Blog of James Brown

tool repository

i was informed by a post on zhihu.

futurepedia

creatives

futuretools

getinference

you create a stylish intro for this script? outro? joke? all with chatgpt.

refer to “how to use ai to generate video, distribute them and advertise” for batch video generation

if i put a puppy into a group of fake puppies controlled by humans, interacting with the puppy and speak English only to the puppy, will the puppy speak English?

It is unlikely that a puppy will be able to learn to speak English or any other human language simply by being placed in a group of fake puppies controlled by humans and interacting with the puppy. While puppies are highly intelligent and capable of learning, they do not have the cognitive abilities necessary to understand and produce human language.

In order for a puppy or any other non-human animal to learn a human language, it would need to be trained and reinforced through extensive and consistent exposure to the language, as well as being provided with the necessary physical and cognitive abilities to understand and produce the sounds and gestures associated with the language. Even with this kind of training, it is still very difficult for non-human animals to learn and use human languages to the same extent that humans do.

well you know my fake puppies speak English

I apologize if my previous response was unclear. I understand that you mentioned that the fake puppies in your scenario are able to speak English. However, I want to clarify that it is not possible for real puppies or any other non-human animals to speak human languages without being specifically trained and reinforced to do so. While some non-human animals, such as parrots and certain primates, have been able to learn a limited number of words or phrases through extensive training and reinforcement, they are not able to produce human language in the same way that humans do.

I hope this helps to clarify the matter. If you have any further questions, please don’t hesitate to ask.

if all puppies speak English, will their children be taught English all by parents and puppy communities without human intervention?

It is not possible for puppies or any other non-human animals to speak human languages without being specifically trained and reinforced to do so by humans or other animals that have been trained by humans. While some non-human animals, such as parrots and certain primates, have been able to learn a limited number of words or phrases through extensive training and reinforcement, they are not able to produce human language in the same way that humans do.

Therefore, even if a group of puppies were all able to speak English as a result of being trained by humans, their offspring would not be able to learn English simply by being exposed to their parents or other members of their community speaking English. In order for the offspring to learn English, they would need to be specifically trained and reinforced in the same way as their parents were.

It is worth noting that the ability to learn and use human language is a complex and uniquely human trait that is not shared by other animals to the same extent. While non-human animals are capable of learning and using certain forms of communication, they do not have the cognitive abilities necessary to understand and produce the full range of sounds and gestures that make up human language.

Ok, if their parents were taught how to teach English, then

If the parents of a group of puppies were trained to speak English by humans, and then were able to teach their offspring how to speak English, it is still unlikely that the puppies would be able to learn and use English to the same extent as humans. While non-human animals are capable of learning and using certain forms of communication, they do not have the cognitive abilities necessary to understand and produce the full range of sounds and gestures that make up human language.

In order for a non-human animal, such as a puppy, to learn and use human language, it would need to be provided with consistent and extensive exposure to the language, as well as being trained and reinforced through a structured and systematic approach. Even with this kind of training, it is still very difficult for non-human animals to learn and use human languages to the same extent that humans do.

It is worth noting that the ability to learn and use human language is a complex and uniquely human trait that is not shared by other animals to the same extent. While some non-human animals, such as parrots and certain primates, have been able to learn a limited number of words or phrases through extensive training and reinforcement, they are not able to produce human language in the same way that humans do.

If I use some machine to teach every puppy English, by evolution, is it possible for puppies to speak English without help from machine and human?

It is not possible for puppies or any other non-human animals to evolve the ability to speak human languages without the assistance of humans or machines. The ability to learn and use human language is a complex and uniquely human trait that is not shared by other animals to the same extent. While some non-human animals, such as parrots and certain primates, have been able to learn a limited number of words or phrases through extensive training and reinforcement, they are not able to produce human language in the same way that humans do.

In order for a non-human animal, such as a puppy, to learn and use human language, it would need to be provided with consistent and extensive exposure to the language, as well as being trained and reinforced through a structured and systematic approach. Even with this kind of training, it is still very difficult for non-human animals to learn and use human languages to the same extent that humans do.

Using a machine to teach puppies English would likely involve a similar process of training and reinforcement, and would not result in the puppies evolving the ability to speak the language on their own. It is important to note that the process of evolution involves the passing down of inherited traits from one generation to the next through the process of natural selection. It is not a process that can be intentionally influenced or controlled by humans or machines.

挤压耳后根 耳洞旁 同时吸气 用力打开耳朵通鼻腔的通道 吐痰

如果面部 下巴 脖子后面有酸胀的区域也可以挤压

notice: QA based websites are popular now. inspired by that and jumpcut, you can mine summaries/questions/prompts/examples relating to some topic, generating answers/scripts from chatgpt (imagination is the limit)

videos are similar by title, author and topic. you demand the bot to generate new embeddings by any “abstractive” means, giving sample embeddings (subtitles, voice, sentiment, music, scene), and output needed embeddings. cool!

