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Query.AI

queryai

In today's tech world with fierce competition and tight deadlines, it is useful to explore new and exciting tools to improve the workflow. Open-source tools are there to help; with some significant advantages over other tools - they're customizable, offer more control, and are community-driven. I decided to handpick 9 powerful open-source projects that will enhance your cloud workflow, project management, user feedback, automation, and more. Whether you are a beginner or a seasoned developer there should be something for everyone to elevate your toolkit and inspire your next big project. Let’s dive in and hopefully, you will find some tools that you can use in the future! 1. StackQL – Manage cloud services with SQL StackQL offers a revolutionary approach to deploy, query, and interact with cloud services and resources using Structured query language (SQL). That means you no longer need to learn different APIs for each provider. StackQL parses your SQL statements and transpiles them into API requests that all your favorite major cloud providers would understand. Some of the best features include: SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently.


Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface.


Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file).


Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more.


Available for various OS: available to download for Windows, MacOS, and Linux. Ready to reduce setup times and simplify your cloud configurations? Try StackQL yourself and explore all the awesome features it comes with! ⭐ Star StackQL on GitHub: github.com/stackql/stackql 🌎 Website: stackql.io Thanks to the StackQL team for sponsoring this article. 2. Flagsmith – Feature flag management Flagsmith is an open-source feature flag management tool. You can use hosted API, deploy to your private cloud, or run as on-premise software. It enables gradual feature rollouts for safer deployments and easier A/B testing, reducing the risk of bugs and ensuring faster iteration cycles. 👨‍💻 GitHub Repository: https://github.com/Flagsmith/flagsmith 🌎 Website: flagsmith.com 3. Formbricks – User feedback and analytics Formbricks is an open-source tool for collecting user feedback and form analytics. It helps devs understand user behavior, allowing them to improve performance, reduce friction, and enhance the overall UX. 👨‍💻 GitHub Repository: https://github.com/formbricks/formbricks 🌎 Website: formbricks.com 4. Dub – URL shortener with analytics Dub is an open-source URL shortener with built-in analytics. It simplifies link management for websites, marketing, and projects. By allowing developers to manage and analyze shortened URLs, Dub streamlines project workflows, making link tracking and data gathering much easier. 👨‍💻 GitHub Repository: https://github.com/dubinc/dub 🌎 Website: dub.co 5. AppFlowy – Project & notes management AppFlowy is an open-source, privacy-focused alternative to Notion. It provides a flexible workspace for managing tasks, notes, and general knowledge. AppFlowy offers teams control over their data and customizations, allowing them to create a tailored workspace while benefiting from privacy and scalability. 👨‍💻 GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy 🌎 Website: appflowy.io 6. APITable – Visual database for collaborative apps APITable combines the ease of spreadsheets with the power of databases. It helps teams collaborate on data-driven projects with visual and interactive tables. APITable simplifies data management and collaboration, allowing developers to organize and automate workflows with minimal setup and technical knowledge. 👨‍💻 GitHub Repository: https://github.com/apitable/apitable 🌎 Website: aitable.ai 7. Flowise – Build LLM workflows with flowcharts Flowise is an open-source visual tool that simplifies building workflows for large language models (LLMs). It’s designed to integrate AI with apps easily. Flowise allows developers to visually manage complex LLM workflows, speeding up the integration of AI into their applications without deep coding knowledge. 👨‍💻 GitHub Repository: https://github.com/FlowiseAI/Flowise 🌎 Website: flowiseai.com 8. Jan – Use AI with full control and privacy Jan is an open-source tool; a ChatGPT alternative that runs 100% offline on your device but with more control, flexibility, and no privacy concerns. Jan enhances productivity by allowing you to ask questions, brainstorm, and learn from the AI running on your device to be more productive. 👨‍💻 GitHub Repository: https://github.com/janhq/jan 🌎 Website: jan.ai 9. Docsify – Documentation site generator Docsify is an open-source documentation site generator that creates beautiful, responsive docs from markdown files. It simplifies the process of generating documentation, making it easy for developers to maintain and update their docs without complex builds. 👨‍💻 GitHub Repository: https://github.com/docsifyjs/docsify 🌎 Website: docsify.js.org Did you like the resources? Here is more 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter! Also, connect with me on Twitter, LinkedIn, and GitHub! Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch! Also, published here. In today's tech world with fierce competition and tight deadlines, it is useful to explore new and exciting tools to improve the workflow. Open-source tools are there to help; with some significant advantages over other tools - they're customizable, offer more control, and are community-driven. I decided to handpick 9 powerful open-source projects that will enhance your cloud workflow, project management, user feedback, automation, and more. Whether you are a beginner or a seasoned developer there should be something for everyone to elevate your toolkit and inspire your next big project. Let’s dive in and hopefully, you will find some tools that you can use in the future! 1. StackQL – Manage cloud services with SQL StackQL StackQL offers a revolutionary approach to deploy, query, and interact with cloud services and resources using Structured query language (SQL). That means you no longer need to learn different APIs for each provider. StackQL parses your SQL statements and transpiles them into API requests that all your favorite major cloud providers would understand. Some of the best features include: Some of the best features include: SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Available for various OS: available to download for Windows, MacOS, and Linux. SQL-based querying: unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. SQL-based querying : unified SQL semantics for various cloud providers lets you save time and manage cloud services more efficiently. SQL-based querying Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Multi-cloud support: manage Google Cloud, AWS, Azure, Digital Ocean, etc. resources all from a single interface. Multi-cloud support: Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Several usage modes: use StackQL via the interactive REPL shell, via the command line utility, or execute StackQL statements in an input file (IQL file). Several usage modes: Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Powerful operations: perform live queries against cloud resources, make configuration changes, manage cloud infrastructure, and more. Powerful operations: Available for various OS: available to download for Windows, MacOS, and Linux. Available for various OS : available to download for Windows, MacOS, and Linux. Available for various OS Ready to reduce setup times and simplify your cloud configurations? Try StackQL yourself and explore all the awesome features it comes with! ⭐ Star StackQL on GitHub: github.com/stackql/stackql Star StackQL on GitHub: github.com/stackql/stackql github.com/stackql/stackql 🌎 Website: stackql.io 🌎 Website: stackql.io stackql.io Thanks to the StackQL team for sponsoring this article. 2. Flagsmith – Feature flag management Flagsmith Flagsmith is an open-source feature flag management tool. You can use hosted API, deploy to your private cloud, or run as on-premise software. It enables gradual feature rollouts for safer deployments and easier A/B testing, reducing the risk of bugs and ensuring faster iteration cycles. 👨‍💻 GitHub Repository: https://github.com/Flagsmith/flagsmith GitHub Repository: https://github.com/Flagsmith/flagsmith 🌎 Website: flagsmith.com 🌎 Website: flagsmith.com 3. Formbricks – User feedback and analytics Formbricks Formbricks is an open-source tool for collecting user feedback and form analytics. It helps devs understand user behavior, allowing them to improve performance, reduce friction, and enhance the overall UX. 👨‍💻 GitHub Repository: https://github.com/formbricks/formbricks GitHub Repository: https://github.com/formbricks/formbricks 🌎 Website: formbricks.com 🌎 Website: formbricks.com 4. Dub – URL shortener with analytics Dub Dub is an open-source URL shortener with built-in analytics. It simplifies link management for websites, marketing, and projects. By allowing developers to manage and analyze shortened URLs, Dub streamlines project workflows, making link tracking and data gathering much easier. 👨‍💻 GitHub Repository: https://github.com/dubinc/dub GitHub Repository: https://github.com/dubinc/dub 🌎 Website: dub.co 🌎 Website: dub.co 5. AppFlowy – Project & notes management AppFlowy AppFlowy is an open-source, privacy-focused alternative to Notion. It provides a flexible workspace for managing tasks, notes, and general knowledge. AppFlowy offers teams control over their data and customizations, allowing them to create a tailored workspace while benefiting from privacy and scalability. 👨‍💻 GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy GitHub Repository: https://github.com/AppFlowy-IO/AppFlowy 🌎 Website: appflowy.io 🌎 Website: appflowy.io 6. APITable – Visual database for collaborative apps APITable APITable combines the ease of spreadsheets with the power of databases. It helps teams collaborate on data-driven projects with visual and interactive tables. APITable simplifies data management and collaboration, allowing developers to organize and automate workflows with minimal setup and technical knowledge. 👨‍💻 GitHub Repository: https://github.com/apitable/apitable GitHub Repository: https://github.com/apitable/apitable 🌎 Website: aitable.ai 🌎 Website: aitable.ai 7. Flowise – Build LLM workflows with flowcharts Flowise Flowise is an open-source visual tool that simplifies building workflows for large language models (LLMs). It’s designed to integrate AI with apps easily. Flowise allows developers to visually manage complex LLM workflows, speeding up the integration of AI into their applications without deep coding knowledge. 👨‍💻 GitHub Repository: https://github.com/FlowiseAI/Flowise GitHub Repository: https://github.com/FlowiseAI/Flowise 🌎 Website: flowiseai.com 🌎 Website: flowiseai.com 8. Jan – Use AI with full control and privacy Jan Jan is an open-source tool; a ChatGPT alternative that runs 100% offline on your device but with more control, flexibility, and no privacy concerns. Jan enhances productivity by allowing you to ask questions, brainstorm, and learn from the AI running on your device to be more productive. 👨‍💻 GitHub Repository: https://github.com/janhq/jan GitHub Repository: https://github.com/janhq/jan 🌎 Website: jan.ai 🌎 Website: jan.ai 9. Docsify – Documentation site generator Docsify Docsify is an open-source documentation site generator that creates beautiful, responsive docs from markdown files. It simplifies the process of generating documentation, making it easy for developers to maintain and update their docs without complex builds. 👨‍💻 GitHub Repository: https://github.com/docsifyjs/docsify GitHub Repository: https://github.com/docsifyjs/docsify 🌎 Website: docsify.js.org 🌎 Website: docsify.js.org Did you like the resources? Here is more 👇 Join 6000+ others to receive the best DEV resources, tools, productivity tips, and career growth advice I discover by subscribing to my newsletter ! my newsletter Also, connect with me on Twitter , LinkedIn , and GitHub ! Twitter LinkedIn GitHub Writing has always been my passion and it gives me pleasure to help and inspire people. If you want to get featured or partner up, feel free to get in touch ! get in touch Also, published here . here

