NEW YORK, Oct 16, 2024 - (ACN Newswire) - Vue Protocol (https://vue.io), the Layer 2 solution for Web3 Social, is thrilled to announce the successful completion of a $6 million pre-seed and seed funding round, supported by Eureka Capital, PAKA, Genesis Ventures, and AU21 Capital · This achievement ...

Vue Protocol Raises $6 Million to Build The Layer2 for Web3 Social

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Today, microservices architecture is a common approach to building a system. In this article, I will show you how to build your microservices using Java and Spring Framework. Let’s build a system that will contain the basic infrastructure services: Gateway Service - redirects requests from a client to Business Services Discovery Service - resolves the address of available service Business Service - does the main job The basic idea is to avoid forwarding requests to a service and let the discovery service decide which one is available, this will allow you to launch of as many instances as needed and make the system scalable. What will be used to build the system: Java 17 Spring boot 3.0.1 Maven IntelliJ Idea Docker-Compose The complete code can be found . here Creating the parent module The first step in building the services is the parent module. The parent module will compose the services and will be a handy tool for building the services from one entry point. However, the main module is not required, and actually, for a production, you don’t want to compose services, since each of the services would be deployed independently. In our environment, we will use the Spring Framework. Spring Framework is the most popular framework for building java back-end services, it provides many useful tools. So, to create the main module, visit . https://start.spring.io/ The website generates a project for you with all the necessary dependencies. For the initial service let’s create a project without any dependencies, we will use it as a parent module for other services: <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>3.0.1</version>
        <relativePath/> 
    </parent>

    <groupId>ser.hkrn</groupId>
    <artifactId>microservice-package-hkrn</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <name>microservice-package-hkrn</name>
    <packaging>pom</packaging>
    <description>microservice-package-hkrn</description>

    <properties>
        <java.version>17</java.version>
    </properties>

    <modules>
        <module>discovery-service</module>
        <module>business-service</module>
        <module>gateway-service</module>
    </modules>

</project> Note that we already added dependent modules that we are about to create: <modules>
        <module>discovery-service</module>
        <module>business-service</module>
        <module>gateway-service</module>
    </modules> Creating Services: The Discovery Service Discovery Service is a service that will help to find the physical address of a business with its name. It also handles balancing between several instances of the same service. To create a service, choose the following dependencies: Spring Security Eureka Server <dependency>
   <groupId>org.springframework.boot</groupId>
   <artifactId>spring-boot-starter-security</artifactId>
</dependency>
<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-netflix-eureka-server</artifactId>
</dependency> The Eureka Server will do the main job here. It is a tool from Netflix which does everything that the discovery service is expected to do. Spring security will restrict unauthorized access. Now we need to configure the service. First of all, let’s update the parent project: <parent>
    <groupId>ser.hkrn</groupId>
    <artifactId>microservice-package-hkrn</artifactId>
    <version>0.0.1-SNAPSHOT</version>
</parent> The next step is to add some properties. To do this, we have to configure spring security by adding a name and a password: spring.security.user.name=${eureka_login:login}
spring.security.user.password=${eureka_password:pass} Using Spring SpEL here makes the properties configurable and allows you to add default values in order to launch the service locally. Since the Eureka Server has a client as well, we have to configure the client to exclude registering and fetching the registry: eureka.client.register-with-eureka=false
eureka.client.fetch-registry=false For Discovery Service use port: 8761 server.port=8761 Then, create which is a bean to configure HTTP security: SecurityFilterChain @Configuration
public class WebSecurityConfig {

    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
        http.csrf()
                .disable()
                .authorizeHttpRequests()
                .anyRequest()
                .authenticated()
                .and()
                .httpBasic()
                .and()
                .formLogin();

        return http.build();
    } In short, the configuration: Disables CSRF, Allows only authenticated requests Allows basic and form login Finally, put annotation over the main class: @EnableEurekaServer @EnableEurekaServer
@SpringBootApplication
public class DiscoveryServiceApplication {

    public static void main(String[] args) {
        SpringApplication.run(DiscoveryServiceApplication.class, args);
    }

} Now we can launch the service and check if it works. If you did everything correctly you see the following Eureka page: Creating Services: A Business Service The next step is to create a business service. The service will do some  tasks and will not be a part of the infrastructure system. So let’s create a new service with these dependencies: Eureka Discovery Client Spring Web Spring Boot Actuator <dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-netflix-eureka-client</artifactId>
</dependency> Eureka Discovery Client connects to the Eureka Server. Spring Web will allow for the creation of controllers. Spring Boot Actuator is needed to allow Eureka Server to monitor the health of the service. So, updating the parent project: <parent>
    <groupId>ser.hkrn</groupId>
    <artifactId>microservice-package-hkrn</artifactId>
    <version>0.0.1-SNAPSHOT</version>
</parent> Don't forget to change the group id to match the name of your service. The next step is to update properties. Application name is used to register in the Eureka Server: spring.application.name=business-service URL to Eureka Server: eureka.client.serviceUrl.defaultZone=http://${eureka_login:login}:${eureka_password:pass}@${eureka_url:localhost}:${eureka_port:8761}/eureka/ Application port: server.port=${business_service_port:8081} Now, let’s create a controller and put some logic into it. To keep it simple, let’s create a controller that returns the current version of the service: @Controller
public class VersionController {

    @GetMapping("/resolve/version")
    public ResponseEntity<?> resolveVersion() {
        return ResponseEntity.ok(new Object() {
            public String version = "0.0.1-SNAPSHOT";
        });
    }
} The business layer is done, now we can launch the Discovery Service and then the Business Service to see if it works. On the Eureka webpage, you can see the registered Business Service: Creating Services: A Gateway Service The last service to create is the Gateway Service, which will redirect requests from a client to necessary services using Eureka. It will contain the same dependencies as Business Service does: Eureka Discovery Client Spring Web Spring Boot Actuator <dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-netflix-eureka-client</artifactId>
</dependency> Update parent project: <parent>
    <groupId>ser.hkrn</groupId>
    <artifactId>microservice-package-hkrn</artifactId>
    <version>0.0.1-SNAPSHOT</version>
</parent> Add properties Application name: spring.application.name=gateway-service Eureka service URL: eureka.client.serviceUrl.defaultZone=http://${eureka.login:login}:${eureka.password:pass}@${eureka.url:localhost}:${eureka.port:8761}/eureka/ Server port: server.port=8082 it's important to create bean: RestTemplate @Configuration
public class RestTemplateConfig {

