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Hi, there! Occasionally, there arises a need for swift load testing, whether it be in a local environment or on a testing platform. Typically, such tasks are tackled using specialized tools that demand thorough prior comprehension. However, within enterprises and startups where rapid time-to-market and prompt hypothesis validation are paramount, excessive tool familiarization becomes a luxury. This article aims to spotlight developer-centric solutions that obviate the necessity for profound engagement, allowing for rudimentary testing without delving into pages of documentation. local running You should install:: Docker — all services and tools are required for it. Java 19+ — for kotlin service. Also, you can try to use the Java 8 version; it should work, but you have to change the Gradle settings. Golang — one of the services is golang service =) Python 3+ — for the Yandex tank. Tech Requirements Prior to embarking on our journey, it is advisable to generate a couple of services that can serve as illustrative examples for testing purposes. Kotlin + webflux. r2dbc + postgres. Stack: Our service has: – get all stocks (limit 10) GET – get stock by name GET__ /api/v1/stocks /api/v1/stock ?name=appl – save stock POST / api/v1/stock It should be an easy service because we have to focus on load testing =) Kotlin and the HTTP Service Let's start by creating a small service with some basic logic inside. We'll prepare a model for this purpose: @Table("stocks")
data class Stock(
    @field:Id val id: Long?,
    val name: String,
    val price: BigDecimal,
    val description: String
) Simple router: @Configuration
@EnableConfigurationProperties(ServerProperties::class)
class StockRouter(
    private val properties: ServerProperties,
    private val stockHandler: StockHandler
) {

    @Bean
    fun router() = coRouter {
        with(properties) {
            main.nest {
                contentType(APPLICATION_JSON).nest {
                    POST(save, stockHandler::save)
                }
                GET(find, stockHandler::find)
                GET(findAll, stockHandler::findAll)
            }
        }
    }
} and handler: @Service
class StockHandlerImpl(
    private val stockService: StockService
) : StockHandler {

    private val logger = KotlinLogging.logger {}

    private companion object {
        const val DEFAULT_SIZE = 10
        const val NAME_PARAM = "name"
    }

    override suspend fun findAll(req: ServerRequest): ServerResponse {
        logger.debug { "Processing find all request: $req" }

        val stocks = stockService.getAll(DEFAULT_SIZE)
        return ServerResponse.ok()
            .contentType(MediaType.APPLICATION_JSON)
            .body(stocks, StockDto::class.java)
            .awaitSingle()
    }

    override suspend fun find(req: ServerRequest): ServerResponse {
        logger.debug { "Processing find all request: $req" }

        val name = req.queryParam(NAME_PARAM)

        return if (name.isEmpty) {
            ServerResponse.badRequest().buildAndAwait()
        } else {
            val stocks = stockService.find(name.get())

            ServerResponse.ok()
                .contentType(MediaType.APPLICATION_JSON)
                .body(stocks, StockDto::class.java)
                .awaitSingle()

        }
    }

    override suspend fun save(req: ServerRequest): ServerResponse {
        logger.debug { "Processing save request: $req" }

        val stockDto = req.awaitBodyOrNull(StockDto::class)
        return stockDto?.let { dto ->
            stockService.save(dto)

            ServerResponse
                .ok()
                .contentType(MediaType.APPLICATION_JSON)
                .bodyValue(dto)
                .awaitSingle()
        } ?: ServerResponse.badRequest().buildAndAwait()
    }

} Full code here: GitHub Create a docker file: FROM openjdk:20-jdk-slim
VOLUME /tmp
COPY build/libs/*.jar app.jar
ENTRYPOINT ["java", "-Dspring.profiles.active=stg", "-jar", "/app.jar"] Then, build a docker image and tune it 🤤 docker build -t ere/stock-service .
docker run -p 8085:8085 ere/stock-service But for now, it's better to stick with the idea of running everything through Docker containers and migrate our service into a Docker Compose setup. version: '3.1'

services:

  db:
    image: postgres
    container_name: postgres-stocks
    ports:
      - "5432:5432"
    environment:
      POSTGRES_PASSWORD: postgres

  adminer:
    image: adminer
    ports:
      - "8080:8080"

