Text Blaze

textblaze

In the fast-paced world of digital workflows, having the right tools at your fingertips can make all the difference. I've been asked what extensions I use while browsing, so I decided to compile a list of some of my favorites that contribute the most to in my workflow. productivity Whether you're a developer, designer, or an avid multitasker, these extensions might improve your experience. I've included direct links, descriptions, and visuals so that you can get an initial impression of each extension right away. Hopefully, these will be useful to you! 1. (Sponsored) Hitab Hitab is a versatile browser extension designed to enhance your new tab experience. It transforms your browser's new tab page into a powerful productivity hub, offering features that go beyond traditional tab management. Here's a closer look at what Hitab is and its key functionalities: Hitab provides a customizable dashboard on your new tab page, allowing you to organize and access your most frequently visited websites, bookmarks, and widgets effortlessly. Personalized Dashboard: Seamlessly integrate task management into your browsing experience with Hitab. Create to-do lists, set priorities, and stay organized directly from your new tab. Task Management: Easily navigate through your bookmarks with a visually appealing layout. Hitab ensures quick access to your favorite websites and resources with just a click. Quick Access to Bookmarks: Boost your productivity by jotting down quick notes and setting reminders directly within your new tab. Hitab acts as a digital scratchpad to capture your thoughts on the fly. Notes and Reminders: Hitab includes a convenient search bar, allowing you to perform quick searches directly from your new tab. Additionally, stay updated on the weather with an integrated widget for real-time information. Search Bar and Weather Widget: Hitab is available as an extension in , , and a . Chrome Edge Web App 2. OctoTree OctoTree is a GitHub browser extension that provides a tree view of a GitHub repository's code. It simplifies code navigation, making it easier to explore and understand large codebases on GitHub. 3. ColorZilla ColorZilla is a color picker extension that allows users to identify and copy colors from any webpage. It includes an eyedropper tool for precise color selection and a color history feature. 4. WhatFont WhatFont helps identify fonts used on web pages. By simply hovering over text, it provides information about the font type, size, line height, and color, aiding designers and developers in font selection. 5. JSON Formatter JSON Formatter is a tool that beautifies and formats JSON data for better readability. It organizes JSON objects in a tree-like structure, making it easier for developers to analyze and debug JSON content. 6. Window Resizer Window Resizer is an extension that allows users to resize their browser window to predefined dimensions. It aids developers and designers in testing responsive designs for various screen sizes. 7. GoFullPage GoFullPage is a screenshot tool that captures entire web pages, even those that require scrolling. It's useful for documenting web content and sharing full-page views with others. 8. TextBlaze TextBlaze is a productivity extension that allows users to create and use text templates, snippets, and shortcuts for faster typing and communication. It's particularly handy for repetitive tasks. 9. Mobile Simulator Mobile Simulator is a responsive design testing tool that emulates various mobile device screens, helping developers ensure their websites look and function well on different devices. 10. AgreeTo AgreeTo is an extension designed for scheduling purposes. It simplifies the process of finding suitable meeting times by showing the availability of participants. 11. Dark Reader Dark Reader is a browser extension that enables dark mode on websites, reducing eye strain in low-light environments. It provides a customizable dark theme for an improved browsing experience. 12. Session Buddy Session Buddy is a session manager that allows users to save, restore, and organize browser sessions. It's beneficial for those who work with multiple open tabs and want to manage their browsing sessions efficiently. 13. Screely Screely is an instant browser mockup tool. It captures screenshots and adds device frames and background effects to create visually appealing mockups of web designs or applications. 14. Loom Loom is a screen recorder extension that allows users to create and share video recordings of their screens. It's often used for tutorials, demos, or collaborative work. 15. CheckerPlus CheckerPlus for Gmail is an extension that enhances Gmail management. It provides desktop notifications, quick email access, and additional features to streamline Gmail usage. 16. Bitwarden Bitwarden is a free password manager that helps users securely store and manage their passwords. It offers features like password generation and syncing across devices for improved password security. 17. Grammarly Grammarly for Chrome is a writing assistance tool that checks spelling, grammar, and punctuation as users type. It helps improve the quality of written content and communication. Writing has always been my passion, and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Make sure to receive the best resources, tools, productivity tips, and career growth tips I discover by subscribing to ! my newsletter Also, connect with me on , , and ! Twitter LinkedIn GitHub Also published . here