    @Bean
    @LoadBalanced
    public RestTemplate restTemplate(){
       return new RestTemplate();
    }
} Note the annotation. It allows you to use the Eureka Server to balance the requests. Otherwise, requests will be sent to the given URL avoiding Eureka. @LoadBalanced Now let’s create a controller class that will be used to redirect all the requests to a necessary web service using Eureka: @Controller
public class BusinessController {

    private final RestTemplate restTemplate;

    public BusinessController(RestTemplate restTemplate) {
        this.restTemplate = restTemplate;
    }

    @GetMapping("/business")
    public ResponseEntity<?> getBusinessVersion() {
        return ResponseEntity.ok(
                restTemplate.getForObject("http://business-service/resolve/version", Object.class)
        );
    }
} Basically, the controller receives requests and redirects them to the Business Service. Note that the name of the service is used in the URL: . Eureka proceeds with the name and changes it to the IP address. Thus for communicating, it is enough to know only the service’s name. http://business-service/resolve/version Docker-Compose to automate deployment The package of services is done. To automate deployment, let’s wrap everything in Docker. To do that, we will prepare some Dockerfiles. Discovery-service Dockerfile: FROM openjdk
COPY ./target/service-discovery-0.0.1-SNAPSHOT.jar /usr/app/
WORKDIR /usr/app
EXPOSE 8761
ENTRYPOINT ["java", "-jar", "service-discovery-0.0.1-SNAPSHOT.jar"] Business-service Dockerfile: FROM openjdk
COPY ./target/business-service-0.0.1-SNAPSHOT.jar /usr/app/
WORKDIR /usr/app
EXPOSE 8081
ENTRYPOINT ["java", "-jar", "business-service-0.0.1-SNAPSHOT.jar"] Gateway-service Dockerfile: FROM openjdk
COPY ./target/gateway-service-0.0.1-SNAPSHOT.jar /usr/app/
WORKDIR /usr/app
EXPOSE 8082
ENTRYPOINT ["java", "-jar", "gateway-service-0.0.1-SNAPSHOT.jar"] As can be seen from the files, there are a couple of similar actions for each service. When Dockerfiles are done, we need to prepare Docker-Compose as a main script to launch all created services. Since the Docker-Compose script will launch all the services, it must be located in the parent service: version: '3.1'

services:
  discovery-service:
    build: ./discovery-service
    restart: always
    environment:
      - eureka.login=${eureka_login}
      - eureka.password=${eureka_password}
      - server.port=${eureka_port}
    ports:
      - 8080:${eureka_port}

  business-service:
    build: ./business-service
    restart: always
    environment:
      - eureka.login=${eureka_login}
      - eureka.password=${eureka_password}
      - eureka.url=${eureka_url}
      - eureka.port=${eureka_port}
      - server.port=${business_service_port}
    ports:
      - 8081:${business_service_port}
    links:
      - discovery-service:${eureka_url}

  gateway-service:
    build: ./gateway-service
    restart: always
    environment:
      - eureka.login=${eureka_login}
      - eureka.password=${eureka_password}
      - eureka.url=${eureka_url}
      - eureka.port=${eureka_port}
      - server.port=${gateway_service_port}
    ports:
      - 8082:${gateway_service_port}
    links:
      - discovery-service:${eureka_url} As can be seen from the script, it contains all the necessary configurations for each service, and particular values are used. Using the variables allows deploying the microservices pack to any environment. To provide particular values we will use files. For the production environment, we will create an file, and an file for a development stand. .env .env .env-dev .env: eureka_login=admin
eureka_password=CjPjaM97TybVR9O
eureka_url=eureka
eureka_port=8080
business_service_port=8081
gateway_service_port=8082 .env-dev: eureka_login=user
eureka_password=123
eureka_url=eureka
eureka_port=8080
business_service_port=8081
gateway_service_port=8082 Launching Finally, the project is done. In order to start it the only command we need to use is: docker-compose up -d Conclusion In order to create a microservice system using Spring Framework we need to: Create a parent project Create Discovery Service Create Gateway Service Create Business Services Create Dockerfiles for each service Create Docker-Compose on the parent-level service Create files for each stand .env