  stock-service:
    image: ere/stock-service
    container_name: stock-service
    ports:
      - "8085:8085"
    depends_on:
      - db Moving forward: how can we proceed with testing? Specifically, how can we initiate a modest load test for our recently developed service? It’s imperative that the testing solution is both straightforward to install and user-friendly. Given our time constraints, delving into extensive documentation and articles isn’t a viable option. Fortunately, there’s a viable alternative — enter Yandex Tank. The tank is a powerful instrument for testing and has important integrations with , but in the article, we will use it as a simple tool. JMeter https://github.com/yandex/yandex-tank?embedable=true Docs: https://yandextank.readthedocs.org/en/latest/ Let's kick off by creating a folder for our tests. Once we've placed the configs and other essential files—fortunately, just a couple of them—we'll be all set. For our service, we need to test methods “get-all” and “save.” The first config for find method. phantom:
  address: localhost
  port: "8085"
  load_profile:
    load_type: rps
    schedule: line(100, 250, 30s)
  writelog: all
  ssl: false
  connection_test: true
  uris:
    - /api/v1/stocks
overload:
  enabled: false
telegraf:
  enabled: false  
autostop:
  autostop:
    - time(1s,10s) # if request average > 1s
    - http(5xx,100%,1s) # if 500 errors > 1s
    - http(4xx,25%,10s) # if 400 > 25%
    - net(xx,25,10) # if amount of non-zero net-codes in every second of last 10s period is more than 25 Key settings for configuration: Address and port: Same as our application. Load test profile (load_profile): We'll use the 'lined' type, ranging from 100 requests per second to 250 with a 30-second limit. URIs: A list of URLs to be tested. Autostop pattern: No need to stress-test if our service has already gone down! =) Copy and paste the bash script (tank sh): docker run \
    -v $(pwd):/var/loadtest \
    --net="host" \
    -it yandex/yandex-tank And run! What will we see as a result? Yandex Tank will log everything it deems worthy during the test. We can observe metrics such as the 99th percentile and requests per second (rps). So, are we stuck with the terminal now? I want a GUI! Don't worry, Yandex Tank has a solution for that too. We can utilize one of the overload plugins. Here's an example of how to add it: overload:
  enabled: true
  package: yandextank.plugins.DataUploader
  job_name: "save docs"
  token_file: "env/token.txt" We should add our token; just go here and logic by GitHub: https://overload.yandex.net Okay, dealing with a GET request is straightforward, but what about POST? How do we structure the request? The thing is, you can't just throw the request into the tank; you need to create patterns for it! What are these patterns? It's simple — you need to write a small script, which you can again fetch from the documentation and tweak a bit to suit our needs. https://yandextank.readthedocs.io/en/latest/ammo_generators.html?source=post_page-----77157bf4a779--------------------------------&embedable=true And we should add our own body and headers: #!/usr/bin/env python3
# -*- coding: utf-8 -*-

import sys
import json

# http request with entity body template
req_template_w_entity_body = (
        "%s %s HTTP/1.1\r\n"
        "%s\r\n"
        "Content-Length: %d\r\n"
        "\r\n"
        "%s\r\n"
    )

# phantom ammo template
ammo_template = (
        "%d %s\n"
        "%s"
    )

method = "POST"
case = ""
headers = "Host: test.com\r\n" + \
          "User-Agent: tank\r\n" + \
          "Accept: */*\r\n" + \
          "Connection: Close\r\n" 


def make_ammo(method, url, headers, case, body):
    """ makes phantom ammo """
    req = req_template_w_entity_body % (method, url, headers, len(body), body)
    return ammo_template % (len(req), case, req)


def generate_json():
    body = {
        "name": "content",
        "price": 1,
        "description": "description"
    }
    url = "/api/v1/stock"
    h = headers + "Content-type: application/json"
    s1 = json.dumps(body)
    ammo = make_ammo(method, url, h, case, s1)
    sys.stdout.write(ammo)
    f2 = open("ammo/ammo-json.txt", "w")
    f2.write(ammo)


if __name__ == "__main__":
    generate_json() Result: 212 
POST /api/v1/stock HTTP/1.1
Host: test.com
User-Agent: tank
Accept: */*
Connection: Close
Content-type: application/json
Content-Length: 61