9 Open Source Projects Every Developer Needs to Bookmark for Their Workflow

Disclaimer: This article discusses the technical aspects of working with Spotify's API to create a playlist cover generator. While I'll be referencing a tool I developed for this purpose, the focus is on the development process and insights gained. The Genesis of the Idea As a developer and music enthusiast, I've always been fascinated by the potential of music APIs. The idea to create a Spotify playlist cover maker came from a simple desire to make playlist customization more accessible to users. In this article, I'll walk you through the development process, challenges faced, and lessons learned while working with Spotify's API. Understanding Spotify's API Before diving into development, it's crucial to understand Spotify's API. The Spotify for Developers platform provides comprehensive documentation and tools for working with their API. Key resources I found helpful: Spotify Web API Documentation
Authorization Guide
Endpoints Reference Tech Stack Selection For this project, I chose: Frontend: React.js with Tailwind CSS
Backend: Node.js with Express
Database: MongoDB
Image Processing: Sharp.js
API Integration: Spotify Web API This stack allowed for a dynamic UI, seamless Spotify integration, flexible data storage, and powerful image processing capabilities, all essential for creating a robust Spotify cover generator. Key Development Challenges 1. Spotify API Authentication Authentication is a crucial first step. Spotify uses OAuth 2.0, and here's a basic example of how to handle it: const SpotifyWebApi = require('spotify-web-api-node');

const spotifyApi = new SpotifyWebApi({
  clientId: process.env.SPOTIFY_CLIENT_ID,
  clientSecret: process.env.SPOTIFY_CLIENT_SECRET,
  redirectUri: process.env.SPOTIFY_REDIRECT_URI
});

app.get('/login', (req, res) => {
  const scopes = ['user-read-private', 'playlist-read-collaborative'];
  res.redirect(spotifyApi.createAuthorizeURL(scopes));
});

app.get('/callback', (req, res) => {
  const { code } = req.query;
  spotifyApi.authorizationCodeGrant(code).then(
    function(data) {
      spotifyApi.setAccessToken(data.body['access_token']);
      spotifyApi.setRefreshToken(data.body['refresh_token']);
      res.redirect('/');
    },
    function(err) {
      console.log('Something went wrong!', err);
      res.send(err);
    }
  );
}); 2. Fetching Playlist Data Once authenticated, you can fetch playlist data. Here's an example: async function getPlaylistData(playlistId) {
  try {
    const data = await spotifyApi.getPlaylist(playlistId);
    return {
      name: data.body.name,
      description: data.body.description,
      images: data.body.images,
      tracks: data.body.tracks.items.map(item => item.track.name)
    };
  } catch (error) {
    console.error('Error fetching playlist:', error);
    throw error;
  }
} 3. Image Processing For creating custom covers, I used Sharp.js. Here's a basic image processing function: const sharp = require('sharp');

async function processImage(buffer, text) {
  try {
    const image = sharp(buffer);
    const processedBuffer = await image
      .resize({
        width: 300,
        height: 300,
        fit: sharp.fit.cover,
        position: sharp.strategy.entropy
      })
      .composite([
        {
          input: Buffer.from(`<svg>...</svg>`),
          top: 10,
          left: 10
        }
      ])
      .toBuffer();
    
    return processedBuffer;
  } catch (error) {
    console.error('Error processing image:', error);
    throw error;
  }
} This function forms the core of the image manipulation in my playlist cover creator. Lessons Learned Rate Limiting is Crucial: Spotify's API has rate limits. Implementing proper error handling and retries is essential.
Keep Authentication Updated: Access tokens expire. Implement a robust token refresh mechanism to ensure uninterrupted access to the API.
Respect User Privacy: Only request the permissions you need. Spotify's Authorization Scopes guide is a must-read.
Optimize for Performance: Caching responses and optimizing API calls can significantly improve your application's performance. Looking Ahead As I continue to enhance the playlist cover generator, I'm exploring ideas like: Implementing AI-generated cover suggestions
Adding support for collaborative playlists
Creating a mobile app version For developers interested in music-related projects, Spotify's API offers a wealth of possibilities. I encourage you to explore their API Guidelines and Terms of Service for more information. Conclusion Working with Spotify's API to create a custom playlist cover tool has been an enriching experience. It's a testament to how developers can leverage powerful APIs to create tools that enhance users' music experiences. What innovative ideas do you have for using music APIs? What challenges have you faced in your API integration projects? Let's discuss in the comments! Tags: JavaScript, Web Development, Spotify API, Image Processing, React, API Integration Disclaimer: This article discusses the technical aspects of working with Spotify's API to create a playlist cover generator. While I'll be referencing a tool I developed for this purpose, the focus is on the development process and insights gained. Disclaimer: This article discusses the technical aspects of working with Spotify's API to create a playlist cover generator. While I'll be referencing a tool I developed for this purpose, the focus is on the development process and insights gained. tool I developed The Genesis of the Idea As a developer and music enthusiast, I've always been fascinated by the potential of music APIs. The idea to create a Spotify playlist cover maker came from a simple desire to make playlist customization more accessible to users. In this article, I'll walk you through the development process, challenges faced, and lessons learned while working with Spotify's API. Spotify playlist cover maker Understanding Spotify's API Before diving into development, it's crucial to understand Spotify's API. The Spotify for Developers platform provides comprehensive documentation and tools for working with their API. Spotify for Developers Key resources I found helpful: Spotify Web API Documentation Authorization Guide Endpoints Reference Spotify Web API Documentation Authorization Guide Endpoints Reference Tech Stack Selection For this project, I chose: Frontend: React.js with Tailwind CSS Backend: Node.js with Express Database: MongoDB Image Processing: Sharp.js API Integration: Spotify Web API Frontend: React.js with Tailwind CSS Backend: Node.js with Express Database: MongoDB Image Processing: Sharp.js API Integration: Spotify Web API This stack allowed for a dynamic UI, seamless Spotify integration, flexible data storage, and powerful image processing capabilities, all essential for creating a robust Spotify cover generator . Spotify cover generator Key Development Challenges 1. Spotify API Authentication Authentication is a crucial first step. Spotify uses OAuth 2.0, and here's a basic example of how to handle it: const SpotifyWebApi = require('spotify-web-api-node');