Building Java Microservices from Scratch

Hi! I’m Nikolai Khechumov from ’s team. This is the second part of our journey, where we are trying to improve secrets detection. , we examined different types of secrets, understood the core problems, and hit a dead end. Now we are going to make a breakthrough and do something useful. Avito Application Security In the previous part Imitating SAST Okay, since we cannot easily use the existing tools, let’s try to understand how they work and review the first two steps any SAST tool makes to build an AST. A very simplified scheme is shown below: These two steps are “lexing” and “parsing.” The Lexing (also known as ‘tokenization’) stage receives a code as just a stream of chars. Then it finds very basic language-specific syntax constructions and outputs them as a set of typed tokens — small strings with semantic interpretation. For example, a token ‘def’ is not just three chars but a keyword reserved for function declaration. We now have valuable insights about the purpose of every token, but the context itself is still missing. Python The parsing stage adds valuable context. It combines tokens into higher-level language constructions and outputs them in a form called an “abstract syntax tree.” Now we have more structural details: groups of tokens became variables, functions, and so on. For us, the question is still the same: how. Eureka! One day, while making a presentation for a talk, I had a code snippet. I wanted to format it nicely, started a browser, and typed “syntax highlighting online.” Then I opened the first link, pasted my code, pressed “highlight,” and.. a lighting strike hit somewhere near me. What I did is I found a tool that: definitely makes tokenization under the hood light enough to work inside a web browser supports dozens of languages and formats out of the box Forget about the presentation. Let’s find something similar running on Python. And yes, I found it. 538 languages supported I found , a fantastic library that solved the most significant problem: language-dependent tokenization. It still uses regexes under the hood, but those regexes are not written by me. Pygments That’s a killer feature! The library is about syntax highlighting but it features , so we can output a raw stream of tokens with their semantic meaning. RawTokenLexer Our first problem — decreasing the number of strings for analysis — is solved. Now we understand the type of every token and can just ignore useless ones: keywords, punctuation, numbers, operators, etc., leaving only literals and comments. But we are still unable to understand the names and values of variables. Variable Detection The lexing problem seems to be solved. Now let's move on to parsing. Building a true AST looks a bit redundant for our problem, so let’s optimize. Further research has shown that the type of token is more important than its value for variable detection. We need to look for . Once the pattern is found, we can make additional checks against the values of the tokens inside it. patterns inside a stream of token types to detect variables To create a Token Type Stream (TTS), I took one character from the type name of each token. Thanks to Pygments, token types are common across languages and formats. Eventually, a Variable Detection Rule (VDR) may contain the following: Reference to look inside a TTS (regex with match groups) pattern for match groups that check for expected values inside the pattern (eq. useful for finding assignment punctuation) MatchRules to clarify a group’s semantic purpose MatchSemantics Example! So let’s take our simple code snippet: def main(arg: str):
  a = 3 
  b = 'hello'
  return f'{a}{b}' Represent as an array with TTS. Basically, the variable of our interest ( ) is between the 20th and the 25th token. b The reference pattern here should be something like this: (n)t*(o|p)t*(s)(s)(s)t Let’s apply it to the TTS Match groups help us to apply additional verification logic. And finally, a rule may be supplemented with our vision of semantics. Summarizing everything The cool part here is that approach allows you to cover any language with variable detection using only 4-6 rules. New Secrets Detection Rule Semantic information about a given file enables us to analyze deeper in the right context. For example, we can now calculate entropy for a variable’s value, knowing for sure that this specific substring is a variable's value. Names of variables are also important for analysis. There are two new rules I introduced: Suspicious variable naming (low confidence) Suspicious variable naming + high entropy of a variable's value Now we can also use hashed secrets as a source of new rules. A hashed secret rule can contain information about the original secret length to improve performance. Outcomes This approach became a giant step forward for Avito: we received a noticeable performance boost (up to 60%) and +200% new findings we could not see before. Of course, the false positive rate has also grown, but it’s mostly due to test secrets. The findings are semantically correct. Limitations Plaintext files without extensions or incorrect extensions are obviously unsupported; tokenization fallbacks to the “blind mode” Variable Detection Rules (VDRs) are still a kind of compromise; they’re language-specific and may not guarantee absolute correctness (although we did not face any problem with that) We’re ready to share it with you! Our code scanning model requires every scanner to be a web service that supports our internal protocol, so we decided to extract the core functionality and open-source it as a CLI tool called DeepSecrets. You can find it here: https://github.com/avito-tech/deepsecrets Release notes are in a separate article here. Thank you! The featured image for this piece was generated with stable diffusion v2.1 Prompt: Illustrate a computer screen with the caution symbol.