Boost Your Productivity in 2024 With These 17 Chrome Extensions 🔥🚀

We reverse engineered 10 fastest-growing startups from YCombinator W21 to uncover 14 effective SEO content frameworks The inspiration behind this article got , and we wanted to find out why. The top 10 YCombinator alumni from Winter 21 an average of 400% annual growth spent the last month getting under the skin of some of the world’s best-performing startups to discover successful that our SaaS clients can use – in a scalable way. Code200 SEO content frameworks We for traffic growth from YC’s W21 batch. identified 14 frameworks used by the top 10 companies Here we introduce the most popular approaches. Which ones will become part of your SEO content strategy? How it worked We decided to examine YC startups because only the best of the best meet this incubator’s exacting submission criteria. We chose the because all the businesses are roughly the same age and, being around two years old, they will have established a market and be looking to scale up. YC W21 batch From the original 319, we chose the 10 startups with the fastest traffic growth in 2022, using Ahrefs for our analysis. The figures: How much did YC startups grow their traffic in 2022? Search traffic 8 out of 10 of the startups studied grew their traffic by over 550%, with achieving a stunning 11,377% increase. Betterhalf Organic pages 8 out of 10 startups increased the number of pages on their websites that ranked. On average they added 405 pages each. Keywords 9 of the 10 startups increased the number of keywords they rank for, adding on average 29,230 new keywords. Referring domains 10 of the 10 startups increased the number of sites that linked back to them. (We don’t know if this is the result of organic growth or targeted backlinking campaigns.) Domain Rank (DR) They often say that Domain Rating (which is based on the number of backlinks to your site) must hit 80 in order to rank highly on Google. However, although 8 of our 10 startups did increase their DR, their scores averaged out at 41. For some niches, then, a DR as low as 25–30 may still be enough to rank highly. Conclusion: if you want more clicks and rankings, publish more content On average, each startup added an enormous 546 pages to their website. There are always new topics to cover and new keywords to rank for. Read on to discover the most popular SEO content frameworks used by some of 2022’s most successful startups. 14 SEO content strategies top YCombinator startups used to achieve 400% average growth We analyzed the content strategies and keywords targeted by each of the 10 websites on their top-performing pages. We categorized the approaches and ended up with 14 different SEO content frameworks that can be applied to virtually any type of startup. If you can spot any other frameworks, let us know in the comments! 1. How-to guides Appearing on 4 of the 10 startups' websites, this was the most popular framework of them all. has hundreds of guides on how to write emails, such as to and to . Text Blaze say thank you after a meeting ask for a raise ’s huge range of how-tos for Spanish speakers includes detailed commentary on different ways to translate and Poliglota “buenas noches” “papa.” 2. Long-tail topics Targeting niche keywords better addresses user search intent and increases conversion rate, although ultra-specific pages receive a relatively low volume of traffic, so you’ll need lots of them to make this approach worthwhile. Indian matchmaking site has numerous very specific collections of women looking to get married; examples include , those , and brides. Betterhalf widows awaiting divorce Tamil The vast selection of topics on the website includes supremely detailed explanations of even the humble and functionalities. FlutterFlow Form List View 3. High-volume blog content At the opposite end of the funnel, popular topics can be targeted with blog posts. Three of the many highly clickable articles from men’s health specialists were on , , and . Phoenix condom size sexual enhancement pills penis pumps covers all sorts of high-volume topics such as , , and the . Text Blaze Pomodoro timers tone of voice Eisenhower matrix 4. Location-based pages Pages can target users differently depending on which country, state, region, or city they’re in. It's an easy way to scale your content, if your product or service has a geographical component. uses unique , , and templates for every one of the many cities it serves. Zingbus booking route visitor guide targets delivery of Viagra and other medications to men in various cities across Canada, including and . Phoenix Toronto Vancouver 5. Solution-based pages Focus on a pain point your target audience may be experiencing and offer advice. You could write a general, top-of-the-funnel piece that doesn’t directly sell to the reader, or you could make sure your product features prominently in the list of solutions. has a wide range of pages that are each dedicated to an individual health issue, such as and . Phoenix hair loss ED has tons of troubleshooting content, with pages focusing on particular software issues such as and . FlutterFlow gray screen APKs 6. Product-based & feature-based pages Take one product and do a deep dive into its features; alternatively, take a feature and round up all the products that have that feature. has pages covering each of its medications in detail, such as and . Phoenix Viagra Cialis (tadalafil) 7. Listicles Some approaches are evergreen. Listicles continue to be a popular framework – you're reading one now! has a list for every occasion. A of the best places to visit in a given destination city can be multiplied by the number of cities the company serves. Zingbus rundown ’s many listicles include recommendations for and . Poliglota easy English books online translators 8. Documentation Reappropriate existing content from the documentation that has yet to be "shown to Google" – such as product specs and user manual pages – to help people looking for answers to super-specific questions. ’s enormous range of support articles, such as and , act both as documentation and traffic generators. QuickNode eth_gasPrice eth_getBlockByHash Some examples from ’s expansive “Docs” repository include pages on the and . FlutterFlow Stack widget Facebook Sign-In 9. Compatibility Create pages that discuss how well two things go together; they can either be directly related to your business or of more general interest. ’s exhaustive range of astrological sign compatibility are the perfect example of a compatibility framework that is related to the business. Betterhalf pages Frameworks 10 - 14 We’ve tried to keep things brief here, but we’ll happily send you the full list of 14 SEO content frameworks for free. Get our full list of 14 SEO content frameworks for free. This is how we do it Why do some startups choose to ignore SEO? Here at we prove, again and again, how powerful good SEO can be. Code200 We chose two startups at random and considered how we could apply some of the SEO approaches used by the YC W21 cohort. The businesses in our study used between 2 and 7 of the SEO content frameworks featured in our list, so as a rule, we would recommend using 3 or 4. Clay3D.io - 3D model reviewing tool Long tail Many more keywords can be targeted by creating pages on other kinds of 3D models; the possibilities are endless! 3D model heart 3D model skull 3D model skeleton How-to guides Do a little research into 3D modeling to come up with plenty more actions to add to this list! How to make 3D model How to animate 3D model How to scale 3D model Listicles Just mix and match words to make an infinite number of interesting new listicle ideas. Best 3D model sites Best 3D model software Best 3D skull models Free tool or products If it exists, it can be modeled. If it can be modeled, you can conceivably give away a free version. Free 3D model of a car Free 3D skull model Lionpose.com - Skincare for hyperpigmentation How-to guides Even if you want to concentrate on a small number of keywords, there are likely many common ways to fit them into a query or article title. how to get rid of hyperpigmentation how to treat hyperpigmentation how to remove hyperpigmentation how to reduce hyperpigmentation Long-tail topics Just combining these in different ways gives “ ,” “ ,” and “ .” how long does hyperpigmentation last on black skin how long does hyperpigmentation last around mouth how to get rid of hyperpigmentation on black skin around mouth And that’s before introducing multiple skin tones, body parts, and question formats. how to get rid of hyperpigmentation on black skin how to get rid of hyperpigmentation around mouth how long does hyperpigmentation last Listicles Listicles can be inspired either by long-tail or high-volume subjects. best products for hyperpigmentation best vitamin c serum for hyperpigmentation best hyperpigmentation treatment best products for hyperpigmentation in black skin Calculators If time or price is involved, a range of calculators will probably be an appropriate addition to your site. how much does laser cost for hyperpigmentation how many chemical peels to get rid of hyperpigmentation how much is laser hyperpigmentation removal Comparison Lion Pose could make content on a variety of symptoms and conditions. melasma vs hyperpigmentation acne scar vs hyperpigmentation Alternatives Pages can focus on any combination of two Individual treatments; the number of pages is only limited by the number of treatments available. dermal hyperpigmentation laser alternative alternative hydroquinone for hyperpigmentation Conclusions To grow organic search, on your site. Unless you already have 1000 pages, creating new content should be your first priority. increase the number of content pages Backlink numbers are important, but the W21 YC startups' average DR of 42 is enough to rank in many niches. Check your competition: do you really need to go all-in on backlinks? Use frameworks that allow you to . target keywords and topics at scale The fastest-growing W21 YC startups we’ve listed. use between 2 and 7 of the content frameworks Let us apply these frameworks to your product. Talk to us

SEO Strategies of YCombinator’s Fastest-growing Startups in 2022

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YCombinator 2022 年增长最快的初创公司的 SEO 策略

If you frequently write copy, Web posts or articles & SEO optimization is important to you then you might want to give ‘Textalyzer’ a try. This Web-based tool will measure readability, check your readability score & analyze text against an important number of parameters, improving it immediately. The way Textalyzer works is, you write a focus keyword into the tool’s 1st text field, then an article title & finally, the article’s text (up to 600 words). You then get an analysis details summary detailing the number of words, characters & sentences, together with the reading time. Most importantly, SEO content & keyword analysis is provided. Here you’re get info about the title & the article’s recommended length, correct usage of the focus keyword & keyword density (content title optimization for focus keyword). Keyword usage is also broken down by density percentage. To read the rest of this article, . click here

