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Booksy UK and Ireland

booksy

Effective trading mechanisms are crucial to DeFi, as without them, the entire ecosystem would struggle to operate efficiently and attract users, ultimately limiting its growth and impact. Currently, DeFi primarily relies on Automated Market Makers (AMMs), while Central Limit Order Books (CLOBs) are emerging as an alternative. Automated Market Makers (AMMs) revolutionized DeFi asset trading by enabling straightforward swaps without the need for traditional order books. Their user-friendly approach has made AMMs the dominant trading method in DeFi, allowing users to trade directly from their wallets without needing to navigate complex order types. However, a new mechanism is gaining traction across DeFi: Central Limit Order Books (CLOBs). While CLOBs have long been a staple in traditional finance, their adoption in DeFi is still in its early stages. CLOBs require high transaction throughput and low latency to match and execute orders efficiently, which poses a challenge for most blockchains—especially Ethereum—before the advent of rollups and sharding. As blockchain scalability improves, L2 solutions like Fuel are making CLOBs a viable option, by allowing developers to build smart contracts that require high performance virtual machines to be efficient. The emergence of DeFi based CLOBS signals that the DeFi sector is maturing and positioning itself for a wave of institutional engagement. Additionally, the integration of both AMMs and CLOBs can help create a robust DeFi ecosystem that accommodates a diverse user base, leveraging the strengths of each to meet varying trading needs. How do Automated Market Makers (AMMs) work? AMMs use an algorithm to price assets based on the constant product formula and its derivatives, the most popular being, x*y=k. AMMs enable traders to exchange assets directly from liquidity pools without requiring a counterparty, unlike traditional order books. Instead, on AMMs traders trade against the liquidity of passive liquidity providers who earn fees for providing liquidity to the protocol. This approach has been revolutionary in DeFi, allowing anyone to provide liquidity and participate in trading without needing to be a professional trader or quant. How do Central Limit Order Books (CLOBs) work? CLOBs facilitate efficient and transparent trading by matching buy and sell orders based on price and time priority. Traders submit various types of orders, including market, limit, and stop orders. CLOBs continuously update to reflect current market conditions, showing outstanding buy and sell orders and providing insights into market depth. For instance, if a trader wants to buy 1 Bitcoin at $60,000, they submit  a limit order to  the CLOB. If a seller is willing to sell 1 Bitcoin at $60,000, the two orders can be  matched, and the trade is executed. If no seller is available at that price, the order remains in the book until a matching seller is found. The  speed and precision of CLOBs make them ideal for high-frequency and algorithmic trading, attracting professional traders and institutional investors by offering greater flexibility and control. Key Differences Between CLOBs and AMMs Feature

Automated Market Makers (AMMs)

Central Limit Order Books (CLOBs)



Mechanism

Algorithmic pricing based on the constant product formula (x*y=k)

Order matching based on price-time priority



Users

Suitable for both casual and advanced users; offers customizable strategies, especially with advancements like UniV4.

Favoured by professional traders for precise control but also adaptable for various user levels depending on the platform.



Order Types

Simple swaps, no advanced order types

Advanced order types like limit orders, stop orders, and conditional orders



Capital Efficiency

Capital inefficient due to most liquidity sitting idle along the bonding curve

More capital efficient, matching buyers and sellers directly,



Trading Strategies

Supports both basic and advanced trading strategies with customizable liquidity provision in UniV4.

Supports sophisticated trading strategies and high-frequency trading



User Expertise

Easy to use, typically features simpler UI

Requires more expertise, designed for advanced trading capabilities



Decentralization

Completely decentralized, governed by smart contracts

Can be decentralized but typically involves a more complex architecture



Advanced Features

Supports advanced features like buy/sell limit orders and customizable liquidity provision, especially with UniV4

Offers better price discovery and enhanced order types, reducing arbitrage opportunities and benefiting from more precise market equilibrium Why Will CLOBs Play an Important Role in the Next Phase of DeFi? The next wave of DeFi growth is expected to be driven by increasing institutional investment and interest. According to Northern Trust, financial institutions are preparing for significant adoption of digital assets and tokenization, which is expected to accelerate institutional DeFi over the next few years. Additionally, industry experts predict substantial growth in DeFi due to the tokenization of real-world assets, further attracting institutional investors. Binance Research notes that the total value locked (TVL) in DeFi has surged by 75.1% year-to-date, reaching $94.9 billion, with significant capital inflows. It also highlights that yield-bearing assets saw a 148.6% increase, now making up $9.1 billion of the TVL. This influx of capital has benefited nearly every DeFi sector, making previously inaccessible financial primitives available on chain. To accommodate this shift, DeFi must evolve beyond the limitations of AMMs, which are well-suited for everyday users, and develop infrastructure that meets the demands of more sophisticated traders. This is where CLOBs have the potential to become a game-changer for DeFi, offering advanced trading capabilities, superior price discovery, and improved capital efficiency to support the needs of institutional participants. Effective trading mechanisms are crucial to DeFi, as without them, the entire ecosystem would struggle to operate efficiently and attract users, ultimately limiting its growth and impact. Currently, DeFi primarily relies on Automated Market Makers (AMMs), while Central Limit Order Books (CLOBs) are emerging as an alternative. Automated Market Makers (AMMs) revolutionized DeFi asset trading by enabling straightforward swaps without the need for traditional order books. Their user-friendly approach has made AMMs the dominant trading method in DeFi, allowing users to trade directly from their wallets without needing to navigate complex order types. However, a new mechanism is gaining traction across DeFi: Central Limit Order Books (CLOBs). While CLOBs have long been a staple in traditional finance, their adoption in DeFi is still in its early stages. CLOBs require high transaction throughput and low latency to match and execute orders efficiently, which poses a challenge for most blockchains—especially Ethereum—before the advent of rollups and sharding. As blockchain scalability improves, L2 solutions like Fuel are making CLOBs a viable option, by allowing developers to build smart contracts that require high performance virtual machines to be efficient. The emergence of DeFi based CLOBS signals that the DeFi sector is maturing and positioning itself for a wave of institutional engagement. Additionally, the integration of both AMMs and CLOBs can help create a robust DeFi ecosystem that accommodates a diverse user base, leveraging the strengths of each to meet varying trading needs. How do Automated Market Makers (AMMs) work? How do Automated Market Makers (AMMs) work? AMMs use an algorithm to price assets based on the constant product formula and its derivatives, the most popular being, x*y=k. AMMs enable traders to exchange assets directly from liquidity pools without requiring a counterparty, unlike traditional order books. Instead, on AMMs traders trade against the liquidity of passive liquidity providers who earn fees for providing liquidity to the protocol. This approach has been revolutionary in DeFi, allowing anyone to provide liquidity and participate in trading without needing to be a professional trader or quant. How do Central Limit Order Books (CLOBs) work? How do Central Limit Order Books (CLOBs) work? CLOBs facilitate efficient and transparent trading by matching buy and sell orders based on price and time priority. Traders submit various types of orders, including market, limit, and stop orders. CLOBs continuously update to reflect current market conditions, showing outstanding buy and sell orders and providing insights into market depth. For instance, if a trader wants to buy 1 Bitcoin at $60,000, they submit  a limit order to  the CLOB. If a seller is willing to sell 1 Bitcoin at $60,000, the two orders can be  matched, and the trade is executed. If no seller is available at that price, the order remains in the book until a matching seller is found. The  speed and precision of CLOBs make them ideal for high-frequency and algorithmic trading, attracting professional traders and institutional investors by offering greater flexibility and control. Key Differences Between CLOBs and AMMs Key Differences Between CLOBs and AMMs Feature

Automated Market Makers (AMMs)

Central Limit Order Books (CLOBs)



Mechanism

Algorithmic pricing based on the constant product formula (x*y=k)

Order matching based on price-time priority



Users

Suitable for both casual and advanced users; offers customizable strategies, especially with advancements like UniV4.

Favoured by professional traders for precise control but also adaptable for various user levels depending on the platform.



Order Types

Simple swaps, no advanced order types

Advanced order types like limit orders, stop orders, and conditional orders



Capital Efficiency

Capital inefficient due to most liquidity sitting idle along the bonding curve

More capital efficient, matching buyers and sellers directly,



Trading Strategies

Supports both basic and advanced trading strategies with customizable liquidity provision in UniV4.