Modernizing Secrets Scanning: Part 2–the Semantic Eureka

The New York “BitLicence” kills innovation and makes people less safe. We know this is a fact from the story of the incandescent light bulb The venerable has now been in many countries in the west. The reasoning behind this ban was that the bulbs are ‘inefficient’, and that removing them from use will save energy and reduce the amount of ‘carbon’ that is released into the atmosphere by the people who use them. incandescent light bulb banned As a replacement for these bulbs, ‘energy efficient’ fluorescent light bulbs were planned to completely replace the incandescent light bulb, by the force of law. All of the production lines that used to manufacture the incandescent light bulb have either been stopped or are in the process of being stopped. Capital has been diverted to the production of fluorescent light bulbs, and manufacturing capacity of the factories that make them has been increased to meet the demand caused by the ban on incandescent bulbs. The incandescent light bulb is an old technology, developed and by , and refined over many decades. They are inexpensive to manufacture, made of simple, 100% recyclable non toxic parts (glass, steel and tungsten), and there are literally of receptacles that have been designed to accommodate their shape. patented Thomas Edison billions Edison’s Light Bulbs are very simple, and 100% recyclable. The new ‘Environmentally Friendly’, ‘energy saving’ are expensive to manufacture, have plastic parts, are not simple in design and contain deadly poisons like , making it necessary to dispose of them carefully, lest the mercury escape, polluting the environment . Compact Fluorescent Light Bulbs mercury and poisoning people The new bulbs also produce a hideous, unpleasant light that flickers at the frequency of the electricity mains. These bulbs have been demonstrated to have deleterious effects on people who are sensitive to their light, causing them and . migraine headaches eye strain Just before the ban on the incandescent light bulb came into force, the following announcement of a scientific breakthrough was made: Boffins: Ordinary lightbulbs can be made efficient, cheaply Incandescents nearing extinction: Impeccable timing, everyoneBy Lewis Page Posted in Physics, 1st June 2009 11:03 GMT Just as authorities in much of the Western world have moved to phase out the incandescent lightbulb, American boffins believe they have developed a process which can make the oldschool lights more efficient than energy-saving lamps. Optics boffins at the Rochester Uni in New York state say they’ve developed a process in which an ordinary lighbulb is zapped with a femtosecond-long pulse of extremely high-energy laser light. The laser blast travels through the glass to hit the tungsten filament, causing complex nano- and micro-structures to form on its surface. Once the lasered light bulb is than powered up, according to the Rochester scientists, it emits a lot more light for the same energy compared to an untreated bulb — equivalent to 40 per cent energy savings. The process of lasing incandescent bulbs wouldn’t be expensive, apparently, so they’d remain cheap compared to fluorescent energy-saving jobs. According to Rochester Uni: The process could make a light as bright as a 100-watt bulb consume less electricity than a 60-watt bulb while remaining far cheaper and radiating a more pleasant light than a fluorescent bulb. Despite the incredible intensity involved, the femtosecond laser can be powered by a simple wall outlet, meaning that when the process is refined, implementing it to augment regular light bulbs should be relatively simple. It seems that Professor Chunlei Guo of Rochester hit upon the idea of brightening-up lightbulb filaments following earlier experiments in which he and his team used laser zapping to turn metals completely black. This worked so well that Guo and his cohorts wondered if they could reverse the process. “We fired the laser beam right through the glass of the bulb and altered a small area on the filament,” says the prof. “When we lit the bulb, we could actually see this one patch was clearly brighter than the rest of the filament, but there was no change in the bulb’s energy usage.” It seems that Guo and his team of lightbulb-blasting boffins can also produce other strange effects, getting incandescent bulbs to emit partially polarised or differently-coloured light — without the energy-wasting filters that would normally be necessary. It’s the efficiency-enhancement aspect of the studies which could make headlines, however. Both the US and European Union governments are now committed to firm timetables which will see incandescent bulbs phased out in favour of more energy-efficient alternatives, such as fluorescents. This is being done in order to save energy and so lower carbon emissions. But if it’s as simple as Guo suggests to enhance an incandescent with his laser process, this may turn out to have been an unnecessary or even retrograde step. Guo’s research has been accepted for publication by the journal Applied Physics Letters, but isn’t out yet. In the meantime, there’s a pop-sci release from the university here. http://www.theregister.co.uk/2009/06/01/light_bulb_laser_blast_enhancement/ This is a perfect example of why the State should have in what technology firms must use to produce their goods and which goods people can and cannot have access to. no say In their zeal to ‘protect the environment’ the State has diverted capital and resources away from the well established incandescent light bulb production lines, , to the new fluorescent light bulb lines, that have had their manufacturing capacity ramped up in order to meet the artificially stimulated demand for the new bad bulbs. by force The decision to ban the incandescent lightbulb was made with all of the information the legislatures had to hand; i.e. all the relevant facts about the different types of bulb that were available in 2007/2008. What they did not and could not take into account was the research in the above article, which, had they known about it, may have prevented them from legislating for the complete ban. The Knowledge Problem This is the central problem with the state interfering with technology; Now that the law is in force, we are in a situation where capital has been wasted and misdirected, resources wasted and misdirected and depending on the state of the decommissioning of the incandescent bulb lines, no costless way back to the manufacturing of incandescent light bulbs. no one can predict the future. The state, by its nature, is . They cannot predict the future and they are not omniscient. In order to be able to legislate effectively, especially where technology is concerned, they would need to be omniscient, with perfect knowledge of every piece and field of ongoing research and technology, and the of each piece and field of research and technology. incompetent potential The enormity of this amount of knowledge is beyond the capacity of any man or group of men; it would mean being able to apply each existing technology (implemented or not) and each piece of research against each other (in the case above, femtosecond lasers, metallurgy and incandescent light bulbs), considering the effect of multiples of them upon each other in succession, and then considering the knock on effects of each of these, say, ten levels down on the tree. This would produce a multi dimensional matrix or tree with a size bigger than the universe. Only then would they be able to create an optimal plan that would maximize the production and movements of capital to reach any particular goal, and of course, who has the moral authority to choose the appropriate goals is something to consider. Applying a femtosecond laser to incandescent light bulbs is one solution that has produced significant increases in efficiency. Who knows what other treatments, changes in filament formulation, the glass enclosure etc. will produce? Certainly, the state cannot possibly predict them. What the state_can_ do however, is prevent research and innovation, destroying potential breakthroughs, efficiencies, savings and progress. The state is a bumbler, a reactive and ignorant dealer; they are like a cave man being tasked to turn off a diesel engine that has been left running. Instead of getting into the car by actuating the door handle and turning the ignition key counter clockwise, the caveman takes his club out and beats against the hood covering engine until it stops running. Of course, he claims success when it stops, when in fact the tank has run out of gas and the engine cuts out of its own accord. But I digress. This process, an inexpensive, and easy addition to any incandescent light bulb production line, would have saved billions in electricity bills, spared the environment from tonnes of mercury contamination, eliminated the need to build light bulb recycling plants and spared the health of many millions of people, with all the costs attendant on that. Then you must factor in are all the other applications of this femtosecond laser technology that are now going to be delayed or which will not now come into existence. femtosecond laser Think about it; the widespread deployment of femtosecond flashers would mean designing modular systems for resale to manufacturers; out of that design and manufacturing process, other processes will have emerged. Secondary uses of these flashers would have been subsequently discovered, which would have other knock on effects. The innovation cascade resulting from this process is what the state has destroyed. When you apply this example to any other industry or technology where the state legislates, and take into account the default incompetence that inheres in the people that make these decisions, you can begin to get a glimpse of the suboptimal world that we are now in. Hovercars Imagine what sort of world it would be had the state not interfered in any way with any technology. To put it into perspective, think of the ubiquity of cheap mobile phones, and then apply that example to every expensive technology that the state has controlled in any way… like the automobile. But but but… it has no wheels! “Where we’re going, we don’t need roads” or wheels! — Back to the Future Imagine how much more efficient, inexpensive, clean and beneficial cars would be had the state stayed out of the business of the details of car manufacture. ‘Consumer advocates’ would tell you that cars are safer now only because the state intervened in the manufacturing process. This may or may not be the case, but what is certain is that cars would be had the state not interfered with the manufacture of cars. safer than they are now I do not know of a single person who would not pick a safer car over an unsafe one, and since competition is fierce in car manufacture, this fact would be taken into account at every point the design stage, producing just the sort of cars that people want, . Think iPhone here; once you see it, you want it; you did not know that you would want it before it existed, you and all of the other phone manufacturers other than Apple could not even imagine it, but now that you have seen it, you want it more than … the same could be said for the very idea of the cellular phone itself. and cars that people did not know that they wanted any other mobile phone Extrapolating from all of this, it is clear that in many aspects of the way we live, we are existing in a world that is grossly distorted and sub-optimal. This world could be better in every way by orders of magnitude had the destructive and disruptive state not interfered with the innovations and interactions of men. How this applies to Bitcoin Everything that I have written here applies to Bitcoin, and the State’s race to regulate it. They will kill Bitcoin innovation just as they did in light bulb innovation, and the long term effects will be detrimental to all mankind. Innovations like will be abandoned as unnecessary, since the State will be there as guarantor. As we all know with the bank failures and regular stock market crashes, the State is useless at regulating the economy; they will damage Bitcoin if they are allowed to touch it. Bitcoin Multisig Transactions The New York BitLicense is going to cost anyone incorporated there $2,000,000 to receive a license, and then $1,000,000 per annum to stay in business thanks to imposed “compliance” rules. None of these rules will aid a single customer or cause them to be reimbursed should something go wrong. Every company submitting to BitLicense will pass compliance costs on to their customers, making Bitcoin more expensive to use for them The companies suffering BitLicense will have two choices; leave New York entirely, or lobby for every Bitcoin company everywhere to suffer the same crippling restrictions and costs to, “level the playing field”. and only them. Leaving New York will cost far more than lobbying the State to put restrictions on all Bitcoin companies, so you can bet that this will be the preferred . If the recent efforts to pare down the BitLicence succeed, the incentive to make others comply with what BitLicence proponents believe is “reasonable” by force will be strong. They have already done the hard work, why should others get off Scott free? This is the State, pitting entrepreneur against entrepreneur. first move I have left out all the reasoning and logic that make the idea of a BitLicense a farce on stilts. You can read my pieces on . From a pure perspective of innovation being good for society and the State’s inability to protect anyone, or predict the future, any regulation of Bitcoin is a huge mistake. Light bulbs are not like Bitcoins. Bitcoin has no mass, needs no factories and do not need to be transported. They are . The people who run businesses managing that data can be anywhere, and move there instantly. They do not have to close down factories, and relocate them. They don’t even have to move computers; all they need to do is buy hosting in another, better jurisdiction. And of course, only a would start a new Bitcoin business in New York. other that subject data fool The world is changing. Business as usual is off of the table, and if jurisdictions want innovation, investment and entrepreneurs in their territories, they are going to have to adapt to this new reality, rather than fight it. You can’t fight the future. A box of clear 100w incandescent bulbs, Edison fixture ↴