‘Textalyzer’ checks & improves your text’s readability

How to Create Scrollable Lists with Protocol-Oriented Design & UICollectionViewCompositionalLayout

প্রোটোকল-ওরিয়েন্টেড ডিজাইন এবং UICollectionViewCompositional Layout সহ কীভাবে স্ক্রোলযোগ্য তালিকা তৈরি করবেন

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プロトコル指向設計と UICollectionViewCompositionalLayout を使用してスクロール可能なリストを作成する方法

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Recommendations by Concise User Profiles from Review Text: Experimental Results

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de;
(2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl;
(3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de;
(4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction
Related Work
Methodology
Experimental Design
Experimental Results
Conclusion
Ethics Statement and References Abstract Recommender systems are most successful for popular items and users with ample interactions (likes, ratings etc.). This work addresses the difficult and underexplored case of supporting users who have very sparse interactions but post informative review texts. Our experimental studies address two book communities with these characteristics. We design a framework with Transformer-based representation learning, covering user-item interactions, item content, and user-provided reviews. To overcome interaction sparseness, we devise techniques for selecting the most informative cues to construct concise user profiles. Comprehensive experiments, with datasets from Amazon and Goodreads, show that judicious selection of text snippets achieves the best performance, even in comparison to LLMgenerated rankings and to using LLMs to generate user profiles. Index Terms—recommender, sparse data, user text, language model, transformer I. INTRODUCTION A. Motivation Recommender systems have become a mature technology and are ubiquitous in applications [1]. Methodologies fall into two major families or hybrid combinations: i) interactionbased recommenders that leverage binary signals (e.g., membership in personal playlists or libraries) or numeric ratings for user-item pairs, and ii) content-based recommenders that exploit item features and user-provided content, ranging from meta-data attributes (e.g., item categories) all the way to textually rich reviews. In many settings, where user-item signals are easily collectable and abundant, interaction-based recommenders achieve excellent performance and are hard to beat. Some applications combine interaction data with informative item features, again relying on massive data (e.g., [2]). In contrast, the data-poor regime of long-tail users and long-tail items has received less attention. Some works advocate countering data sparseness by clustering long-tail items (e.g., [3]). However, this is not sufficient to gear up for users with few interactions but diverse tastes. Approaches for cold-start and cross-domain recommendations and zero-shot learning (e.g., [4]–[7]), are often focused on the item side only: handling new items by transferring (latent) knowledge from existing ones with dense data. Note that sparseness is not just a lack of user-item interactions, but also refers to the users and items themselves: users with merely a handful of items do not exhibit a clear picture of their tastes, and items that are completely unseen at training time are a challenging case. Moreover, even users with a good number of items can be a major problem when the preferred items are highly diverse, such as less known books from all kinds of genres. To address these difficulties, the most promising approach is to leverage review texts by users. In settings where users spend substantial time per item, such as books or travel destinations (as opposed to items with short attention spans, like music or restaurants), even data-poor users with few interactions may leave rich texts that express their interests and tastes. The approach has been studied before (e.g., [8]–[11]), but the performance is much lower than for settings with ample interaction data. This paper aims to advance the state of the art on this theme, by tackling both issues of data sparseness and diversity of tastes via textbased concise user profiles. B. Research Questions Our approach constructs and encodes concise user profiles from reviews of data-poor text-rich users, focusing on the book domain for concreteness (using data from Goodreads and Amazon). Unlike movies or restaurants, books have a much longer tail of popularity and exhibit huge diversity of user tastes, making books a particularly challenging domain for data-poor recommendations. Also, negative reviews are very rare (most have ratings like 4 or 5 stars), so that we deal with binary data and have no negative samples. This approach raises several research questions (RQs): RQ1: Learning with Language Models. How can we best incorporate language models, like BERT, T5 or ChatGPT, for item ranking or for encoding the gist of a user’s reviews, enhanced with the LM’s world knowledge? RQ2: Long Text. Since neural architectures can consume only limited input size (e.g., 512 tokens), we have to select a subset of snippets. Even with a large capacity, the computational and energy cost is typically quadratic in the number of input tokens, so that being selective has benefits. What are the most informative cues that should be fed into the model? RQ3: Review Aspects. User reviews express a mix of aspects, including personal background (e.g., “I’m a retired teacher”), sentiment expressions (e.g., “what a great story”), general emotions (e.g., “brings back lovely memories”), and comments about the book contents. As sentiments do not add value over the already known fact that the book was liked, only the content comments yield cues towards new recommendations. How should this issue be considered in constructing a concise user profile with informative cues? C. Approach We devise a general framework, called CUP, for constructing Concise User Profiles, and encoding them in a recommender system, which allows us to configure a variety of methods. We propose a Tranformer-based architecture that supports end-to-end learning of item and user encodings, making use of a language model. Our choice for the language model is BERT; alternatives such as T5 can be easily plugged in but incur higher computational cost. The end-to-end learning operates on sufficiently short, judiciously constructed profiles, and leverages the available user item interactions as well. On top of the Tranformer, we place feed-forward layers for a supervised classifier. The classifier and the representation learning are jointly trained end-to-end. The prediction scores for user-item pairs yield the per-user ranking of items. This architecture can take as input a spectrum of user and item information including user reviews and item descriptions and metadata (e.g., categories). The rationale for this design is twofold: i) ensuring that inference-time computations are efficient, leveraging trainingtime-computed vectors for users and items and solely computing a scalar product at run-time, and ii) being able to express a wide spectrum of user profiling techniques within the same architecture. Alternative architectures, such as CNNbased, or LLM-based with smart prompts, are baselines in our experiments. We specifically devise new techniques for coping with the case where users have few reviews with long and noisy text (see RQ2 and RQ3), such that their total length exceeds the number of tokens that the encoders can consume. Our techniques are based on information-retrieval-style statistics (like idf weights) for n-grams or sentences, or on neural similarity measures (using SentenceBERT), or on running review texts through a generative language model, like T5 or ChatGPT, for generating user profiles. Table VII illustrates the role of concise user profiles, by giving examples for two users of Amazon books and the Goodreads community, with profiles computed by different methods. In experiments, we limit the number of tokens per user profile, as a stress-test. The main argument for this choice is to keep the system as light-weight as possible. The computational cost of the Transformer, and thus also the environmental footprint, are quadratic in the number of input tokens. Section V-D experimentally shows that a small token budget is competitive to larger ones, while having advantages in efficiency. D. Contributions The salient contributions of this work are the following: • a new framework, called CUP, for Transformer-based recommenders that leverage language models and concise user profiles from reviews; • judicious techniques for selecting and encoding informative cues from the long and noisy text of user reviews, outperforming prior baselines as well as methods based on large language models (LLMs); • comprehensive experiments with focus on data-poor but text-rich users with highly diverse preferences, and difficult predictions (e.g., recommending new authors, not just new books by previously seen authors). We make code, data and experimental details available on our project web page[1]. [1] https://personalization.mpi-inf.mpg.de/CUP This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ghazaleh