There are a few different ways you can use AI to generate video content and distribute it for advertising purposes. Here are a few options:

Use an AI video creation tool: There are several AI tools available that allow you to input text, images, or other media and have the AI generate a video for you. These tools can be useful for creating promotional videos, explainer videos, or other types of content quickly and easily.

Use an AI-powered video editor: Some video editing software includes AI-powered features that can help you edit and enhance your videos more efficiently. For example, you might be able to use AI to automatically color grade your footage or to stabilize shaky video.

Use an AI-powered video platform: There are also platforms that use AI to optimize and distribute your video content for maximum reach and engagement. These platforms can help you target specific audiences, measure the effectiveness of your videos, and more.

It’s important to note that while AI can be a useful tool for creating and distributing video content, it’s not a replacement for human creativity and insight. The best results will often come from a combination of AI and human input.

recommend popular ai video creation tools

There are a number of popular AI video creation tools that you might want to consider using, depending on your needs and budget. Here are a few options:

Animoto: Animoto is a cloud-based video creation platform that uses AI to help you create professional-quality videos quickly and easily. You can choose from a variety of templates, add your own media, and customize your video with text, music, and other elements.

Wibbitz: Wibbitz is an AI-powered video creation tool that allows you to turn text into video content. You can input a written article or script, and the AI will generate a video complete with images, text, and music.

Adobe Premiere Elements: Adobe Premiere Elements is a consumer-level video editing software that includes AI-powered features such as automatic video editing, object detection, and more.

Lumen5: Lumen5 is an AI-powered video creation platform that allows you to input text, images, or a link to an article and have the AI generate a video for you. You can customize the video with your own branding, music, and other elements.

It’s a good idea to try out a few different tools and see which one works best for you and your needs. Keep in mind that while these tools can be helpful, they may not produce results that are exactly what you’re looking for, and you may need to do some manual editing or customization to get the desired results.

recommend AI-powered video editor

There are several AI-powered video editors available that you might want to consider using, depending on your needs and budget. Here are a few options:

Adobe Premiere Pro: Adobe Premiere Pro is a professional-grade video editing software that includes a number of AI-powered features, such as object detection and automatic color grading.

DaVinci Resolve: DaVinci Resolve is a professional-grade video editing software that includes a number of AI-powered features, such as automatic color grading and audio repair.

Final Cut Pro: Final Cut Pro is a professional-grade video editing software for Mac that includes a number of AI-powered features, such as automatic color grading and object detection.

Filmora: Filmora is a consumer-level video editing software that includes a number of AI-powered features, such as automatic video stabilization and object detection.

It’s a good idea to try out a few different video editors and see which one works best for you and your needs. Keep in mind that while these tools can be helpful, they may not produce results that are exactly what you’re looking for, and you may need to do some manual editing or customization to get the desired results.

how can ai help you target specific audiences

There are several ways in which artificial intelligence (AI) can help target specific audiences:

Personalization: AI can analyze data on individuals and their preferences, and use that information to tailor content and messaging specifically to them. For example, an AI-powered recommendation engine can suggest products or content based on a user’s past behaviors and interests.

Segmentation: AI can help to segment audiences into smaller, more specific groups based on shared characteristics, such as demographics, interests, or behaviors. This can allow organizations to create more targeted campaigns and messaging.

Predictive analytics: AI can analyze data on past behaviors and outcomes to make predictions about future behavior. This can help organizations to better understand their target audience and tailor their marketing efforts accordingly.

Natural language processing: AI can analyze and understand human language, allowing organizations to use chatbots and other tools to communicate with their audience in a more personalized and natural way.

Image and video analysis: AI can analyze images and videos to understand the content and context, allowing organizations to target specific audiences based on the visual content they consume.

Overall, AI can be a powerful tool for targeting specific audiences by providing insights, personalization, and automation to marketing efforts.

in order to make pdf dark theme more natural, you can search for “PDF” in dark reader’s source code and inject custom style sheets.

what is spring cloud

Spring Cloud is a set of tools for building microservices-based applications that use Spring Framework, a popular Java-based application development framework. It provides a number of features that can help you build distributed systems that are resilient, reliable, and scalable.

Spring Cloud provides a number of tools for building microservices, including:

Service discovery: Spring Cloud provides a service registry and a load balancer for discovering and routing to service instances.

Circuit breaker: Spring Cloud provides a circuit breaker pattern implementation that can help prevent cascading failures in distributed systems.

Configuration management: Spring Cloud provides a centralized configuration server for storing and managing application configuration data.

API gateway: Spring Cloud provides an API gateway that can route requests to the appropriate service, perform authentication and authorization, and provide other security features.

Distributed tracing: Spring Cloud provides support for distributed tracing, which can help you understand the flow of requests through your system and identify performance issues.