Supports sophisticated trading strategies and high-frequency trading



User Expertise

Easy to use, typically features simpler UI

Requires more expertise, designed for advanced trading capabilities



Decentralization

Completely decentralized, governed by smart contracts

Can be decentralized but typically involves a more complex architecture



Advanced Features

Supports advanced features like buy/sell limit orders and customizable liquidity provision, especially with UniV4

Offers better price discovery and enhanced order types, reducing arbitrage opportunities and benefiting from more precise market equilibrium Feature

Automated Market Makers (AMMs)

Central Limit Order Books (CLOBs) Mechanism

Algorithmic pricing based on the constant product formula (x*y=k)

Order matching based on price-time priority Users

Suitable for both casual and advanced users; offers customizable strategies, especially with advancements like UniV4.

Favoured by professional traders for precise control but also adaptable for various user levels depending on the platform. Order Types

Simple swaps, no advanced order types

Advanced order types like limit orders, stop orders, and conditional orders Capital Efficiency

Capital inefficient due to most liquidity sitting idle along the bonding curve

More capital efficient, matching buyers and sellers directly, Trading Strategies

Supports both basic and advanced trading strategies with customizable liquidity provision in UniV4.

Supports sophisticated trading strategies and high-frequency trading User Expertise

Easy to use, typically features simpler UI

Requires more expertise, designed for advanced trading capabilities Decentralization

Completely decentralized, governed by smart contracts

Can be decentralized but typically involves a more complex architecture Advanced Features

Supports advanced features like buy/sell limit orders and customizable liquidity provision, especially with UniV4

Offers better price discovery and enhanced order types, reducing arbitrage opportunities and benefiting from more precise market equilibrium Feature Automated Market Makers (AMMs) Central Limit Order Books (CLOBs) Feature Feature Feature Automated Market Makers (AMMs) Automated Market Makers (AMMs) Automated Market Makers (AMMs) Central Limit Order Books (CLOBs) Central Limit Order Books (CLOBs) Central Limit Order Books (CLOBs) Mechanism Algorithmic pricing based on the constant product formula (x*y=k) Order matching based on price-time priority Mechanism Mechanism Mechanism Algorithmic pricing based on the constant product formula (x*y=k) Algorithmic pricing based on the constant product formula (x*y=k) Order matching based on price-time priority Order matching based on price-time priority Users Suitable for both casual and advanced users; offers customizable strategies, especially with advancements like UniV4. Favoured by professional traders for precise control but also adaptable for various user levels depending on the platform. Users Users Users Suitable for both casual and advanced users; offers customizable strategies, especially with advancements like UniV4. Suitable for both casual and advanced users; offers customizable strategies, especially with advancements like UniV4. Favoured by professional traders for precise control but also adaptable for various user levels depending on the platform. Favoured by professional traders for precise control but also adaptable for various user levels depending on the platform. Order Types Simple swaps, no advanced order types Advanced order types like limit orders, stop orders, and conditional orders Order Types Order Types Order Types Simple swaps, no advanced order types Simple swaps, no advanced order types Advanced order types like limit orders, stop orders, and conditional orders Advanced order types like limit orders, stop orders, and conditional orders Capital Efficiency Capital inefficient due to most liquidity sitting idle along the bonding curve More capital efficient, matching buyers and sellers directly, Capital Efficiency Capital Efficiency Capital Efficiency Capital inefficient due to most liquidity sitting idle along the bonding curve Capital inefficient due to most liquidity sitting idle along the bonding curve More capital efficient, matching buyers and sellers directly, More capital efficient, matching buyers and sellers directly, Trading Strategies Supports both basic and advanced trading strategies with customizable liquidity provision in UniV4. Supports sophisticated trading strategies and high-frequency trading Trading Strategies Trading Strategies Trading Strategies Supports both basic and advanced trading strategies with customizable liquidity provision in UniV4. Supports both basic and advanced trading strategies with customizable liquidity provision in UniV4. Supports sophisticated trading strategies and high-frequency trading Supports sophisticated trading strategies and high-frequency trading User Expertise Easy to use, typically features simpler UI Requires more expertise, designed for advanced trading capabilities User Expertise User Expertise User Expertise Easy to use, typically features simpler UI Easy to use, typically features simpler UI Requires more expertise, designed for advanced trading capabilities Requires more expertise, designed for advanced trading capabilities Decentralization Completely decentralized, governed by smart contracts Can be decentralized but typically involves a more complex architecture Decentralization Decentralization Decentralization Completely decentralized, governed by smart contracts Completely decentralized, governed by smart contracts Can be decentralized but typically involves a more complex architecture Can be decentralized but typically involves a more complex architecture Advanced Features Supports advanced features like buy/sell limit orders and customizable liquidity provision, especially with UniV4 Offers better price discovery and enhanced order types, reducing arbitrage opportunities and benefiting from more precise market equilibrium Advanced Features Advanced Features Advanced Features Supports advanced features like buy/sell limit orders and customizable liquidity provision, especially with UniV4 Supports advanced features like buy/sell limit orders and customizable liquidity provision, especially with UniV4 Offers better price discovery and enhanced order types, reducing arbitrage opportunities and benefiting from more precise market equilibrium Offers better price discovery and enhanced order types, reducing arbitrage opportunities and benefiting from more precise market equilibrium Why Will CLOBs Play an Important Role in the Next Phase of DeFi? Why Will CLOBs Play an Important Role in the Next Phase of DeFi? The next wave of DeFi growth is expected to be driven by increasing institutional investment and interest. According to Northern Trust , financial institutions are preparing for significant adoption of digital assets and tokenization, which is expected to accelerate institutional DeFi over the next few years. Additionally, industry experts predict substantial growth in DeFi due to the tokenization of real-world assets, further attracting institutional investors. Binance Research notes that the total value locked (TVL) in DeFi has surged by 75.1% year-to-date, reaching $94.9 billion, with significant capital inflows. It also highlights that yield-bearing assets saw a 148.6% increase, now making up $9.1 billion of the TVL. This influx of capital has benefited nearly every DeFi sector, making previously inaccessible financial primitives available on chain. Northern Trust Northern Trust industry experts predict substantial growth in DeFi industry experts predict substantial growth in DeFi Binance Research Binance Research To accommodate this shift, DeFi must evolve beyond the limitations of AMMs, which are well-suited for everyday users, and develop infrastructure that meets the demands of more sophisticated traders. This is where CLOBs have the potential to become a game-changer for DeFi, offering advanced trading capabilities, superior price discovery, and improved capital efficiency to support the needs of institutional participants.

The Maturity of DeFi Requires a Shift from Automated Market Makers to Central Limit Order Books