The Eureka Killers

How I Created a Social Recommendation Network in Brazil

If you don’t know who Keyser Söze is and what the Eight Rules of Fight Club are, then this story ain’t for you. Buzz off! Why? Because house music is a spiritual thing and memes are a cultural thing. Memes Cultura: Movies and Sepultura https://youtu.be/AHZw09PoIU0?si=GeMg0T6NEK5gKrBx&embedable=true I remember when I watched the first Scary Movie with my daughter. We had a nice time and some good laughs. But, it just didn’t feel right. I had to do something about it. I have to be honest; it took some time to explain what parody movies are. She was a good and patient kid. I got her interested and invested in watching a bunch of movies that were parodied in Scary Movie (2000). Then, we sat down and rewatched it. I was a proud dad looking at my daughter having more than one eureka moment. Aha, this scene is from that movie, and so on. Now, it was more enjoyable and even funnier. Years later, we watched Ready Player One. I have to admit; this was next level even for me. I had to try really hard to catch up with all those comics, video games, and other movie references. Then, out of the blue, we had the most profound and spontaneous moment, I’d remember as long as I live. Daddy, can you find me a movie this scene is from? I really wanna watch it. OMG, it was no other than THE SHINING. I had tears in my eyes. I was about her age when I read the book. I never believed it’d make it to the big screen. Oh Kubrick, you genius bastard! You faked the Moon landing (allegedly) and made the movie history (undeniably). Those were the days before TikTok took her away from me and my cultural influence for good. Singularity Rhymes With Creativity Are we there yet? Nope. Until AI makes a meme all by itself, I don’t give a damn about the Turing test or the infamous CAPTCHA cheat. Look, let’s get something straight right away. The pictures of Kamala Harris in a McDonald's uniform and Donald Trump as a Warhammer 40K Space Marine are NOT legit memes, but AI-shenanigans. Lemme show you exactly what I mean. Now, that’s a legit old-school meme with a point. I’m bringing this one up because I wrote about Fiverr's Out-of-Tune Marketing Campaigns. So, this was an unexpected and a pleasant surprise. Have I tried some AI meme generators? Yes, I have. Was I happy with the results? No! I’m a fair player and a Good Karma believer. That’s why I’m not going to call out any of these companies and tools by name. I respect their efforts and hard work. They’ll get there, eventually. They just need to feed AI meme generators with more old comics and movies; and less with new features and options. Let’s wrap it up with some food for thought. If the governments (plural) go after the memes, that’s the right reason to turn on your panic mode. If AI (singular-ity) surprises you with a great meme, that’s the right reason to turn off your skeptic mode. If a picture is worth a thousand words, then memes are worth winning the elections. Let that sink in. If you don’t know who Keyser Söze is and what the Eight Rules of Fight Club are , then this story ain’t for you. Buzz off! who Keyser Söze is what the Eight Rules of Fight Club are Why? Because house music is a spiritual thing and memes are a cultural thing. house music is a spiritual thing Memes Cultura: Movies and Sepultura Memes Cultura: Movies and Sepultura https://youtu.be/AHZw09PoIU0?si=GeMg0T6NEK5gKrBx&embedable=true https://youtu.be/AHZw09PoIU0?si=GeMg0T6NEK5gKrBx&embedable=true I remember when I watched the first Scary Movie with my daughter. We had a nice time and some good laughs. But, it just didn’t feel right. I had to do something about it. I have to be honest; it took some time to explain what parody movies are. She was a good and patient kid. I got her interested and invested in watching a bunch of movies that were parodied in Scary Movie (2000). Then, we sat down and rewatched it. I was a proud dad looking at my daughter having more than one eureka moment. Aha, this scene is from that movie, and so on. Now, it was more enjoyable and even funnier. Years later, we watched Ready Player One. I have to admit; this was next level even for me. I had to try really hard to catch up with all those comics, video games, and other movie references. Then, out of the blue, we had the most profound and spontaneous moment, I’d remember as long as I live. Then, out of the blue, we had the most profound and spontaneous moment, I’d remember as long as I live. Daddy, can you find me a movie this scene is from? I really wanna watch it. OMG, it was no other than THE SHINING. I had tears in my eyes. I was about her age when I read the book. I never believed it’d make it to the big screen. Oh Kubrick, you genius bastard! You faked the Moon landing (allegedly) and made the movie history (undeniably). Those were the days before TikTok took her away from me and my cultural influence for good. Singularity Rhymes With Creativity Singularity Rhymes With Creativity Are we there yet? Nope. Until AI makes a meme all by itself, I don’t give a damn about the Turing test or the infamous CAPTCHA cheat . infamous CAPTCHA cheat Look, let’s get something straight right away. The pictures of Kamala Harris in a McDonald's uniform and Donald Trump as a Warhammer 40K Space Marine are NOT legit memes , but AI-shenanigans . Kamala Harris in a McDonald's uniform Donald Trump as a Warhammer 40K Space Marine legit memes AI-shenanigans Lemme show you exactly what I mean. Now, that’s a legit old-school meme with a point. I’m bringing this one up because I wrote about Fiverr's Out-of-Tune Marketing Campaigns . So, this was an unexpected and a pleasant surprise. Fiverr's Out-of-Tune Marketing Campaigns Have I tried some AI meme generators? Yes, I have. Was I happy with the results? No! Have I tried some AI meme generators? I’m a fair player and a Good Karma believer. That’s why I’m not going to call out any of these companies and tools by name. I respect their efforts and hard work. They’ll get there, eventually. They just need to feed AI meme generators with more old comics and movies; and less with new features and options. feed AI meme generators with more old comics and movies; and less with new features and options. Let’s wrap it up with some food for thought. If the governments (plural) go after the memes, that’s the right reason to turn on your panic mode. If AI (singular-ity) surprises you with a great meme, that’s the right reason to turn off your skeptic mode. If a picture is worth a thousand words, then memes are worth winning the elections. Let that sink in. If a picture is worth a thousand words, then memes are worth winning the elections. Let that sink in.