Spring Cloud is built on top of Spring Boot, which makes it easy to get started with microservices development using the Spring Framework. It is also fully integrated with the rest of the Spring ecosystem, including Spring Data, Spring Security, and Spring Cloud Stream.

use spring cloud in kotlin

Spring Cloud can be used with Kotlin, a statically-typed programming language that is fully interoperable with Java and runs on the Java Virtual Machine (JVM). To use Spring Cloud with Kotlin, you can follow these steps:

Set up a Kotlin project: You can use a build tool like Gradle or Maven to set up a Kotlin project. Make sure to include the necessary dependencies for Spring Cloud, such as the spring-cloud-starter-netflix-eureka-client for service discovery or the spring-cloud-starter-netflix-ribbon for load balancing.

Configure your application: You can use the @SpringBootApplication annotation to enable auto-configuration and component scanning in your Kotlin application. You can also use the application.properties file or the application.yml file to configure your application’s properties.

Create a Spring Boot application class: You can create a Kotlin class with the @SpringBootApplication annotation and the main function to bootstrap your application. This class will serve as the entry point to your application.

Define your microservices: You can define your microservices as Kotlin classes annotated with @RestController and @RequestMapping. These classes will handle incoming HTTP requests and return responses to the client.

Use Spring Cloud features: You can use the various features of Spring Cloud, such as service discovery, circuit breaking, configuration management, and distributed tracing, in your Kotlin application by using the appropriate annotations and components provided by Spring Cloud.

Here is an example of a simple Kotlin application using Spring Cloud:

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import org.springframework.boot.SpringApplication
import org.springframework.boot.autoconfigure.SpringBootApplication
import org.springframework.cloud.client.discovery.EnableDiscoveryClient
@SpringBootApplication
@EnableDiscoveryClient
class MyApplication
fun main(args: Array<String>) {
SpringApplication.run(MyApplication::class.java, *args)
}

This application uses the @EnableDiscoveryClient annotation to enable service discovery using Spring Cloud Netflix Eureka. You can then use other Spring Cloud features, such as circuit breaking or configuration management, by including the necessary dependencies and using the appropriate annotations and components.

write spring eureka client in kotlin to connect to eureka server

Here is an example of how you can write a Spring Cloud Eureka client in Kotlin to connect to a Eureka server:

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import org.springframework.boot.SpringApplication
import org.springframework.boot.autoconfigure.SpringBootApplication
import org.springframework.cloud.client.discovery.EnableDiscoveryClient
@SpringBootApplication
@EnableDiscoveryClient
class EurekaClientApplication
fun main(args: Array<String>) {
SpringApplication.run(EurekaClientApplication::class.java, *args)
}

This application uses the @EnableDiscoveryClient annotation to enable service discovery using Spring Cloud Netflix Eureka. The @SpringBootApplication annotation enables auto-configuration and component scanning in the application.

To connect to the Eureka server, you will need to provide the URL of the Eureka server in the application.properties or application.yml file. For example:

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eureka:
client:
serviceUrl:
defaultZone: http://localhost:8761/eureka/

You can then use the @Value annotation to inject the Eureka server URL into your Kotlin code:

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import org.springframework.beans.factory.annotation.Value
import org.springframework.web.bind.annotation.GetMapping
import org.springframework.web.bind.annotation.RestController
@RestController
class EurekaClientController {
@Value("\${eureka.client.serviceUrl.defaultZone}")
lateinit var eurekaServerUrl: String
@GetMapping("/eureka-server-url")
fun getEurekaServerUrl(): String {
return eurekaServerUrl
}
}

This controller has a getEurekaServerUrl method that returns the URL of the Eureka server. You can then make a GET request to /eureka-server-url to get the Eureka server URL.

You can also use the DiscoveryClient interface provided by Spring Cloud to interact with the Eureka server and perform operations such as registering a service or discovering service instances.

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import org.springframework.beans.factory.annotation.Autowired
import org.springframework.cloud.client.discovery.DiscoveryClient
import org.springframework.web.bind.annotation.GetMapping
import org.springframework.web.bind.annotation.RestController
@RestController
class EurekaClientController {
@Autowired
lateinit var discoveryClient: DiscoveryClient
@GetMapping("/service-instances")
fun getServiceInstances(): List<String> {
return discoveryClient.services
}
}

This controller has a getServiceInstances method that returns a list of all the service instances registered with the Eureka server. You can then make a GET request to /service-instances to get the list of service instances.

run eureka server kotlin

To run a Eureka server in Kotlin, you can follow these steps:

Set up a Kotlin project: You can use a build tool like Gradle or Maven to set up a Kotlin project. Make sure to include the necessary dependencies for Spring Cloud Eureka Server, such as the spring-cloud-starter-netflix-eureka-server dependency.