How it all started many years ago I was a happy student when I started to develop for money. It was a lot of fun, and it was super interesting: a lot of new technologies and a lot of challenges. I had no family at that time (too early), and I had all the time to develop things. I was kind of a geek, and I was always super strict with myself on deliverability. The company I was working for was small and full of entrepreneurship, or better to say, startups, which was awesome. Otherwise, I would leave it earlier. But even though it was a lot of fun, I decided to go further into the world of freelance. I was aiming to become an owner and boss to myself. Owning the life being a freelancer There were always a lot of "benefits" to being a freelancer; ones could imagine a person sitting on a beach of an island drinking a mango smoothie and working on a project... This is a nice picture to imagine, but for most freelancers, it's not like this at all. It was a long standup for me. It was necessary to produce good reviews and build a few products for free. But later, I managed to find one customer and then another. Then accidentally, I was working with ten different customers at the same time: you know that, handling support vs active development. Did I become super-independent? No, not really. Instead of one boss at my previous job, I got ten. Yes, I have increased my expertise quite a lot because I have joined more than 30 different projects in different niches, especially in e-commerce, travel-related, and fin-tech. But I didn’t get any way to become happier in that setup. It didn't work for me. So I had to quit and find a better focus for me. It was not something I would like to do forever. I didn't want to come to work at the office as well. So it was the moment when I had to switch to the product. How do you get to product ideas? I believe that the best product ideas could be born at the center of your expertise. While working on different client projects, I always repeat the same routine again and again. Their routing is the thing that every developer does every day. Every developer reinvents the wheel every day to build the bicycle. How much time it's necessary to spend to build a simple ToDo list with backend and frontend, DB, Email notifications, and such? Well, it could be various, but it will take at least one day if you are familiar with the deployment, you have good knowledge and high expertise on many aspects like Example: Hosting DNS Frontend Frameworks, HTML + CSS + JS Backend Frameworks Email Providers, Email templates Etc. How much time do you need to spend to get such expertise and deliver faster enough? Remember, we are talking about a ToDo list, the simplest app we can imagine. It was a nice match; he got into the same set of questions but went a step further - he started to deliver a product that could solve this. These days I met , who is started his work on . John Mars Let's build a ToDo List as quickly as possible. Let's talk about the ToDo list again. One day I asked myself what could be the easiest option to develop a ToDo list app. For me, the answer is: don't develop it -> Reuse something that is already developed/tested/production ready. The question is how? From top to bottom in terms of the amount of knowledge required You could find a few ready-to-go or *any-ecosystem* packages to get it faster npm You could build something with AI - just ask to build UI and API and then improve. You could find a seed project GitHub You can use a low-code solution You can use a no-code solution So which option to pick depends on many factors like Ability to customize Amount of efforts Running costs (cost of hosting and maintaining ) Long-term support and improvements Let's focus on Customisability VS Speed of development I have tried to compare options and made this simple chart From my experience, the more you want the solution to be different from others - the more time you need to spend. I have tried many no-code platforms, and they gave me good speed in the beginning, but I always had a lack of customization and simplicity of updating as soon as the project grew. At the same time, the fully custom solution I have developed for my clients is something I'm not able to support as well because the amount of custom functionality the product eventually introduce is huge, you need a team to manage this, which is ok, but it creates bigger running costs. Then I took a look at low-code platforms; it was a bit "unclear" thing for me. In simple words, it's a kind of platform where you can code, but at the same time, you can use a no-code approach. It seems like a perfect combination for me, and that is where Mars is sitting. How it works there Everything on Mars is a micro-app. You can combine them, fork, reconfigure, etc. You can do a lot of changes without coding, and you can write a thing from scratch if it's necessary. This is how I came to the product idea. Mars seemed like an awesome platform for me, but it's just a tool, the same as many others. If I want to build a ToDo list on Mars, I need somebody to develop such a micro-app for me. Then I made a simple approximation on bigger products: an e-commerce store like Amazon, service marketplaces like Booksy, or Renting platform like Airbnb. And I got this: Many people develop similar big and complex apps today. And all of them repeat the logic. Every founder pays for this. So, to make such founders' and developers' lives better, I could provide them with ready-to-go reusable micro apps built by niche experts. Being a developer myself and having quite a huge experience in many niches after ten years of product development for clients, I was in the right shape to take it off. That is how has been founded. Or flew up! Fleexy The Team, or Crew Teams build things together, and Crews starts the spaceships. From the products I have worked with, I remember a few very talented guys. Perfect tech experience, passion for creating things, open-minded and honest vision of things. Together with and , we founded Fleexy. Alexey Andrey We aim to build the biggest foundation of micro-apps for Your Future Product. It was a nice moment. It was always so inspiring to build a new thing. You start light, you have no dependencies, and you feel like you can fly. So we did. But we are a self-bootstrapped company. We have no investors, which means we don't have a lot of money to burn. That means that we have to find a customer sooner than later. Building a product is a lot of fun. We started with an obvious thing: testing different niches where we could be helpful. That is how we get into 2 of them. A Services-Marketplace A Social-Marketplace By doing these demos, we also created a solid number of micro apps we can use now for our next project/product. We were happy to share that with the world. So we built a website and described what we are and why. And guess what? We got 0 visitors. How Coders Switch to Marketing That's where we are now. We are in marketing mode. We do posts; we do tweets; we literally spend days writing blog posts (and we still do), we do keyword analyses, and we do back-link building to get higher domain authority. That already gave us some leads! This helped us to get our first customers and even released one of them, . It has been built on top of the Services-Marketplace solution we have. It has a lot of customizations, especially in terms of UI, and it looks and works awesome. The Daba The AI Era is here; why can’t we add it to the solution we have? This pivot happened just a few weeks ago. We were sitting on our weekly call with guys and thinking aloud about the lead we had and the things we built. We were looking for a product we can easily sell which gives instant value to the customer, something that: could be super useful for customers from the first step will be based on micro-apps we have mind-blowing to have a marketing effect That’s how we come to . Marketsy Marketsy.ai and the Future We just released an AI marketplace builder. It’s something we actually super proud of already. We got people to after the start. build almost 500 stores on the platform just two weeks That’s what we are focused on, 200% right now. We believe there is a lot of interesting impact we can generate on the industry of marketplaces/e-commerce this way. So we will do that until we know more. We will continue on this path. And as for me By checking out past years, I can confirm: It was a lot of pressure, and it still is. I lost a bigger part of the income I had, I had to learn new things for me, I had to adapt a lot, and now I have much more: Having a mission I believe could impact many people’s life tomorrow The crew I can trust Freedom to do things and be able to define my next step The long-term journey I'm happy to be part of I do share the progress on . Follow to get updated. Twitter Also published . here

How I Went From a Freelance Developer to a Product Founder, My 10 Year Journey

আমি কিভাবে একজন ফ্রিল্যান্স ডেভেলপার থেকে একজন প্রোডাক্ট ফাউন্ডারে গেলাম, আমার 10 বছরের যাত্রা

Wie ich vom freiberuflichen Entwickler zum Produktgründer wurde, meine 10-jährige Reise

Cómo pasé de desarrollador independiente a fundador en 10 años

Comment je suis passé d'un développeur indépendant à un fondateur en 10 ans

मैं 10 वर्षों के भीतर एक फ्रीलांस डेवलपर से संस्थापक कैसे बन गया

私が 10 年以内にフリーランスの開発者から創業者になった方法

프리랜서 개발자에서 제품 창업자가 되기까지의 10년 여정

Como passei de desenvolvedor freelancer a fundador em 10 anos

Serbest Geliştiriciden Ürün Kurucusuna Nasıl Geçtim, 10 Yıllık Yolculuğum

Tôi đã đi từ một nhà phát triển tự do thành một nhà sáng lập như thế nào trong vòng 10 năm