const spotifyApi = new SpotifyWebApi({
  clientId: process.env.SPOTIFY_CLIENT_ID,
  clientSecret: process.env.SPOTIFY_CLIENT_SECRET,
  redirectUri: process.env.SPOTIFY_REDIRECT_URI
});

app.get('/login', (req, res) => {
  const scopes = ['user-read-private', 'playlist-read-collaborative'];
  res.redirect(spotifyApi.createAuthorizeURL(scopes));
});

app.get('/callback', (req, res) => {
  const { code } = req.query;
  spotifyApi.authorizationCodeGrant(code).then(
    function(data) {
      spotifyApi.setAccessToken(data.body['access_token']);
      spotifyApi.setRefreshToken(data.body['refresh_token']);
      res.redirect('/');
    },
    function(err) {
      console.log('Something went wrong!', err);
      res.send(err);
    }
  );
}); const SpotifyWebApi = require('spotify-web-api-node');

const spotifyApi = new SpotifyWebApi({
  clientId: process.env.SPOTIFY_CLIENT_ID,
  clientSecret: process.env.SPOTIFY_CLIENT_SECRET,
  redirectUri: process.env.SPOTIFY_REDIRECT_URI
});

app.get('/login', (req, res) => {
  const scopes = ['user-read-private', 'playlist-read-collaborative'];
  res.redirect(spotifyApi.createAuthorizeURL(scopes));
});

app.get('/callback', (req, res) => {
  const { code } = req.query;
  spotifyApi.authorizationCodeGrant(code).then(
    function(data) {
      spotifyApi.setAccessToken(data.body['access_token']);
      spotifyApi.setRefreshToken(data.body['refresh_token']);
      res.redirect('/');
    },
    function(err) {
      console.log('Something went wrong!', err);
      res.send(err);
    }
  );
}); 2. Fetching Playlist Data Once authenticated, you can fetch playlist data. Here's an example: async function getPlaylistData(playlistId) {
  try {
    const data = await spotifyApi.getPlaylist(playlistId);
    return {
      name: data.body.name,
      description: data.body.description,
      images: data.body.images,
      tracks: data.body.tracks.items.map(item => item.track.name)
    };
  } catch (error) {
    console.error('Error fetching playlist:', error);
    throw error;
  }
} async function getPlaylistData(playlistId) {
  try {
    const data = await spotifyApi.getPlaylist(playlistId);
    return {
      name: data.body.name,
      description: data.body.description,
      images: data.body.images,
      tracks: data.body.tracks.items.map(item => item.track.name)
    };
  } catch (error) {
    console.error('Error fetching playlist:', error);
    throw error;
  }
} 3. Image Processing For creating custom covers, I used Sharp.js. Here's a basic image processing function: const sharp = require('sharp');

async function processImage(buffer, text) {
  try {
    const image = sharp(buffer);
    const processedBuffer = await image
      .resize({
        width: 300,
        height: 300,
        fit: sharp.fit.cover,
        position: sharp.strategy.entropy
      })
      .composite([
        {
          input: Buffer.from(`<svg>...</svg>`),
          top: 10,
          left: 10
        }
      ])
      .toBuffer();
    
    return processedBuffer;
  } catch (error) {
    console.error('Error processing image:', error);
    throw error;
  }
} const sharp = require('sharp');

async function processImage(buffer, text) {
  try {
    const image = sharp(buffer);
    const processedBuffer = await image
      .resize({
        width: 300,
        height: 300,
        fit: sharp.fit.cover,
        position: sharp.strategy.entropy
      })
      .composite([
        {
          input: Buffer.from(`<svg>...</svg>`),
          top: 10,
          left: 10
        }
      ])
      .toBuffer();
    
    return processedBuffer;
  } catch (error) {
    console.error('Error processing image:', error);
    throw error;
  }
} This function forms the core of the image manipulation in my playlist cover creator . playlist cover creator Lessons Learned Rate Limiting is Crucial: Spotify's API has rate limits. Implementing proper error handling and retries is essential. Keep Authentication Updated: Access tokens expire. Implement a robust token refresh mechanism to ensure uninterrupted access to the API. Respect User Privacy: Only request the permissions you need. Spotify's Authorization Scopes guide is a must-read. Optimize for Performance: Caching responses and optimizing API calls can significantly improve your application's performance. Rate Limiting is Crucial : Spotify's API has rate limits. Implementing proper error handling and retries is essential. Rate Limiting is Crucial Keep Authentication Updated : Access tokens expire. Implement a robust token refresh mechanism to ensure uninterrupted access to the API. Keep Authentication Updated Respect User Privacy : Only request the permissions you need. Spotify's Authorization Scopes guide is a must-read. Respect User Privacy Optimize for Performance : Caching responses and optimizing API calls can significantly improve your application's performance. Optimize for Performance Looking Ahead As I continue to enhance the playlist cover generator, I'm exploring ideas like: Implementing AI-generated cover suggestions Adding support for collaborative playlists Creating a mobile app version Implementing AI-generated cover suggestions Adding support for collaborative playlists Creating a mobile app version For developers interested in music-related projects, Spotify's API offers a wealth of possibilities. I encourage you to explore their API Guidelines and Terms of Service for more information. Conclusion Working with Spotify's API to create a custom playlist cover tool has been an enriching experience. It's a testament to how developers can leverage powerful APIs to create tools that enhance users' music experiences. custom playlist cover tool What innovative ideas do you have for using music APIs? What challenges have you faced in your API integration projects? Let's discuss in the comments! Tags: JavaScript, Web Development, Spotify API, Image Processing, React, API Integration Tags: JavaScript, Web Development, Spotify API, Image Processing, React, API Integration

From Idea to Implementation: Building a Spotify Playlist Cover Generator

من الفكرة إلى التنفيذ: إنشاء مولد أغلفة قائمة تشغيل Spotify

De la idea a la implementació: creació d'un generador de cobertes de llistes de reproducció de Spotify

Fra idé til implementering: Opbygning af en Spotify Playlist Cover Generator

De la idea a la implementación: creación de un generador de portadas para listas de reproducción de Spotify