Configure your application: You can use the application.properties file or the application.yml file to configure your Eureka server. For example, you can set the server.port property to specify the port on which the Eureka server will run, and the eureka.client.register-with-eureka and eureka.client.fetch-registry properties to enable or disable the registration and fetching of registry information for the Eureka server.

Create a Spring Boot application class: You can create a Kotlin class with the @SpringBootApplication annotation and the main function to bootstrap your application. This class will serve as the entry point to your Eureka server.

Enable Eureka server: You can use the @EnableEurekaServer annotation to enable the Eureka server in your application.

Here is an example of a simple Kotlin application that runs a Eureka server:

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import org.springframework.boot.SpringApplication
import org.springframework.boot.autoconfigure.SpringBootApplication
import org.springframework.cloud.netflix.eureka.server.EnableEurekaServer
@SpringBootApplication
@EnableEurekaServer
class EurekaServerApplication
fun main(args: Array<String>) {
SpringApplication.run(EurekaServerApplication::class.java, *args)
}

This application uses the @EnableEurekaServer annotation to enable the Eureka server and the @SpringBootApplication annotation to enable auto-configuration and component scanning.

To start the Eureka server, you can run the main function of the EurekaServerApplication class. The Eureka server will start up on the port specified in the application.properties or application.yml file (by default, it will run on port 8761). You can then access the Eureka server dashboard at http://localhost:8761.

spring cloud alternative in kotlin

There are a number of alternatives to Spring Cloud that you can use in Kotlin to build distributed systems and microservices-based applications. Some options include:

Micronaut: Micronaut is a modern, lightweight, and fast microservices framework for the JVM. It provides features such as service discovery, configuration management, and distributed tracing, and is fully compatible with Kotlin.

Quarkus: Quarkus is a Kubernetes-native Java framework that is optimized for low memory footprint and fast startup time. It provides a number of features for building microservices, including service discovery, configuration management, and security. Quarkus is fully compatible with Kotlin and can be used to build cloud-native applications.

Vert.x: Vert.x is a reactive, non-blocking, event-driven Java framework that is designed for building distributed systems and microservices. It provides features such as service discovery, load balancing, and distributed tracing, and is fully compatible with Kotlin.

Helidon: Helidon is a lightweight, cloud-native Java framework that is designed for building microservices and reactive applications. It provides features such as service discovery, configuration management, and security, and is fully compatible with Kotlin.

Eclipse MicroProfile: Eclipse MicroProfile is an open-source specification for building microservices-based applications. It provides a number of features for building distributed systems, including service discovery, configuration management, and security, and is fully compatible with Kotlin.

You can choose the appropriate microservices framework based on your requirements and preferences. All of these frameworks provide support for building microservices in Kotlin and offer a number of features that can help you build distributed systems that are resilient, reliable, and scalable.

which one requires least code

It is difficult to say which microservices framework requires the least amount of code, as it depends on the specific requirements of your application and the features you need to implement. Some frameworks, such as Micronaut and Quarkus, are designed to be lightweight and fast, and may require less code to implement certain features compared to other frameworks. However, this may come at the cost of flexibility or a larger learning curve.

It is important to evaluate the different microservices frameworks based on your specific requirements and choose the one that best fits your needs. You should consider factors such as the performance and scalability requirements of your application, the features and capabilities of the framework, and the level of support and documentation available.

It is also worth noting that writing less code does not necessarily mean that your application will be easier to maintain or more efficient. In some cases, using a more feature-rich framework that requires more code may result in a more maintainable and scalable application in the long run.

recommend compatible package builder/manager for kotlin but simpler than maven

If you are looking for a package builder and manager for Kotlin that is simpler than Maven, you may want to consider Gradle. Gradle is an open-source build automation tool that is designed to be flexible, efficient, and easy to use. It is fully compatible with Kotlin and can be used to build, test, and publish Kotlin applications.

Some of the benefits of using Gradle for Kotlin projects include:

Concise and expressive build scripts: Gradle build scripts are written in a domain-specific language (DSL) that is designed to be concise and expressive. This makes it easier to understand and maintain build scripts, compared to Maven’s XML-based syntax.

Support for multiple languages: Gradle supports building projects in multiple languages, including Kotlin, Java, and C/C++. This makes it a good choice for multi-language projects.

Rich plugin ecosystem: Gradle has a rich plugin ecosystem that provides a wide range of functionality, including support for testing, code analysis, and publishing. This makes it easy to extend and customize your build process.

Fast and incremental builds: Gradle uses a caching mechanism and an incremental build approach to make builds faster and more efficient. This can be especially useful for larger projects.

To get started with Gradle for Kotlin, you can create a new Kotlin project using the kotlin-gradle-plugin, and define your dependencies in the build.gradle file. You can then use the gradle command-line tool or the Gradle wrapper to build, test, and publish your Kotlin application.