我如何在 10 年内从一名自由开发者变成一名创始人

FRESH SUSPICIONS

Versioning database changes is as important as versioning source code. By using a database migration tool we can safely manage how the database evolves, instead of running a bunch of non versioned loose files. In some frameworks like , database versioning occurs along the development. But when it comes to world, I don't see it happening so often. SQL Ruby On Rails Java In this article we’ll see how to integrate with to evolve the database schema of a application using . Liquibase Spring Boot Java MySQL Meet Liquibase Currently, the most popular database tools are and . I've choose the latter due to these benefits: Flyway Liquibase - It's database agnostic - it works for all major database vendors; - You can specify your changes in , , and formats. XML YAML JSON SQL We'll use format. YAML Liquibase concepts These are the key concepts: - : a file that keeps track of all changes that need to run to update the DB; changeLog - : these are atomic changes that would be applied to the database. Each is uniquely identified by 'id' and 'author'. Each is run as a single transaction. changeSet changeSet changeset The Domain Model This is our initial domain model that will be evolved during this article: The ' ' table has a relationship with ' ' table. Library One To Many Book The Rails way When I had my first experience with , back in 2007, the first feature that caught my attention was . is the framework shipped with . Ruby On Rails Active Record Migrations Active Record ORM Ruby On Rails So, for the given domain model above, to generate a migration file that create the two tables: A migration file is created. Then, we add instructions: Alright. Let's fire up the server: Then, using , if we try to access ' ' resource, for example: cURL Book We'll get an error, as we can see at server's log: Self-explanatory: we need to run the migration that will create the tables. Like this: Let's check in . Both tables were created: MySQL OK. suppose that we need to add two fields to ' ' table: ' ' and ' '. To accomplish this, we create another migration file like this: Books isbn publisher Then we open the migration file and add the instructions: Let's run this migration: If we check the table again, both fields were added: What if ' ' field is not necessary anymore? Let's create a migration to fix this: publisher This is the migration file: Let's run this migration: Now if we check the table, ' ' field was removed: publisher The Java way How can we do the same in a Java application? can be seamless integrated with , so let's begin. Liquibase Spring Boot Creating the project is our start point: Spring Initializr We've choose the following dependencies: - : to add 'mysql-connector-java' jar to our project; MySQL - : Starter for using Spring Data JPA with Hibernate; JPA - : Starter for using ; Liquibase Liquibase - : Starter for exposing Spring Data repositories over REST using Spring Data REST. Rest Repositories Additionally, I'm using to ease calling from command line as we'll see in the following examples. Liquibase Maven plugin Liquibase This is the dependency: org.liquibase liquibase-maven-plugin 3.6.3 < > dependency < > groupId </ > groupId < > artifactId </ > artifactId < > version </ > version </ > dependency And this is its configuration: org.springframework.boot spring-boot-maven-plugin org.liquibase liquibase-maven-plugin 3.6.3 src/main/resources/liquibase.yml < > build < > plugins < > plugin < > groupId </ > groupId < > artifactId </ > artifactId </ > plugin < > plugin < > groupId </ > groupId < > artifactId </ > artifactId < > version </ > version < > configuration < > propertyFile </ > propertyFile </ > configuration </ > plugin </ > plugins </ > build As stated in a , I rather not to expose entities or repositories directly, but just for the sake of demonstration, I'll use this time. It makes it easy to build hypermedia-driven REST web services on top of repositories. previous post Rest Repositories Spring Data Configuration This is our ' ' file: src/main/resources/application.yml spring: datasource: url: jdbc:mysql://localhost:3306/liquibase_test?useSSL=false username: root password: jpa: hibernate: dialect: org.hibernate.dialect.MySQL5InnoDBDialect ddl-auto: none liquibase: change-log: classpath:db/liquibase-changelog.yml A few notes: - by setting ' ' to ' ', the schema generation will be delegated to ; spring.jpa.hibernate.ddl-auto none Liquibase - by default, 's changeLog file is expected to be in ' ; but we are changing it by setting ' ' to put it in ' '. Liquibase db/changelog/db.changelog-master.yaml' spring.liquibase.change-log src/main/resources/db/liquibase-changelog.yml And this is our ' ' file, which is used by : src/main/resources/liquibase.yml Liquibase Maven plugin url: jdbc:mysql://localhost:3306/liquibase_test?useSSL=false username: root password: driver: com.mysql.cj.jdbc.Driver outputChangeLogFile: src/main/resources/db/liquibase-OutputChangelog.yml The entities This is our ' ' entity: Book com.tiago.entity; javax.persistence.Column; javax.persistence.Entity; javax.persistence.GeneratedValue; javax.persistence.GenerationType; javax.persistence.Id; javax.persistence.JoinColumn; javax.persistence.ManyToOne; { (strategy = GenerationType.IDENTITY) Long id; (nullable= ) String title; (name= ) Library library; { id;
  } { .id = id;
  } { title;
  } { .title = title;
  } { library;
  } { .library = library;
  }
} package import import import import import import import /**
 * Entity for table "Book" 
 * 
 * Tiago Melo (tiagoharris@gmail.com)
 *
 */ @author @Entity public class Book @Id @GeneratedValue private @Column false private @ManyToOne @JoinColumn "library_id" private Long public getId () return public void setId (Long id) this String public getTitle () return public void setTitle (String title) this Library public getLibrary () return public void setLibrary (Library library) this And this is our ' ' entity: Library com.tiago.entity; java.util.List; javax.persistence.Column; javax.persistence.Entity; javax.persistence.GeneratedValue; javax.persistence.GenerationType; javax.persistence.Id; javax.persistence.OneToMany; { (strategy = GenerationType.IDENTITY) Long id; (nullable= ) String name; (mappedBy = ) List<Book> books; { id;
  } { .id = id;
  } { name;
  } { .name = name;
  } { books;
  } { .books = books;
  }
} package import import import import import import import /**
 * Entity for table "Library" 
 * 
 * Tiago Melo (tiagoharris@gmail.com)
 *
 */ @author @Entity public class Library @Id @GeneratedValue private @Column false private @OneToMany "library" private Long public getId () return public void setId (Long id) this String public getName () return public void setName (String name) this List<Book> public getBooks () return public void setBooks (List<Book> books) this The repositories As mentioned earlier, since we are using , there's no need to write controllers; the repositories will be exposed directly. Rest Repositories This is our ' ': BookRepository com.tiago.repository; org.springframework.data.repository.CrudRepository; com.tiago.entity.Book; { } package import import /**
 * Repository for { Book} entity.
 * 
 * Tiago Melo (tiagoharris@gmail.com)
 *
*/ @link @author public < , > interface BookRepository extends CrudRepository Book Long And this is our ' ': LibraryRepository com.tiago.repository; org.springframework.data.repository.CrudRepository; com.tiago.entity.Library; { } package import import /**
 * Repository for { Library} entity.
 * 
 * Tiago Melo (tiagoharris@gmail.com)
 *
*/ @link @author public < , > interface LibraryRepository extends CrudRepository Library Long Organizing our changelogs (migration files) Let's take a look at our directory structure: This is ' ' file: src/main/resources/db/liquibase-changelog.yml databaseChangeLog: - includeAll: path: db/changelog/ We are telling to execute all changelog files in ' ' directory. Liquibase src/main/resources/db/changelog/ Changelog #1: creating the tables As a general rule, let's adopt the following naming convention: <migration_number>_<what_does_this_migration_do>.yml This way, will execute changelogs ordered by its number. Liquibase This is our ' ' file. As the name implies, it creates the tables: 1_create_book_and_library_tables.yml databaseChangeLog: - changeSet: author: "tiago" id: "creates_library_table" changes: - createTable: tableName: "library" columns: - column: name: "id" type: "BIGINT" autoIncrement: "true" constraints: primaryKey: "true" - column: name: "name" type: "VARCHAR(255)" constraints: nullable: "false" unique: "true" - changeSet: author: "tiago" id: "creates_book_table" changes: - createTable: tableName: "book" columns: - column: name: "id" type: "BIGINT" autoIncrement: "true" constraints: primaryKey: "true" - column: name: "title" type: "VARCHAR(255)" constraints: nullable: "false" unique: "true" - column: name: "library_id" type: "BIGINT" constraints: foreignKeyName: "fk_book_library" references: "library(id)" Differently from , when we fire up the server, the changelogs will be automatically executed. Let's see: Rails $ mvn spring-boot:run Taking a look at server's log, we notice that the two tables were created: : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : SELECT COUNT(*) FROM liquibase_test.DATABASECHANGELOG : : INFO --- [           main] l.c.StandardChangeLogHistoryService      : Reading liquibase_test.DATABASECHANGELOG : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : SELECT * FROM liquibase_test.DATABASECHANGELOG ORDER BY DATEEXECUTED ASC, ORDEREXECUTED ASC : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : SELECT COUNT(*) FROM liquibase_test.DATABASECHANGELOGLOCK : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : CREATE TABLE liquibase_test.library (id BIGINT AUTO_INCREMENT NOT NULL, name VARCHAR( ) NOT NULL, CONSTRAINT PK_LIBRARY PRIMARY KEY (id), UNIQUE (name)) : : INFO --- [           main] liquibase.changelog.ChangeSet            : Table library created : : INFO --- [           main] liquibase.changelog.ChangeSet            : ChangeSet db/changelog/ _create_book_and_library_tables.yml::creates_library_table::tiago ran successfully ms : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : SELECT MAX(ORDEREXECUTED) FROM liquibase_test.DATABASECHANGELOG : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : INSERT INTO liquibase_test.DATABASECHANGELOG (ID, AUTHOR, FILENAME, DATEEXECUTED, ORDEREXECUTED, MD5SUM, , COMMENTS, EXECTYPE, CONTEXTS, LABELS, LIQUIBASE, DEPLOYMENT_ID) VALUES ( , , , NOW(), , , , , , NULL, NULL, , ) : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : CREATE TABLE liquibase_test.book (id BIGINT AUTO_INCREMENT NOT NULL, title VARCHAR( ) NOT NULL, library_id BIGINT NULL, CONSTRAINT PK_BOOK PRIMARY KEY (id), CONSTRAINT fk_book_library FOREIGN KEY (library_id) REFERENCES liquibase_test.library(id), UNIQUE (title)) : : INFO --- [           main] liquibase.changelog.ChangeSet            : Table book created : : INFO --- [           main] liquibase.changelog.ChangeSet            : ChangeSet db/changelog/ _create_book_and_library_tables.yml::creates_book_table::tiago ran successfully ms : : INFO --- [           main] liquibase.executor.jvm.JdbcExecutor      : INSERT INTO liquibase_test.DATABASECHANGELOG (ID, AUTHOR, FILENAME, DATEEXECUTED, ORDEREXECUTED, MD5SUM, , COMMENTS, EXECTYPE, CONTEXTS, LABELS, LIQUIBASE, DEPLOYMENT_ID) VALUES ( , , , NOW(), , , , , , NULL, NULL, , ) : : INFO --- [           main] l.lockservice.StandardLockService        : Successfully released change log lock 2019 -03 -06 23 17 14.103 29090 2019 -03 -06 23 17 14.104 29090 from 2019 -03 -06 23 17 14.105 29090 2019 -03 -06 23 17 14.106 29090 2019 -03 -06 23 17 14.123 29090 255 2019 -03 -06 23 17 14.145 29090 2019 -03 -06 23 17 14.145 29090 1 in 23 2019 -03 -06 23 17 14.146 29090 2019 -03 -06 23 17 14.148 29090 `DESCRIPTION` 'creates_library_table' 'tiago' 'db/changelog/1_create_book_and_library_tables.yml' 1 '8:a4b142ffda1ccd5c1840ddad83e249b5' 'createTable tableName=library' '' 'EXECUTED' '3.6.3' '1925034107' 2019 -03 -06 23 17 14.151 29090 255 2019 -03 -06 23 17 14.173 29090 2019 -03 -06 23 17 14.174 29090 1 in 24 2019 -03 -06 23 17 14.175 29090 `DESCRIPTION` 'creates_book_table' 'tiago' 'db/changelog/1_create_book_and_library_tables.yml' 2 '8:a54634bf0781ac011818976bf5e36351' 'createTable tableName=book' '' 'EXECUTED' '3.6.3' '1925034107' 2019 -03 -06 23 17 14.186 29090 Let's check them in : MySQL Great. Now, using , let's access the ' ' resource: cURL Book No books as expected. The same occurs with ' ' resource: Library Changelog #2 and #3: initialization data Let's see how we can initialize our tables. This is ' ': 2_insert_data_books.yml databaseChangeLog: - changeSet: author: "tiago" id: "insert_data_books" changes: - insert: tableName: "book" columns: - column: name: "title" value: "Test Book 1" And this is ' ': 3_insert_data_library.yml databaseChangeLog: - changeSet: author: "tiago" id: "insert_data_library" changes: - insert: tableName: "library" columns: - column: name: "name" value: "Library 1" Now let's try something different. Instead of firing up the server to make to run these changelogs, we'll do it by using the : Liquibase Maven plugin $ mvn liquibase:update -Dliquibase.changeLogFile=db/liquibase-changelog.yml This is the output: [INFO] INSERT INTO book (title) VALUES ( )
[INFO] New row inserted into book
[INFO] INSERT INTO book (title) VALUES ( )
[INFO] New row inserted into book