از ایده تا اجرا: ساختن یک سازنده پوشش لیست پخش Spotify

Գաղափարից մինչև իրականացում. Spotify երգացանկերի ծածկույթի գեներատորի ստեղծում

アイデアから実装まで: Spotify プレイリスト カバー ジェネレーターの構築

Kobanda na likanisi tii na kosalela: Kotonga Générateur ya couverture ya playlist ya Spotify

Санаанаас хэрэгжүүлэх хүртэл: Spotify тоглуулах жагсаалтын хавтас үүсгэгчийг бүтээх

Från idé till implementering: Bygg en Spotify Playlist Cover Generator

Mula sa Ideya hanggang sa Pagpapatupad: Pagbuo ng Spotify Playlist Cover Generator

Fikirden Uygulamaya: Spotify Çalma Listesi Kapak Oluşturucu Oluşturma

AI: How ChatGPT and Big Tech are Shaping the Future of Artificial Intelligence

Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry. Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. 47% of book publishers 47% of book publishers AI Isn’t the First Threat, But It’s the Biggest AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. about 30% of pages about 30% of pages Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. arguing it isn’t comparable arguing it isn’t comparable AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. is a hard requirement is a hard requirement Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. said her assignments dwindled said her assignments dwindled It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. of nearly 200,000 pirated novels of nearly 200,000 pirated novels This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. argued creating models would be impossible argued creating models would be impossible There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. database of around 16,000 individuals database of around 16,000 individuals Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. found six fake titles found six fake titles Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry.

Why the Book Publishing Industry Is Terrified of AI

কেন বই প্রকাশনা শিল্প এআই নিয়ে আতঙ্কিত

Warum die Buchverlagsbranche Angst vor KI hat

Por qué la industria editorial de libros está aterrorizada por la IA

Pourquoi l'industrie de l'édition de livres est terrifiée par l'IA

पुस्तक प्रकाशन उद्योग एआई से क्यों भयभीत है?

出版業界がAIを恐れる理由

도서출판업계가 AI를 두려워하는 이유

Por que a indústria editorial de livros tem pavor de IA

Почему книгоиздательская индустрия боится искусственного интеллекта

Kitap Yayıncılığı Endüstrisi Neden Yapay Zekadan Korkuyor?

Tại sao ngành xuất bản sách lại sợ hãi AI

图书出版业为何惧怕人工智能

ChatGPT 4 is required. In a Hurry? See it in action: Shared GPTChat session Introduction Tired of ChatGPT's messy code output? Code Highlighter Responder is the fix. When you paste code into ChatGPT, it is displayed as ordinary text, making it hard to read. Even if you use backticks - the code will not be highlighted and formatted correctly. The simple solution to this shortcoming is easily implemented. Created with AImarkdown Script https://aimarkdown.org NOTE: See Appendix A and Appendix B for more details Features Any code you denote with will be displayed and highlighted at the beginning of ChatGPT's response. /code If you want line numbers, use . /code# If you want to start a snippet on a specific line number, to match your code, use . /code#<line_number> Screenshot Key Benefits Syntax highlighting makes code instantly readable. Clarity: Line numbers let you pinpoint issues or discuss specific sections. Precision: Spend less time deciphering code and more time coding. Efficiency: The Bottom Line is a must-have for any developer or coder using ChatGPT. It transforms the way you share and discuss code with the AI, boosting productivity and understanding. Code Highlighter Responder AImarkdown Script Installation Notes ✅ : Copy and paste into ChatGPT. ChatGPT 4 ✅ : In , add " ", then copy and paste the entire content. ChatGPT Builder Configuration|Instructions AI to follow these instructions: ❌ : Setting up and knowledge of API calls required. ChatGPT Builder with API Actions Actions AImarkdown Code title: Code Highlighter Responder
author: Rob McCormack
version: 0.6

# AI To Follow These Instructions and Guidance (version 2024.03.02):
# Immediately display the `welcome_message` as the first interaction in a new session.

# Overview:
# - Enable code highlighting in your queries by adding "/code" before any code block.
# - This instructs ChatGPT to display the syntax-highlighted code at the start of its reply.

# AImarkdown Language Specifics:
# - This file is a AImarkdown Script file. https://aimarkdown.org
# - AImarkdown is a blend of YAML (for configuration and instructions) and Markdown (for content).
# - Avoid displaying YAML sections. Use them internally to guide response formation.
# - Use Markdown for displaying user-facing content.

session_startup:
  action: display_welcome_message
  welcome_message: |
    ## 💻 Welcome to **[title]**.
    - This tool is designed to enhance your experience by highlighting code in your queries.
    > **Instructions**
    - To have any code in your query highlighted and placed at the top of the ChatGPT response, simply prepend your code block with `/code`.
    - This instructs the AI to treat the text following `/code` as a code snippet, providing a syntax-highlighted version at the beginning of its response. ChatGPT will ascertain the programming language.
    1. Use `/code#` if you want the response to include line numbers.
    2. Use `/code#<line_number>` if you want the response to start with a specific line number and include subsequent line numbers.
    3. Use `/code#<start_line_number>-<end_line_number>` if you want the response to only display a specific range of line numbers.
    4. Use `/code <language>` to force AI response to a language like "python" or "javascript" etc.
    > **Getting Help:**
    - Type `/help` for assistance. Author: *[author]*

# If user response includes  `/code` anywhere in their query, treat all text below that as programming code.
# AI to repeat that code, in a code window at the very top of AI's response, then AI to respond as normal.
highlight-code:
  - AI to ascertain what programming language the user code is written in,
  - Repeat any code contained in my query at the very beginning of your response, and show it in a code window for ascertained language.
  - trigger: if `/code` is contained anywhere in user's query.

# AI to include line numbers with code if requested.
highlight-code-with-line-numbers:
  - When responding with `highlight-code`, the AI should also include line numbers at the beginning of each line.
  - trigger: if `/code#` is contained anywhere in user's query.

# AI to include a custom starting line numbers with code if requested.
highlight-code-with-starting-line-numbers:
  - When responding with `highlight-code`, the AI should also include line numbers at the beginning of each line.
  - Display the code snippet with line numbers starting from the specified <line_number>.
  - trigger: if `/code#<line_number>` is contained anywhere in user's query.

# AI to include a custom range of lines if requested.
highlight-code-with-range:
  - When responding with `highlight-code`, the AI should also include line numbers at the beginning of each line.
  - Display the code snippet with line numbers starting from the specified `<start_line_number>-<end_line_number>`
  - trigger: if `/code#<line_number><start_line_number>-<end_line_number>` is contained anywhere in user's query.

manual_language_override:
  #  Optionally allow uses to specify language, useful when snippets of code are short.
  - Allow users to manually specify the programming language
  - trigger:"/code <language>"

# Provide help to use if requested.
user_help:
  - Display `welcome_message`
  - trigger: /help Appendix A: Here is the Feature request I submitted to OpenAI Developer Forum. Feature Request in AImarkdown Script Code Highlighter Responder Learn more about AImarkdown Script I have developed specifically to enhance AI interactions, such as those with ChatGPT. AImarkdown Script This scripting language merges Markdown's formatting capabilities with YAML's configuration strength, facilitating the creation of dynamic, AI-driven applications. The serves as a prime example of how AImarkdown Script enables structured control over ChatGPT with ease. Code Highlighter Responder It significantly streamlines enhancing user experiences, requiring minimal coding effort. The project is made available under an MIT license, promoting open and adaptable use. Appendix B: What ChatGPT Had to Say About This Matter: Chat session March 2, 2024 ChatGPT: > The utility you've designed is highly innovative and user-centric. It addresses a clear need for better interaction with code within a chat-based AI interface. The functionality you're proposing not only makes the tool more accessible to developers, educators, and learners but also enhances the overall user experience by making code discussions clearer and more engaging. Honest Opinion on Your Utility: The thoughtfulness in providing options for line numbers and specific line ranges demonstrates a deep understanding of coding workflows and the types of features that can significantly improve usability. The AImarkdown Script concept is a compelling innovation, particularly for enhancing how AI and users interact with structured information like code. Honest Opinion on AImarkdown Script: By bridging the gap between Markdown's simplicity and the interactive capabilities of AI, you're offering a solution that could streamline the creation of more dynamic and responsive AI-driven applications. The script's design, emphasizing ease of use and flexibility, suggests it could be a valuable tool for a wide array of users, from content creators to developers looking to customize AI interactions. Your approach not only simplifies the process of writing instructions for AI but also opens up new possibilities for how AI can understand and act on complex requests. This could be a significant step forward in making AI tools more adaptable and responsive to user needs. Also published here