H. Torbati, Max Planck Institute for Informatics Saarbrucken, Germany & ghazaleh@mpi-inf.mpg.de; (2) Andrew Yates, University of Amsterdam Amsterdam, Netherlands & a.c.yates@uva.nl; (3) Anna Tigunova, Max Planck Institute for Informatics Saarbrucken, Germany & tigunova@mpi-inf.mpg.de; (4) Gerhard Weikum, Max Planck Institute for Informatics Saarbrucken, Germany & weikum@mpi-inf.mpg.de. Table of Links Abstract and Introduction Related Work Methodology Experimental Design Experimental Results Conclusion Ethics Statement and References Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Design Experimental Design Experimental Results Experimental Results Conclusion Conclusion Ethics Statement and References Ethics Statement and References Abstract Recommender systems are most successful for popular items and users with ample interactions (likes, ratings etc.). This work addresses the difficult and underexplored case of supporting users who have very sparse interactions but post informative review texts. Our experimental studies address two book communities with these characteristics. We design a framework with Transformer-based representation learning, covering user-item interactions, item content, and user-provided reviews. To overcome interaction sparseness, we devise techniques for selecting the most informative cues to construct concise user profiles. Comprehensive experiments, with datasets from Amazon and Goodreads, show that judicious selection of text snippets achieves the best performance, even in comparison to LLMgenerated rankings and to using LLMs to generate user profiles. Index Terms —recommender, sparse data, user text, language model, transformer Index Terms I. INTRODUCTION A. Motivation A. Motivation Recommender systems have become a mature technology and are ubiquitous in applications [1]. Methodologies fall into two major families or hybrid combinations: i) interactionbased recommenders that leverage binary signals (e.g., membership in personal playlists or libraries) or numeric ratings for user-item pairs, and ii) content-based recommenders that exploit item features and user-provided content, ranging from meta-data attributes (e.g., item categories) all the way to textually rich reviews. In many settings, where user-item signals are easily collectable and abundant, interaction-based recommenders achieve excellent performance and are hard to beat. Some applications combine interaction data with informative item features, again relying on massive data (e.g., [2]). In contrast, the data-poor regime of long-tail users and long-tail items has received less attention. Some works advocate countering data sparseness by clustering long-tail items (e.g., [3]). However, this is not sufficient to gear up for users with few interactions but diverse tastes. Approaches for cold-start and cross-domain recommendations and zero-shot learning (e.g., [4]–[7]), are often focused on the item side only: handling new items by transferring (latent) knowledge from existing ones with dense data. Note that sparseness is not just a lack of user-item interactions, but also refers to the users and items themselves: users with merely a handful of items do not exhibit a clear picture of their tastes, and items that are completely unseen at training time are a challenging case. Moreover, even users with a good number of items can be a major problem when the preferred items are highly diverse, such as less known books from all kinds of genres. To address these difficulties, the most promising approach is to leverage review texts by users. In settings where users spend substantial time per item, such as books or travel destinations (as opposed to items with short attention spans, like music or restaurants), even data-poor users with few interactions may leave rich texts that express their interests and tastes. The approach has been studied before (e.g., [8]–[11]), but the performance is much lower than for settings with ample interaction data. This paper aims to advance the state of the art on this theme, by tackling both issues of data sparseness and diversity of tastes via textbased concise user profiles. B. Research Questions B. Research Questions Our approach constructs and encodes concise user profiles from reviews of data-poor text-rich users, focusing on the book domain for concreteness (using data from Goodreads and Amazon). Unlike movies or restaurants, books have a much longer tail of popularity and exhibit huge diversity of user tastes, making books a particularly challenging domain for data-poor recommendations. Also, negative reviews are very rare (most have ratings like 4 or 5 stars), so that we deal with binary data and have no negative samples. This approach raises several research questions (RQs): RQ1: Learning with Language Models. How can we best incorporate language models, like BERT, T5 or ChatGPT, for item ranking or for encoding the gist of a user’s reviews, enhanced with the LM’s world knowledge? Learning with Language Models. RQ2: Long Text. Since neural architectures can consume only limited input size (e.g., 512 tokens), we have to select a subset of snippets. Even with a large capacity, the computational and energy cost is typically quadratic in the number of input tokens, so that being selective has benefits. What are the most informative cues that should be fed into the model? Long Text. RQ3: Review Aspects. User reviews express a mix of aspects, including personal background (e.g., “I’m a retired teacher”), sentiment expressions (e.g., “what a great story”), general emotions (e.g., “brings back lovely memories”), and comments about the book contents. As sentiments do not add value over the already known fact that the book was liked, only the content comments yield cues towards new recommendations. How should this issue be considered in constructing a concise user profile with informative cues? Review Aspects. C. Approach C. Approach We devise a general framework, called CUP, for constructing Concise User Profiles, and encoding them in a recommender system, which allows us to configure a variety of methods. We propose a Tranformer-based architecture that supports end-to-end learning of item and user encodings, making use of a language model. Our choice for the language model is BERT; alternatives such as T5 can be easily plugged in but incur higher computational cost. The end-to-end learning operates on sufficiently short, judiciously constructed profiles, and leverages the available user item interactions as well. On top of the Tranformer, we place feed-forward layers for a supervised classifier. The classifier and the representation learning are jointly trained end-to-end. The prediction scores for user-item pairs yield the per-user ranking of items. This architecture can take as input a spectrum of user and item information including user reviews and item descriptions and metadata (e.g., categories). The rationale for this design is twofold: i) ensuring that inference-time computations are efficient, leveraging trainingtime-computed vectors for users and items and solely computing a scalar product at run-time, and ii) being able to express a wide spectrum of user profiling techniques within the same architecture. Alternative architectures, such as CNNbased, or LLM-based with smart prompts, are baselines in our experiments. We specifically devise new techniques for coping with the case where users have few reviews with long and noisy text (see RQ2 and RQ3), such that their total length exceeds the number of tokens that the encoders can consume. Our techniques are based on information-retrieval-style statistics (like idf weights) for n-grams or sentences, or on neural similarity measures (using SentenceBERT), or on running review texts through a generative language model, like T5 or ChatGPT, for generating user profiles. Table VII illustrates the role of concise user profiles, by giving examples for two users of Amazon books and the Goodreads community, with profiles computed by different methods. In experiments, we limit the number of tokens per user profile, as a stress-test. The main argument for this choice is to keep the system as light-weight as possible. The computational cost of the Transformer, and thus also the environmental footprint, are quadratic in the number of input tokens. Section V-D experimentally shows that a small token budget is competitive to larger ones, while having advantages in efficiency. D. Contributions D. Contributions The salient contributions of this work are the following: • a new framework, called CUP, for Transformer-based recommenders that leverage language models and concise user profiles from reviews; • judicious techniques for selecting and encoding informative cues from the long and noisy text of user reviews, outperforming prior baselines as well as methods based on large language models (LLMs); • comprehensive experiments with focus on data-poor but text-rich users with highly diverse preferences, and difficult predictions (e.g., recommending new authors, not just new books by previously seen authors). We make code, data and experimental details available on our project web page[1]. [1] https://personalization.mpi-inf.mpg.de/CUP