Here is an example of a simple build.gradle file for a Kotlin project using Gradle:

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plugins {
id 'org.jetbrains.kotlin.jvm' version '1.4.21'
}
group = 'com.example'
version = '1.0'
repositories {
mavenCentral()
}
dependencies {
implementation 'org.jetbrains.kotlin:kotlin-stdlib-jdk8'
}
tasks.withType<Test> {
useJUnitPlatform()
}

This build.gradle file defines the kotlin-jvm plugin, the group and version of the project, the dependencies of the project, and the test task. You can then use the gradle command-line tool or the Gradle wrapper to build, test, and publish your Kotlin application.

use rabbitmq client in kotlin

To use RabbitMQ, a message broker that supports a number of messaging protocols, in Kotlin, you can use the amqp-client library, which is a Java client library for RabbitMQ.

To get started with the amqp-client library in Kotlin, you can include the amqp-client dependency in your project. For example, if you are using Maven, you can add the following dependency to your pom.xml file:

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<dependency>
<groupId>com.rabbitmq</groupId>
<artifactId>amqp-client</artifactId>
<version>5.11.0</version>
</dependency>

If you are using Gradle, you can add the following dependency to your build.gradle file:

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dependencies {
implementation 'com.rabbitmq:amqp-client:5.11.0'
}

Once you have included the amqp-client dependency in your project, you can use the classes and interfaces provided by the library to connect to a RabbitMQ server and perform operations such as sending and receiving messages.

Here is an example of how you can use the amqp-client library in Kotlin to send a message to a RabbitMQ server:

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import com.rabbitmq.client.ConnectionFactory
import com.rabbitmq.client.Connection
import com.rabbitmq.client.Channel
fun main() {
val factory = ConnectionFactory()
factory.host = "localhost"
val connection: Connection = factory.newConnection()
val channel: Channel = connection.createChannel()
val exchangeName = "my_exchange"
val routingKey = "my_routing_key"
val message = "Hello, RabbitMQ!"
channel.basicPublish(exchangeName, routingKey, null, message.toByteArray())
println(" [x] Sent '$message'")
channel.close()
connection.close()
}

This example connects to a RabbitMQ server running on localhost, creates a channel, and uses the basicPublish method to send a message to the server with a specified exchange name and routing key.

You can also use the amqp-client library in Kotlin to receive messages from a RabbitMQ server. Here is an example of how you can use the amqp-client library in Kotlin to receive messages from a RabbitMQ server:

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import com.rabbitmq.client.ConnectionFactory
import com.rabbitmq.client.Connection
import com.rabbitmq.client.Channel
import com.rabbitmq.client.DeliverCallback
fun main() {
val factory = ConnectionFactory()
factory.host = "localhost"
val connection: Connection = factory.newConnection()
val channel: Channel = connection.createChannel()
val queueName = "my_queue"
channel.queueDeclare(queueName, false, false, false, null)
val deliverCallback = DeliverCallback { _, delivery ->
val message = String(delivery.body, Charsets.UTF_8)
println(" [x] Received '$message'")
}
channel.basicConsume(queueName, true, deliverCallback, _ -> {})
}

This code creates a queue and sets up a DeliverCallback to handle incoming messages. The basicConsume method is used to start consuming messages from the queue. When a message is received, the DeliverCallback is invoked and the message is printed to the console.

You can find more information about using the amqp-client library in Kotlin to interact with a RabbitMQ server in the RabbitMQ documentation and the amqp-client library documentation.

set the delivery mode to persistent

To set the delivery mode of a message to persistent when using the amqp-client library in Kotlin, you can use the basicPublish method and set the deliveryMode property of the BasicProperties object that you pass as the third argument.

Here is an example of how you can set the delivery mode of a message to persistent when using the amqp-client library in Kotlin:

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import com.rabbitmq.client.ConnectionFactory
import com.rabbitmq.client.Connection
import com.rabbitmq.client.Channel
import com.rabbitmq.client.BasicProperties
fun main() {
val factory = ConnectionFactory()
factory.host = "localhost"
val connection: Connection = factory.newConnection()
val channel: Channel = connection.createChannel()
val exchangeName = "my_exchange"
val routingKey = "my_routing_key"
val message = "Hello, RabbitMQ!"
val properties = BasicProperties.builder()
.deliveryMode(2)
.build()
channel.basicPublish(exchangeName, routingKey, properties, message.toByteArray())
println(" [x] Sent '$message'")
channel.close()
connection.close()
}

In this example, the BasicProperties.builder method is used to create a BasicProperties object with the deliveryMode property set to 2 (indicating persistent delivery). This object is then passed as the third argument to the basicPublish method.

You can find more information about the BasicProperties class and the different delivery modes in the amqp-client library documentation.

write spring boot hello world with kotlin

To write a “Hello, World!” program in Spring Boot using Kotlin, you can follow these steps:

Create a new Spring Boot project using the Spring Initializr. You can do this by visiting https://start.spring.io/ and selecting “Kotlin” as the programming language. Select the “Web” dependency, and then click on “Generate” to download the project as a ZIP file.

