n} and row-gap-{n} utilities.

By default both classes will exist, but the old utilities will be removed in Tailwind v2.0.

To migrate to the new class names, simply replace any existing usage of the old names with the new names:

- <div class="col-gap-4 row-gap-2 ...">
+ <div class="gap-x-4 gap-y-2 ...">

To opt-in to the new names now, enable the removeDeprecatedGapUtilities flag in your tailwind.config.js file:

// tailwind.config.js
module.exports = {
  future: {
    removeDeprecatedGapUtilities: true,
  },
}

Experimental features

Tailwind v1.7.0 introduces a new experimental feature system that allows you to opt-in to new functionality that is coming to Tailwind soon but isn’t quite stable yet.

It’s important to note that experimental features may introduce breaking changes, do not follow semver, and can change at any time.

If you like to live on the wild side though, you can enable all of them like so:

// tailwind.config.js
module.exports = {
  experimental: 'all',
}

With that out of the way, here is some of the fun stuff we’re working on that we’re pumped you can finally play with…

Use @apply with variants and other complex classes

This is a huge one — you can finally use @apply with responsive variants, pseudo-class variants, and other complex classes!

.btn {
  @apply bg-indigo hover:bg-indigo-700 sm:text-lg;
}

There are a lot of details to understand with this one, so I recommend reading the pull request to learn about how it all works.

This introduces breaking changes to how @apply worked before, so be sure to read all of the details before just flipping the switch.

To enable this feature, use the applyComplexClasses flag:

// tailwind.config.js
module.exports = {
  experimental: {
    applyComplexClasses: true,
  },
}

New color palette

We’ve added a teaser of the new Tailwind 2.0 color palette that you can start playing with today using the uniformColorPalette flag:

// tailwind.config.js
module.exports = {
  experimental: {
    uniformColorPalette: true,
  },
}

The idea behind the new palette is that every color at every shade has a similar perceived brightness. So you can swap indigo-600 with blue-600 and expect the same color contrast.

We do expect these colors to continue to change a lot as we iterate on them, so use these at your own risk.

Extended spacing scale

We’ve added a much bigger spacing scale that includes new micro values like 0.5, 1.5, 2.5, and 3.5, as well as new large values like 72, 80, and 96, and added percentage based fractional values to the whole spacing scale (1/2, 5/6, 7/12, etc.)

You can enable the extended spacing scale using the extendedSpacingScale flag:

// tailwind.config.js
module.exports = {
  experimental: {
    extendedSpacingScale: true,
  },
}

This is pretty stable, I would be surprised if we change this.

Default line-heights per font-size by default

We’ve added recommended default line-heights to every built-in font-size, which can be enabled using the defaultLineHeights flag:

// tailwind.config.js
module.exports = {
  experimental: {
    defaultLineHeights: true,
  },
}

This is a breaking change and will impact your designs, as previously all font sizes had a default line-height of 1.5.

Extended font size scale

We’ve added three new font sizes (7xl, 8xl, and 9xl) to keep up with the latest huge-as-hell-hero-text trends. They include default line-heights as well.

You can enable them under the extendedFontSizeScale flag:

// tailwind.config.js
module.exports = {
  experimental: {
    extendedFontSizeScale: true,
  },
}

#tailwindcss #css #web-development #developer

What is GEEK

Buddha Community

Tailwind CSS v1.7.0

Semantic Similarity Framework for Knowledge Graph

Introduction

Sematch is an integrated framework for the development, evaluation, and application of semantic similarity for Knowledge Graphs (KGs). It is easy to use Sematch to compute semantic similarity scores of concepts, words and entities. Sematch focuses on specific knowledge-based semantic similarity metrics that rely on structural knowledge in taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents (corpus-IC and graph-IC). Knowledge-based approaches differ from their counterpart corpus-based approaches relying on co-occurrence (e.g. Pointwise Mutual Information) or distributional similarity (Latent Semantic Analysis, Word2Vec, GLOVE and etc). Knowledge-based approaches are usually used for structural KGs, while corpus-based approaches are normally applied in textual corpora.

