Genius Returnsis in the E-Commerce Enablers Industry

Genius Returns

It was Friday evening and I needed to make myself lunch after work. I found myself staring into my fridge wondering what to cook. I had leftovers from last night, however, I needed to make something fresh and exciting. I thought to myself, why not build a personal AI kitchen assistant to analyze the ingredients I have and suggests delicious dishes tailored precisely to my pantry? This is where Large Language Models (LLMs) stepped into the kitchen with me. LLMs are a type of artificial intelligence that have been trained on a massive dataset of text (and code), including a vast trove of recipes; meaning that they can do so much more than just finding a suitable recipe. They can become your culinary genius on demand. literally So, how does it all come together? In this article, I'll detail how I: Developed an LLM app utilizing GPT-3.5 turbo LLM to assess available ingredients and recommend recipes Deploy the LLM app on for production, enabling integration into a user interface for seamless communication via my mobile device Agenta Developing The LLM App I’ll be using OpenAI gpt-3.5-turbo in this application, however, I’d also be including various directives of the turbo model and also gpt-4. Use whichever model you think is appropriate for the task. Additionally, I will make use of to deploy my LLM application, as it is my preferred open-source platform for creating robust AI applications with LLMs. Agenta provides the necessary tools for managing and evaluating prompts. Agenta Here are the capabilities of Agenta: Quickly experiment with and compare prompts in any LLM workflow (chain-of-prompts, Retrieval Augmented Generation (RAG), LLM agents...) Swiftly generate test sets and golden datasets for evaluation Evaluate your application using pre-existing or custom evaluators Annotate and A/B test your applications with human feedback Collaborate with product teams on prompt engineering and evaluation Deploy your application with a single click through the UI, CLI, or github workflows By using Agenta, I am able to concentrate on constructing my LLM app and streamline the entire development cycle of the application, thereby enhancing the speed of experimentation. I’d be using Python to build the kitchen assistant. Creating LLM app directory and dependency file Create a directory named and a file named and add the following packages inside the file: and kitchen_assistant requirements.txt agenta openai If you have something like the image attached below, you’re good to go. Environment variable & LLM app development Now that you have the assistant folder and have included the packages inside the file, proceed to create a new file named and include your OPENAI secret key to make the LLM app work. requirements.txt .env Ensure that the file is inside the folder. You should have something like this: .env kitchen_assistant OPENAI_API_KEY=sk-vxxxxxxxxxxxxx # replace with your secret key Next would be to create a file named , and copy the following code into it. I’ll explain what the code is doing in a minute. app.py # Stdlib Imports
from typing import Dict, Any

# Third Party Imports
import agenta as ag # type: ignore
from openai import AsyncOpenAI


SYSTEM_PROMPT = "You are my AI kitchen assistant. Help me find creative recipes based on my ingredients, provide step-by-step cooking instructions, and advise me on food safety practices."
CHAT_LLM_GPT = [
    "gpt-3.5-turbo-16k",
    "gpt-3.5-turbo-0301",
    "gpt-3.5-turbo-0613",
    "gpt-3.5-turbo-16k-0613",
    "gpt-4",
]

# Initialize agenta sdk and update the configuration parameters
ag.init()
ag.config.default(
    temperature=ag.FloatParam(0.2),
    max_tokens=ag.IntParam(-1, -1, 4000),
    prompt_system=ag.TextParam(SYSTEM_PROMPT),
    model=ag.MultipleChoiceParam("gpt-3.5-turbo", CHAT_LLM_GPT),
)

# Initialize async openai sdk
client = AsyncOpenAI()


@ag.entrypoint
async def suggest(inputs: ag.MessagesInput = ag.MessagesInput()) -> Dict[str, Any]:
    """
    Suggest  delicious dish tailored precisely based on the ingredients (inputs).

    Args:
        inputs (ag.MessagesInput): messages input for chat completion

    Returns:
        dict: The llm response (along with additional information).
    """