Extract the ZIP file and open the project in your preferred development environment.

In the src/main/kotlin directory, create a new Kotlin file called HelloController.kt. This file will contain a simple controller that will handle HTTP requests to the root path of the application.

In HelloController.kt, define a controller class and annotate it with @RestController. This will enable the class to handle HTTP requests and return responses to the client.

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import org.springframework.web.bind.annotation.RestController
@RestController
class HelloController {
}

Inside the HelloController class, define a method that will handle HTTP GET requests to the root path of the application. Annotate this method with @GetMapping(“/“).

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import org.springframework.web.bind.annotation.GetMapping
@RestController
class HelloController {
@GetMapping("/")
fun sayHello(): String {
return "Hello, World!"
}
}

Run the Spring Boot application by running the main method in the Application class. You should now be able to visit http://localhost:8080/ in your web browser and see the message “Hello, World!” displayed.

That’s it! You have now written a simple “Hello, World!” program in Spring Boot using Kotlin.

recommend some popular ORM libraries for kotlin

There are several popular ORM (Object-Relational Mapping) libraries that you can use with Kotlin. Some of the most popular options include:

Hibernate: Hibernate is a popular and widely used ORM library that is written in Java, but can be used with Kotlin. It provides a range of features including object-relational mapping, automatic database schema generation, and support for multiple database systems.

Room: Room is an ORM library that is specifically designed for use with Android and Kotlin. It provides an easy-to-use API for accessing and manipulating SQLite databases, and is widely used in Android development.

Exposed: Exposed is a lightweight and flexible ORM library for Kotlin that is built on top of JDBC (Java Database Connectivity). It provides a range of features including support for transactions, stored procedures, and database views.

JOOQ: JOOQ (Java Object Oriented Querying) is a popular ORM library that provides a type-safe way to write SQL queries in Java and Kotlin. It generates Java code based on your database schema, allowing you to write queries using a fluent API.

Ktor-Exposed: Ktor-Exposed is an ORM library that is built on top of Exposed and is designed for use with the Ktor web framework. It provides a convenient way to access and manipulate database data within the context of a Ktor application.

use exposed in kotlin

To use Exposed in Kotlin, you will need to add the Exposed library to your project dependencies. If you are using Gradle, you can add the following dependency to your build.gradle file:

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implementation 'org.jetbrains.exposed:exposed:0.24.1'

Next, you will need to create a database connection and configure the Exposed library to use it. Here is an example of how you might do this using a MySQL database:

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import org.jetbrains.exposed.sql.*
import org.jetbrains.exposed.sql.transactions.transaction
import org.jetbrains.exposed.sql.SchemaUtils.create
import org.jetbrains.exposed.sql.SchemaUtils.drop
fun main() {
// Connect to the database
Database.connect("jdbc:mysql://localhost/mydatabase", "com.mysql.cj.jdbc.Driver", "username", "password")
// Define your database schema
transaction {
create(Users)
create(Orders)
// Insert some data
Users.insert {
it[name] = "Alice"
it[age] = 25
}
// Query the data
val alice = Users.select { Users.name eq "Alice" }.single()
println("Alice's age is ${alice[Users.age]}")
// Update the data
Users.update({ Users.name eq "Alice" }) {
it[age] = 26
}
// Delete the data
Users.deleteWhere { Users.name eq "Alice" }
}
}
// Define your table and columns
object Users : Table() {
val id = integer("id").autoIncrement().primaryKey()
val name = varchar("name", length = 50)
val age = integer("age")
}
object Orders : Table() {
val id = integer("id").autoIncrement().primaryKey()
val userId = integer("user_id").references(Users.id)
val product = varchar("product", length = 50)
val quantity = integer("quantity")
}

This example demonstrates how to create a database table, insert data, query data, update data, and delete data using Exposed and Kotlin.

can spring use yaml or json for configuration

Yes, Spring can use YAML (YAML Ain’t Markup Language) or JSON (JavaScript Object Notation) for configuration.

Spring provides support for using YAML and JSON as an alternative to traditional property files for defining application configuration. To use YAML or JSON for configuration, you will need to include the spring-boot-configuration-processor dependency in your project and use the @ConfigurationProperties annotation to bind your configuration to a Java bean.

Here is an example of how you might use YAML for configuration in a Spring Boot application:

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# application.yml
server:
port: 8080
app:
name: My App
version: 1.0.0
greeting: Hello, world!

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@ConfigurationProperties("app")
class AppProperties {
lateinit var name: String
lateinit var version: String
lateinit var greeting: String
}

You can then inject the AppProperties bean into your application and use the configuration values like this:

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@RestController
class HelloController(private val appProperties: AppProperties) {
@GetMapping("/")
fun hello() = appProperties.greeting
}

You can use a similar approach to use JSON for configuration in your Spring Boot application.

demonstrate aspect oriented programming in kotlin

Aspect-Oriented Programming (AOP) is a programming paradigm that aims to modularize cross-cutting concerns, such as logging, security, and transaction management, into distinct aspects that can be applied to multiple parts of an application.