Recommendations by Concise User Profiles from Review Text: Abstract and Introduction

Creating Scrollable Lists using Protocol-Oriented Programming and UICollectionViewFlowLayout

It is an undeniable fact that the future belongs to technology and tech-savvy individuals. As more and more businesses and companies currently rely on apps, either mobile or web, the demand for app developers is increasing and will continue to grow in the years to come. A has also made the salaries go up. iOS developers are among those who get well. higher demand for app developers paid This article will provide you with a comprehensive guide to becoming an iOS app developer and become a lucrative part of the unending tech market. The Importance of an iOS developer The importance of an iOS developer is directly connected to the importance of the apps that are available on the App Store for businesses and companies. It is commonly believed that Apple devices are more reliable and safe to use, and iOS apps are more secure than those developed for other operating systems. As mentioned above, companies are relying on these apps more and more for their business activities, better ROI, and customer satisfaction. This dependency makes iOS developers important for businesses and individuals. These individuals are important because they develop apps that are user-friendly and there are lower chances of data loss or theft. This importance and shortage of iOS developers are also the for them to be in demand. reasons Basic Requirements to Become an iOS Developer What do you need (as the bare minimum) to have before you can become an iOS developer? Let's go through it all from equipment to programming languages. Equipment Mac Computer The most basic equipment you need to become an iOS developer is a Macintosh computer. You need a computer system that runs on macOS because the software and systems that are used to develop iOS apps are only compatible with Mac operating systems. Although, there is a way to run macOS on other computers but you can only do that by getting a license from Apple, and that is not easy. In other words, if you do not have a licensed Mac, you cannot upload the app on the App Store. Software Xcode The first and foremost software that you need on your computer is . It is available for free on the App Store for Mac computers. This Integrated Development Environment (IDE) provides developers with multiple tools i.e. debugger, instruments, simulator, and more, that help in app development for Apple products. Xcode is a complete and comprehensive system of developing and testing iOS apps and making sure they’re submitted and uploaded on the App Store. There are multiple available templates to build and develop apps for different devices, platforms, and app types to make the development process easy. Xcode Swift Playgrounds To become a professional app developer for Apple devices, you need to learn the most important programming language in the Apple ecosystem, Swift. is probably the best way to learn Swift for iOS app programming. It helps to learn the language through different challenges and puzzles, without even requiring the developer to have the basic programming knowledge. You’ll use Swift when you become a professional developer by learning how to generate code with this language. You can install Swift Playgrounds from the App Store. Swift Playgrounds Programming Languages Swift Swift is the best programming language to learn to develop apps for iOS and other operating systems, like iPadOS or watchOS, running on Apple devices. Apps developed with Swift only work on devices running on iOS 7 or later - of iPhone devices issued in the last four years by Apple are running iOS 13. It is a multi-paradigm, object-oriented, and protocol-oriented programming language which uses the open-source LLVM compiler. 92% If you do not have enough adequate experience and knowledge of programming languages then you will need to learn different object-oriented concepts and commands to become a coding expert and start programming with Swift. Swift Playgrounds plays an important role in helping you learn these commands, signs, and concepts. There are also official comprehensive available to learn this language on iBooks. guides Swift is an open-source language developed by Chris Latner at Apple in 2014. However, it was initially a proprietary language that Apple to open source on in December 2015. Since then Swift language has become part of many other development platforms as well which are not owned by Apple like and . It maintains a vibrant online community at swift.org. decided GitHub Vapor Kitura Almost all of the new libraries and development by Apple is now done in Swift. Objective-C Another important programming language that you need to learn to become a . It was a significant part of Apple’s app development and programming process before the introduction of Swift. Some people suggest against learning this language as Swift has replaced it, but you should have a basic understanding of coding and programming because it has been Apple’s primary programming language in the past. professional iOS developer is Objective-C Both Swift and Objective-C can be used together as , a front end which parses C, C++ and Objective C code, can translate all of the languages and convert them into a representation suitable for LLVM. CLang Objective-C was developed in the 1980s but as a language has not evolved much since then. The primary frameworks we use as part of Cocoa and Cocoa Touch for development of iOS and other platforms including FoundationKit, UIKit, AVFoundation, CoreImage have been written in C and Objective-C for years. So an understanding of Objective-C can help not only to develop apps in Objective-C but understanding of these libraries as well. Step-by-step guide for beginner iOS Developers to learn Swift coding Swift is the language of the present and future for iOS developers. It is advancing and there are the latest versions of the language being introduced after intervals. What is constant is the functionality and appropriateness of Swift for iOS development. Below is a list of what a beginner needs to learn to become an iOS developer at least at a basic level. For advanced development, the sky's the limit. Basic syntax Learning Swift coding begins with basic syntax as it provides comprehensive knowledge about the language. Without learning the syntax, an individual won’t be able to learn the basics of the language and write even a single piece of code. These are terms, keywords, and commands used in the codes you write. You can learn basic syntax using the official Swift handbook, or using the tutorials provided by and by Bart Jacobs. RayWanderlich CocoaCasts Variables and constants The variables and constants in Swift are different from other languages. One needs to understand that there are different . In Swift, the variable values are created with the keyword ‘var’, and the constant values are created with the ‘let’ keyword. The value assigned to a constant can’t be changed, otherwise there will be a syntax error in the code you create. types of variables and constants Appropriate usage of let and var help Swift to effectively manage the memory. The rule of thumb is to use the ‘let’, unless you need the ‘var’. This is how you can simply create a constant and variable in Swift: var name = "kooper" let x = 1 One needs to understand Swift uses type inference to automatically understand the type of data in a variable or constant. This is a new feature that was not part of Objective-C. In some cases, a developer would need to explicitly provide by adding a colon. Like here we want to differentiate between Float and Double: let pi: Double = 3.14 Loops Loops in Swift are similar to other languages. You can add a for loop to repeat a piece of code more than once. A loop repeats executing a chunk of code unless a condition is met. Here is the simplest way to create a loop that repeats 100 times: list = ..< item list { ( )