In text analysis applications, a common pipeline is adopted in using semantic similarity from concept level, to word and sentence level. For example, word similarity is first computed based on similarity scores of WordNet concepts, and sentence similarity is computed by composing word similarity scores. Finally, document similarity could be computed by identifying important sentences, e.g. TextRank.

logo

KG based applications also meet similar pipeline in using semantic similarity, from concept similarity (e.g. http://dbpedia.org/class/yago/Actor109765278) to entity similarity (e.g. http://dbpedia.org/resource/Madrid). Furthermore, in computing document similarity, entities are extracted and document similarity is computed by composing entity similarity scores.

kg

In KGs, concepts usually denote ontology classes while entities refer to ontology instances. Moreover, those concepts are usually constructed into hierarchical taxonomies, such as DBpedia ontology class, thus quantifying concept similarity in KG relies on similar semantic information (e.g. path length, depth, least common subsumer, information content) and semantic similarity metrics (e.g. Path, Wu & Palmer,Li, Resnik, Lin, Jiang & Conrad and WPath). In consequence, Sematch provides an integrated framework to develop and evaluate semantic similarity metrics for concepts, words, entities and their applications.


Getting started: 20 minutes to Sematch

Install Sematch

You need to install scientific computing libraries numpy and scipy first. An example of installing them with pip is shown below.

pip install numpy scipy

Depending on different OS, you can use different ways to install them. After sucessful installation of numpy and scipy, you can install sematch with following commands.

pip install sematch
python -m sematch.download

Alternatively, you can use the development version to clone and install Sematch with setuptools. We recommend you to update your pip and setuptools.

git clone https://github.com/gsi-upm/sematch.git
cd sematch
python setup.py install

We also provide a Sematch-Demo Server. You can use it for experimenting with main functionalities or take it as an example for using Sematch to develop applications. Please check our Documentation for more details.

Computing Word Similarity

The core module of Sematch is measuring semantic similarity between concepts that are represented as concept taxonomies. Word similarity is computed based on the maximum semantic similarity of WordNet concepts. You can use Sematch to compute multi-lingual word similarity based on WordNet with various of semantic similarity metrics.

from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()

# Computing English word similarity using Li method
wns.word_similarity('dog', 'cat', 'li') # 0.449327301063
# Computing Spanish word similarity using Lin method
wns.monol_word_similarity('perro', 'gato', 'spa', 'lin') #0.876800984373
# Computing Chinese word similarity using  Wu & Palmer method
wns.monol_word_similarity('狗', '猫', 'cmn', 'wup') # 0.857142857143
# Computing Spanish and English word similarity using Resnik method
wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng', 'res') #7.91166650904
# Computing Spanish and Chinese word similarity using Jiang & Conrad method
wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn', 'jcn') #0.31023804699
# Computing Chinese and English word similarity using WPath method
wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng', 'wpath')#0.593666388463

Computing semantic similarity of YAGO concepts.

from sematch.semantic.similarity import YagoTypeSimilarity
sim = YagoTypeSimilarity()

#Measuring YAGO concept similarity through WordNet taxonomy and corpus based information content
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Actor109765278', 'wpath') #0.642
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Singer110599806', 'wpath') #0.544
#Measuring YAGO concept similarity based on graph-based IC
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Actor109765278', 'wpath_graph') #0.423
sim.yago_similarity('http://dbpedia.org/class/yago/Dancer109989502','http://dbpedia.org/class/yago/Singer110599806', 'wpath_graph') #0.328

Computing semantic similarity of DBpedia concepts.

from sematch.semantic.graph import DBpediaDataTransform, Taxonomy
from sematch.semantic.similarity import ConceptSimilarity
concept = ConceptSimilarity(Taxonomy(DBpediaDataTransform()),'models/dbpedia_type_ic.txt')
concept.name2concept('actor')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'path')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'wup')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'li')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'res')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'lin')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'jcn')
concept.similarity('http://dbpedia.org/ontology/Actor','http://dbpedia.org/ontology/Film', 'wpath')

Computing semantic similarity of DBpedia entities.

from sematch.semantic.similarity import EntitySimilarity
sim = EntitySimilarity()
sim.similarity('http://dbpedia.org/resource/Madrid','http://dbpedia.org/resource/Barcelona') #0.409923677282
sim.similarity('http://dbpedia.org/resource/Apple_Inc.','http://dbpedia.org/resource/Steve_Jobs')#0.0904545454545
sim.relatedness('http://dbpedia.org/resource/Madrid','http://dbpedia.org/resource/Barcelona')#0.457984139871
sim.relatedness('http://dbpedia.org/resource/Apple_Inc.','http://dbpedia.org/resource/Steve_Jobs')#0.465991132787