    messages = [{"role": "system", "content": ag.config.prompt_system}] + inputs
    max_tokens = ag.config.max_tokens if ag.config.max_tokens != -1 else None
    chat_completion = await client.chat.completions.create(
        model=ag.config.model,
        messages=messages,
        temperature=ag.config.temperature,
        max_tokens=max_tokens,
    )
    token_usage = chat_completion.usage.dict() # type: ignore
    return {
        "message": chat_completion.choices[0].message.content,
        **{"usage": token_usage},
        "cost": ag.calculate_token_usage(ag.config.model, token_usage),
    } The imports are essentially importing the required modules that are needed to ensure the code works correctly. System prompt sets the context for every conversation that happens between the user (you and I) and the LLM application. You’d be pissed at your friend who in the middle of a finance conversation with you starts talking about something unrelated, right? Imagine a Large Language Model (LLM) as a super-powered reader. To really grasp what it's reading, it needs context – the words and sentences surrounding whatever it's focused on. This 'context window' is like a spotlight: the bigger the window, the more information the LLM can use to understand and generate meaningful responses. Next to the system prompt is a list of defined models that can be used when testing the user prompt for the LLM app in Agenta. You get to choose whichever model to see how it performs. The command is used to initialize the Agenta SDK. Subsequently, is where you define crucial parameters that dictate the behavior of the kitchen AI assistant. These parameters include: ag.init() ag.config.default(...) Regulates the creativity of responses (lower temperature = more predictable, higher = more unexpected). temperature: Determines the maximum length of the AI's response (measured in tokens, which roughly correspond to words). max_tokens: Sets the initial instructions or context provided to the LLM, specifying it as your kitchen assistant. prompt_system: Specifies the LLM to be used (with a range of GPT model versions as options). model: The line initializes the OpenAI Async SDK, enabling your code to establish a connection and interact with OpenAI's language models. client = AsyncOpenAI() To register your LLM app with Agenta and use it in their playground, you need to define an entry point. I used the decorator on my function. This tells Agenta that this function is where the action starts. Agenta then automatically converts this function into an API endpoint (using FastAPI), making it accessible through their web interface. @ag.entrypoint suggest Publishing The LLM App Now that your AI kitchen assistant is ready, we should publish it to Agenta for experimentation. To do this, you can install the Agenta CLI in a virtual environment using your preferred Python package installer or globally on your system. However, I advise against the latter to avoid potential conflicts with your system packages. Once the installation is complete, make sure to activate the virtual environment. I will proceed with publishing the LLM app to Agenta Cloud, enabling me to deploy the LLM application to a production environment and later, create a UI application for mobile use. If you wish to publish to the cloud version of Agenta as well, I suggest creating an first and then returning to follow the steps below. account If you prefer not to deploy the app to the cloud version of agenta, you can the open-source version on your local machine by following these steps. It will be a slightly different process, but not overly complex. run Create a new project for the LLM app To publish the application in Agenta, we must first create an empty project. Execute the following command in the directory where your application code is located: (.venv) [kitchen_assistant]$ agenta init This will prompt you to enter the project name, Agenta host, API key (if using the cloud or enterprise version), organization, and how you want to initialize the app. Once the app initialization is successfully done, a blank project will be generated in Agenta along with a config.toml file in the same directory, containing all project information. The config.toml file and the empty project in Agenta Cloud will look like the following. Publishing/Serving the first app variant With the project created in Agenta cloud, we need to add the first app variant to it. This can be done by running the following command: agenta variant serve app.py This will create a new app variant in Agenta under the name . Here, is the name of the codebase containing the LLM app logic, while is a default configuration created for that codebase. Each new app variant created from the web interface or from the CLI will always have the name format . app.default app default <codebase_name>.<configuration_name> An is a distinct version of an LLM app, featuring different parameters or code but keeping the same inputs and outputs. This feature enables you to experiment with alternative methods within a single app. For instance, you could have two variants of the kitchen assistant app, each using a different system prompt. app variant Executing the serve command above will create a container for the application featuring a REST API endpoint. This endpoint will be used in Agenta when interacting with the application. The CLI will also display the URL of the endpoint, which can be used to navigate to the application API documentation or playground in Agenta. Copy the playground URI and paste it into your browser. After doing so, you will see the following user interface. It may take 10-20 seconds to load as it sets up the playground for you. You have successfully published your LLM app to Agenta and would need to experiment with it to ensure that it functions as intended. In the chat section right after “User”, write the ingredients and click on the “Run” button. You’ll see that the AI kitchen assistant is considering what you can make with the ingredients: When it’s done processing the ingredients that you inputted, you’ll get suggestion on what you can make. And so, you own a Kitchen AI assistant that suggests recipes based on your ingredients. Did you observe something after each use? You view the total tokens used, the run's cost, and the time taken! Don't hold back now, keep experimenting with the LLM app. Customize the system prompt to match a particular culture that you want, adjust the temperature, and switch the model if needed. Deploying the LLM App To An Environment After using the playground to find a good configuration for your AI Kitchen assistant, it’s time to deploy the application. By deploying the application, you can integrate it to a UI (web and/or mobile) application or anywhere since the deployed app will be an endpoint. You can also later change the configuration from the playground without having to update the code. Clicking on the “Publish” button will bring up a modal with environments you want to deploy the LLM application to. Agenta provides a way to deploy an application to multiple environments: development, staging, and production. Each environment having its own unique configuration. Since the LLM app works and it’s the weekend, we can deploy to production :-). Click on the "production" checkbox and then the "Publish" button. You will receive a success notification informing you that the LLM app variant has been deployed. Look at the side navbar, you’re going to see different sections. I’ll keep what each of them do: The playground is a user-friendly environment in Agenta where you can create and test new app variants. Here, you can adjust parameters, input data, and observe the outputs from your app. Playground: The test set is another user-friendly environment where you can create high-quality datasets. A golden test set, also known as a ground truth test set, consists of various inputs and their expected correct answers. Test Sets: Another environment that enables you to generate automated assessments using various evaluators: , , , , , , and . The purpose of conducting evaluations is to methodically evaluate the outcomes of the LLM application and compare various versions to determine the most optimal one. Another objective is to set a benchmark for the application and evaluate any potential risks. Evaluations: exact match similarity match regex test JSON field match AI critique code evaluation webhook test At times, you might require assessing your models' performance through human judgement. This is where the Human Evaluation environment becomes valuable. It enables you to carry out and to gauge your models' performance using human judgement. Annotations: A/B tests single model tests Endpoints are where you can access the deployed application code. You can view the code only after configuring your application successfully and deploying it to a specific environment. Currently, Agenta supports just three languages to integrate the deployed LLM app: Python, cURL, and TypeScript. If you’d like to add more languages or a specific language, feel free to contribute by raising an and tackling it. ;-) issue To ensure the deployed LLM app works in production, choose cURL in the languages drop-down, click "Copy," update the content value with the ingredients you have, and run it in your terminal. You should receive a result similar to the one below. Conclusion How does it feel to have a Personal Kitchen AI assistant? My partner feels like she has a sous chef that handles recipe making for her. Quite amusing, isn't it? XD In this article, you learnt what LLMs are, how to create a kitchen assistant app using the GPT-3.5-turbo model to analyze your available ingredients and offer recipe suggestions. You also learned how to deploy the LLM app on Agenta for practical use. As an extra tip, I will be a user interface using Svelte and deploy it on Github Pages. I will design it to allow you to input the URI of your LLM app, enabling personal interaction. In the upcoming article, I will demonstrate how to generate test sets for conducting human evaluations to ensure that the LLM app's output aligns with your expectations. building

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Genius Tool To Predict Number Of Covid Cases: Leading Time-Series Multi-Step Models