{"name": "content", "price": 1, "description": "description"} That’s it! Just run the script, and we will have ammo-json.txt. Just set new params to config, and delete the URLs: phantom:
  address: localhost:9001
  ammo_type: phantom
  ammofile: ammo-json.txt And run it one more time! It’s Time to Test the GRPC! Having acquainted ourselves with loading HTTP methods, it’s natural to consider the scenario for GRPC. Are we fortunate enough to have an equally accessible tool for GRPC, akin to the simplicity of a tank? The answer is affirmative. Allow me to introduce you to ‘ghz’. Just take a look: https://ghz.sh/?source=post_page-----77157bf4a779--------------------------------&embedable=true But before we do that, we should create a small service with Go and GRPC as a good test service. Prepare a small proto file: syntax = "proto3";

option go_package = "stock-grpc-service/stocks";

package stocks;

service StocksService {
  rpc Save(SaveRequest) returns (SaveResponse) {}
  rpc Find(FindRequest) returns (FindResponse) {}
}

message SaveRequest {
  Stock stock = 1;
}

message SaveResponse {
  string code = 1;
}

message Stock {
  string name = 1;
  float price = 2;
  string description = 3;
}

message FindRequest {
  enum Type {
    INVALID = 0;
    BY_NAME = 1;
  }
  message ByName {
    string name = 1;
  }
  Type type = 1;

  oneof body {
    ByName by_name = 2;
  }
}

message FindResponse {
  Stock stock = 1;
} And generate it! (also, we should install ) protoc protoc --go_out=. --go_opt=paths=source_relative --go-grpc_out=. --go-grpc_opt=paths=source_relative stocks.proto Our results: Coding Time! Next steps: Create services as fast as we can. Create dto (stock entity for db layer) package models

// Stock – base dto
type Stock struct {
 ID          *int64  `json:"Id"`
 Price       float32 `json:"Price"`
 Name        string  `json:"Name"`
 Description string  `json:"Description"`
} Implement server // Server is used to implement stocks.UnimplementedStocksServiceServer.
type Server struct {
 pb.UnimplementedStocksServiceServer
 stockUC stock.UseCase
}

// NewStockGRPCService stock gRPC service constructor
func NewStockGRPCService(emailUC stock.UseCase) *Server {
 return &Server{stockUC: emailUC}
}

func (e *Server) Save(ctx context.Context, request *stocks.SaveRequest) (*stocks.SaveResponse, error) {
 model := request.Stock

 stockDto := &models.Stock{
  ID:          nil,
  Price:       model.Price,
  Name:        model.Name,
  Description: model.Description,
 }

 err := e.stockUC.Create(ctx, stockDto)
 if err != nil {
  return nil, err
 }

 return &stocks.SaveResponse{Code: "ok"}, nil
}

func (e *Server) Find(ctx context.Context, request *stocks.FindRequest) (*stocks.FindResponse, error) {
 code := request.GetByName().GetName()

 model, err := e.stockUC.GetByID(ctx, code)
 if err != nil {
  return nil, err
 }

 response := &stocks.FindResponse{Stock: &stocks.Stock{
  Name:        model.Name,
  Price:       model.Price,
  Description: model.Description,
 }}

 return response, nil
} Full code here: click, please! Test It! Install GHz with brew (as usual): link Let’s check a simple example: link Now, we should change it a little bit: move to the folder with the proto files. add method: . stocks.StocksService.Save add simple body: ‘{“stock”: { “name”:”APPL”, “price”: “1.3”, “description”: “apple stocks”} }’. connections will be shared among goroutine workers. Each pair of goroutines will share a single connection. 10 20 2 set service’s port and the result: cd .. && cd stock-grpc-service/proto

ghz --insecure \
  --proto ./stocks.proto \
  --call stocks.StocksService.Save \
  -d '{"stock": { "name":"APPL", "price": "1.3", "description": "apple stocks"} }' \
  -n 2000 \
  -c 20 \
  --connections=10 \
  0.0.0.0:5007 Run it! Summary:
  Count:        2000
  Total:        995.93 ms
  Slowest:      30.27 ms
  Fastest:      3.11 ms
  Average:      9.19 ms
  Requests/sec: 2008.16

Response time histogram:
  3.111  [1]   |
  5.827  [229] |∎∎∎∎∎∎∎∎∎∎∎
  8.542  [840] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
  11.258 [548] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
  13.973 [190] |∎∎∎∎∎∎∎∎∎
  16.689 [93]  |∎∎∎∎
  19.405 [33]  |∎∎
  22.120 [29]  |∎
  24.836 [26]  |∎
  27.551 [6]   |
  30.267 [5]   |

Latency distribution:
  10 % in 5.68 ms 
  25 % in 6.67 ms 
  50 % in 8.27 ms 
  75 % in 10.49 ms 
  90 % in 13.88 ms 
  95 % in 16.64 ms 
  99 % in 24.54 ms 