...
[INFO] INSERT INTO library (name) VALUES ( )
[INFO] New row inserted into library
.... 'Test Book 1' 'Test Book 2' 'Library 1' Then, if we fire up the server and access ' ' resource again... Book Take a look at this: : { : } "library" "href" "http://localhost:8080/books/1/library" We'll use this URL to associate this book to ' ' soon. Library 1 Calling ' ' resource: Library Now we'll associate ' ' to ' ': Test Book 1 Library 1 $ curl -i -X PUT -H -d http: "Content-Type:text/uri-list" "http://localhost:8080/libraries/1" //localhost:8080/books/1/library Then we can check if it worked, by querying the ' ' association of our book: library Changelog #4: adding columns During the development, we found necessary to add ' ' and ' ' fields to our ' ' table. isbn publisher book The first step is to create the changelog file. This is ' ': 4_add_isbn_and_publisher_to_book.yml databaseChangeLog: - changeSet: author: "tiago" id: "add_isbn_and_publisher_to_book" changes: - addColumn: columns: - column: name: "isbn" type: "VARCHAR(255)" constraints: nullable: "false" - column: name: "publisher" type: "VARCHAR(255)" constraints: nullable: "false" tableName: "book" Then, let's run the changelog: $ mvn liquibase:update -Dliquibase.changeLogFile=db/liquibase-changelog.yml This is the output: [INFO] ALTER TABLE book ADD isbn VARCHAR( ) NOT NULL, ADD publisher VARCHAR( ) NOT NULL
[INFO] Columns isbn(VARCHAR( )),publisher(VARCHAR( )) added to book
[INFO] ChangeSet db/changelog/ _add_isbn_and_publisher_to_book.yml::add_isbn::tiago ran successfully ms 255 255 255 255 4 in 60 Let's check it in : MySQL Great. The second step is to change our ' ' entity to add these two new fields: Book com.tiago.entity; javax.persistence.Column; javax.persistence.Entity; javax.persistence.GeneratedValue; javax.persistence.GenerationType; javax.persistence.Id; javax.persistence.JoinColumn; javax.persistence.ManyToOne; { (strategy = GenerationType.IDENTITY) Long id; (nullable= ) String title; (name= ) Library library; (nullable= ) String isbn; (nullable= ) String publisher; { id;
  } { .id = id;
  } { title;
  } { .title = title;
  } { library;
  } { .library = library;
  } { isbn;
  } { .isbn = isbn;
  } { publisher;
  } { .publisher = publisher;
  }
} package import import import import import import import /**
 * Entity for table "Book" 
 * 
 * Tiago Melo (tiagoharris@gmail.com)
 *
 */ @author @Entity public class Book @Id @GeneratedValue private @Column false private @ManyToOne @JoinColumn "library_id" private @Column false private @Column false private Long public getId () return public void setId (Long id) this String public getTitle () return public void setTitle (String title) this Library public getLibrary () return public void setLibrary (Library library) this String public getIsbn () return public void setIsbn (String isbn) this String public getPublisher () return public void setPublisher (String publisher) this Now let's update our book to set ' ' and ' ': isbn publisher Changelog #5: dropping a column The ' ' field is not necessary anymore. publisher The first step is to create the changelog file. This is ' ': 5_drop_publisher_from_book.yml databaseChangeLog: - changeSet: author: "tiago" id: "drop_publisher_from_book" changes: - dropColumn: columnName: "publisher" tableName: "book" Then, let's run the changelog: $ mvn liquibase:update -Dliquibase.changeLogFile=db/liquibase-changelog.yml This is the output: [INFO] ALTER TABLE book DROP COLUMN publisher
[INFO] Column book.publisher dropped Let's check it in : MySQL OK. The second step is to change our ' ' entity to remove ' ' property: Book publisher com.tiago.entity; javax.persistence.Column; javax.persistence.Entity; javax.persistence.GeneratedValue; javax.persistence.GenerationType; javax.persistence.Id; javax.persistence.JoinColumn; javax.persistence.ManyToOne; { (strategy = GenerationType.IDENTITY) Long id; (nullable= ) String title; (name= ) Library library; (nullable= ) String isbn; { id;
  } { .id = id;
  } { title;
  } { .title = title;
  } { library;
  } { .library = library;
  } { isbn;
  } { .isbn = isbn;
  }
} package import import import import import import import /**
 * Entity for table "Book" 
 * 
 * Tiago Melo (tiagoharris@gmail.com)
 *
 */ @author @Entity public class Book @Id @GeneratedValue private @Column false private @ManyToOne @JoinColumn "library_id" private @Column false private Long public getId () return public void setId (Long id) this String public getTitle () return public void setTitle (String title) this Library public getLibrary () return public void setLibrary (Library library) this String public getIsbn () return public void setIsbn (String isbn) this Now let's fire up the server and check our ' ' resource: Book Great! The ' ' property does not exists anymore. publisher Conclusion Versioning database changes is as important as versioning source code, and tools like makes it possible to do it in a safe and manageable way. Liquibase Through this simple example we learnt how we can evolve database in a Java application by integrating with . Liquibase Spring Boot Download the source Here: https://bitbucket.org/tiagoharris/liquibase-hibernate-example/src/master/

Integrate Java Database Versioning with Liquibase using MySQL [A Step by Step Guide]