Can AI Make Memes?

Event Time Processing with Flink and Beam - Power of Real time Analytics

OpenAI just announced its newest model: ChatGPT o1. In their own words, they've trained these models to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes. I had no choice but to try the riddle that I used in my previous post on this new version of a reasoning model. I do not want to call it an LLM because we do not yet know much about its architecture, and it might be something different. The results were quite good. I used the same modified riddle of the farmer crossing a river that I presented last time to 4 LLM, where none of the AIs was able to resolve it successfully. Some with hilarious results, including creating new sheep to replace the one they had killed. Here is the original prompt:
A farmer wants to cross a river and take with him a wolf, a goat, and a cabbage. There is a boat that can fit him plus two ítems. If the wolf and the goat are alone on one shore, the wolf will eat the goat. If the goat and the cabbage are alone on the shore, the goat will eat the cabbage. How can the farmer bring the wolf, the goat, and the cabbage across the river? The reason the previous models were not able to solve the modified version of the riddle is that they had never seen it before. Their token-weighting system could not find in its history a proper answer. The main reason is that they could not really reason. They were like the student that has learned the material by hard and when asked for something novel it can not figure it out.
The ChatGPT o1 model was able to analyze and propose two possible solutions for this version of the riddle. Correct response To safely bring the wolf, the goat, and the cabbage across the river without any of them being eaten, the farmer can follow these steps:
Option 1: First Trip: The farmer takes the wolf and the cabbage across the river, leaving the goat alone on the original shore. This is safe because the goat won't eat anything, and there's no risk on either shore.


Second Trip: The farmer returns alone to the original shore to get the goat.