Status code distribution:
  [OK]   2000 responses And what, stare at everything in the terminal again? No, with ghz, you can also generate a report, but unlike Yandex, it will be generated locally and can be opened in the browser. Just set it: ghz --insecure -O html -o reports_find.html \
... -O + html → output format -o filename Conclusion In summary, when you need a swift assessment of your service's ability to handle a load of 100+ requests per second or identify potential weaknesses, there's no need to initiate intricate processes involving teams, seeking assistance from AQA, or relying on the infrastructure team. More often than not, developers have capable laptops and computers that can execute a small load test. So, go ahead and give it a shot—save yourself some time! I trust you found this brief article beneficial. Valuable Documentation I Recommend Reading: Yandex Tank: docs link Yandex Tank GitHub: GitHub link Yandex Tank Setting: link ghz official page: link ghz setting: ghz config: link link May the Force Be With You! Thanks once again, and best of luck! 🍀🕵🏻

Turbocharge Load Testing: YandexTank + ghz Combo for Lightning–Fast Code Checks!

টার্বোচার্জ লোড টেস্টিং: লাইটনিং-ফাস্ট কোড চেকের জন্য ইয়ানডেক্সট্যাঙ্ক + গিগাহার্টজ কম্বো!

Turbolader-Lasttest: YandexTank + GHz-Kombination für blitzschnelle Codeprüfungen!

Prueba de carga de turbocompresor: combinación YandexTank + ghz para verificaciones de códigos ultrarrápidas.

Test de charge Turbocharge : YandexTank + ghz Combo pour des vérifications de code ultra-rapides !

टर्बोचार्ज लोड परीक्षण: लाइटनिंग-फास्ट कोड जांच के लिए YandexTank + ghz कॉम्बो!

ターボチャージ負荷テスト: YandexTank + ghz コンボによる超高速コード チェック!

터보차지 부하 테스트: 매우 빠른 코드 검사를 위한 YandexTank + ghz 콤보!

Teste de carga turboalimentada: combinação YandexTank + ghz para verificações de código extremamente rápidas!

Турбо-нагрузочное тестирование: комбинация ЯндексТанк + ghz для молниеносной проверки кода!

Turboşarj Yük Testi: Hızlı Kod Kontrolleri için YandexTank + ghz Kombinasyonu!

Kiểm tra tải tăng áp: Combo YandexTank + ghz để kiểm tra mã nhanh như chớp!

涡轮增压负载测试：YandexTank + ghz 组合可实现闪电般的快速代码检查！

The Noonification: Elon Musk vs. Mainstream Media (4/18/2024)