I summed up the most important reasons why Google Assistant is soon going to change the game for good. Now let me take you a step deeper into the rabbit hole, and explain what it takes to integrate with it, and what business opportunities it opens for you. In the last article, Assistant is everywhere — use it to reach your customers There are already over 1 billion Assistant-enabled devices — including smartphones, smart speakers, headphones, TVs, smartwatches, smart home appliances, and more! Every day brings news of new ones being released. Integrating with Google assistant means your business will be there to assist your customers whenever and wherever they need it. Ordering products, finding stores, scheduling appointments, giving instructions, accessing content — it is only a fraction of what can be done with Google Assistant. Whatever the focus of your business is, I am fairly sure Google Assistant can help you boost it in a way you might not have thought of yet. Below, I will give you plenty of such examples. But first, I will briefly explain how you can write your own Google Assistant extension. Actions on Google — Cloud platform of the future Actions on Google is a platform that allows you to extend the functionality of Google Assistant with your own applications and deploy them for all Assistant-enabled devices. It is a cloud-based platform which means that the apps you are writing are never really installed on any of the end devices. This makes it extremely convenient for both the developers and the users. First of all, you don’t have to worry about many things that are big issues for mobile or desktop apps, like supporting older versions of the hardware, running updates, etc. Moreover, users don’t have to install your actions to use them — all that is required to open an action is to say: “Ok Google, talk to…” and name the action you want to invoke. When it comes to the development effort, actions are far easier to implement than any mobile or web application, and their user interface is simple and standardised, thus eliminating much of the standard UI work. What it takes to create an action for Google Assistant What is really great about actions development is that Google’s tools and machine learning algorithms do most of the heavy lifting for you. They handle both speech-to-text and text-to-speech, and most importantly, they can cleverly guess user’s intents and choose the best path of the conversation. Don’t worry, you don’t have to provide every possible combination of user’s words for the algorithms to work — they manage to correctly infer intents even if a user doesn’t use the exact words you provide in the console. The most important of these tools is definitely Dialogflow. It is a natural language understanding tool, where most of this magic happens. The biggest challenge during the creation process of an action is to design the conversational experience that is both efficient and pleasant for its users. This requires an in-depth knowledge of the capabilities, restrictions and Google’s guidelines for the platform. Another challenge is to provide a business logic that will deliver content for the action, perform operations and integrate with external services. This logic is handled by a backend application called a fulfillment service. A fulfillment service is basically a piece of software that receives requests from the Assistant, processes them and responds. It can be written in any technology you prefer, as long as it can communicate with Google’s API. There are also Node.js and Java/Kotlin client libraries that further simplify the implementation. Possible use cases for integration with Google Assistant I do not intend to cover all possible use cases here. I just want to point out a few popular scenarios that might apply to your business, or at least will move your imagination. – The most straightforward scenario is an intelligent FAQ bot, which can share its “knowledge” and answer questions about your service or products. FAQ Examples: Visa Guru, Pringles. – Going further, a virtual assistant can aid your call centre, staff or even banking system in some of their duties. Customer service Examples: Bank of America, Mr. Spring, AGL, U. S. Bank. – Ordering a cab, coffee, pizza or even flowers? You can do all of that with Google Assistant in a matter of seconds! Ordering Examples: Uber, Starbucks, Domino’s Pizza, 1–800-Flowers. – From groceries through tickets to pretty much everything… You can buy them just by using your voice or screen interface. Purchases Examples: Asda, Fred Meyer, Ticketmaster, Fandango, eBay. – Checking flights, air quality, the weather or movie recommendations can be done on the go using voice interface. Information Examples: Singapore Airlines, World Air Quality Index, AccuWeather, Itcher. – You can listen to and control your music and video streaming services. There are also use cases for audiobooks, children stories background music or even meditation! Multimedia Examples: Spotify, Netflix, YouTube, Google Play, Disney, StoryCastle, Headspace. – Shopping lists, work trackers, notes — Assistant can be the fastest and most natural way to interact with those. Lists, tasks and notes Examples: Todoist, Evernote, Trello, Timesheets Companion. – You can give your users instructions on pretty much everything. The most common scenarios would probably involve cooking and workouts. But assistant could be easily used to make quick, hands-free guides for things like hair colouring or newborn bathing. Instructions Examples: Tasty, Fitness Buddy, Clairol Color Expert, Johnson’s. – Assistant can be a quick way to send and receive messages and emails, add posts on your social app, or even find a date._Examples: WhatsApp, Twitter, Viber, Sonus Mail, Only One._ Communication & Social media – Finding nearest branch offices, restaurants, dental providers, events, or dogs to adopt — you can write an action that will direct clients to your business. Location Examples: OCBC Bank, Hipster Cat, Sunfish Events, Petfinder, MetLife. – Dining, activities, beauty and fitness — the Reserve with Google service allows your clients to book appointments with Google Assistant. There are also many existing services that provide appointment scheduling actions. Using one of them will allow you to reach your clients even without writing your own action. Reservations Examples: Appointy, Booksy, MINDBODY, Tiqets, TripAdvisor. – Once you try it, voice becomes the most natural interface to control your smart home appliances like light, entertainment systems, heating, cleaning robots and more. But it doesn’t really stop with home smart devices there. Many car brands like Lexus, Mercedes or Volvo provided integrations with Google Assistant that let their users check fuel level or turn on heating remotely. Smart home Examples: Philips Hue, Harmony, Nest, Roomba, Volvo on call, Mercedes me. These are just a taste of what you can do with Google Assistant. Basically, if you can think of any fast and frequently used scenario within your service where you interact with its users, it is a likely candidate for an action! How to make your action profitable? There are a few ways to monetise your action directly. If it is an extension or another interface of your premium service, you can introduce account linking to allow for authorisation. There is also an option to integrate payments via Google Pay, enabling users to pay for access to premium features directly from Google Assistant. Purchasing is another example. With the Google Pay integration mentioned above, the checkout process can be pretty seamless. But if you don’t want to integrate Google Pay, you can as easily implement only part of the flow in Google Assistant, and when the user is ready to check out, ask him to use their phone (if one isn’t already using one), and continue the process in a browser opened directly from your action. But the business value can also be gained through different ways than online payment. There are countless use cases which prove that assistant can bring profit by allowing users to connect to your service easily, find your business locations, or just by increasing your brand recognition. From Zyrtec’s allergy forecasts to Johnson’s newborn bathing instructions — there are many actions that are not only useful but are actually very smart marketing campaigns. Wrap-up Google Assistant can provide a powerful interface for your business to connect with your clients, and creating an action is simpler and faster than writing an app for other platforms like mobile, web and desktop. Start small, think of a simple scenario, and release your first app to the Actions Directory. It won’t cost you much, and the results can exceed your expectations! Want to develop an action? If you’re interested in writing a Google Assistant application for your business, reach out to — . And if you have any questions about Google Assistant, drop me a note at . Miquido Actions on Google development experts jerzy.biernacki@miquido.com What’s next? I’m far from done when it comes to Google Assistant and actions. The next piece in the series is going to be about Google’s guidelines and tips on how to design a high-quality action for the pleasure of its users and Google’s reviewers. Stay tuned and follow ! Miquido’s blog Read more: How Google Assistant is about to disrupt your business Miquido joins an exclusive group of Actions on Google experts