This is the first part in a multi-part series on building Agents with OpenAI's Assistant API using the Python SDK. What Are Agents? The way I like to look at it, an agent is really just a piece of software leveraging an LLM (Large Language Model) and trying to mimic human behavior. That means it can not only converse and understand language, but it can also perform actions that have an impact on the real world. These actions are typically called tools. In this blog post, we will explore how to build an agent using OpenAI's Assistant API using their Python SDK. Part 1 will be just the skeleton of the assistant. That is, just the conversational part. I chose to build a CLI app on purpose to be framework agnostic. We will purposefully call our implementation an Agent and refer to the OpenAI SDK implementation as an Assistant to easily distinguish between the two. I use the terms and interchangeably when it comes to functions that the Agent is able to call. Part 2 will cover function callin in more detail. tools functions Prerequisites To follow along with this tutorial, you will need the following: Python3 installed on your machine An OpenAI API key Basic knowledge of Python programming OpenAI Assistant Concepts : An Assistant in the Assistants API is an entity configured to respond to user messages. It uses instructions, a chosen model, and tools to interact with functions and provide answers. Assistant : A Thread represents a conversation or dialogue in the Assistants API. It is created for each user interaction and can contain multiple Messages, serving as a container for the ongoing conversation. Thread : A Message is a unit of communication in a Thread. It contains text (and potentially files in the future) and is used to convey user queries or assistant responses within a Thread. Message : A Run is an instance of the Assistant processing a Thread. It involves reading the Thread, deciding whether to call tools, and generating responses based on the model's interpretation of the Thread's Messages. Run Setting Up the Development Environment The first step is to create a virtual environment using and activate it. This will ensure that our dependencies are isolated from the system Python installation: venv python3 -m venv venv
source venv/bin/activate Let's install our only dependency: the package: openai pip install openai Create a file. Let's populate with some basic runtime logic for our CLI app: main.py while True:
    user_input = input("User: ")
    if user_input.lower() == 'exit':
        print("Exiting the assistant...")
        break
    print(f"Assistant: You said {user_input}") Try it out by running : python3 main.py python3 main.py
User: hi
Assistant: You said hi As you can see, the CLI accepts a User message as input, and our genius Assistant doesn't have a brain 🧠 yet so he just repeats the message right back. Not so smart yet. The Agent Now, the fun 😁 (or headaches 🤕) begins. I'll provide all the imports needed for the final class right now, so you don't rack your brain on where things are coming from since I kept imports out of code samples for brevity. Let's start by building an class in a new file : Agent agent.py import time
import openai
from openai.types.beta.threads.run import Run

class Agent:
    def __init__(self, name: str, personality: str):
        self.name = name
        self.personality = personality
        self.client = openai.OpenAI(api_key="sk-*****")
        self.assistant = self.client.beta.assistants.create(
            name=self.name,
            model="gpt-4-turbo-preview"
        ) In the class constructor, we initialize the OpenAI client as a class property by passing our OpenAI API key. Next, we create an class property that maps to our newly created Assistant. We store and as class properties for later use. assistant name personality The argument we are passing to the create method is just for identifying the Assistant in the OpenAI dashboard, and the AI is not actually aware of it at this point. You actually have to pass the name to the which we will see later. name instructions You could already set when creating the Assistant, but it will actually make your Assistant less flexible to dynamic changes. instructions You can update an Assistant by calling , but there is a better place to pass in dynamic values that we will see when we get to Runs. client.beta.assistants.update Note that if you pass here and then again when creating a Run, the Assistant's will be overwritten by the of the run. They do not complement each other, so choose one based on your needs: Assistant level for static instructions or Run level for dynamic instructions. instructions instructions instructions For the model, I chose the model so that we can add function calling in part 2 of this series. You could use if you want to save a few fractions of a penny while giving yourself a migraine of pure frustration down the line when we implement tools. gpt-4-turbo-preview gpt-3.5-turbo GPT 3.5 is terrible at calling tools; the hours I've lost trying to deal with it allow me to say that. 😝 I'll leave it at that, and more on this later. Creating a Thread, Adding Messages, and Retrieving the Last Message After we create an agent, we will need to start a conversation thread. class Agent:
    # ... (rest of code)
    def create_thread(self):
        self.thread = self.client.beta.threads.create() And we will want a way to add messages to that thread: class Agent:
    # ... (rest of code)
    def add_message(self, message):
        self.client.beta.threads.messages.create(
            thread_id=self.thread.id,
            role="user",
            content=message
        ) Note that at the moment, it is only possible to add messages with the role . I believe OpenAI plans on changing this in a future release as this is pretty limiting. user Now, we can get the last message in the thread: class Agent:
    # ... (rest of code)
    def get_last_message(self):
        return self.client.beta.threads.messages.list(
            thread_id=self.thread.id
        ).data[0].content[0].text.value Next, we create an entry point method to test out what we have so far. Currently, the method just returns the last message in the thread. It doesn't actually perform a Run. It's still brainless. run_agent run_agent class Agent:
    # ... (rest of code)
    def run_agent(self):
        message = self.get_last_message()
        return message Back in , we create the agent and our first thread. We add a message to the thread. Then return that same message back to the user, but this time, coming from that live thread. main.py from agent import Agent

agent = Agent(name="Bilbo Baggins",
                      personality="You are the accomplished and renowned adventurer from The Hobbit. You act like you are a bit of a homebody, but you are always up for an adventure. You worry a bit too much about breakfast.")

agent.create_thread()

while True:
    user_input = input("User: ")
    if user_input.lower() == 'exit':
        print("Exiting the agent...")
        break
    agent.add_message(user_input)
    answer = agent.run_agent()
    print(f"Assistant: {answer}") Let's run it: python3 main.py
User: hi
Assistant: hi Still not very smart. Closer to a parrot 🦜 than a hobbit. In the next section, the real fun begins. Creating and Polling a Run When you create a run, you need to periodically retrieve the object to check the status of the run. This is called polling, and it sucks. You need to poll in order to determine what your agent should do next. OpenAI plans to add support for streaming to make this simpler. In the meantime, I will show you how to set up polling in this next section. Run Note the on the following method names which is the standard in Python for indicating that the method is intended for internal use and should not be accessed directly by external code. _ First, let's create a helper method for creating a , and update to call this method: _create_run Run run_agent class Agent:
    # ... (rest of code)
    def get_breakfast_count_from_db(self):
        return 1

    def _create_run(self):
        count = self.get_breakfast_count_from_db()
        return self.client.beta.threads.runs.create(
            thread_id=self.thread.id,
            assistant_id=self.assistant.id,
            instructions=f"""
                Your name is: {self.name}
                Your personality is: {self.personality}