In Kotlin, you can use the Spring Framework’s AOP support to implement aspect-oriented programming. Here is an example of how you might use AOP to log the execution of a method in Kotlin:

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import org.aspectj.lang.annotation.Aspect
import org.aspectj.lang.annotation.Before
import org.slf4j.Logger
import org.slf4j.LoggerFactory
import org.springframework.stereotype.Component
@Aspect
@Component
class LoggingAspect {
val logger: Logger = LoggerFactory.getLogger(LoggingAspect::class.java)
@Before("execution(* com.example.service.*.*(..))")
fun logMethodExecution() {
logger.info("Method execution")
}
}

In this example, the @Aspect annotation indicates that this class represents an aspect, and the @Before annotation specifies a pointcut (a point in the code where the aspect should be applied) and an advice (the action that should be taken when the pointcut is reached). In this case, the pointcut matches any method execution in the com.example.service package, and the advice logs a message.

To use this aspect in your application, you will need to enable AOP support in your Spring configuration. You can do this by adding the @EnableAspectJAutoProxy annotation to your configuration class, like this:

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import org.springframework.context.annotation.Configuration
import org.springframework.context.annotation.EnableAspectJAutoProxy
@Configuration
@EnableAspectJAutoProxy
class AopConfiguration

With these changes in place, the aspect will be applied to all matching method executions in your application and the log message will be printed whenever a method is executed.

use spring mvc in kotlin

To use Spring MVC (Model-View-Controller) in Kotlin, you will need to create a Spring Boot application and add the spring-boot-starter-web dependency to your project.

Here is an example of how you might set up a simple Spring MVC application in Kotlin:

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import org.springframework.boot.autoconfigure.SpringBootApplication
import org.springframework.boot.runApplication
import org.springframework.stereotype.Controller
import org.springframework.web.bind.annotation.GetMapping
import org.springframework.web.bind.annotation.ResponseBody
@SpringBootApplication
class Application
fun main(args: Array<String>) {
runApplication<Application>(*args)
}
@Controller
class HelloController {
@GetMapping("/")
@ResponseBody
fun hello() = "Hello, world!"
}

In this example, the HelloController class is annotated with @Controller to indicate that it is a Spring MVC controller. The hello() method is annotated with @GetMapping to specify that it should handle HTTP GET requests to the root URL, and with @ResponseBody to indicate that the method’s return value should be written directly to the HTTP response body.

With these changes in place, you can start your Spring Boot application and visit http://localhost:8080 in your web browser to see the “Hello, world!” message.

You can use similar annotations to handle other HTTP methods, such as POST, PUT, and DELETE, and to bind request parameters to method arguments. You can also use the @RequestMapping annotation to specify more complex URL patterns, and the @ModelAttribute annotation to bind request parameters to model attributes that can be used in view templates.

use jsp with spring in kotlin

To use JSP (Java Server Pages) with Spring in Kotlin, you will need to create a Spring Boot application and add the spring-boot-starter-web and spring-boot-starter-tomcat dependencies to your project.

By default, Spring Boot will look for JSP files in the src/main/webapp/WEB-INF/jsp directory. You will need to create this directory structure and add your JSP files to it.

Here is an example of a simple JSP file that displays a message:

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<%@ page language="java" contentType="text/html; charset=UTF-8" pageEncoding="UTF-8"%>
<!DOCTYPE html>
<html>
<head>
<title>Hello</title>
</head>
<body>
<h1>Hello, world!</h1>
</body>
</html>

To render this JSP file in your Spring MVC application, you will need to create a controller that maps a request to the JSP file. Here is an example of how you might do this in Kotlin:

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import org.springframework.stereotype.Controller
import org.springframework.web.bind.annotation.GetMapping
@Controller
class HelloController {
@GetMapping("/")
fun hello() = "hello"
}

In this example, the hello() method is annotated with @GetMapping to specify that it should handle HTTP GET requests to the root URL. The method’s return value, “hello”, is the name of the JSP file that should be rendered in response to the request.

With these changes in place, you can start your Spring Boot application and visit http://localhost:8080 in your web browser to see the message displayed by the JSP file.