         } let 0 100 for in print "item is \(item)" A loop is fundamental in programming as we can use it to populate a list or keep updating the background in a game. Functions Functions in your code make the app or software perform a particular task. As a developer, . Defining a function means to prepare the function and make grounds to call and perform it. You can repeat several functions. Means you can call a function many times because it is the same every time you have to call it. You don’t have to create a new code for a repetitive function and can just copy it. A function in Swift starts with the ‘func’ keyword and followed by its name and (). If the function returns some data we can mention its type after an arrow -> operator first you need to define the function and then call it func pet() -> String { return "Kooper" } Optionals Optionals is a great addition in Swift that is a good programming practice to help developers reduce nil/null errors that could cause sudden crashes in apps for users. This is because an optional can hold nil without a problem. A good way is to keep all of your variables as optional variables while you declare them or pass them as a method parameter. You need to only unwrap the Optionals when you have to use the data in variables like while populating the UI or doing some business logic. With that, we can save tons of use cases and scenarios that would cause a crash and only focus when we are using them. Furthermore, Optionals can also help us to create in Swift methods. default parameters To use Optionals, you only need to put a “?” question mark operator at the end of your data type declaration in a variable. The “?” will show that we are unsure if the variable has a value or not. Here is an example of Optionals. A variable of String type is created for ‘currentAddress’ but with a “?” which shows this var can have a nil. var oldAddress: String? You can also use Optionals to create a , which means you can also return an optional object while initializing an object. failable initializer UITableView and UICollectionView If you know how to make var, let, function, loop and if-else and optionals. You can start making your first simple application like Notes or To-do list. Try to use UITableView or UICollectionView which can show a list of items in the view of the screen. A user can then select an item to see the detail. You can finish your first basic app and it to the App Store for publishing. submit -> { .data. } -> { cell: = .tableView.dequeueReusableCell(withIdentifier: cellReuseIdentifier) !
            cell.textLabel?.text = .data[indexPath.row] cell
        } { ( )
        } func tableView ( tableView: UITableView, numberOfRowsInSection    section: Int) _ Int return self count func tableView ( tableView: UITableView, cellForRowAt indexPath: IndexPath) _ UITableViewCell let UITableViewCell self as UITableViewCell self return func tableView ( tableView: UITableView, didSelectRowAt indexPath: IndexPath) _ print "Selected: \(indexPath.row)." A link to the demo app is given at the end of this article. New Skills for iOS Developers to learn in 2021 You need to have a good grasp of networking libraries like AlamoFire and Kingfisher, maintaining codebase in version control like Git, design patterns, and architectural patterns like MVVM. However, here are few skills you need to learn that truly belongs to Swift or Xcode: SwiftUI skills Among the most important and significant skills an iOS developer should learn to become valuable is . It provides the developers with multiple capabilities for a responsive design and appearance of the app. App developers are the ones to develop the UI of the app and place an image, graphic, text box, button, and table, etc. in an app, and learning SwiftUI makes this job easy. Having the skills and expertise of this platform to design apps, an iOS developer’s job of designing the best apps through Swift would become easier. learning the SwiftUI SwiftUI is the latest step by Apple to help developers in creating responsive UI and binding them with the controllers. SwiftUI is a declarative UI framework like XML which is used by Android. A declarative framework focuses only on how to populate the data and not what to populate. Before SwiftUI, developers had the option to create UI using Storyboards, Xibs, and using the code. All of these approaches are great and have their own set of pros and cons. SwiftUI brings “Bindings” which has become my new favorite . This was something we could only do using RxSwift in the past. feature Code and app testing Testing is a significant part of the whole app design and development process. Every iOS developer needs to learn how to test the codes they have written and make necessary changes to it to make sure it works perfectly. Testing requires running the main and alternative use cases again and again to remove the flaws. The developers should be able to create unit test codes to test the app or software. Without having the skills to write test codes, an iOS developer might not be able to succeed in their career. Thankfully Apple provides the XCTest framework which can help you to do unit testing, performance testing and UI testing. The framework is part of Xcode and integrates seamlessly with the testing workflow. You can also run Profile which will run your app on a selected simulator or device along with the of your choice like Time Profiler, Core Animation, Memory Leaks, Allocations, etc. instrument Other than that, you can use run Analyze which will run a static analyzer that will find issues Core Data An iOS developer sometimes would not want the app they have developed to lose all its data after a user closes or kills the app. Learning the Core Data skills helps the developers to make their apps able to retain all the core app and customer data in the device. This retention of data helps the users to continue using the app where they left during the previous session. This is among the primary skills an app developer needs to learn. Grand Central Dispatch An app performs a lot of functions at a time and there is a dire need to align all these functions for smooth app performance. The is one best way to responsibly make all these functions work in perfect harmony and concurrency. A developer needs to have skills and knowledge to make concurrency of these functions possible to make the device run a particular app smoothly. Grand Central Dispatch (GCD) Processing the commands given by users, fetching data from a vast network of information, and displaying all the information on the screen may slow down the device performance. The GCD skills of a developer make it possible for the app and device to perform these functions simultaneously without any glitches or effects on the performance. Technically speaking, GCD allows you to dispatch a part of code on the background thread, foreground/UI thread, or on your own created thread with a set priority. For example, a developer may want to update the UI as the highest priority when a user executes the app but on the contrary, will not mind executing synchronization of profile settings as the lowest priority. Conclusion Mobile and web applications have become a fundamental part of modern-day business cultures and everyday human life. In the wake of all this, an app and software developer is an integral part of the market and society. The number of users using iPhone devices has crossed the 1 mark, and there is an increasing demand for expert iOS developers. The most basic and primary things an individual can do to become an iOS developer is to have a Mac computer, install Xcode in it, and learn Swift coding at the very earliest. Swift is the primary coding and programming language to develop apps for all Apple devices now, and one can’t become an iOS developer without learning Swift coding and programming. billion Here is a sample demo app built using Storyboards and MVVM. You can install it from . For more information please join our community. here