                Metadata related to this conversation:
                {{
                    "breakfast_count": {count}
                }}
            """,
        )

    def run_agent(self):
        run = self._create_run() # add this line
        message = self.get_last_message()
        return message Notice how we pass the and to create a run. thread.id assistant.id Remember how I said at the beginning that there was a better place to pass in dynamic instructions and data? That would be the parameter when creating the Run. In our case, we could have the breakfast be fetched from a database. This will allow you to easily pass in different relevant dynamic data every time you want to trigger an answer. instructions count Now, your agent is aware of the world changing around it and can act accordingly. I like to have a metadata JSON object in my instructions that keeps relevant dynamic context. This allows me to pass in data while being less verbose and in a format that the LLM understands really well. Don't run this yet; it won't work because we aren't waiting for the run to complete when we are getting the last message, so it will still be the last user message. Let's solve this by building out our polling mechanism. First, we will need a way to repeatedly and easily retrieve a run, so let's add a method: _retrieve_run class Agent:
    # ... (rest of code)
    def _retrieve_run(self, run: Run):
        return self.client.beta.threads.runs.retrieve(
            run_id=run.id, thread_id=self.thread.id) Notice how we need to pass both the and to find a specific run. run.id thread.id Add a method to our Agent class: _poll_run class Agent:
    # ... (rest of code)
    def _cancel_run(self, run: Run):
        self.client.beta.threads.runs.cancel(
            run_id=run.id, thread_id=self.thread.id)

    def _poll_run(self, run: Run):
        status = run.status
        start_time = time.time()
        while status != "completed":
            if status == 'failed':
                raise Exception(f"Run failed with error: {run.last_error}")
            if status == 'expired':
                raise Exception("Run expired.")

            time.sleep(1)
            run = self._retrieve_run(run)
            status = run.status

            elapsed_time = time.time() - start_time
            if elapsed_time > 120:  # 2 minutes
                self._cancel_run(run)
                raise Exception("Run took longer than 2 minutes.") 🥵 Phew, that's a lot... Let's unpack it. receives a object as an argument and extracts the current Run . All the available statuses can be found in the OpenAI . We'll just use a few that suit our current purpose. _poll_run Run status docs We now run a while loop to check for a completed status while handling a few error scenarios. The actual billing of the Assistant API is a bit murky, so to be on the safe side, I opted to cancel my runs after 2 minutes. Even though there is an status for when OpenAI cancels runs after 10 minutes. If a run takes more than 2 minutes, you probably have a problem anyway. expired Since I also don't want to poll every few milliseconds, I throttle my request by only polling every 1 second until I hit the 2-minute mark and cancel my run. You can adjust this to whatever you see fit. Each iteration after the delay, we fetch the Run status again. Now, let's plug all that into our method. You will notice we first create the run with then we poll with until we get an answer or an error is thrown, and finally when the polling is finished, we retrieve the last message from the thread which will now be from the agent. run_agent _create_run _poll_run We then return the message to our runtime loop, so it can be sent back to the user. class Agent:
    # ... (rest of code)
    def run_agent(self):
        run = self._create_run()
        self._poll_run(run) # add this line
        message = self.get_last_message()
        return message Voilà, now when you run your agent again, you will get a reply from our friendly Agent: python3 main.py
User: hi
Assistant: Hello there! What adventure can we embark on today? Or perhaps, before we set out, we should think about breakfast. Have you had yours yet? I've had mine, of course – can't start the day without a proper breakfast, you know.
User: how many breakfasts have you had?
Assistant: Ah, well, I've had just 1 breakfast today. But the day is still young, and there's always room for a second, isn't there? What about you? How can I assist you on this fine day? In part 2, we will add the ability for our Agent to call tools. You can find the full code on my . GitHub Thank you for your reading. Happy to hear any thoughts and feedback in the comments. Follow me on Linkedin for more content like this: https://www.linkedin.com/in/jean-marie-dalmasso-1b5473141/

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如何使用 Python 中的 OpenAI 助手构建代理 - 第 1 部分：对话式

