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Welcome to the chaotic realm of online shopping, where consumers are bombarded with an overwhelming number of choices and an avalanche of information. In this post, we'll dive into the messy online shopping experience and explore why consumers find themselves lost amidst a sea of options. Truly understanding where your consumer is coming from can drastically change and improve the way you position and sell your products online. From the trust crisis to the constant barrage of advertisements, we'll unravel the complexities and share some insights that shed light on this shopping frenzy. The Paradox of Choice It's a paradoxical reality: while having numerous options seems like a dream come true, it often leads to indecision and confusion. Research studies have shown that when faced with an excessive number of choices, consumers tend to experience decision fatigue, leading to dissatisfaction with their final selection. According to a conducted by psychologists Sheena Iyengar and Mark Lepper, too many choices can leave consumers feeling overwhelmed and less satisfied with their purchase. study To counter fatigue and make decision-making easier,  brands should provide clear and concise product information, including detailed descriptions, specifications, and customer reviews. This information should highlight key features, benefits, and use cases, enabling consumers to quickly assess the suitability of a product for their needs. One example of a brand that excels in providing clear and concise product information is Apple. Apple's website and product pages are known for their meticulous attention to detail and comprehensive information. Here's how they achieve this: Detailed Descriptions: Apple provides thorough descriptions of each product, highlighting its key features, technical specifications, and functionalities. They use concise and easily understandable language to communicate the product's capabilities and benefits. Interactive Visuals: Apple utilizes interactive visuals, including high-quality images, videos, and 360-degree product views, to showcase the product from various angles. This allows consumers to get a closer look at the product and its design, enhancing their understanding and assessment of its suitability. Use Cases and Examples: Apple includes real-world use cases and examples to demonstrate how their products can be integrated into customers' lives. They showcase different scenarios and highlight the benefits and solutions that the products offer, helping consumers envision the product's potential. Customer Reviews and Ratings: Apple incorporates customer reviews and ratings into their website, allowing potential buyers to read feedback from other users. This social proof helps build trust and provides additional insights into the product's performance and user experiences. Simplified Comparison: Apple facilitates product comparison by offering side-by-side comparisons on their website. This feature enables consumers to evaluate different models or configurations based on their specifications, helping them make more informed decisions. By presenting comprehensive product information in a user-friendly manner, Apple empowers consumers to quickly assess the suitability of their products and make confident purchase decisions. This approach contributes to a smoother online shopping experience, reducing the feeling of being overwhelmed by excessive choices. The trust crisis In the digital era, trust has become a valuable commodity. With countless online sellers vying for attention, consumers are rightfully cautious about whom to trust. They are no longer willing to rely solely on traditional advertising. Instead, they turn to fellow consumers for guidance. Online reviews have become the go-to resource for building trust. Sites like , , and offer platforms where shoppers can seek authentic feedback and opinions from real people who have experienced the product or service firsthand. YouTube Reddit Trustpilot But very quickly, shoppers end up with dozens of tabs open all at the same time browsing feedback from anonymous strangers online. And while sites like YouTube, Reddit, and Trustpilot provide valuable platforms for seeking feedback, opinions, and reviews, the abundance of information available can indeed become overwhelming for consumers. The sheer volume of content and varying perspectives can make it challenging to navigate and make confident decisions. With countless reviews, comments, and discussions available, consumers can feel inundated by an overwhelming amount of information. Processing and synthesizing this vast array of opinions can be time-consuming and mentally exhausting. To make things more complicated, different individuals have diverse experiences and preferences, leading to a wide range of opinions on products or services. Shoppers often encounter conflicting viewpoints, making it challenging to determine which sources to trust and which reviews align with their own needs and preferences. These online platforms also often contain subjective reviews influenced by personal biases, emotions, or even sponsored content. Consumers need to filter through biased or exaggerated opinions to find genuine and objective feedback that aligns with their decision-making process. Context is a less talked about but important factor: online reviews may lack the necessary context for consumers to fully assess their relevance to their specific situation. Factors such as the reviewer's background, expectations, or usage scenario may differ from their own, making it difficult to apply the information directly to their purchasing decision. The role of social media In the age of social media, brands are constantly competing for attention in users' feeds. While these platforms allow for targeted advertising, the incessant stream of ads can be overwhelming. Consumers find themselves bombarded with enticing offers and new options, making it increasingly challenging to filter through the noise. According to , the average person is exposed to more than 6,000 to 10,000 ads per day! This saturation of ads can lead to ad fatigue, making it difficult for consumers to find their "aha" moment (more on this below). Ad Lock To combat the constant stream of advertisements, consumers actively take measures to reduce ad exposure. They employ ad blockers, browser extensions or plugins that filter out intrusive ads during their online browsing. Additionally, consumers skip or fast-forward through ad breaks in videos and TV shows, utilizing features that allow them to bypass advertisements. Some choose to pay for ad-free versions of platforms or services, enjoying uninterrupted content for a fee. ‍ Consumers also selectively consume content on platforms that prioritize ad-free experiences, seeking out channels and services that align with their preferences. These actions reflect a growing desire among consumers to regain control over their online experiences and minimize the overwhelming presence of ads. Seeking Validation and the Proverbial “Aha” Moment To make informed decisions, consumers often seek validation from multiple sources. They browse social media pages, read product reviews, and ask for recommendations from friends. The desire for a magical realization, the "aha" moment when they discover a brand that resonates with them, drives them to gather as much information as possible. According to a survey by BrightLocal, 91% of consumers read online reviews regularly or occasionally to help them make purchasing decisions. These diverse sources of information can create both excitement and confusion, contributing to the overall messiness of the online shopping experience. ​​The quest for the elusive "aha" moment - that moment when consumers find a brand that truly resonates with them to the point where they’re ready to add-to-cart and checkout, fuels their desire to gather as much information as possible. Engaging in conversations with other customers becomes a valuable avenue for achieving this magical realization more quickly. By actively seeking out the experiences and opinions of fellow shoppers, consumers can tap into real-world insights that go beyond marketing messages. That’s where a platform that facilitates these conversations like Moast becomes critical to a brand’s online shopping journey. Engaging in these conversations allows consumers to gain a deeper understanding of how a brand's products align with their needs, preferences, and values. It enables them to leverage the collective wisdom of other customers, accelerating the process of finding that "aha" moment and discovering a brand that feels just right. Conclusion Online shopping, while convenient, can be an overwhelming journey. The surplus of options, the trust crisis, and the constant barrage of advertisements all contribute to the messy nature of the experience. As consumers, it's important to be mindful of the challenges and take steps to navigate this jungle effectively. Brands, on the other hand, must strive to build trust, offer personalized experiences, and simplify the decision-making process for their customers. In this digital age, where messiness seems to prevail, let's embrace the adventure of online shopping, armed with the knowledge and tools to make informed choices and find that magical "aha" moment amidst the chaos.