The Tech Company News Brief is Changing! Starting soon, you’ll receive a new (and revamped!) version of HackerNoon’s editorial driven, once-weekly newsletter. Keep your eyes peeled! What happens when the world's richest man gets caught in the crosshairs of one of the oldest and most reputable news organizations in the world? Fireworks 🎆 It was only recently that Reuters amongst Tesla investors by reporting that Elon Musk's crown jewel was scrapping plans to produce a cheaper version of the company's electric car, causing all hell to break loose, albeit briefly. caused panic The news report prompted a decline in Tesla's stock price (~6% or so), before Elon Musk jumped in and addressed the article, reversing the downward slide. Musk's response? . Fake news According to Reuters, the reason why Tesla (allegedly) is no longer producing a cheaper electric vehicle is because it can't compete with Chinese companies that are flooding the market with their own battery-powered cars. And to drive that point home, you need look no further than Xiaomi. You see, the world's third-largest seller of smartphones is also now doubling down as an electric vehicle manufacturer, in what Chief Executive Lei Jun calls the " " of his life. And boy, Lei has reason to be proud and die peacefully, because the company's very first electric car, the SU7, has already garnered more than 100,000 orders. last major entrepreneurship project The SU7 would retail at under $30,000, below Tesla's Model 3 in China. Tesla have competed with the car with its now-scrapped cheaper version, the Model 2 electric vehicle, which was supposedly going to retail at about $25,000. But that would have just been the tip of the iceberg since some Chinese cars go for as low as $10,000 and Musk's company cannot compete at THAT price point. could certainly But all that's beside the point; the reason why everyone wanted Tesla to produce an inexpensive model was because the company's growth depends so heavily on it. In corporate America, if number not going up, it going down. And at Tesla, number definitely going down, leaving everyone very, very unhappy. real Teslas , and everyone knows it. And given that some of the higher-end models of the car are woefully out of the average consumer's reach, it makes sense for the company to focus on bringing its prices down. aren't cheap Abandoning plans for a cheaper Tesla would also serve as a reversal of Musk's vision for the company, since the eccentric billionaire had always on creating a low-cost family car after using profits from luxury vehicles. planned Anyway, news at Tesla hasn't been all that positive ever since Reuters reported the company was (allegedly) no longer going to produce a cheaper electric car. Ironically, Reuters has once again been at the forefront of all the crazy stuff going down at Tesla: Worryingly still, for Tesla at least, the company's stock has lost 35% of its value since the start of the year is still one of the most shorted stocks in American history. and Meanwhile, Musk's just sharing memes: ranked #5 on this week. Tesla HackerNoon's Tech Company Rankings 👋 You’re reading HackerNoon's , a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe to receive the newsletter in your inbox every Tuesday. Tech Company News Brief here complete 😒 It's a Bad Time to be an Alexa Developer Were you one of the developers on 's payroll to create apps for Alexa? Well, we've got bad news for you. Amazon Amazon that it will stop paying developers to build apps for its Alexa voice-activated digital assistant at the end of June. The company will also no longer offer free credits that Alexa developers could use to power their programs with Amazon Web Services. recently announced "These older programs have simply run their course, so we decided to sunset them," an Amazon spokesperson was quoted as saying by . media outlets Apps created for Alexa were called "skills" and saw some developers make hundreds if not thousands of dollars every month. The $100 monthly credit to Amazon Web Services was the icing on top. But as with all things corporate America, Amazon saw that there really wasn't enough interest in its program and certainly not enough of a return to justify paying peanuts from the billions it makes every year. Oh well. Amazon ranked #14 on HackerNoon's __ __this week. Tech Company Rankings Spotify, Create a Playlist for HackerNoon!🎵 Ooh boy. I guess it was a matter of time before jumped in on the generative AI craze. Spotify Well, the company's take on generative AI is certainly more unique than others. If you're a premium user in the UK and Australia, you can now access a feature that can let youj create music playlists using prompts. That's right, anything from places, animals, genres or mood, can be used to create a playlist. So something like, a playlist for cats or tigers or for rainy days or the Eiffel Tower are all game! The tool will not produce results for non-music-related prompts, like current events or specific brands, however. So if you wanted a playlist for Louis Vuitton or Prada, you're out of luck. In Other News.. 📰 Bitcoin Could Surge to $120K on 'Doomsday Rally,' Trader Says — via CoinDesk Elon Musk plans to charge new X users to enable posting — via TechCrunch Facebook and Instagram’s response to deepfake porn is under review by oversight board  — via CNN Baidu says AI chatbot 'Ernie Bot' has attracted 200 million users — via Reuters "Top secret" is no longer the key to good intel in an AI world: report — via Axios AMD rolls out its latest chips for AI PCs as competition with Nvidia and Intel heats up — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's page. Tech Company Rankings