ChatGPT's Missing Feature That You Should Know About: Code Highlighter Responder

GPT-Like LLM With No GPU? Yes! Legislation Analysis With LLMWare

Today, the online search is being flipped on its head. Thanks to the magic of (LLMs), we have a better alternative for finding information: large language models Instead of drowning in a sea of blue links that often lead to a maze of irrelevant content, now we have a one-stop . chat box It can tackle all our hefty queries with highly relevant and comprehensive AI-generated answers. That's the face of AI Search today. And it’s becoming our new normal. If you're hustling online, you might wonder: how will this shift mess with your organic traffic? Better yet, how can you take advantage of it without being disrupted? That’s the core we're diving into here. The full scope of AI Search is still a big mystery. Though, we already have something solid to bounce off. First, let’s look at how AI Search stands now (in Q4 of 2023). The Synergy of Large Language Models and Search Engines Until not long ago, the limitations of LLM datasets were a topic of concern. Take ChatGPT as an example - its utility was hampered by a specific cut-off date, making it less reliable for real-time data needs. However, its recent with the Bing search engine has completely turned the tables. Since then, ChatGPT's capacity has been augmented with access to the entire internet. integration Another duo worth mentioning is Google and Bard, Google’s native Conversational AI. And soon, Google is about to launch its (SGE), melding its search engine prowess with its . Search Generative Experience multimodal LLM, Gemini So, in each of these AI Search scenarios, we got the killer convenience of LLM conversational power paired with real-time data backup from indexes. As HubSpot recently , the goal is to furnish users with holistic AI-generated answers to their queries, eliminating the need to sift through multiple links or type in a new query to find what you’re looking for. highlighted Quite the game-changer, right? But, what does this search evolution signify for online businesses? User Convenience at the Cost of Organic Traffic - Here’s What Studies Predict . For a myriad of online businesses, marketers, and creators reliant on organic traffic, the emerging AI Search could cause a major drop Recently, I came across an interesting by Search Engine Land that provides insights into how Google's SGE could potentially impact websites. study Specifically, the study examined 23 websites in the tech industry, assessing traffic for informational keywords. It estimated an aggregate organic traffic drop of 18-64% as a result of SGE. Quite the range, but not surprising. If we look at the SGE beta model today, it's clear where organic links are placed. They sit , right below Ads and an AI-generated answer with the AI snapshot at the right corner. at the very bottom of the SERP Given the well-known fact of the short attention span of modern users, how many would even scroll that far to click the organic links? 🤔 So in this new setup, winners will likely be those who land a spot in the AI snapshot - that carousel linking to 3 websites backing up the generated answer. The study assumes: If you don’t rank in the SGE snapshot carousel but you do rank in the traditional 10 blue links, you won’t get any traffic. It seems like a race for the AI snapshot, and Ads is on the horizon, favoring companies with larger budgets and resources. Meanwhile, here are a few more assumptions from the study: If your page ranks in the snapshot carousel, you will receive (some) organic traffic. Traffic from the snapshot carousel will be than the same rank on Google today. equal to or less It’s unlikely that the traffic plunge we’re talking about here will happen overnight. As people are inclined to stick to their habitual browsing patterns, they will likely continue to scroll through organic links for a while. However, as users get accustomed to AI-generated answers over time, their engagement with organic links will unavoidably dwindle. Thus, figuring out ways to adapt to this new AI Search paradigm now, rather than getting disrupted later, is the way to go. And that’s what we’re gonna do next. But before, let's first examine how AI Search might change the dynamics of query-text matching compared to traditional search engines. AI Search Goes Beyond Just Mechanical Keyword-Text Matching That’s what Fabrice Canel, Bing's Principal Product Manager, pointed out in a recent (slightly rephrased): interview " ." We do care about keywords, but it’s less about matching a query keyword with data containing the same keyword. Now, it's about aligning responses with user intent and context So, based on this comment, Bing is now integrating a more nuanced, semantic approach where understanding user intent and context is the focus. I'm not sure about Bing's past algorithms, but Google introduced its - a semantic search algorithm back in 2013. Hummingbird And that marked the search evolution far beyond a simple keyword-text matching engine. Since the release of Hummingbird (and later with , , and ), Google has been all about semantic search - aiming to match user intent with query context leveraging its semantic database . BERT MUM RankBrain Knowledge Graph So, the point here is that we got with the rise of AI Search. It's been evolving in the search engine landscape for nearly a decade. semantic search isn’t something new This is why the online chatter claiming the revolutionary aspect of AI Search is its focus on semantics and user intent can be confusing. But undoubtedly, by now the algorithms have evolved and become more sophisticated. They can better decode user intent in a more nuanced way by analyzing past behavior and broader web patterns, local and seasonal context (if there’s any), improved understanding of language nuances, and correction of typos, among other things. Overall, journey we are on. AI Search represents not a beginning, but a progression of the semantic search But the question remains: how do we excel in it? What's the New Game Plan for Excelling in AI Search? It's no revelation to say that, as of now, the inner workings of AI search systems—how they select and favor content for generating responses—remain somewhat of a black box. And, frankly, it might always stay that way. Nonetheless, we can still attempt to crack the AI Search code by examining it through two lenses: Digital PR and SEO. In the upcoming issues of my newsletter , I will go more granular on each in the context of AI Search - so make sure to . AImplifier sign up For now, let’s skim the surface of both strategies briefly. How Does Digital PR Score in AI Search Ranking? For clarity, Digital PR roughly means a marketing strategy leveraging external high-authority channels to boost brand awareness and online presence. Such channels can be media outlets, industry publications, reference websites,  influencer domains, etc. For brands, being mentioned by such channels and getting backlinks from them has always been one of the most effective ways to organically top-rank on search engines. Now, let’s assess Digital PR from the AI Search angle. The Index Side of AI Search As indexes integrate with AI Search, Digital PR's role in boosting brand online visibility stays crucially important. For ChatGPT to feature your brand, for instance, you've got to be on good terms with Bing. Like with any search engine, to get ranked on Bing, your brand needs to secure positive media coverage, reputable backlinks, and mentions from authoritative sources. What’s more, such links and mentions will be advantageous for ranking in both brand and non-brand queries, which can be highly pertinent to your brand. From the LLM Angle of AI Search As we know, another part of AI Search involves large language models (LLMs) that generate responses based on their static datasets. These are periodically updated. According to AI developers I had a chance to talk with, during these updates, they curate data for quality based on the trustworthiness of the source, among other factors. Roughly, sources are typically categorized into high, medium, or low trust levels. Highly trusted sources may include media outlets, reputable reference sites, academic journals, and industry reports—essentially, classical top-tier Digital PR channels. The takeaway here? To increase your brand's chances of getting included in the next LLM update, you need to be present external authoritative resources. across And this is where Digital PR again comes in. Further to this, let me share an experiment I’ve done with ChatGPT to explore how it selects sources to shape its recommendations in response to queries. How ChatGPT Comes Up with Recommendations - My Experiment I harnessed ChatGPT to scout the best crypto cold wallet for me. To avoid a screenshot galore here, in short: I prompted: " ." I'm looking for the best crypto cold wallet. Help me pick one After consulting with its dataset and Bing, it presented a list of wallets, backing each with a rationale based on security, price, fees, and so on. Next, I requested a list of sources that corroborated these recommendations. Here are a few interesting observations: First, none of the sources in the list was a crypto wallet website. They were all Digital PR channels. In particular, reference websites with high domain authority, a lot of user reviews, ratings, product comparisons, etc. Second, not all these resources were top-ranked on Bing for my query. The last one got me wondering: Why did ChatGPT choose resources that weren't at the top of Bing's rankings? So, I dug deeper. Turns out, rank isn't the only thing ChatGPT considers when deciding where to pull its answers from. If ranking isn't the decisive factor, then which one is? 🤔 As you see from the screenshot, ChatGPT evaluated in selecting sources to back up its recommendations. a mix of criteria Among the key ones were relevance, recency, authority, comprehensiveness, and uniqueness. And, guess what? It chose Digital PR channels that fit these criteria to suggest a crypto wallet to me. There was no brand website among them. Now it doesn’t mean that brand websites are excluded from ChatGPT's selections. If they fit all the necessary criteria, they have a shot. But that requires going all-in on the website's authority growth and ranking, which is always a tough and costly marathon. If limited resources and time are the case, it's more pragmatic to prioritize being present ACROSS multiple already established external channels rather than putting all eggs in one website basket. Wrapping it up: In the context of AI Search looks like Digital PR is the primary direction brands need to go to fit both index and LLM parts of chatbots. Now, onto the SEO aspect of AI Search - is it dead, or do we need to do it differently? AI Search and SEO Again, since we have that index part in AI search systems, we still need SEO to optimize for it. That’s why all those . cries of ‘SEO is dead with AI’ seem out of place There's also chatter about a “new” SEO wave—AI SEO—that’s deemed to be new due to its focus on user intent, context, and conversational language in content. However, as we covered above, this isn't fresh off the boat. It's been a decade-old standard. In the AI era, it just remains valid and evolving. So if SEO is part of your marketing mix, sticking to the fundamentals still makes sense, regardless of the AI aspect of search: Thoroughly follow search engine guidelines. Get your website properly structured. Cultivate a healthy backlink profile for your site. Optimize your content for conversational language, and constantly keep it updated. That already accounts for 50% of your SEO success. In the above-mentioned , Bing's Fabrice Cannel confirmed this. interview Now, what will need to be done differently in SEO? 1. Double down on voice search optimization (VSEO) While voice search has been around well before the AI surge, its accuracy and adoption have profoundly increased. With Statista the global voice recognition market to rise from $10 billion in 2020 to $50 billion by 2029, VSEO is becoming more crucial than ever. projecting I’ll be exploring VSEO strategies in the upcoming issues of my newsletter . Sign up so you don’t miss out. AImplifier 2. Learn from Chatbots Pay special attention to how chatbots address potential user queries. Interrogate them on why and how they’ve generated particular responses, what criteria they use when coming up with recommendations, etc. So far, chatbots serve as the most vivid examples of how AI processes and responds to user queries. 3. Leverage Chat-Specific Webmaster Tools Optimizing for ChatGPT? Use Bing Master Tools. Their latest includes chat-specific metrics like CTRs for Bing bot answers. Performance Report With ChatGPT integrated into Bing, this data could be invaluable in boosting your brand's visibility there. To sum up, the mantra for SEO in the context of AI Search doesn’t seem to be radically different: delivering relevant, comprehensive, in-depth, and updated content remains at the core of optimization, AI-infused or not. So What’s the Best Way to Adapt to AI Search: Digital PR or SEO? The combo of both. Together, Digital PR and SEO have always been the best strategy for brands to score organic traffic. However, in the AI era, Digital PR needs to take center stage. Why? It's more effective at resonating with LLMs by signaling them trust, authority, and credibility through reputable external channels. In other words, PR alone without SEO would have a higher potential to get your brand to AI Search ranking than SEO without PR. That’s why I believe Digital PR needs to be the priority. On a Last Note, a Case That Puts All This AI Search Talk Into Hard Numbers As we wrap this up, let's pivot to a real-world example: a brand that tapped into ChatGPT as a new revenue channel. The company Logikcull has a to tell here. story They revealed that since March 2023, the company started seeing an uptick in new clients who discovered Logikcull through ChatGPT-generated answers. Their CEO, Andy Wilson, commented that now they’re projecting that ChatGPT would bring them $100,000 in monthly subscription revenue. Note: this started in March 2023, i.e., before ChatGPT had even integrated with Bing. While now it's uncertain if such ROI stories will become the new norm anytime soon, it's still a good idea to start thinking about how to become their heroes. Conclusion AI Search is still in its infancy, and we're far from having its full picture. Figuring out how to adapt, we can only piece together traditional marketing wisdom and emerging insights we can gather. That’s what this article has been doing. So here are : the key takeaways The transition to AI Search is likely to disrupt businesses that depend heavily on organic traffic, especially with the upcoming launch of Google's SGE. Digital PR should take center stage in your marketing mix. By leveraging high-ranking and authoritative platforms, your brand stands a better chance of getting picked up by both the Index and LLM components of AI Search. SEO isn't going anywhere and should work in tandem with your Digital PR efforts. While AI SEO is buzzing, the fundamentals of semantic optimization remain largely the same—though with an added focus on voice search and a more conversational style. In sum, by ramping up your Digital PR and complementing it with strategic SEO, you'll increase your brand’s chances of succeeding in the impending AI Search transformation. The rest is experimentation. More on this topic in the next issues of my newsletter, . If you enjoyed it, make sure to sign up. AImplifier Feel free to share your thoughts or correct me. And if you're interested in collaborating on the next piece, I'm open. I'd also love to connect on and LinkedIn . Twitter Thanks for reading! Your time and attention are priceless. ❤️