Evaluate semantic similarity metrics with word similarity datasets

from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
evaluation = WordSimEvaluation()
evaluation.dataset_names()
wns = WordNetSimilarity()
# define similarity metrics
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
# evaluate similarity metrics with SimLex dataset
evaluation.evaluate_metric('wpath', wpath, 'noun_simlex')
# performa Steiger's Z significance Test
evaluation.statistical_test('wpath', 'path', 'noun_simlex')
# define similarity metrics for Spanish words
wpath_es = lambda x, y: wns.monol_word_similarity(x, y, 'spa', 'path')
# define cross-lingual similarity metrics for English-Spanish
wpath_en_es = lambda x, y: wns.crossl_word_similarity(x, y, 'eng', 'spa', 'wpath')
# evaluate metrics in multilingual word similarity datasets
evaluation.evaluate_metric('wpath_es', wpath_es, 'rg65_spanish')
evaluation.evaluate_metric('wpath_en_es', wpath_en_es, 'rg65_EN-ES')

Evaluate semantic similarity metrics with category classification

Although the word similarity correlation measure is the standard way to evaluate the semantic similarity metrics, it relies on human judgements over word pairs which may not have same performance in real applications. Therefore, apart from word similarity evaluation, the Sematch evaluation framework also includes a simple aspect category classification. The task classifies noun concepts such as pasta, noodle, steak, tea into their ontological parent concept FOOD, DRINKS.

from sematch.evaluation import AspectEvaluation
from sematch.application import SimClassifier, SimSVMClassifier
from sematch.semantic.similarity import WordNetSimilarity

# create aspect classification evaluation
evaluation = AspectEvaluation()
# load the dataset
X, y = evaluation.load_dataset()
# define word similarity function
wns = WordNetSimilarity()
word_sim = lambda x, y: wns.word_similarity(x, y)
# Train and evaluate metrics with unsupervised classification model
simclassifier = SimClassifier.train(zip(X,y), word_sim)
evaluation.evaluate(X,y, simclassifier)

macro averge:  (0.65319812882333839, 0.7101245049198579, 0.66317566364913016, None)
micro average:  (0.79210167952791644, 0.79210167952791644, 0.79210167952791644, None)
weighted average:  (0.80842645056024054, 0.79210167952791644, 0.79639496616636352, None)
accuracy:  0.792101679528
             precision    recall  f1-score   support

    SERVICE       0.50      0.43      0.46       519
 RESTAURANT       0.81      0.66      0.73       228
       FOOD       0.95      0.87      0.91      2256
   LOCATION       0.26      0.67      0.37        54
   AMBIENCE       0.60      0.70      0.65       597
     DRINKS       0.81      0.93      0.87       752

avg / total       0.81      0.79      0.80      4406

Matching Entities with type using SPARQL queries

You can use Sematch to download a list of entities having a specific type using different languages. Sematch will generate SPARQL queries and execute them in DBpedia Sparql Endpoint.

from sematch.application import Matcher
matcher = Matcher()
# matching scientist entities from DBpedia
matcher.match_type('scientist')
matcher.match_type('científico', 'spa')
matcher.match_type('科学家', 'cmn')
matcher.match_entity_type('movies with Tom Cruise')

Example of automatically generated SPARQL query.

SELECT DISTINCT ?s, ?label, ?abstract WHERE {
    {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/NuclearPhysicist110364643> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Econometrician110043491> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Sociologist110620758> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Archeologist109804806> . }
 UNION {  
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://dbpedia.org/class/yago/Neurolinguist110354053> . } 
    ?s <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#Thing> . 
    ?s <http://www.w3.org/2000/01/rdf-schema#label> ?label . 
    FILTER( lang(?label) = "en") . 
    ?s <http://dbpedia.org/ontology/abstract> ?abstract . 
    FILTER( lang(?abstract) = "en") .
} LIMIT 5000

Entity feature extraction with Similarity Graph

Apart from semantic matching of entities from DBpedia, you can also use Sematch to extract features of entities and apply semantic similarity analysis using graph-based ranking algorithms. Given a list of objects (concepts, words, entities), Sematch compute their pairwise semantic similarity and generate similarity graph where nodes denote objects and edges denote similarity scores. An example of using similarity graph for extracting important words from an entity description.

from sematch.semantic.graph import SimGraph
from sematch.semantic.similarity import WordNetSimilarity
from sematch.nlp import Extraction, word_process
from sematch.semantic.sparql import EntityFeatures
from collections import Counter
tom = EntityFeatures().features('http://dbpedia.org/resource/Tom_Cruise')
words = Extraction().extract_nouns(tom['abstract'])
words = word_process(words)
wns = WordNetSimilarity()
word_graph = SimGraph(words, wns.word_similarity)
word_scores = word_graph.page_rank()
words, scores =zip(*Counter(word_scores).most_common(10))
print words
(u'picture', u'action', u'number', u'film', u'post', u'sport', 
u'program', u'men', u'performance', u'motion')

Publications

Ganggao Zhu, and Carlos A. Iglesias. "Computing Semantic Similarity of Concepts in Knowledge Graphs." IEEE Transactions on Knowledge and Data Engineering 29.1 (2017): 72-85.