Let's learn about via these 63 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Space /Learn Repo 1. Exodus from Mars Mother Earth, the womb of the solar system. Every other place, foster homes for her unwanted children. What is next? 2. The Impact of Web3 and Blockchain on the Space Industry How can Web3 and Blockchain, two new technologies that are more associated with grifting and scams have anything to do with the space race? 3. Lets Go Beyond Nuclear Fusion and Extract Energy from Blackholes I actually have been thinking about this for many years, and up until recently it has been confirmed. This is nothing new, as cosmologist and thermonuclear physicist Yakov Zeldovich have discussed this nearly half a century ago. 4. The Westall UFO Incident Explained In Under 5 Minutes The Westall UFO [incident] occurred on April 6, 1966, in the Melbourne suburb of Westall, Victoria, Australia. The incident involved multiple reports of... 5. DIY Tutorial: Building a System to Track and Communicate with Satellites in Orbit The purpose of my project is to build a system for tracking satellites in orbit and will make it possible to communicate with the satellites 6. Why Every Company Will Eventually Become a Space Company At some point, every company will become a space company. Sounds stupid at the moment but soon all companies will want to be part of the industry. Here's how. 7. How Twilio Sent Code to Space In a first of its kind mission, Twilio invited participants to launch their code into the stratosphere, and have it run in space. 8. “A robot army isn’t an ideal opponent, but I would have a water gun” Naturally, another good portion of the population would be trying to destroy everything around them for stupidity’s sake. 9. I'll pick up the check - from space! Everyone is all like, don't come back from space Jeff, but let me you tell you something, I am single and ready to mingle. 10. Can Mankind Survive as an Interplanetary Species? Humanity is well on the way to becoming an interplanetary species, but can we overcome the challenges of survival on another planet? 11. With Close to $100 million in Total Sales in Metaverses, NFTs Are Boosting Commerce in Metaverses Space is a 3D virtual world experience which provides its citizens powerful tools to create content, build businesses, interact collaboratively, and own and mon 12. Nominee Paul Bailey is Building the Future of Automated Space Systems Paul Bailey from the United States has been nominated for a 2020 Noonie in the Software Development category. He is a System Architect at CognitiveSpace, a company performing R&D in the field of aerospace engineering. 13. How to Build SpaceX’s Starship The Starship is a reusable heavy-lift launch vehicle (rocket) designed to travel to space with cargo and humans on board. 14. What Is Elon Musk's Deal with Space Colonization and Underpopulation? Elon Musk is just too busy to explain why we should worry more about underpopulation than the overpopulation problem. 15. Videos About Space We Shouldn't Undervalue ▶️ Ok, let's explore a few cool videos that I think a lot of addicted to space people should watch. 16. This Swiss Company is Building a Constellation of Nano-Satellites Astrocast is a company working to provide 100% of global coverage for IoT with an innovative constellation of nano-satellites. 17. Student Spaceshot Rocket Clubs — Part 1 (USA) In the past few years, liquid propulsion and spaceshot rocket clubs have popped up at high schools and colleges worldwide. Let's take a look at some of them. 18. Everything You Need to Know About Nasa's Perseverance Rover NASA's latest and greatest $2.7 Billion rover, Perseverance, was launched on July 30, 2020. It was a huge deal, and you're probably confused as to why. NASA's Jet Propulsion Lab manufactured Percy, and it is expected to land on Mars on February 18, 2021. Though it looks like a tiny robot to the eye, it is the size of an SUV and weighs 1025 kg (2260 lb). Let's start with the purpose of the whole mission. Perseverance serves four purposes. 19. Starship Transformer: Launch And Land Anywhere  Even In Wind The moment SpaceX mastered rocket engine vectoring all kinds of cool new space ship design possibilities suddenly opened. 20. What is SpaceX's Starlink Project? - A Brief Summary SpaceX is preparing to deliver high-speed internet to the world by sending about 30 thousand satellites into the Earth's orbit. 21. Tesla Cybertruck: Built for Mars, Available on Earth Elon Musk is known for his incredibly ambitious, multi-year visions for the future. Tesla’s initial Master Plan, funding cheap vehicles with expensive ones, was realized with the Model 3. Now the Cybertruck is part of a whole new Master Plan that has much more to do with SpaceX than Tesla. You might love the Cybertruck, or you might hate it, but it was never designed for you. Cybertruck was designed to be extremely rugged, Mars rugged. 22. Merging The Galaxies With The Metaverse Space research meets the metaverse using advanced technologies like blockchain, AI, VR and cryptocurrency. Explore multiple use cases of metaverse in space and 23. We Need Some SPACE: What Is Missing From DORA? The SPACE framework offers a holistic approach which takes into account wellbeing, collaboration and satisfaction alongside the usual performance metrics. 24. Fermi Paradox: Is Earth on Quarantine? Someone asks me a year ago about what Fermi Paradox is. And it's not a simple question, especially when you dig deeper. So I'm sharing a list of excellent clips that you might enjoy. Especially with recent news from our science friends - I think this article should be published. 25. Sustainable Agriculture, Space Colonization, And Food Security Challenges As humans colonize space, advances in key agricultural technologies are key to achieving long term survivability and also help Earth at the same time. 26. On MoonDAO's Unending Ambition to Decentralize The Space Industry MoonDAO will stop at nothing to decentralize the space industry, or so it seems, and with a consortium of 1,000 crypto investors throwing 2,620 ETH behind it 27. A Beginner's Guide for Alien Invaders Exclusively on HackerNoon! An original never-before-published science fiction story! 28. The Minkowski's Four-Dimensional Space Relativity: The Special and General Theory by Albert Einstein is part of HackerNoon’s Book Blog Post series. 29. Always Bet Musk's Oxen! Fresh off the Virgin Martian Express, Jackson had a layover at the Blue Origin moon base. He was trying to get to the Starbase Autonomous Zone 30. Why SpaceX’s Historic Starship Flight Wasn't a “Failure” This week, the coverage of one of humanity’s most impressive milestones was reduced to a climax of fire. Starship didn’t fail. Science divulgation, however, might have. 31. The Noonification: Can Mankind Survive as an Interplanetary Species?  (10/20/2022) 10/20/2022: Top 5 stories on the Hackernoon homepage! 32. Advancements in Airplane Tech Could Lead Major Airports to Become Spaceports Hypersonic aviation is going to open up more opportunities than it realises and airports need to be on the ball to become spaceports themselves. 