Elon Musk vs. Mainstream Media

The subject line “How To Bypass The Filter” frequents the search bars of AI chatbot users far and wide. In fact, countless YouTube videos, TikToks, and Wikihow pages are dedicated exclusively to offering detailed tutorials on how to “disable” filters, when realistically they only delay them, at best. Clearly, there’s a high demand for mainstream, uncensored AI chatbot interaction. That’s where Dopple.ai comes in. Dopple.ai is a free AI chatbot that lets you interact with virtual characters based on real and fictional people. Unlike AI chatbots such as ChatGPT and Character.ai, Dopple.ai doesn’t rely on filters and biases to keep its users safe. Instead, it’s equipped with an ETM rating system (“E” for “Everyone,” “T” for “Teen,” and “M” for mature) and a full set of parental controls, operating similarly to comic books and video games. That way, Dopple.ai satisfies the individual interests of all its users. To the avail of millions, Character.ai is less open-minded than its leading competitor. Receiving backlash for its enforcement of NSFW (Not Safe For Work) guidelines and removal at moderators’ discretion, it responded with a subreddit that reads, “We do not encourage discussion of NSFW filters—our stance is final, and requesting them to be removed will result in a ban.” The issue with this stance is that it defeats the entire purpose of custom AI chatbots. Every virtual character has a unique set of personality traits, interests, and interaction styles. You can either customize your own chatbot or chat with AI versions of celebrities, TV/movie characters, and more. By filtering these chats, AI companies crush the very pillar of their undertaking: to have users believe that they’re chatting with real people. Another debate in the AI chatbot community concerns the issue of bias. In a study by U.K. researchers at the University of Anglia, results found that OpenAI’s ChatGPT contains a liberal bias, or “systematic political bias toward the Democrats in the U.S., Lula in Brazil, and the Labour Party in the U.K.,” as they wrote. Because ChatGPT relies on Internet data to generate content, it’s almost impossible to control what assumptions, stereotypes, and beliefs it collects from the Internet. This raises concerns about whether the presence of bias influences users’ views and even political or electoral processes. To mitigate the issue of political bias, Dopple.ai trains its AI chatbots to stay true to the values and beliefs of the characters they represent. As a result, users are exposed to a wide range of political views while also having the ability to choose which they prefer to engage with. Launching AI projects successfully without bias is tough, even for the biggest companies. Google’s rollout of Gemini in late February was halted after an uproar over the model’s image generation functionality. Of course, it’s almost impossible to design an AI tool that’s meant to resemble humans without facing the same issues as humans, such as censorship and bias. However, Dopple.ai is changing the game by allowing free speech laws to permeate the AI space, giving people what they want when they want it. The subject line “How To Bypass The Filter” frequents the search bars of AI chatbot users far and wide. In fact, countless YouTube videos, TikToks, and Wikihow pages are dedicated exclusively to offering detailed tutorials on how to “disable” filters, when realistically they only delay them, at best. Clearly, there’s a high demand for mainstream, uncensored AI chatbot interaction. That’s where Dopple.ai comes in. Dopple.ai is a free AI chatbot that lets you interact with virtual characters based on real and fictional people. Unlike AI chatbots such as ChatGPT and Character.ai, Dopple.ai doesn’t rely on filters and biases to keep its users safe. Instead, it’s equipped with an ETM rating system (“E” for “Everyone,” “T” for “Teen,” and “M” for mature) and a full set of parental controls, operating similarly to comic books and video games. That way, Dopple.ai satisfies the individual interests of all its users. Dopple.ai Dopple.ai To the avail of millions, Character.ai is less open-minded than its leading competitor. Receiving backlash for its enforcement of NSFW (Not Safe For Work) guidelines and removal at moderators’ discretion, it responded with a subreddit that reads, “We do not encourage discussion of NSFW filters—our stance is final, and requesting them to be removed will result in a ban.” The issue with this stance is that it defeats the entire purpose of custom AI chatbots. Every virtual character has a unique set of personality traits, interests, and interaction styles. You can either customize your own chatbot or chat with AI versions of celebrities, TV/movie characters, and more. By filtering these chats, AI companies crush the very pillar of their undertaking: to have users believe that they’re chatting with real people. Another debate in the AI chatbot community concerns the issue of bias. In a study by U.K. researchers at the University of Anglia , results found that OpenAI’s ChatGPT contains a liberal bias, or “systematic political bias toward the Democrats in the U.S., Lula in Brazil, and the Labour Party in the U.K.,” as they wrote. Because ChatGPT relies on Internet data to generate content, it’s almost impossible to control what assumptions, stereotypes, and beliefs it collects from the Internet. This raises concerns about whether the presence of bias influences users’ views and even political or electoral processes. University of Anglia University of Anglia To mitigate the issue of political bias, Dopple.ai trains its AI chatbots to stay true to the values and beliefs of the characters they represent. As a result, users are exposed to a wide range of political views while also having the ability to choose which they prefer to engage with. Launching AI projects successfully without bias is tough, even for the biggest companies. Google’s rollout of Gemini in late February was halted after an uproar over the model’s image generation functionality. Of course, it’s almost impossible to design an AI tool that’s meant to resemble humans without facing the same issues as humans, such as censorship and bias. However, Dopple.ai is changing the game by allowing free speech laws to permeate the AI space, giving people what they want when they want it.

Dopple.ai Overtakes Mainstream Competitors With Unfiltered, Unbiased AI Chatbots

Dopple.ai ফিল্টারহীন, নিরপেক্ষ এআই চ্যাটবটগুলির সাথে মূলধারার প্রতিযোগীদের ছাড়িয়ে গেছে

Dopple.ai überholt Mainstream-Konkurrenten mit ungefilterten, unvoreingenommenen KI-Chatbots

Dopple.ai supera a sus principales competidores con chatbots de IA imparciales y sin filtros

Dopple.ai dépasse ses concurrents traditionnels avec des chatbots IA non filtrés et impartiaux

Dopple.ai ने बिना फ़िल्टर किए, निष्पक्ष AI चैटबॉट के साथ मुख्यधारा के प्रतिस्पर्धियों को पीछे छोड़ दिया

Dopple.ai は、フィルタリングされていない、偏見のない AI チャットボットで主流の競合他社を追い抜く

Dopple.ai는 필터링되지 않고 편견 없는 AI 챗봇으로 주류 경쟁사를 추월했습니다.