How to boost your business with Actions on Google

Intelligent Investing in Cryptocurrencies

As a dad, there's nothing more precious to me than sharing a bedtime story with my son. Those moments of imagination and wonder, when his eyes light up with excitement, are truly priceless. But as a parent, I also know the importance of making stories accessible to children everywhere, especially those who might not have easy access to books. That's why I'm so passionate about the potential of AI in creating and sharing stories. Today, I want to share with you a delightful story created by Storybooks called "Humphrey's Harmonious Hiccups" and my vision for using AI to bring the magic of storytelling to kids around the world. Humphrey's Harmonious Hiccups: A Tale of Fun and Friendship "Humphrey's Harmonious Hiccups" is a charming tale about a hedgehog named Humphrey who can't stop hiccuping, which disrupts his piano practice. With the help of his friend Beatrice, a bakery-owning badger, Humphrey finds a sweet solution to his hiccup problem and goes on to perform at the annual Forest Talent Show. This heartwarming story, created with the help of AI, captures the whimsy and joy of childhood in a way that resonates deeply with me and my son. A Dad's Perspective When I first read "Humphrey's Harmonious Hiccups" to my son, his laughter was infectious. The story's playful narrative and vibrant illustrations brought Humphrey's adventures to life in a way that only a well-crafted tale can. As we turned each page, I saw the wonder in my son's eyes, and it reminded me of the power of a good story to connect and inspire. The Vision for AI-Generated Stories The magic of "Humphrey's Harmonious Hiccups" is just a glimpse into the potential of AI-generated stories. With tools like Gemini and Imagen, we can create personalized, engaging narratives that captivate young minds and foster a love for reading. But beyond just creating stories for our own children, these technologies offer a unique opportunity to address a global need. Bridging the Gap In many parts of the world, access to books and educational resources is limited. AI-generated stories can help bridge this gap by providing children everywhere with access to high-quality, engaging content. Imagine a world where every child, regardless of their circumstances, can enjoy a bedtime story that sparks their imagination and curiosity. Personalizing the Experience As a dad, I want to create stories that resonate with my son's interests and experiences. AI can help personalize these stories, making each tale unique to the reader. Whether it's a story about a hiccuping hedgehog or an adventurous mouse, AI can craft narratives that are tailored to each child's imagination. The Future of AI in Storytelling The future of storytelling is bright, and AI is at the forefront of this transformation. Here are a few ways I envision AI enhancing the world of children's books: Customized Narratives: AI can create stories based on individual preferences, ensuring that each child feels seen and heard in the tales they read.
Rich Illustrations: Advanced AI can generate beautiful, detailed illustrations that bring stories to life, making reading a more immersive experience.
Interactive Storytelling: Future AI-generated books could include interactive elements that allow children to influence the storyline, making reading a dynamic and engaging activity.
Global Accessibility: By making stories available in multiple languages and formats, AI can help ensure that children around the world have access to quality literature.
Educational Integration: AI can seamlessly incorporate educational content into stories, helping children learn while they enjoy their favorite tales. Conclusion As a dad, my dream is to create magical moments for my son and to share the joy of storytelling with children everywhere. With the help of AI, this dream is becoming a reality. Stories like "Humphrey's Harmonious Hiccups" are just the beginning. Together, we can use technology to foster a love of reading, spark imaginations, and ensure that every child has access to the wonderful world of books. Thank you for joining me on this journey. Let's continue to explore the possibilities of AI in storytelling and create a brighter future for our children, one story at a time. What kind of stories would you like to see created with the help of AI? Share your thoughts in the comments below! For more details, you can read the full story of Humphrey's Harmonious Hiccups here. As a dad, there's nothing more precious to me than sharing a bedtime story with my son. Those moments of imagination and wonder, when his eyes light up with excitement, are truly priceless. But as a parent, I also know the importance of making stories accessible to children everywhere, especially those who might not have easy access to books. That's why I'm so passionate about the potential of AI in creating and sharing stories. Today, I want to share with you a delightful story created by Storybooks called "Humphrey's Harmonious Hiccups" and my vision for using AI to bring the magic of storytelling to kids around the world. Storybooks "Humphrey's Harmonious Hiccups" Humphrey's Harmonious Hiccups: A Tale of Fun and Friendship "Humphrey's Harmonious Hiccups" is a charming tale about a hedgehog named Humphrey who can't stop hiccuping, which disrupts his piano practice. With the help of his friend Beatrice, a bakery-owning badger, Humphrey finds a sweet solution to his hiccup problem and goes on to perform at the annual Forest Talent Show. This heartwarming story, created with the help of AI, captures the whimsy and joy of childhood in a way that resonates deeply with me and my son. A Dad's Perspective When I first read "Humphrey's Harmonious Hiccups" to my son, his laughter was infectious. The story's playful narrative and vibrant illustrations brought Humphrey's adventures to life in a way that only a well-crafted tale can. As we turned each page, I saw the wonder in my son's eyes, and it reminded me of the power of a good story to connect and inspire. The Vision for AI-Generated Stories The magic of "Humphrey's Harmonious Hiccups" is just a glimpse into the potential of AI-generated stories. With tools like Gemini and Imagen, we can create personalized, engaging narratives that captivate young minds and foster a love for reading. But beyond just creating stories for our own children, these technologies offer a unique opportunity to address a global need. Bridging the Gap In many parts of the world, access to books and educational resources is limited. AI-generated stories can help bridge this gap by providing children everywhere with access to high-quality, engaging content. Imagine a world where every child, regardless of their circumstances, can enjoy a bedtime story that sparks their imagination and curiosity. Personalizing the Experience As a dad, I want to create stories that resonate with my son's interests and experiences. AI can help personalize these stories, making each tale unique to the reader. Whether it's a story about a hiccuping hedgehog or an adventurous mouse, AI can craft narratives that are tailored to each child's imagination. The Future of AI in Storytelling The future of storytelling is bright, and AI is at the forefront of this transformation. Here are a few ways I envision AI enhancing the world of children's books: Customized Narratives: AI can create stories based on individual preferences, ensuring that each child feels seen and heard in the tales they read. Rich Illustrations: Advanced AI can generate beautiful, detailed illustrations that bring stories to life, making reading a more immersive experience. Interactive Storytelling: Future AI-generated books could include interactive elements that allow children to influence the storyline, making reading a dynamic and engaging activity. Global Accessibility: By making stories available in multiple languages and formats, AI can help ensure that children around the world have access to quality literature. Educational Integration: AI can seamlessly incorporate educational content into stories, helping children learn while they enjoy their favorite tales. Customized Narratives : AI can create stories based on individual preferences, ensuring that each child feels seen and heard in the tales they read. Customized Narratives Rich Illustrations : Advanced AI can generate beautiful, detailed illustrations that bring stories to life, making reading a more immersive experience. Rich Illustrations Interactive Storytelling : Future AI-generated books could include interactive elements that allow children to influence the storyline, making reading a dynamic and engaging activity. Interactive Storytelling Global Accessibility : By making stories available in multiple languages and formats, AI can help ensure that children around the world have access to quality literature. Global Accessibility Educational Integration : AI can seamlessly incorporate educational content into stories, helping children learn while they enjoy their favorite tales. Educational Integration Conclusion As a dad, my dream is to create magical moments for my son and to share the joy of storytelling with children everywhere. With the help of AI, this dream is becoming a reality. Stories like "Humphrey's Harmonious Hiccups" are just the beginning. Together, we can use technology to foster a love of reading, spark imaginations, and ensure that every child has access to the wonderful world of books. Thank you for joining me on this journey. Let's continue to explore the possibilities of AI in storytelling and create a brighter future for our children, one story at a time. What kind of stories would you like to see created with the help of AI? Share your thoughts in the comments below! For more details, you can read the full story of Humphrey's Harmonious Hiccups here . Humphrey's Harmonious Hiccups here