33. Why Netflix’s Squid Game is NOT Close to Reality I'm not thrilled, and I'm far away from being obsessed with Netflix's Squid Game. 34. The Universe is So Big, Aliens Must Exist This Slack discussion by Utsav Jaiswal, Arthur, Patrick Lee (Mentor) and Limarc occurred in hackernoon's official #slogging-beta channel. 35. Spacecraft Engineering Models: How to Migrate UML to TypeQL How to map UML to TypeQL. Read how the European Space Agency migrated their spacecraft engineering models from UML to TypeQL for use in a TypeDB database. 36. Big ‘Earth Observation’ Data: Challenges and Applications As nearly a thousand Earth observation satellites currently orbit the planet, terabytes of remote sensing data and satellite imagery of land, vegetation, water bodies, glaciers, urban landscapes, and other geographic features become available for end users across multiple industries. Modern GIS systems allow the collection of all such geospatial data in one place for a comprehensive analysis of the area under study. 37. SpaceX Starlink Master Plan A parody, not written by Elon Musk 38. The Computational Infrastructure Behind Habidatum Location Risk Score Learn about space-time indexing at scale using quadkeys and clickhouse. 39. UFOs: Five things we can be pretty sure about technologically superior civilizations If we believe in evolution then, however, sophisticated form of life they are. They probably evolved from the simplest life forms  daily fighting for survival. 40. Main Reasons To Build Intelligent Space Stations With An Artificial Gravity It’s been the dream of many space enthusiasts from the 1960s to live in an intelligent artificial gravity space station ever since it was popularized by NASA chief scientist Von Braun and the legendary film: ‘Space Odyssey: 2001’. Unfortunately, generating artificial gravity for astronauts was not a priority for NASA as astronauts were able to reduce some of the impact of weightlessness on their bodies thanks to a ruthless exercise regimen. Further, a ship that contains a centrifuge for generating artificial gravity would be extremely expensive to launch to orbit. So, NASA ended up sticking with a compact and modular station that is more affordable with their budget and that doesn’t deal with weightlessness risk and accepted the risks it posed to astronauts in the international space station. Below is a comprehensive list of health risks astronauts face in the zero-g space station. Weightlessness risks are highlighted in the red box. 41. Quantum Origin, CosmicOS, and Lambda Calculus In regards to Quantum Origin, you may have read my post from June. This is a follow up to it. In our observable universe, it seems infinite in terms of the scale everything is in, whether going on in the micro, nano or cosmic scales and beyond. I followed up on the fact that there are certain mathematical patterns in regards to everything and I'm evidentially wanting to make a theory of everything. Proof in regards to my mathematical proposals in terms of computation would be solving the holy grail of man's biggest questions. While I'm confident in regards to the math and the way things are structured, there are things I have thought about in regards to taking things a step further. 42. About Man's Need to Go to Space Human Expansion: the space industrialization 43. Building a Decentralized and Uncensorable Internet — The Nexus Protocol Building a Decentralized and Uncensorable Internet — The Nexus Protocol 44. NASA Discovers Evidence of ‘Water Worlds’ in Deep Space Scientists have spotted two water worlds, Kepler-138 c and Kepler-138 d, that they think could be teeming with water. 45. How Starlink is Disrupting the Internet Starlink, by SpaceX, will provide internet from the constellation of low Earth orbit satellites with low latency and high speed compared to submarine cables. 46. The United States Lost the Space Race With the discovery of water ice on the moon, a self-sufficient base becomes a real possibility. We're going to the moon, but only if some corporation takes us. 47. In Memoriam: the Fallen Defenders of G'oldurree When We Are Gone, Will There Be Anyone to Mourn Our Passing? 48. First-ever Image Of A Multi-planet System Around A Sun-like Star In this thread, the HackerNoon Community discusses their thoughts on scientific evolution, its consequences, and our favorite Hubble images. 49. Spacetex: Clothes Made for Space, the Fiber Behind Gravity Spacetex textile is changing the game in fashion introducing subcategories such as Dystopian Fashion and Techwear. 50. Latin America Seeks its Riches in Space by Reaching Jupiter in 30 Days JOVE is a novel solar-powered mission that will use the solar wind's momentum, speed, and direction as the propulsive thrust to reach Jupiter in 30 days 51. The REAL Life of Pi: Ethereal, Romantic, Mysterious And Completely Memorizing “Probably no symbol in mathematics has evoked as much mystery, romanticism, misconception and human interest as the number Pi” ~William L. Schaaf, Nature and History of Pi. π 52. Space Exploration: New Frontier For Technological Innovations From the earliest known periods of human history, people have always been fascinated by the big yellow ball that provided light and heat, the tiny light dots shimmering in and regularly recurring tailed streaks dashing across the night sky. We wanted to know more about those objects, but the crippling limitations in our knowledge and technology didn’t allow that. 53. Starhopper: The Prototype Of Starship By SpaceX SpaceX is a space exploration company owned by a genius and a visionary Elon Musk who’s also the CEO of Tesla Inc. The main goal of SpaceX is to make space travel and exploration easy and efficient. Throughout the years, Elon Musk has done a lot of amazing things and has stayed completely open to the questions of the people. I think that Elon Musk’s transparency is his best quality. Anyways, let’s take a closer look at the latest advancement of SpaceX. 54. Can Satellites Help Combat Global Warming? In this slogging thread, our technology channel took the chance to discuss this news about the possibility of satellites helping fight global warming. 55. The Ongoing Mars Missions: Are We Ready to Live on the Red Planet? Back in the '60s, we saw the biggest and heated space race to the moon and now we are in the space race to Mars. The coming year is going to bring some exciting news and adventures about life on Mars. NASA is not alone this time either, there are several other space agencies sending Mars missions. But why are these space organizations spending their millions on such missions? Just to know one thing— could humans live on Mars? Does life exist on the red planet? 56. What 2021 Scientific Discovery Excited You the Most? In this slogging thread, our community shared their favorite scientific discoveries of the year. 57. The Long Game in the Space Industry: Why the Need to Transcend Requires Time and Effort Monica’s log, Stardate… I have been wanting to articulate some of my thoughts about the notion of time and effort for the space industry. Allow me to state some of my premises in advance. 58. Houston, We Have a Problem. There’s Not Enough Women on this Spaceship! Celebrate this Intentional Holiday with Girls that Rule the World! 