Dopple.ai supera os principais concorrentes com chatbots de IA imparciais e não filtrados

Dopple.ai обгоняет основных конкурентов благодаря нефильтрованным и объективным чат-ботам с искусственным интеллектом

Dopple.ai, Filtrelenmemiş, Tarafsız Yapay Zeka Sohbet Robotlarıyla Ana Rakiplerini Geride Bırakıyor

Dopple.ai vượt qua các đối thủ cạnh tranh chính thống bằng các chatbot AI không thiên vị, không được lọc

Dopple.ai 凭借未经过滤、公正的人工智能聊天机器人超越主流竞争对手

Satoshi Nakamoto is far from being the sole anonymous figure in the cypherpunk arena. They’re privacy activists, so they value their own privacy more than anything. That’s also the case of Eric Hughes, known for being one of the founders of the cypherpunks movement and creator of the first anonymous remailer. Beyond this, we know quite little about him. He's an American cryptographer, mathematician, and programmer. He graduated from the University of California, Berkeley, and worked for a while in the Netherlands with David Chaum, a known cryptographer and precursor . of cypherpunks Unlike Satoshi, we at least know his face and voice. at his own house in Oakland, California. Those meetings soon extended to Cygnus Solutions . This was a company dedicated to supporting free software and was founded by Gilmore himself. the group now called “cypherpunks” by arranging physical meetings with Tim May, John Gilmore and other programmers He started in Sunnyvale The famous cypherpunk mailing list to which Satoshi belonged and where he first shared the Bitcoin whitepaper was an evolution of those first meetings, created by Tim May, John Gilmore, and Hughes in 1992. Hughes was, indeed, the admin of the mailing list. , . Hal Finney, another remarkable cypherpunk, helped him to improve the code. An anonymous remailer is a service that first receives an email, deletes the identifying information (such as the sender's email address), and then forwards it to its destination or to another remailer in the network. This way, the sender's identity is protected. In the same forum, he also shared his own creation for privacy: the first anonymous remailer in 1992 A year later, Hughes wrote and published the Cypherpunk Manifesto, a sort of guide that would stay forever with cypherpunks and crypto enthusiasts alike. A Cypherpunk Manifesto outlines the principles and beliefs of the Cypherpunk movement, emphasizing the importance of privacy, cryptography, and anonymous transaction systems in maintaining individual freedoms and an open society in the digital age. It hasn't only served as a guiding philosophy for the Cypherpunk movement but it has also **influenced the development of a wide array of privacy-enhancing technologies and advocacy for digital rights.
\ This short text “Cypherpunks write code” is a motto that comes from this manifesto. It reads: “Privacy is necessary for an open society in the electronic age (…) We cannot expect governments, corporations, or other large, faceless organizations to grant us privacy (…) We must defend our own privacy if we expect to have any (…) Cypherpunks write code. We know that someone has to write software to defend privacy, and (…) we're going to write it.” They have written it, indeed. These words and this movement have inspired figures and software such as Julian Assange (WikiLeaks), Adam Back (Hashcash & Blockstream), Eric Blossom (GNU Radio Project), Phil Zimmerman (PGP Protocol), Bram Cohen (BitTorrent & Chia), Hal Finney (First Proof-of-Work), Nick Szabo (Smart Contracts), Wei Dai (B-Money), Zooko Wilcox (Zcash), and, of course, Satoshi Nakamoto (Bitcoin). Most of them are still alive and active in 2023. A Cypherpunk Incarnation Beyond cypherpunk meetings, Hughes has rarely appeared in public. He was masked in a photo of three cypherpunks in Wired (1993), and delivered the keynote address, " ", at the Amsterdam CryptoParty in 2012. He’s a quite private person. However, after reading his opinions and creations on the mailing list, we can guess that he’s still working for the cypherpunk dream of online and financial freedom and privacy. Putting the Personal Back in Personal Computers At its core, the cypherpunk philosophy advocates for the empowerment of individuals through privacy-enhancing technologies and decentralized systems. Obyte embodies these ideals by offering a platform that prioritizes decentralization in its architecture and governance. We can say that is working to reach the same dreams, as a cypherpunk incarnation. Obyte By leveraging a Directed Acyclic Graph (DAG) ledger, Obyte creates a network where transactions are added directly by users, without middlemen involved —rather than being reliant on an intermediary (like miners). This decentralization helps to prevent single points of failure and reduces the risk of censorship or manipulation. From a privacy standpoint, Obyte integrates features that enable users to transact and communicate privately and securely. They include an encrypted chat, the option to use Tor with the wallet, and the opportunity to create private assets. In essence, Obyte's commitment to decentralization and privacy embodies the core principles of the cypherpunk movement. By empowering individuals with greater control over their digital interactions and data, Obyte contributes to the advancement of a more open, free, and equitable society in line with cypherpunk ideals. Read more from series: Cypherpunks Write Code Tim May & Crypto-anarchism Wei Dai & B-money Nick Szabo & Smart Contracts Adam Back & Hashcash Jude Milhon & Community Memory Featured Vector Image by Garry Killian / Freepik Eric Hughes Photograph by ReasonTV / YouTube