Oscar Araque, Ganggao Zhu, Manuel Garcia-Amado and Carlos A. Iglesias Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis, ICDM sentire, 2016.

Ganggao Zhu, and Carlos Angel Iglesias. "Sematch: Semantic Entity Search from Knowledge Graph." SumPre-HSWI@ ESWC. 2015.


Support

You can post bug reports and feature requests in Github issues. Make sure to read our guidelines first. This project is still under active development approaching to its goals. The project is mainly maintained by Ganggao Zhu. You can contact him via gzhu [at] dit.upm.es


Why this name, Sematch and Logo?

The name of Sematch is composed based on Spanish "se" and English "match". It is also the abbreviation of semantic matching because semantic similarity metrics helps to determine semantic distance of concepts, words, entities, instead of exact matching.

The logo of Sematch is based on Chinese Yin and Yang which is written in I Ching. Somehow, it correlates to 0 and 1 in computer science.

Author: Gsi-upm
Source Code: https://github.com/gsi-upm/sematch 
License: View license

#python #jupyternotebook #graph 

Hire CSS Developer

Want to develop a website or re-design using CSS Development?

We build a website and we implemented CSS successfully if you are planning to Hire CSS Developer from HourlyDeveloper.io, We can fill your Page with creative colors and attractive Designs. We provide services in Web Designing, Website Redesigning and etc.

For more details…!!
Consult with our experts:- https://bit.ly/3hUdppS

#hire css developer #css development company #css development services #css development #css developer #css

Tailwind CSS tutorial

In this tutorial I would like to introduce you to one of the fastest growing and promising CSS Frameworks at the moment, Tailwind CSS. It is different from other frameworks, such as Bootstrap, because it is built on a new way of building user interfaces using a utility-first CSS classes structure, as opposed to the OOCSS structure from other frameworks.

By the end of this guide you will be able to install, configure and build a responsive hero section (live demo) using the utility-first classes from Tailwind CSS and configure the project using the recommended PostCSS powered Tailwind configuration file for better maintainability and versatility.

Here’s the table of contents for this tutorial for Tailwind CSS:

  • Introducing Tailwind CSS
  • Adding Tailwind CSS to your project via a package manager
  • Creating the configuration file and process your CSS with Tailwind
  • Building a responsive hero section using the utility-first classes from Tailwind
  • Customize fonts, colors and add extra classes using the configuration file
  • Reduce loading time and file size by purging the unused classes from your CSS
  • Conclusion and summary

Read the full tutorial from Themesberg.

#tailwind #tailwind-css #tailwind-css-tutorial #tutorial #open-source

Popular Tailwind CSS Plugins and Extensions - Themesberg Blog

By reading this article you will browse a list of the most popular plugins and extensions for the utility-first CSS framework, Tailwind CSS. Although the default code base of the framework already covers a lot of the needs when building user interfaces, you can never get enough plugins and extensions powered by the open-source community.

One of the requirements for a plugin to appear on this list is to be open-source with no other strings attached so that the developers browsing this list can stay assured that they can use the plugin for their Tailwind CSS powered project.

Check out the list of Tailwind CSS Plugins and Extensions on Themesberg.

#tailwindcss #tailwind #tailwind-css #tailwind-css-plugins #themesberg

Tailwind CSS Cheatsheet

Master Tailwind CSS with this Cheatsheet.

Tailwind CSS allows us to build modern websites with the respective classes without writing a single native CSS.

Just like other CSS libraries like Bootstrap, Tailwind also has responsive classes that we only need to specify in our application.

This article will get you up and started quickly with Tailwind to make beautiful websites and UIs.

Tailwind is amazing since we have to only to specify the respective classes and Tailwind CSS will take care of everything.

On top of that, we don’t have to worry about responsivity as all is taken care of by Tailwind on different devices.

A utility-first CSS framework packed with classes like flex, pt-4, text-centre and rotate-90 that can be composed to build any design, directly in your markup. From Tailwind.com.

In this article, we will see the most basic CSS classes and their relative properties in CSS.

#tailwind #css #tailwind-css #tailwindcss #web-development