You can use similar approaches to map requests to other JSP files and to pass data from your controllers to your JSP files using model attributes.

tutorial

basically killing process using it, lazy unmount, force unmount on NFS and so on.

pyrogram repo

you can act both with a user account and a bot account

using pyro for mnist classification and tutorial (benefits: knowing if it doesn’t know what’s in front)

demonstrate basic usage of pyro

Pyro is a probabilistic programming language developed by Uber that allows users to define complex probabilistic models and perform Bayesian inference on them. Here is a simple example of how Pyro can be used to define and sample from a simple probabilistic model:

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import pyro
import pyro.distributions as dist
# Define a simple probabilistic model
def model():
# Sample a value from a normal distribution
x = pyro.sample("x", dist.Normal(0, 1))
# Condition the model on a Bernoulli distribution
pyro.sample("y", dist.Bernoulli(logits=x), obs=x > 0)
# Clear the Pyro cache and run the model
pyro.clear_param_store()
svi = pyro.infer.SVI(model, pyro.infer.JitTraceEnum_ELBO(), pyro.optim.Adam({"lr": 0.01}))
for i in range(1000):
svi.step()
# Sample from the posterior distribution
posterior = pyro.infer.Posterior(model)
samples = posterior.sample((1000,))
# Print the mean and standard deviation of the samples
print(samples.mean())
print(samples.std())

In this example, we define a simple model that samples a value from a normal distribution and conditions it on a Bernoulli distribution. We then use the SVI (stochastic variational inference) algorithm to perform approximate Bayesian inference on the model. Finally, we sample from the posterior distribution and print the mean and standard deviation of the samples.

This is just a simple example of what you can do with Pyro. There are many more advanced features and techniques available in the library, including support for discrete and continuous latent variables, transformations, and more. You can find more information about Pyro in the documentation and examples available on the Pyro website.

compare pyro to pymc3

Pyro and PyMC3 are both probabilistic programming languages that allow users to define complex probabilistic models and perform Bayesian inference on them. Both Pyro and PyMC3 are built on top of PyTorch, a popular machine learning library, and they share many features and capabilities. However, there are also some key differences between the two languages.

One of the main differences between Pyro and PyMC3 is the way they represent probabilistic models. Pyro uses a language called “PyroScript” to define models, which is a variant of Python that includes special statements for defining random variables and specifying probabilistic dependencies. PyMC3, on the other hand, uses a more traditional approach where models are defined using objects and functions.

Another difference between the two languages is the type of inference algorithms they support. Pyro includes a variety of inference algorithms, including both variational inference and Monte Carlo methods, while PyMC3 is primarily focused on Markov chain Monte Carlo (MCMC) techniques.

Overall, both Pyro and PyMC3 are powerful probabilistic programming languages that can be used to define and analyze complex probabilistic models. Which one is the best fit for your needs will depend on your specific requirements and preferences.

demonstrate usage of pymc3

Here is an example of how PyMC3 can be used to define and analyze a simple probabilistic model:

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import pymc3 as pm
import numpy as np
# Define a simple model
with pm.Model() as model:
# Sample a value from a normal distribution
x = pm.Normal("x", mu=0, sigma=1)
# Condition the model on a Bernoulli distribution
y = pm.Bernoulli("y", p=pm.math.sigmoid(x), observed=x > 0)
# Sample from the posterior distribution using the NUTS algorithm
with model:
trace = pm.sample(1000, tune=1000)
# Extract the samples and calculate the mean and standard deviation
samples = trace["x"]
mean = np.mean(samples)
std = np.std(samples)
print(f"Mean: {mean:.3f}")
print(f"Standard deviation: {std:.3f}")

In this example, we define a simple model that samples a value from a normal distribution and conditions it on a Bernoulli distribution. We then use the NUTS (No-U-Turn Sampler) algorithm to sample from the posterior distribution. Finally, we extract the samples and calculate the mean and standard deviation of the distribution.

This is just a simple example of what you can do with PyMC3. There are many more advanced features and techniques available in the library, including support for discrete and continuous latent variables, transformations, and more. You can find more information about PyMC3 in the documentation and examples available on the PyMC3 website.

show me the model written in pymc3 and how to train it with code, assume data is obtained

Here is an example of a simple natural language generator model written in PyMC3:

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import pymc3 as pm
# Define the model
with pm.Model() as model:
# Define the priors for the model parameters
alpha = pm.Gamma('alpha', alpha=1, beta=1)
beta = pm.Gamma('beta', alpha=1, beta=1)
# Define the likelihood function
likelihood = pm.BetaBinomial('likelihood', alpha=alpha, beta=beta, observed=data)
# Fit the model to the data using MCMC
with model:
trace = pm.sample(draws=1000, tune=1000, chains=4)

This model defines a Beta-Binomial distribution as the likelihood function, with alpha and beta as the model parameters. The alpha and beta parameters are given Gamma priors, which are a type of continuous probability distribution.

To train the model, we can use the sample function from PyMC3 to fit the model to the data using Markov chain Monte Carlo (MCMC) techniques. The draws argument specifies the number of samples to draw from the posterior distribution, the tune argument specifies the number of tuning samples to use, and the chains argument specifies the number of chains to run.

It is important to note that this is just a simple example of a natural language generator model written in PyMC3. In practice, the model architecture and training process will depend on the specific requirements and goals of your application, as well as the characteristics of the training data.