Cypherpunks Write Code: Eric Hughes, Remailer and Author of "A Cypherpunk's Manifesto" 

**TORTOLA, British Virgin Islands, March 12th, 2024/Chainwire/--**Nibiru Chain,a developer and user-centric Layer 1 platform, has officially launched its public mainnet. Nibiru Chain stands out by offering a secure and efficient environment for building highly-performant decentralized applications. It is distinguished by its robust smart contract ecosystem offering superior throughput and unparalleled security, making it the go-to platform for builders in gaming, real-world assets (RWAs), NFTs, DeFi, and more. Empowering Users with a Rich Ecosystem At launch, Nibiru Chain offers a wide range of functionalities to its community and prospective builders. Users can engage in staking with NIBI validators and participate in decentralized governance. Nibiru Chain will also introduce competitive Web3 gaming through , allowing players to learn the game of chess, compete in tournaments sponsored by communities, streamers, or brands, and earn rewards. Additionally, Nibiru Chain supports the minting and trading of NFTs on , secure “.nibi” namespaces through Nibiru ID, and real estate opportunities through . Chess3 Dropspace Coded Estate Nibiru Chain encourages innovation by rewarding developers with a portion of transaction fees from their smart contracts, offering built-in value accrual. In addition to providing developers with tools to build applications, Nibiru Chain introduces its Super Chain, which includes perpetuals, spot, swap, and stablecoin functionalities. These features offer greater opportunities for dApps to expand and integrate with DeFi. The Super Chain initiative aims to deliver a seamless retail trading and investment experience and is designed to serve users across more than 40 blockchains. Nibiru's Strategic Vision for 2024 Nibiru Chain’s key initiatives include rolling out its genesis NFT collection and expansion into the APAC region with an initial focus on Korea, China, India, Japan, Vietnam, and Thailand. Central to Nibiru Chain's mission is the launch of parallel optimistic execution, which enables the simultaneous processing of multiple independent transactions. Launching Nibiru Chain marks a significant stride toward increasing blockchain scalability by bridging gaps across the blockchain landscape. Nibiru is engineered to dismantle hurdles that have isolated applications and users within their ecosystems and offer an intuitive, straightforward entry point into a more thoroughly connected Web3 for users and developers alike. Additionally, Nibiru Chain is set to announce details on its upgrade enabling full Ethereum Virtual Machine compatibility, ensuring further interoperability with Ethereum-based dApps. This integration will lower the barrier to entry for Ethereum developers and facilitate a smoother transition to Nibiru Chain’s more efficient and cost-effective Layer 1 solution, making crypto more accessible and user-friendly for the general public. About Nibiru Nibiru Chain is a breakthrough L1 blockchain and smart contract ecosystem sporting superior throughput and unparalleled security. Nibiru aims to be the most developer-friendly and user-friendly smart contract ecosystem, leading the charge toward mainstream Web3 adoption by innovating at each layer of the stack: dApp development, infra, consensus, a comprehensive dev toolkit, and value accrual. | | | | Website Twitter LinkedIn Telegram Discord Contact PR & Media Inquiries media@nibiru.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Nibiru Chain Debuts Public Mainnet Along With Four Major Exchange Listings

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