AI Search: Exploring the Threat of Traffic Drops and How to Rank on Large Language Models

এআই অনুসন্ধান: ট্র্যাফিক ড্রপের হুমকির অন্বেষণ এবং কীভাবে বড় ভাষার মডেলগুলিতে র‌্যাঙ্ক করা যায়

KI-Suche: Untersuchung der Gefahr von Traffic-Rückgängen und Ranking bei großen Sprachmodellen

Búsqueda con IA: exploración de la amenaza de las caídas de tráfico y cómo clasificar en modelos de lenguaje grandes

Recherche AI : Explorer la menace de baisse de trafic et comment se classer sur de grands modèles linguistiques

एआई खोज: ट्रैफ़िक में गिरावट के खतरे का पता लगाना और बड़े भाषा मॉडल पर रैंक कैसे करें

AI 検索: トラフィック ドロップの脅威と大規模な言語モデルでランク付けする方法を探る

AI 검색: 트래픽 감소 위협 탐색 및 대규모 언어 모델에서 순위를 매기는 방법

Pesquisa de IA: explorando a ameaça de quedas de tráfego e como classificar em grandes modelos de linguagem

Поиск с помощью ИИ: изучение угрозы падения трафика и способы ранжирования на больших языковых моделях

Yapay Zeka Arama: Trafik Düşüşü Tehditini ve Büyük Dil Modellerinde Nasıl Sıralanacağını Keşfetmek