How to Become an iOS Developer: An Essential Guide

Meet Bond and ReactiveKit Mobile app consumers have raised the bar for their expectations while scrolling through an app. Nowadays, everyone wants to see smooth transitions, pixel-perfect apps, and fewer steps. Even a small visual distraction can make the user delete your app. In other words, . your app has to be perfect How do you handle your data changes? There is also another important thing, and that is . I have met tons of apps with poor handling of data changes. For example, you are pulling to refresh the table view content and suddenly the whole content disappears and reappears after 2–3 seconds. In most cases, you are getting the same results back as before the "pull to refresh", so nothing has really changed. Sounds familiar? how you handle the data changes to the UI Let's avoid this kind of situations, and handle data changes properly by using . 👇 bindings and reactive data sources Reactive Programming For those who aren't familiar with the term, reactive is an asynchronous programming paradigm concerned with and the propagation of change. For example, all changes to the model will be automatically reflected in the associated view. 📡 programming data streams If you are interested to find out more about reactive programming click here . Bind with Bond is a binding framework that takes binding concepts to a whole new level. It's simple, powerful, type-safe and multi-paradigm - just like Swift. It simplifies state changes by using bindings and reactive data sources. Bond Swift This library is in my all-time favorites. You can attach it to any component that you like. I will show you how to use Bond with few examples, so you can get the idea of how it's working. Installation For the installation, I am using . I highly suggest that you do the same. Just add the below code in your Podfile and run .👇 CocoaPods pod install target 'YOUR_TARGET' do use_frameworks! pod 'Bond', '~> 6.4.3' end I'm using the latest version of the framework from the day I wrote this post. For version changes, please check here . Add to the top of your class so you can use all the Bond methods. import Bond _Many iOS apps use Google Maps. This is a very common feature, so I have decided to prepare an ultimate guide on Google…_theappspace.com Google Maps SDK for iOS using Swift 4 - The App Space Events I will start by showing you how to get rid of the annoying process of and use the simplified approach from Bond instead. IBAction _ = yourButton.reactive.tap.observeNext { print("Button tapped.") } This will observe your button tap. It listens to because it's the most frequently used event. If you want to handle other events you can use: touchUpInside yourButton.reactive.controlEvents(.touchUpInside).observeNext { e inprint("Button tapped.")} Another example would be observing the text changes in a . Knowing that there is no native approach to this situation, Bond provides us with a simple solution. UITextField _ = yourTextField.reactive.text.observeNext { text inprint(text!)} Now, let me show you a simple binding of the typed text into a . The text will get transformed by using the closure and propagated to the label. is the typed string, so you can add any string transformation that you need. UILabel .map $0 yourTextField.reactive.text.map{ $0?.capitalized }.bind(to: yourLabel) Or, you can use the above example but provide a condition instead. Let's say you need to change the button enabled state as someone types in the text field. txtField.reactive.text.map { $0!.characters.count != 0 }.bind(to: yourButton.reactive.isEnabled) Observable Observables are signals and a typealias for the ReactiveKit type. Whenever the state of the changes, an observer will be notified. Property Observable let animal = Observable("Dog") animal.observeNext { value inprint("Hi \(value)!")} If you can see from the example, whenever the animal observable changes its value an observer will be triggered. To change the value just call . To bind an Observable with a UI component just use . animal.value="Cat" animal.bind(to: yourLabel) Bond also supports two-way binding by using . It means that if the view value changes, it will update the Observable value and vice versa. bidirectionalBind animal.bidirectionalBind(to: animalTextField.reactive.text) _This tutorial is about Instagram API Authentication using Swift and will teach you how to retrieve the ACCESS_TOKEN…_theappspace.com Instagram API Authentication using Swift - The App Space Reactive Data Sources By using reactive data sources, it allows us to easily observe changes in a table or collection view. That means whenever the array changes, the data source methods will update automatically. No need of adding on each array change, and it will update only the changes that were made. 🤘🤘🤘 reloadData() We need to use or that will observe for changes and pass the changes automatically to the data source methods. MutableObservableArray ObservableArray let array = MutableObservableArray([“John”,”Michael”,”Steven”]) I will now present you how easy and short it is to implement the data source of a table view. You don't need to connect the UITableViewDataSource with the table view, as Bond handles that for us. The mandatory and , are "packed" under the array method. In the closure, you can add your own cell logic. See the example below. 👇 numberOfRowsInSection() cellForRowAtIndexPath() bind() array.bind(to: tableView, animated: true) { dataSource, indexPath, tableView in let cell = UITableViewCell(style: .subtitle, reuseIdentifier: “cell”) cell.textLabel?.text = dataSource[indexPath.row] return cell } Now, let's say that the array needs to be updated. At this point, we need to , and forget about the table view and whether it's going to update or not. only focus on the array Here are some methods that you can use to make changes to the array. array.append("Brad") /* appends array with a new object */ array.insert("Brad", at: 2) /* inserts new object at the given index */ array.replace(with: [“John”,"Brad",”Michael”,”Steven”], performDiff: true) /* replaces the array but updates only rows that weren't present in the previous array (in our case index 1) */ array.moveItem(from: 1, to: 2) /* items in array will change places */ array.remove(at: 2) /* removes an item from the array */ array[1] = "David" /* replaces the value at the given index */ By using these methods properly, we provide the best user experience possible. The data will be updated, and the user won't get distracted in any way. I have presented just a few of the many features that Bond offers. Binding and the reactive paradigm can bring your app to the next level, so I would strongly suggest using this approach. If you want to spread the message, please don't forget to 👏 or share this story. Thank you for your attention! 🚀 Check out my latest project: _This app is only available on the App Store for iOS devices. Increase your profits by following our professional BET…_apple.co ‎VIP Sports Bet Tips & Scores on the App Store Read more of my writing on Medium: _Forget MVC, now!_hackernoon.com Introducing Clean Swift Architecture (VIP) _Many iOS apps use Google Maps. This is a very common feature, so I have decided to prepare an ultimate guide on the…_medium.freecodecamp.org Your ultimate guide to the Google Maps SDK on iOS, using Swift 4 _Custom UIView with XIB file is a very common practice in iOS Development. Custom UIView classes don’t contain XIB files…_medium.com SWIFT — Custom UIView with XIB file _A Swift tutorial that will make your app available in Spotlight search_hackernoon.com How to add Spotlight support to your iOS app _Understanding One-to-One and One-To-Many relationships_hackernoon.com Core Data Relationships _All you need to know about Auto Layout_hackernoon.com Understanding Auto Layout in Xcode 9 Subscribe to my Newsletter:

Reactive Programming and Binding in Swift

Haskell is not without its faults. One of the most universally acknowledged annoyances, even for pros, is keeping track of the . There are, in total, five different types representing strings in Haskell. Remember Haskell is strongly typed. So if we want to represent strings in different ways, we have to have different types for them. This motivates the need for these five types, all with slightly different use cases. It’s not so bad when you’re using any one of them. But when you constantly have to convert back and forth between them, it can be a major hassle. In this article we’ll go over these five different types. We’ll examine their different use cases, and observe how to convert between them. different string types Strings The type is the most basic form of representing strings in Haskell. It is a simple type synonym for a (the type). So whenever you see in your compile errors, know this refers to the basic type. By default, when you enter in a string literal in your Haskell code, the compiler infers it as a . String list of unicode characters Char [Char] String String myFirstString :: StringmyFirstString = “Hello” The list representation of strings gives us some useful behavior. Since a string is actually a list, we can use all kinds of familiar functions from Data.List. >> let a = “Hello”>> map Data.Char.toUpper a“HELLO”>> ‘x’ : a“xHello”>> a ++ “ Person!”“Hello Person!”>> Data.List.sort “dbca”“abcd” The main drawback of the vanilla string type is its . This comes as a consequence of immutability. For instance suppose we have: inefficiency myFirstString :: StringmyFirstString = “Hello”

myModifiedString :: StringmyModifiedString = map toLower (sort (myFirstString ++ “ Person!”)) This will allocate a total of 4 strings. The first is . The second is the “ Person!” literal. We can then append these without making another string. But then the sorted version will be a third allocation, and the fourth will be the lowercased version. This constant allocation can make our code non-performant and . myFirstString inappropriate for heavy duty operations Text The family of string types solves this dilemma. There are two types: strict and lazy. Most often, you will use the strict form. However, the lazy form is useful in certain circumstances where you know you won’t need the full string. Text Text The main advantage has is that its functions are subject to “fusion”. This means the compiler can actually of multiple allocations we saw in the last example. For instance, if we look at this: Text prevent the issue import qualified Data.Char as Cimport qualified Data.Text as ToptimizedTextVersion :: T.TextoptimizedTextVersion = T.cons ‘c’ (T.map C.toLower (T.append (T.Text “Hello “) (T.Text “ Person!”))) This will only actually allocate a single object at runtime. This will make it it than the version. So for industrial use of heavy text processing, you are much better off using the type than the type. Text substantially more efficient String Text String ByteString The third family of types fall into the category. As with , there are strict and lazy variants of bytestrings. Lazy bytestrings are a bit more common than lazy text values though. Bytestrings are the of the characters. It is the closest you can get to the real machine level interpretation of them. At their core, bytestrings are a list of objects. A is simply an 8-bit number representing a unicode character. ByteString Text lowest level representation Word8 Word8 Most networking libraries will use bytestrings, as they make the most sense for . When you send information across platforms, you can’t be sure about the encoding on the other end. If you store information in a database, you will often want to use bytestrings as well. Like types, they are generally far more efficient than strings. serialization Text Conversion So with all these types floating around, the real problem is converting between them. It can be enormously frustrating when you want to write some basic code but you have a different string type. You’ll have to look up the conversion if you don’t remember, and this can be annoying. Our first example will be with and . This is quite straightforward. The package exports these two functions, which do exactly what you want: String Text Data.Text pack :: String -> Textunpack :: Text -> String There are equivalents in . We’ll find similar functions for going between and . They exist in the package: Data.Text.Lazy ByteStrings Strings Data.ByteString.Char8 pack :: String -> ByteStringunpack :: ByteString -> String Note these only work with strict . To convert between strict and lazy, you'll want functions from the version of a text type. For instance, exports: ByteStrings .Lazy Data.Text.Lazy toStrict :: Data.Text.Lazy.Text -> Data.Text.Text -- (Lazy to strict)fromStrict :: Data.Text.Text -> Data.Text.Lazy.Text -- (Strict to lazy) There are equivalents in . The final conversion we’ll go over is between and . You could use as an intermediate type with the functions above. But this makes certain assumptions about the encoding and is subject to failure. Going from to is straightforward, assuming . The following functions exist in : Data.ByteString.Lazy Text ByteString String Text ByteString you know your data format Data.Text.Encoding encodeUtf8 :: Text -> ByteString

-- LE = Little Endian format, BE = Big Endian

encodeUtf16LE :: Text -> ByteStringencodeUtf16BE :: Text -> ByteStringencodeUtf32LE :: Text -> ByteStringencodeUtf32BE :: Text -> ByteString In general, you’ll use UTF8 encoded text and thus . Decoding is a little more complicated. Simple functions exist in this same library: encodeUtf8 decodeUtf8 :: ByteString -> TextdecodeUtf16LE :: ByteString -> TextdecodeUtf16BE :: ByteString -> TextdecodeUtf32LE :: ByteString -> TextdecodeUtf32BE :: ByteString -> Text But these can throw errors if your bytestring does not match the format. Run-time exceptions are bad, so for UTF8, we have this function: decodeUtf8’ :: ByteString -> Either UnicodeException Text Which let’s us wrap this in an and handle possible errors. For the other formats, we have to rely on functions like: Either decodeUtf16LEWith :: OnDecodeError -> ByteString -> Text Where is a specific type of handler. These functions can be particularly cumbersome and difficult to deal with. Luckily, you’ll most often be using UTF8. OnDecodeError OverloadedStrings So we haven’t touched too much on language extensions yet in my articles. But here’s our first real example of one. It’s intended to show you ! As we saw earlier, Haskell will in general interpret string literals in your code as the type. This means you are unable to have the following code: language extensions aren’t particularly scary String -- FailsmyText :: TextmyText = “Hello”

myBytestring :: ByteStringmyBytestring = “Hello” The compiler expects both of these values to be of type, and not the types you gave. So this will normally throw compiler errors. However, with the extension, you can fix this! Extensions use tags like . They are generally added at the top of your source file. String OverloadedStrings {-# LANGUAGE … #-} {-# LANGUAGE OverloadedStrings #-}-- This works!myText :: TextmyText = “Hello”

myBytestring :: ByteStringmyBytestring = “Hello” In fact, for any type you make, you can create an instance of the typeclass. This will allow you to use string literals to represent it. IsString {-# LANGUAGE OverloadedStrings #-}

import qualified Data.String (IsString(..))

data MyType = MyType String

instance IsString MyType where  fromString s = MyType s

myTypeAsString :: MyTypemyTypeAsString = “Hello” You can also enable this extension within GHCI. You need to use the command . :set -XOverloadedStrings Summary Haskell uses 5 different types for representing strings. Two of these are lazy versions. The type is a type synonym for a list of characters, and is generally inefficient. represents strings somewhat differently, and can fuse operations together for efficiency. is a low level representation most suited to serialization. There are a lot of ways to convert between types, and it's hard to keep them straight. Finally, the compiler can make your life easier. It allows you to use a string literal refer to any of your different string types. String Text ByteString OverloadedStrings If you haven’t had a chance to get started with Haskell yet, you should get our . It will guide you through installation and some basics! Getting Started Checklist Be sure to check back next week! Now that we understand strings, we’ll divide into another potentially thorny system of different types. We’ll investigate the different numeric types and the simple conversions we can run between them. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising & sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Untangling Haskell’s Strings

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