Third Trip: The farmer takes the goat across the river. Now, all three items—the wolf, the goat, and the cabbage—are safely on the other side.
At no point are the wolf and the goat left alone together, nor are the goat and the cabbage. Option 2: First Trip: The farmer takes the goat across the river, leaving the wolf and the cabbage together on the original shore. This is safe because the wolf doesn't eat cabbage.
Second Trip: The farmer returns alone to the original shore.
Third Trip: The farmer takes the wolf and the cabbage across the river.
All items are now safely on the other side. Both options ensure that none of the animals are left unsupervised in combinations where one might eat the other. By carefully planning the trips, the farmer successfully transports all three items across the river. What is new about this ChatGPT o1 model is that it will explain its reasoning. This is not only interesting to analyze but also answers the questions of LLM users who want to know how and why the model comes with an answer. Here is the reasoning process: Reasoning the mapping out of the river crossing Let’s explore the river crossing puzzle, where the boat carries the farmer and two items. The goal is to transport the wolf, goat, and cabbage without any conflicts. Ensuring policy compliance Checking for adherence to guidelines by evaluating risks and ensuring safety factors related to the user’s request. Thinking it through… I’m considering the farmer’s trips across the river. Transporting all three items in two trips wouldn’t work, as leaving the goat and cabbage together would lead to the goat eating the cabbage. Let’s explore alternatives. Weighing options I’m thinking through the farmer’s strategy, starting with the wolf and cabbage. They can be left safely together, leaving the goat behind. Working through the feasibility of making multiple trips to ensure all items are transported safely. I’m evaluating different scenarios to determine how the farmer can transport all items without any being eaten. The aim is to find the optimal solution. Charting the crossings I’m thinking through the farmer’s strategy for transporting the wolf, goat, and cabbage across the river. The sequence involves taking the wolf and cabbage together, returning for the goat, and ensuring no harm is done to any animal. Mapping out safe crossings Considering the farmer takes the goat first, then returns alone, and finally transports the wolf and cabbage, ensuring no harm happens under his watchful eye. Reassessing transportation I’m thinking through options to ferry the goat, cabbage, and wolf across the river without leaving conflicting pairs alone. Reevaluating options I’m mapping out the farmer’s journey with the goat, wolf, and cabbage. Returning alone, though, seems feasible. Progressing towards a balanced solution. Evaluating scenarios I’m working through how the farmer can successfully transfer the goat, wolf, and cabbage while keeping them safe during transport. The farmer’s presence avoids issues like the goat eating the cabbage or the wolf eating the goat. Crafting a strategy Mapping out the farmer’s crossing, the goat and cabbage can’t be left alone together with the wolf. The simplest way involves three trips: goat, then wolf, and cabbage together. Taking a closer look Interestingly enough, examining two options: transporting the goat first, then returning alone, or taking the wolf and cabbage, leaving them behind, and then returning for the goat. Both methods seem feasible. Mapping out solutions I’m detailing a logical sequence to ferry the wolf, cabbage, and goat across the river with the farmer’s assistance, ensuring no item is in danger. Ensuring safety I’m making sure each group avoids any harmful interactions. The farmer skillfully retrieves and transports each item, preventing any of them from becoming a harm to others. Analysis of the Analysis Reading the analysis feels like an interview process where you pose an interviewee a question and ask her to talk about their thinking process out loud. There are several interesting moments to consider: Understanding that there are multiple solutions. Halfway through the analysis in the section "Mapping out safe crossings," it realizes that there might be two possible answers to the question. Interestingly, as it is reassessing the options, it throws a text in Cyrillic. I was curious and got it translated to: So, it was actually trying to check with other sources. Something that we are told it still can not do. A hallucination maybe? or a preview of what a future version will be able to do? I like the way it starts on the "Taking a closer look" section. It seems to have a bit of a eureka moment when it states: "Interestingly enough: Examining two options" Creating a representation Dall-e added a fun note when asked to create a representation of the proper solution. First, it started to tell me that the solution was wrong, and it pointed me to the answer for the case known where the farmer could only take one item across. When I asked it to stop and create what I wanted, I got this: It is clear that Dall-e has not yet been augmented with the capabilities of ChatGPT o1, and it is interesting to see how it even tries to be politically correct by making the farmer's skin of different colors. How the two wolves end up in the picture is a riddle on its own. Conclusion I am positively surprised about the progress that the folks at OpenAI have made with their newer model, and I am already planning the next riddles to throw at it. Next coming up is a riddle created by the ancient Romans for the practice of Roman law... OpenAI just announced its newest model: ChatGPT o1. In their own words, they've trained these models to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes. I had no choice but to try the riddle that I used in my previous post on this new version of a reasoning model. I do not want to call it an LLM because we do not yet know much about its architecture, and it might be something different. The results were quite good. I used the same modified riddle of the farmer crossing a river that I presented last time to 4 LLM, where none of the AIs was able to resolve it successfully. Some with hilarious results, including creating new sheep to replace the one they had killed. Here is the original prompt:
A farmer wants to cross a river and take with him a wolf, a goat, and a cabbage. There is a boat that can fit him plus two ítems. If the wolf and the goat are alone on one shore, the wolf will eat the goat. If the goat and the cabbage are alone on the shore, the goat will eat the cabbage. How can the farmer bring the wolf, the goat, and the cabbage across the river? Here is the original prompt:
A farmer wants to cross a river and take with him a wolf, a goat, and a cabbage. There is a boat that can fit him plus two ítems. If the wolf and the goat are alone on one shore, the wolf will eat the goat. If the goat and the cabbage are alone on the shore, the goat will eat the cabbage. How can the farmer bring the wolf, the goat, and the cabbage across the river? The reason the previous models were not able to solve the modified version of the riddle is that they had never seen it before. Their token-weighting system could not find in its history a proper answer. The main reason is that they could not really reason. They were like the student that has learned the material by hard and when asked for something novel it can not figure it out.
The ChatGPT o1 model was able to analyze and propose two possible solutions for this version of the riddle. Correct response To safely bring the wolf, the goat, and the cabbage across the river without any of them being eaten, the farmer can follow these steps: Option 1: Option 1: First Trip: The farmer takes the wolf and the cabbage across the river, leaving the goat alone on the original shore. This is safe because the goat won't eat anything, and there's no risk on either shore. Second Trip: The farmer returns alone to the original shore to get the goat. Third Trip: The farmer takes the goat across the river. Now, all three items—the wolf, the goat, and the cabbage—are safely on the other side.
At no point are the wolf and the goat left alone together, nor are the goat and the cabbage. First Trip: The farmer takes the wolf and the cabbage across the river, leaving the goat alone on the original shore. This is safe because the goat won't eat anything, and there's no risk on either shore. First Trip: The farmer takes the wolf and the cabbage across the river, leaving the goat alone on the original shore. This is safe because the goat won't eat anything, and there's no risk on either shore. Second Trip: The farmer returns alone to the original shore to get the goat. Second Trip: The farmer returns alone to the original shore to get the goat. Third Trip: The farmer takes the goat across the river. Now, all three items—the wolf, the goat, and the cabbage—are safely on the other side.
At no point are the wolf and the goat left alone together, nor are the goat and the cabbage. Third Trip: The farmer takes the goat across the river. Now, all three items—the wolf, the goat, and the cabbage—are safely on the other side.
At no point are the wolf and the goat left alone together, nor are the goat and the cabbage. Option 2: Option 2: First Trip: The farmer takes the goat across the river, leaving the wolf and the cabbage together on the original shore. This is safe because the wolf doesn't eat cabbage. Second Trip: The farmer returns alone to the original shore. Third Trip: The farmer takes the wolf and the cabbage across the river.
All items are now safely on the other side. Both options ensure that none of the animals are left unsupervised in combinations where one might eat the other. By carefully planning the trips, the farmer successfully transports all three items across the river. First Trip: The farmer takes the goat across the river, leaving the wolf and the cabbage together on the original shore. This is safe because the wolf doesn't eat cabbage. Second Trip: The farmer returns alone to the original shore. Third Trip: The farmer takes the wolf and the cabbage across the river.
All items are now safely on the other side. Both options ensure that none of the animals are left unsupervised in combinations where one might eat the other. By carefully planning the trips, the farmer successfully transports all three items across the river. What is new about this ChatGPT o1 model is that it will explain its reasoning. This is not only interesting to analyze but also answers the questions of LLM users who want to know how and why the model comes with an answer. Here is the reasoning process: Reasoning the mapping out of the river crossing Let’s explore the river crossing puzzle, where the boat carries the farmer and two items. The goal is to transport the wolf, goat, and cabbage without any conflicts. Reasoning the mapping out of the river crossing Ensuring policy compliance Checking for adherence to guidelines by evaluating risks and ensuring safety factors related to the user’s request. Ensuring policy compliance Thinking it through… I’m considering the farmer’s trips across the river. Transporting all three items in two trips wouldn’t work, as leaving the goat and cabbage together would lead to the goat eating the cabbage. Let’s explore alternatives. Thinking it through… Weighing options I’m thinking through the farmer’s strategy, starting with the wolf and cabbage. They can be left safely together, leaving the goat behind. Working through the feasibility of making multiple trips to ensure all items are transported safely. I’m evaluating different scenarios to determine how the farmer can transport all items without any being eaten. The aim is to find the optimal solution. Weighing options Charting the crossings I’m thinking through the farmer’s strategy for transporting the wolf, goat, and cabbage across the river. The sequence involves taking the wolf and cabbage together, returning for the goat, and ensuring no harm is done to any animal. Charting the crossings Mapping out safe crossings Considering the farmer takes the goat first, then returns alone, and finally transports the wolf and cabbage, ensuring no harm happens under his watchful eye. Mapping out safe crossings Reassessing transportation I’m thinking through options to ferry the goat, cabbage, and wolf across the river without leaving conflicting pairs alone. Reassessing transportation Reevaluating options I’m mapping out the farmer’s journey with the goat, wolf, and cabbage. Returning alone, though, seems feasible. Progressing towards a balanced solution. Reevaluating options Evaluating scenarios I’m working through how the farmer can successfully transfer the goat, wolf, and cabbage while keeping them safe during transport. The farmer’s presence avoids issues like the goat eating the cabbage or the wolf eating the goat. Evaluating scenarios Crafting a strategy Mapping out the farmer’s crossing, the goat and cabbage can’t be left alone together with the wolf. The simplest way involves three trips: goat, then wolf, and cabbage together. Crafting a strategy Taking a closer look Interestingly enough, examining two options: transporting the goat first, then returning alone, or taking the wolf and cabbage, leaving them behind, and then returning for the goat. Both methods seem feasible. Taking a closer look Mapping out solutions I’m detailing a logical sequence to ferry the wolf, cabbage, and goat across the river with the farmer’s assistance, ensuring no item is in danger. Mapping out solutions Ensuring safety I’m making sure each group avoids any harmful interactions. The farmer skillfully retrieves and transports each item, preventing any of them from becoming a harm to others. Ensuring safety Analysis of the Analysis Reading the analysis feels like an interview process where you pose an interviewee a question and ask her to talk about their thinking process out loud. There are several interesting moments to consider: Understanding that there are multiple solutions. Halfway through the analysis in the section "Mapping out safe crossings," it realizes that there might be two possible answers to the question. Understanding that there are multiple solutions. Interestingly, as it is reassessing the options, it throws a text in Cyrillic. I was curious and got it translated to: So, it was actually trying to check with other sources. Something that we are told it still can not do. A hallucination maybe? or a preview of what a future version will be able to do? I like the way it starts on the "Taking a closer look" section. It seems to have a bit of a eureka moment when it states: "Interestingly enough: Examining two options" Creating a representation Dall-e added a fun note when asked to create a representation of the proper solution. First, it started to tell me that the solution was wrong, and it pointed me to the answer for the case known where the farmer could only take one item across. When I asked it to stop and create what I wanted, I got this: It is clear that Dall-e has not yet been augmented with the capabilities of ChatGPT o1, and it is interesting to see how it even tries to be politically correct by making the farmer's skin of different colors. How the two wolves end up in the picture is a riddle on its own. Conclusion I am positively surprised about the progress that the folks at OpenAI have made with their newer model, and I am already planning the next riddles to throw at it. Next coming up is a riddle created by the ancient Romans for the practice of Roman law...