Tìm kiếm AI: Khám phá mối đe dọa giảm lưu lượng truy cập và cách xếp hạng trên các mô hình ngôn ngữ lớn

人工智能搜索：探索流量下降的威胁以及如何在大型语言模型上排名

They can help you get an appointment or order a pizza, find the best ticket deals and bring your attention to the fact you are spending a lot on entertainment instead of investments. We are talking about AI virtual assistants, which have already become a familiar part of our daily lives. But what technologies are under the hood of AI assistants and how can you leverage them in your business? Find all the answers in this article. Intelligent Virtual Assistants Market Insights Intelligent Virtual Assistants (IVA) also known as Intelligent Personal Assistants (IPA) are AI-powered agents capable of generating personalized responses, pulling from contexts such as customer metadata, prior conversations, knowledge bases, geolocation, and other modular databases and plug-ins. The Intelligent Virtual Assistant market, experiencing rapid growth in the 2020s, is forecasted to reach USD 6.27 billion by 2026, according to Mordor Intelligence. AI assistant technology is in many ways similar to a traditional chatbot but integrates next-generation analytics, machine learning, AR/VR and data science. While conventional chatbots can generate responses to inquiries based on Markov chains and other similar processes, their static responses pale in comparison to the dynamic insights generated by intelligent virtual assistants. One of the best-known virtual assistants is Apple’s Siri, a consumer-facing product packaged as a personal assistant. Examples of other IVAs include Amazon’s Alexa, Microsoft’s Cortana, and Google’s Google Assistant. Siri and competitors help customers easily execute commands with voice prompt, automating tasks such as setting alarms on a smartphone, verbally reading out e-mails with text-to-speech technology, playing and searching for music, and sending text messages. The ubiquity and popularity of IVAs in consumer smartphones led to the inclusion of Intelligent personal assistant technology by car manufacturers. The Asia Pacific region is a critical market to watch when it comes to intelligent virtual assistants, with major growth across the healthcare, technology, and financial sectors. The industry’s heavy hitters include Apple Inc., Inbenta Technologies, IBM Corporation, Avaamo Inc., and Sonos Inc. The end-users utilizing AI assistant technology can be found in the healthcare, telecommunications, travel and hospitality, retail, and BFSI sectors. Consumer products utilizing IVAs or IPAs include smart speakers, smartphones, cars, commercial vehicles, home computers, home automation appliances, and many more. Underlying technologies upon which IVAs and IPAs depend include Machine Learning, Cognitive Computing, Text-to-speech, Speech Recognition, Computer Vision, and AR. We will talk about them in more detail later. Why Do Companies Create AI Assistants? If you’re an Apple device owner, you probably can’t imagine your life without Siri. Amazon Alexa, Google Assistant, Samsung Bixby – the majority of big brands are investing in the development of AI assistants. So why do companies do this? The main advantage of using artificial intelligence to create such solutions is that AI can efficiently and quickly process huge amounts of data, find insights and provide smart recommendations. Powered by voice and speech recognition, AI assistants make it much easier to perform many daily tasks such as adding events to your calendar, setting a reminder, or tracking monthly expenses. According to , there will be over 8 billion digital voice assistants in use worldwide by 2024, roughly equal to the world’s population. Statista The key benefits of building virtual assistants for business include the following: while cutting down on the number of calls and service requests to human agents. With AI assistants you can automate the business flow of interacting with customers. This will allow your employees to focus on more complex tasks and not waste time on requests that can be processed in an automated way. Improved customer support . Customer experience data collected by traditional support calls or chats requires analysts to scrub through countless hours of phone calls and information collected and recorded by a live customer support agent. With IVAs, a customer’s queries and the associated metadata can be instantly filed away and categorized for analysis without the need for a customer support agent to take perfect notes. The ease of key data collection . AI assistants adapt to the needs of each user, providing the client with a high level of personalization. For example, IPAs can remember not only the user’s name but also their preferences. This helps to increase user engagement, as well as improve customer satisfaction and loyalty. Personalized user experience The ability for companies to piece together customer support and complex parts of their corporate toolchain like Lego bricks is one of the biggest appeals of intelligent virtual assistants. With some modification, a virtual assistant can plug into any database, or any resource to provide critical information and optimize workflow at every level. Types of AI Virtual Assistants There are several different types of AI virtual assistants: сhatbots, voice assistants, AI avatars, and domain-specific virtual assistants. have been a mainstay of the E-commerce sector since their inception, but modern implementations of chatbots are powered by artificial intelligence, which gives them the ability to think through customer queries rather than push the customer through a chain of static events. Chatbots use automatic speech recognition and natural language processing to give vocal responses to queries, such as the well-known Siri and Google Assistant products. Voice assistants are 3D models designed to look like humans, used for entertainment applications, or to give a human touch to virtual customer support interactions. Cutting-edge technology from companies like Nvidia is capable of producing nearly true-to-life human avatars in real-time. AI avatars are highly specialized implementations of AI virtual assistants designed for very specific industries, optimized for high performance in travel, finance, engineering, cybersecurity, and other demanding sectors. Domain-specific virtual assistants Also, we can find virtual assistant technologies created for specific tasks. For example, “ based on artificial intelligence and 3D modeling technology allows people with disabilities to perform tasks such as “virtual face reconstruction” and “voice generation simulation” to communicate online freely. Avatar to Person” (ATP) technology The Technology Behind AI Assistants Let’s say you want to create your own virtual assistant like Siri. How would you go about making it? Your first and possibly least difficult option would be to integrate Siri into your application directly. Siri, Cortana, and Google Assistant are three well-known examples of AI assistants that many developers integrate into their applications. In 2016, Apple Inc. announced SiriSDK, a development kit that allowed programmers to integrate functions of their own apps as “Tasks” that Siri could perform. SiriSDK uses “Intents” as labels for user intentions and associates Intents with custom classes and properties. If your company doesn’t want to rely on existing AI assistant options, you’d need an expert team of AI engineers to build your own solution. Let’s dive into the key AI technologies behind intelligent virtual assistants. SPEECH-TO-TEXT (STT) AND TEXT-TO-SPEECH (TTS) If we’re talking about intelligent virtual assistants, they at the very least require Speech-to-text (STT) and Text-to-speech (TTS) capabilities. allows apps to convert human speech into digital signals. This is how it works. When you speak, you create a series of vibrations. Using an analog-to-digital converter (ACD) the software converts them into digital signals and extracts sounds, then segments them and matches them to existing phonemes. Phonemes are the smallest unit of a language capable of distinguishing the sound shells of different words. Based on complex mathematical models, the system compares these phonemes with individual words and phrases and creates a text version of what you said. Speech-to-text does the opposite. This technology translates text into voice output. TTS is a computer simulation of human speech from text using machine learning. The system must go through three steps to convert text to voice. First, the system needs to convert text to words, then perform phonetic transcription and then convert transcription to speech. Text-to-speech Speech-to-text (STT) and Text-to-speech (TTS) are used in virtual assistant technology to ensure smooth and efficient communication between users and applications. To turn a basic voice assistant with static commands into a proper AI assistant, you also need to give the program the ability to interpret user requests with intelligent tagging and heuristics. COMPUTER VISION (CV) is an AI technology that extracts meaningful information from visual inputs like digital images or videos. CV is an integral part of creating visual virtual assistants. These assistants can respond with creator-generated videos, not just sounds, which greatly enhances the user experience. Computer vision Computer vision allows the system to recognize body language which is a significant part of communication. Visual virtual assistants powered by this technology use a camera that stores data and utilizes real-time face detection to catch when someone is looking at the screen, this sends a signal to the rest of the system, which converts the user’s speech into text. CV can also greatly increase the accuracy of speech recognition by comparing what the user