59. The Space Tech Startup Making Our Internet A Better Place With Lasers🖖 Greetings, humans. I remote chat this afternoon with Rohit Jha, the CEO & Co-Founder of Transcelestial Technologies, a startup building light speed communications infrastructure for the next 100 years. 60. Visualizing Asteroids in Python with Bokeh and NASA APIs A GET request to the Astronomy Picture of the Day (APOD) web service on September 18, 2020, returned the above photo of the spiral galaxy Arp 78.  APOD is one of many APIs available through NASA Open APIs.  Like APOD, some include imagery, such as the Earth Polychromatic Imaging Camera (EPIC) API, which returns images like this: 61. Into Space MANY of my readers will remember the mysterious radio messages which were heard by both amateur and professional short wave operators during the night 62. NexOS, Internet of Things (IoT), And Beyond: Part I Understanding the inspiration and history behind Nexus helps convey the current vision. This is the first of three articles covering pertinent aspects of Nexus’ history in which the context is set for the burgeoning NexOS (Nexus Operating System), and its innovative security principles are introduced. 63. Argentina’s Steady And Visionary Path To Space Is An Inspiring Tale Which path is required for us to reach new worlds? Argentina’s history in the space industry shows an unwavering course of perseverance and resilience. Thank you for checking out the 63 most read stories about Space on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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Let's learn about via these 53 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Bitcoin Mining /Learn Repo 1. Who will mine the last mine Bitcoin? Who will mine the last Bitcoin? 2. Bitcoin Mining and Traditional Banking Energy Consumption: The Truth Over the years, analysts and environmentalists have constantly called Bitcoin mining an energy-intensive venture. This has brought a continuous spotlight on Bit 3. Is Bitcoin Really That Bad For The Environment? Questions about the environmental friendliness of the cryptocurrency business appeared 2 years ago. Is the current situation really that bad as many say? 4. 💰 Why ASIC Miners Are a Great Investment for Passive Income ? 🚀 ASIC Miners Are a great investment for passive income, be sure to choose it wisely to reach the best mining profitability. 5. Invest in Yourself. It’ll Pay Dividends For You and Those Around You I don’t know if this is the best advice I’ve ever given someone, but for most people I’d say: INVEST IN YOU 6. Is the End of Bitcoin Near? The price of an asset is determined by supply and demand. BTC is limited to 21 million units, with 18.7 million presently in circulation. 7. Blockchain In Bullet Points The simplest explanation of the blockchain in the world, what its nine main characteristics are, what makes up a blockchain, hashing,  the body of the block. 8. Why Selfish Mining is Detrimental to a Blockchain Network Selfish mining is a scenario where a miner mines a new block but fails to publish it for other miners to see and add it to the chain of blocks on their ends. 9. Is it Still Profitable to Invest in Bitcoin Mining Hardware or Cloud Mining? What do Reports Say? In December 2018, the crypto industry was shaken amid rumors about one of the largest Bitcoin mining firm Bitmain’s plans to lay off 50% of its staff. Earlier in November, one of the most trusted GPU manufacturers Nvidia also reported a decline in the sale of mining chips. This caused a dent of 23 billion in market valuation of the company. 10. Bitcoin Efficient: Introduction to the Concept This is just a concept, but perhaps can lead to a community-driven and open source project that tackles Bitcoin's Energy Efficiency problem. The project, which I dub "Bitcoin Efficient", might do just that, and much more. Unlike other hard forks from BTC such as Bitcoin Cash, Bitcoin Efficient aims to have dramatic changes in regards to hashing and consensus. The project aims to change the way Bitcoin is being mined. 11. #Debunked: 3 False Narratives Surrounding Bitcoin Bitcoin false narratives debunked , bitcoin mining, bitcoin illicit activity, bitcoin CIA, Bitcoin use cases 12. Here’s Why You Should Not Buy Bitcoin 20 reasons not to buy bitcoin. Investing isn't for everyone. Bitcoin has a way of humbling everyone who gets involved with i.t 13. Bitcoin Mining Still Requires Too Much Energy Bitcoin is far less popular now than it was in its heyday, when the world’s leading cryptocurrency was regularly dominating international headlines and leading newcomers into the burgeoning crypto-marketplace. After it caught on like wildfire amongst non-traditional investors, the Bitcoin hype also generated a slew of headlines and controversies surrounding the immense amount of energy that’s needed to mine - or generate - new tokens. These days, that controversy has largely died down, but many people still wonder if Bitcoin mining still presents a serious new global warming problem. 14. Volcanos and New Proof Methods are great, but Bitcoin's Energy Problem Falls on the Community The environmental cost of blockchain technologies and cryptocurrencies is high, but the answer doesn't only lay with energy production and better proof methods 15. If You Want Bitcoin to Succeed, Then F&ck the Institutions Institutional investors don't hold the key to Bitcoin's growth. They're definitely not looking out for you or your interests. 16. Better Cryptography Means a Better Bitcoin Currently, Bitcoin is quite limited in regards to its cryptography and reaching its true decentralization potential. One of my aims is to change all that. Bitcoin has the problem of an increasingly large carbon footprint, potential threats to it being breakable, and specialized brute force attacks that are becoming increasingly complex. 17. Bitcoin's Post-Halving Hash Rate [Analyzed] The cryptocurrency industry has just experienced the most anticipated event, Bitcoin (BTC) 2020 halving. The last 12.5 Bitcoin block has been mined by F2Pool and encoded the message of “NYTimes 09/Apr/2020 With $2.3T Injection, Fed’s Plan Far Exceeds 2008 Rescue.” paying tribute to Satoshi Nakamoto. Antpool was in luck and mined the first 6.25 Bitcoin block. 18. Proof-of-Stake vs Proof-of-Work vs Proof-of-Time: What’s the Difference? We'll look at the difference between these three algorithms - Proof-of-Stake vs Proof-of-Work vs Proof-of-Time and how they compare. 19. The Future of Bitcoin Network Will be Powered by Green Energy: Unhashed #11 Unhashed is a QnA series where I speak with top execs of new and established blockchain projects to unravel the mystery of blockchain & crypto. 20. Top Crypto Miners in the Market for 2022 This guide's selection of bitcoin mining equipment adheres to the most secure cryptographic procedures to keep your cryptocurrency secure. 21. Why Australia Might Become A Bitcoin Mining Superpower Australia seems to have the perfect trifecta of government stability and support, societal adoption, and bountiful sunshine and land for clean energy production 22. What You Need to Know About Bitcoin's Energy Usage Learn the truth about Bitcoin’s energy consumption and understand why its environmental impact isn’t as bad you think. 