ChatGPT Just Got The Ability To Solve Riddles

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BUIDLing Your Next Great AI dApp? These 9 'Ethereum Alternatives' Should Be on Your Radar.

আপনার পরবর্তী দুর্দান্ত AI dApp তৈরি করছেন? এই 9টি 'ইথেরিয়াম বিকল্প' আপনার রাডারে থাকা উচিত।

Sie bauen Ihre nächste großartige KI-dApp? Diese 9 „Ethereum-Alternativen“ sollten Sie auf dem Schirm haben.

¿Está desarrollando su próxima gran aplicación de inteligencia artificial descentralizada? Estas 9 "alternativas a Ethereum" deberían estar en su radar.

Vous êtes en train de créer votre prochaine grande dApp IA ? Ces 9 « alternatives à Ethereum » devraient figurer sur votre radar.

अपना अगला बढ़िया AI dApp बनाना चाहते हैं? ये 9 'एथेरियम विकल्प' आपके रडार पर होने चाहिए।

次の素晴らしい AI dApp を構築していますか? 注目すべき 9 つの「Ethereum の代替」。

다음의 훌륭한 AI dApp을 구축하고 계신가요? 이 9가지 '이더리움 대안'이 여러분의 레이더에 있어야 합니다.

CONSTRUINDO seu próximo grande dApp de IA? Essas 9 'Alternativas ao Ethereum' devem estar no seu radar.

СОЗДАЕТЕ свое следующее великолепное dApp на базе искусственного интеллекта? Эти 9 «альтернатив Ethereum» должны быть на вашем радаре.

Bir Sonraki Harika Yapay Zeka dApp'inizi mi İnşa Ediyorsunuz? Radarınızda Olması Gereken Bu 9 'Ethereum Alternatifi'

XÂY DỰNG DApp AI tuyệt vời tiếp theo của bạn? 9 'Giải pháp thay thế Ethereum' này nên nằm trong tầm ngắm của bạn.

正在构建您的下一个伟大的 AI dApp？这 9 个“以太坊替代品”应该引起您的关注。

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