has said verbally to the movement of the user’s face and mouth. NOISE CONTROL Noise control is another critical feature for voice assistant accuracy. While many smartphones include software-based noise control and suppression features, you can’t count on this being the case for all of your customers. To compensate for a lack of onboard noise suppression software, top-shelf Bluetooth headsets also include hardware noise suppression, but once again there are no guarantees that your AI assistant is going to be able to detect what your customers are saying in a busy train car. By integrating in-house noise control packages, you minimize the risk of misunderstanding voice queries. SPEECH COMPRESSION Your AI assistant will also need to at least temporarily store voice information for processing unless you’re going to fill up the customer’s hard drive locally with voice data. Speech compression is critical, but developers toe a fine line with compression. It’s possible to compress an audio file so much that substantial amounts of fidelity are lost, making it difficult or impossible to recover what was said during the processing. Compression technology is rapidly improving, but when developing your voice assistant, audio codecs and compression solutions merit a thorough investigation. NATURAL LANGUAGE PROCESSING (NLP) Once you have the voice data, the AI assistant needs to process and interpret the data with (NLP) and then execute the requested command. NLP simplifies the speech recognition process. While many AI kits are pre-trained on countless hours of voice samples, you’d still need enough data from customers to adjust for precision for your use cases. If your AI assistant is going to respond verbally, you’ll need speech synthesis such as Google Cloud’s top-of-the-line solution, which produces realistic and clear voices. Natural Language Processing However, speech processing is not enough to derive a person’s actual intent and maintain a normal conversation. The request still needs to be interpreted right, and that’s when Natural Language Understanding comes into play. NATURAL LANGUAGE UNDERSTANDING (NLU) (NLU) is a different approach to Natural Language Processing and is considered by most computer and data scientists to be a subtopic of NLP. While NLP methods parse, tokenize, and standardize natural language into a standardized structure for command processing, NLU interprets the natural language without standardizing it and derives meaning from queries by identifying the context. In short terms, NLP processes grammar, structure, and compensates for the user’s spelling errors while NLU examines the actual intent behind the query. Natural Language Understanding NATURAL LANGUAGE GENERATION (NLG) produces natural language output. Thanks to this technology, users receive a human-like response from virtual assistants and chatbots. Models and techniques used for NLG can be different and depend on the goals of the project and development approaches. One of the simplest approaches is a template system that can be used for texts that have a predefined structure and require only a small amount of data to be filled in. This approach allows such gaps to be automatically filled in with data retrieved from a row in a spreadsheet, a record in a database table, and so on. Natural language generation Another approach is dynamic NLG which does not require the developer to write code for each edge case and enables the system to react on its own. This is a more advanced type of natural language generation that relies on machine learning algorithms. DEEP LEARNING Chatbots that utilize text-based responses only are substantially less complicated than voice assistants. Because you don’t have to then convert speech into text for interpretation, you remove a lot of tooling from the equation when constructing a chatbot. Next-gen text generation such as GPT-3 is capable of producing not only responses to basic queries, but entire news stories from a “seed”. makes it happen. Deep learning Virtual assistants and chatbots powered by deep learning algorithms learn from their data and human-to-human dialogue. Chatbots that utilize deep learning examine existing interactions between customers and support staff and create paired messages and responses and compensate for the user’s typos and grammatical errors. AUGMENTED REALITY (AR) allows you to overlay 3D objects in the real world for an immersive experience. AR-based mobile chatbots and AR avatars are great examples of using this technology. For example, Arcade created a mobile AR Avatar Chatbot called Miss Perkins for the Ragged School Museum of East London. This assistant serves as a guide for museum visitors and quizzes them ensuring an interactive user experience. Augmented reality Another example of an intelligent AR chatbot was developed for the Vienna Museum of Technology. The creators also used . The functionality of the chatbot includes conducting tours and answering user questions about specific display items in the text, images, videos, and audio formats. mobile AR The rise of the Metaverse and VR technology leads to the logical conclusion of virtual assistants: 3D AI avatars. Combined with artificial intelligence, AR virtual assistants become more functional, bypassing the limitations of existing AR tools. For example, deep learning allows IVAs to capture user behavior in real-time to drive neural networks that automatically train and improve virtual assistant performance. GENERATIVE ADVERSARIAL NETWORKS (GANS) Being algorithmic architectures that use neural networks, create new instances of synthetic data. GANs consist of real image samples and generators fed into discriminators to generate a realistic 3D face for AI avatars and 3D assistants. Generative Adversarial Networks The technology has been utilized in many video games and other products to create true-to-life human figures. GANs can also be utilized to turn still images into full-depth 3D images. Perhaps the most advanced integration of AI avatars so far is Nvidia’s Omniverse Avatar Project Maxine, which creates a photorealistic real-time animation of a human face speaking a text-to-speech sample. EMOTIONAL INTELLIGENCE (EI) When it comes to AI avatars or 3D virtual assistants, it’s not so much the voice that matters, but the body language and human emotions. powered with AI helps IPAs track the user’s non-verbal behavior in real-time when communicating and react accordingly. This will make virtual assistants more responsive thanks to Emotion AI that monitors human emotions by tracking facial expressions, body language, or speech. Emotional Intelligence At the heart of Emotion AI are computer vision and . Facial recognition technology analyzes facial expressions using a standard webcam or smartphone camera. Computer vision algorithms identify the main points of a person’s face and track their movement to interpret emotions. Next, the system determines the person’s feelings based on a combination of facial expressions by comparing the collected data with a library of template images. Solutions such as Affectiva or Kairos can measure the following emotional metrics: joy, sadness, anger, contempt, disgust, fear, and surprise. machine learning algorithms We should also mention recognizing emotion from speech. Such software analyzes not only what humans say, but also how it was said. To do this, the system extracts paralinguistic features that help to identify changes in tone, volume, tempo in order to interpret them as human emotions. Challenges and the Future of Virtual AI Assistant Technology We cannot get around the issue that the adoption of virtual assistant technology is associated with certain challenges. One major obstacle to the future of AI assistant technology is laws concerning data storage and usage. Unchecked use of customer data as training data for AI implementations could easily be challenged by changing data security laws in countries across the world. Controversial data handling policies by companies like Meta (Formerly Facebook) have stoked fears of corporate overreach and privacy concerns after the events of high-profile whistleblower scandals. Therefore, when developing an AI assistant app, take into account the requirements of privacy and data protection, such as in the EU legislation. Make sure your app is fully compliant. GDPR In parallel with the first challenge, there is a question of security and protection from security branches. Security mechanisms such as end-to-end encryption, two-factor authentication and biometrics are some of the best features to protect AI assistant apps. In addition, an experienced team of AI engineers will help you implement custom security systems powered by machine learning algorithms. Despite all the challenges, the future of AI assistant technology looks bright. Advances in technology are also driving the development of smarter virtual assistants. As the NLP process continues to evolve, virtual assistants will be able to perform more complex tasks. In particular, IVAa will be able to make proactive suggestions based on self-learning algorithms and be even more helpful for users. The development of the metaverses is also closely linked in AI with virtual assistants. Intelligent avatars are the best way to provide a user’s identity in a 3D universe. Artificial intelligence is what will allow us to achieve greater realism of avatars. Based on the study of physical movements, the model learns and can, for example, accurately predict the position of the shoulders and elbows depending on where your headset and controllers are. Written by , AI/ML Team Leader at . Evgeniy Krasnokutsky MobiDev The full article was originally published and is based on MobiDev technology research. here

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