23. A Water-Cooling Upgrade That Can Give Bitcoin Miners Their Power Back In typical open and transparent fashion Bitcoin mining sales and service company Meta-Luban does another public test of their water-cooling systems... 24. How Chicago Can Nuke Out its Crypto Ambitions Chicago seeks to ride the Bitcoin train back to relevance. Here is how they can do it. 25. The End Of Crypto Mining Is Just The Beginning Of Crypto A soundbyte news item recently took the crypto community by storm: 85% of the world’s Bitcoin supply has been mined. 26. Digging Further Down the Mine: Understanding Bitcoin Difficulty Bitcoin mining difficulty is currently at an all-time high. 27. Is Bitcoin Really Bad for the Environment? While there is some legitimacy to Bitcoin's negative carbon footprint, the claims are overblown and distributed ledgers can actually help the environment. 28. How The Blockchain Works Explained Visually In this article, we will explore how the blockchain helps cryptocurrencies work with both the architecture and how currencies are mined for transactions. 29. Will Liquid-Cooled Miners Dominate the 3rd Hashrate Revolution? Mining hashrate has come to a crossroads. Water-cooled products are being accepted and recognized by mainstream manufacturers, but will they dominate? 30. "Ukrainians [are] among the top adopters of cryptocurrency in the world" Bitcoin among other Cryptocurrencies may soon be used legally in Ukraine, once President Volodymyr Zelensky signs legislation passed by Ukraine’s Parliament. 31. Can bitcoin-denominated UBI fight hyperinflation? A proposal to solve the hyperinflation problem through adopting Bitcoin UBI, powered by Stacks blockchain Proof-of-transfer mechanism. 32. Mainstream Media Can Do Better Than Releasing Hyperbolic Articles Against Bitcoin Major mainstream media made a coordinated attack on Bitcoin’s energy consumption. 33. What Does China’s Crypto Crackdown Mean for the Industry? China is getting tough on crypto. Same as it ever was, right? But this time the pushback is real. What does it mean for bitcoin miners and crypto as a whole? 34. Post Bitcoin Halving: What Lays Ahead? Bright points, potential pitfalls and what you can look out for 35. The Bitcoin Halving: Miner Economics and Institutional Mining At 5:55 PM EST on May 11, 2020, the third Bitcoin Halving will occur. Over the last several weeks, the price of Bitcoin has soared nearly 30% as speculators and long-term hodlers are accumulating ahead of the highly anticipated event. 36. How The Halving Will Impact Bitcoin's Price and Production This year, sometime around mid-May, an event long anticipated in the Bitcoin community will occur: halving, i.e. the reward granted to miners per block added to the already existing blockchain (from 12.5 BTC to 6.25 BTC) will be halved. The history of Bitcoin will see 32 halvings, and we are approaching merely the third one. Very many people continue to wonder about the impact of this halving, about the evolution of the price, about what will happen with the miners, etc. 37. "Without mining, there is no Bitcoin" This article covers everything you need to know about the process, history, and principles behind Bitcoin mining. 38. How to Convert a 110TH Air-Cooled Miner into a 180TH Liquid-Cooled Beast! The Antminer series 19 Antminer S19 Pro can be upgraded with liquid-cooled technology on large scales and this post proves it. 39. Sovereign Currency Development: A Chat with an OG of Bitcoin Mining I remote chat this afternoon with Dunstan Teo, a blockchain architect and consultant for projects and enterprises, big and small. He’s also a builder of sovereign level tech, a creator of new economic models, a president of a multi office advisory, an author, an advisor to exchanges, and more. 40. Bitcoin in 2020: How To Make The Right Valuation Since its launch in 2009, Bitcoin has continued to thrill and confuse the global financial markets. 41. Lowering The Electricity Costs Of Mining Bitcoin [A How-To Guide] Bitcoin remains an incredibly tantalizing digital asset, as those who invest wisely in this cryptocurrency can earn huge sums of money for themselves. Bitcoin miners and investors are nevertheless forced to contend with the fact that their market is incredibly volatile, and that technological changes are constantly upsetting industry practices. New and more efficient bitcoin mining processes, for instance, can help lower the energy costs associated with producing the digital tokens. 42. Bitcoin's Halving and Pricing Through the Prism of Stock to Flow Method A look at pricing methods of Bitcoin in the context of the Stock to Flow Method and the upcoming halving. 43. The Relevance of Bitcoin Halving Bitcoin halvings occur every 210,000 blocks that have been mined on the blockchain, which is roughly at four-year intervals. 44. Could the Bitcoin Halving Cause a Price Halving? On May the 11th, Bitcoin went through its third halving in its albeit short lifetime. Whilst this event was celebrated by most in the crypto community, some bring up some valid concerns about whether or not many miners will be able to survive with their revenues effectively cut by almost 50% in a single day. 45. Bitcoin on the Brink: What Could Trigger a Capitulation? Bitcoin's volatility is nothing new, but it seems to ramp up as we approach, what could be a critical juncture for the cryptocurrency. 46. On Wild IPOs; Tech Addiction; & Airbnb's Policies Post-Shooting —  #StuffIReadThisMorning 47. Bitcoin: Have Incentives Gone Wrong? Only when all coins are minted and miners are only rewarded by transaction fees will we know the extent of the genius embedded in Satoshi Nakamoto's game theory 48. Understanding the Correlation Between the Bitcoin Price and Bitcoin Mining What is Mining: 49. RSK Bitcoin Merged Mining Returns to Miners Beats The Industry Average Bitcoin mining is often touted as one of the safest and quickest ways to earn the world’s leading cryptocurrency. However, due to factors such as enormous energy consumption and requirement for complex technical hardware, Bitcoin mining has many times eluded small miners. 50. "Chancellor on brink of second bailout for banks" - Where To Find this on the Bitcoin Blockchain "Where can I find the famous title of The Times in Bitcoin blockchain? Is the page saved into the blockchain?" 51. Proof of Work is the Solution Not a Problem Proof of Work is the most secure Internet protocol ever created 52. Bitcoin Imaginaries: Do They Even Need the Real World? I read academic papers about blockchain for a living. Some of them are groundbreaking, others are pure garbage, but one paper, written by Yong Ming Kow and Caitlin Lustig, appeared to be hard to forget - I am still not sure to which bucket it belongs. 53. Bitcoin On-Ramp: The Comprehensive Guide On How To Get Bitcoin Cryptocurrency has revolutionized how global citizens view what the definition of currency may mean. Even at a young age, most people understood the power of currency. We perceived money as a mystical object that provided access to our wishes: candy, food, and fun. Thank you for checking out the 53 most read stories about Bitcoin Mining on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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