Creating Children's Books With Gemini and Imagen

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isn't new, I wrote a longer (as we called them several years before) but in 2022 it reached a certain level of quality, that impressed the world. In addition the emergence of ChatGPT (GPT 3.5) and Large Language Models became part of this trend that we call “Generative AI”. Generative AI article about GANs What Techs Are Part of Generative AI? We can find several ways to categorize these technologies, but I think the easiest way to understand them is if we use output categorization: what will be the actual output of these AI models? Using this logic there are AI models for Image generation · Diffusion models Text generation · Large Language models Video generation · Large-scale Video Generation models Speech or audio generation · Diffusion models Most important applications of Generative AI I did collect the most notable AI applications, that are robust enough and they aren't using any third party services. This is my private collection, I also use the majority of these tools personally: OpenAI · GPT-4 (text to text) · LLM (2024) ChatGPT Google (text to text) · LLM (2024) Gemini (text to text) · LLM (2023) Claude.ai (text to text) · LLM (2023) PI.ai Github (text to text/code) · LLM (2023) Copilot (text to image) · Diffusion Model (2023) Dall-E 3 Pro Max (text to image) · Diffusion Model (2023) Stable Diffusion (text to image) · Diffusion Model (2022) Midjourney (text to video) · Large-scale Video Generation model (2024) OpenAI Sora (text to video) · Large-scale Video Generation model (2023) Runway (text to speech) · Voice model (2023) Lovo.ai (text to speech) · Voice model (2022) Amazon Polly What Are Prompts? Think of a prompt like asking your friend a question or telling them to do something. This can be anything from a question you want answered to a request for it to make something, like a story or a picture. The AI then tries to figure out what you're asking for and gives you back an answer or something new based on what you told it. How well you ask or tell the AI what you want can make a big difference in what you get back. What are the best Generative AI Books for Image and Text Generation? Artificial Intelligence & Generative AI for Beginners (Generative AI & Chat GPT Mastery Series Book 1), by David M. Patel (2023) David M. Patel's book is a straightforward guide that helps readers get to grips with artificial intelligence and generative AI. Patel, an Amazon bestselling author and AI consultant, aims to make complex AI concepts accessible to everyone, whether you're starting from scratch or looking to expand your professional skills. The book covers the basics of AI, including its history and main components, and explains different types of machine learning. It also introduces readers to critical AI fields such as natural language processing, computer vision, and robotics, showing how they apply in the real world. Patel's approach is to give readers the knowledge they need to use AI tools like ChatGPT, DALL-E 3, and MidJourney effectively, helping them boost their productivity and achieve personal or business growth. Patel focuses on generative AI, explaining what it is, how it works, and the various types that exist. He offers practical advice on how to come up with business ideas using generative AI and guides readers through building their own generative AI models. The book also covers how generative AI can be applied in areas such as copywriting, graphic design, and video editing. It raises important ethical questions about AI and predicts how generative AI will change industries like healthcare, media, and education. Patel provides a wealth of resources for readers who want to explore further, including podcasts and influencers in the AI field. Generative AI for Beginners, by Ethan James Whitfield (2023) is a book designed to simplify these complexities, making the world of Generative AI approachable for everyone. It aims to alleviate the frustration of not grasping this transformative technology, which can hinder personal and professional growth. Generative AI for Beginners The book covers the basics of Generative AI, including its principles and how it works, along with its impact across various industries. It goes beyond theory, showing how Generative AI is applied in creating art, music, and written content, and highlights the importance of ethical considerations in AI use. Tailored for absolute beginners, the book promises a clear and straightforward learning journey, fitting easily into busy schedules. It positions itself as a tool for anyone looking to understand and engage with the ongoing AI revolution, offering a pathway to new career opportunities and a deeper appreciation of how AI is reshaping our world. The Midjourney Prompt Book, by Shaheed Ullah (2024) This comprehensive guide covers the advanced knowledge of Midjourney prompts, including commands and parameters with detailed step-by-step instructions and practical tips. I found over a thousand  prompts that fit also for Stable Diffusion XL and DALL.E-3, despite being designed for Midjourney. The book, now in its 6th edition, spans 300 pages across eleven chapters. It includes a newly added chapter on Nature, Commercial, and Fashion Photography, and another dedicated to Building Consistent Characters with Midjourney, catering to both beginners and advanced users. This edition discusses the latest Midjourney's Niji models along with advanced prompts, photorealism, and niche-specific guides. What are the best Generative AI Books to Understand the Technology Behind It? Generative Deep Learning, by David Foster (2023) This book aims to equip machine learning engineers and data scientists with the skills to build cutting-edge generative AI models using TensorFlow and Keras. It starts with the fundamentals of deep learning and gradually progresses to complex architectures like VAEs, GANs, and Transformers. Through practical guidance and expert tips, you'll learn to unlock the creative potential of these models, generating images, music, text, and even solving reinforcement learning tasks. The book also delves into the future of generative AI and its potential impact on individuals and businesses. In essence, this book is a hands-on guide to building and harnessing the power of generative AI, equipping readers with the knowledge and skills to explore this exciting field. Generative AI with LangChain, by Ben Auffarth (2023) This book guides readers through the process of mastering the LangChain framework, bridging the gap between the theoretical aspects of Large Language Models (LLMs) and their practical deployment in creating applications ready for production. The book offers updated code examples on GitHub to keep readers informed about the latest in LangChain developments and includes a free PDF eBook with the purchase of the print or Kindle version. It covers how to harness the capabilities of LLMs while navigating their limitations, using the LangChain framework for various tasks such as customer support, software development help, and data analysis, showcasing the wide-ranging utility of LLMs in real-world applications. Designed for developers, researchers, and anyone keen to deepen their understanding of LLMs, the book provides a thorough exploration of generative AI fundamentals, industry trends, and the construction of responsive LLM applications. It teaches about transformer models, attention mechanisms, and how to automate data analysis with Python, offering insights into improving LLM performance and deploying these models effectively. With a basic requirement of Python knowledge and a suggestion of some machine learning background, this guide is a valuable asset for those looking to enhance their skills in the dynamic field of LLMs and LangChain, positioning them at the forefront of technological innovation. Developing Apps with GPT-4 and ChatGPT, by Olivier Caelen (2023) is a comprehensive guide aimed at Python developers interested in leveraging large language models for application development. Authored by Olivier Caelen and Marie-Alice Blete, the minibook delves into the functionalities and advantages of GPT-4 and ChatGPT, offering a clear explanation of their operations. It serves as a practical handbook, providing readers with a detailed tutorial on how to use the GPT-4 and ChatGPT Python library for a variety of tasks, including text generation, question answering, and content summarization. Developing Apps with GPT-4 and ChatGPT The authors ensure clarity and accessibility in their writing, supplementing theoretical knowledge with easy-to-follow examples, all of which are supported by Python code available in a dedicated GitHub repository. What are the best Generative AI Books to Understand the Social Impact? Generative AI for Leaders, by Amir Husain (2023) Generative AI is shaping our future, and this guidebook is here to help organizations make the most of it. Written in clear, straightforward language, covers the basics of what Generative AI is, its advantages like boosting productivity and sparking new product ideas, and the challenges it brings, including bias and security issues. It outlines steps for creating a strategy that makes an organization stand out, how to put together the right team, and when to get outside help. The book goes beyond the basics, giving a closer look at the technical side of things like word embeddings and large language models, and discusses how to spot the use of Generative AI. Generative AI for Leaders The future of Generative AI is exciting, and the book doesn't stop at theory. It offers over 75 practical ideas that leaders can start using right away to integrate Generative AI into their operations. By focusing on real-world applications and addressing both the opportunities and hurdles of Generative AI, the guide aims to prepare leaders for what's coming. It's about making informed decisions, understanding the technology, and using it to improve and innovate, ensuring organizations are ready for the future. Ai For Educators, by Danielle Austen (2024) When I first found this book I thought finally a domain specific approach for AI. Teachers are at the heart of the education system, dedicating their lives to shaping the future one student at a time. They go above and beyond to make sure every lesson resonates and every student feels valued. Despite their passion, the reality of being a teacher often means facing exhaustion from balancing lesson planning, administrative duties, grading, and endless parent-teacher meetings. Every student requires individual attention, and the desire to make a real difference sometimes comes at the cost of a teacher's well-being. It's concerning that 42% of teachers feel a high level of stress daily, which steals their time, energy, and the joy that initially drew them to this noble profession. Artificial Intelligence is emerging as a supportive ally in the educational field, offering a helping hand to teachers. It proposes a way to simplify their workload rather than complicate it. AI can handle repetitive tasks, adapt lessons to meet each student's needs without extra effort from teachers, keep student data safe, and make learning more engaging. This shift towards using AI in education can reduce the long hours teachers work and help avoid burnout. Final Thoughts on Books about Generative AI As I wrap up this exploration of the top Generative AI books, I'm struck by the pace at which the field is advancing. Each book has offered a unique perspective, from technical development to using prompts that utilizes models like GPT-4. I can't stop thinking how it will feel like using a real AGI (Artificial General Intelligence), maybe as it was described in (a visionary sci-fi). Conjunction This AI journey excitement about the possibilities that Generative AI holds for the future. The rapid evolution from early GPT models to the sophisticated diffusion models of today illustrates a trajectory of innovation that's far from plateauing. The knowledge encapsulated in these books equips us with the understanding to not only participate in this transformation but to drive it forward. As a developer, a creator, or simply an enthusiast of AI, the road ahead is rich with potential, and I'm eager to see how we'll continue to push the boundaries of what's possible with Generative AI. If you are looking for more AI related reads check my ultimate selection of (which also includes fiction books). AI books Originally appeared on Joelbooks

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