How Science Can Help Overcome the Messy Online Shopping Experience

Built on Lance, an open-source columnar data format, LanceDB has some interesting features that make it attractive for AI/ML. For example, LanceDB supports explicit and implicit vectorization with the ability to handle various data types. LanceDB is integrated with leading ML frameworks such as and . Cooler still is LanceDB’s fast neighbor search which enables efficient retrieval of similar vectors using approximate nearest neighbor algorithms. All of these combine to create a vector database that is fast, easy to use and so lightweight it can be deployed anywhere. PyTorch TensorFlow LanceDB is capable of querying data in S3-compatible object storage. This combination is optimal for building high-performance, scalable, and cloud-native ML data storage and retrieval systems. MinIO brings performance and unparalleled flexibility across diverse hardware, locations, and cloud environments to the equation, making it the natural choice for such deployments. Upon completion of this tutorial, you will be prepared to use LanceDB and MinIO to joust with any data challenge. What is Lance? The file format is a columnar data format optimized for ML workflows and datasets. It is designed to be easy and fast to version, query, and use for training, and is suitable for various data types, including images, videos, 3D point clouds, audio, and tabular data. Additionally, it supports high-performance random access: with of 100 times faster than Parquet in queries. Lance’s speed is in part the result of being implemented in Rust, and its cloud-native design which includes features like zero-copy versioning and optimized vector operations. Lance Lance reporting benchmarks One of its is the ability to perform vector search, allowing users to find in under 1 millisecond and combine OLAP-queries with vector search. Other production applications for the lance format include edge-deployed low-latency vector databases for ML applications, large-scale storage, retrieval, and processing of multi-modal data in self-driving car companies, and billion-scale+ vector personalized search in e-commerce companies. Part of the appeal of the Lance file format is its compatibility with popular tools and platforms, such as Pandas, , Polars, and . Even if you don’t use LanceDB, you can still leverage the Lance file format in your data stack. key features nearest neighbors DuckDB Pyarrow Built for AI and Machine Learning Vector databases like LanceDB offer distinct advantages for AI and machine learning applications, thanks to their efficient and compute architectures and retrieval of high-dimensional vector representations of data. Here are some key use cases: decoupled storage Natural Language Processing (NLP): Find documents or passages similar to a query based on meaning, not just keywords. This powers chatbot responses, personalized content recommendations, and knowledge retrieval systems. Semantic Search: Understand and answer complex questions by finding relevant text passages based on semantic similarity. Question Answering: Discover latent topics in large text collections, useful for document clustering and trend analysis. Topic Modeling: Computer Vision: Search for similar images or videos based on visual content, crucial for content-based image retrieval, product search, and video analysis. Image and Video Retrieval: Improve the accuracy of object detection and classification models by efficiently retrieving similar training data. Object Detection and Classification: : Recommend similar videos based on the visual content of previously watched videos. Video Recommendation Among the plethora of vector databases on the market, LanceDB is particularly well suited for AI and machine learning, because it supports querying on S3- compatible storage. Your data is everywhere, your database should be everywhere too. Architecting for Success Using MinIO with LanceDB offers several benefits, including: : MinIO’s cloud-native design is built for scale and high-performance storage and retrieval. By leveraging MinIO's scalability and performance, LanceDB can efficiently handle large amounts of data, making it well-suited for modern ML workloads. Scalability and Performance : MinIO is highly available, immutable, and highly durable. This ensures that data stored in MinIO is protected against hardware failures and provides high availability and fault tolerance, which are crucial for data-intensive applications like LanceDB. High Availability and Fault Tolerance : Multi-site, active-active replication enables near-synchronous replication of data between multiple MinIO deployments. This robust process ensures high durability and redundancy, making it ideal for shielding data in mission-critical production environments. Active-active replication The combination of MinIO and LanceDB provides a high-performance scalable cloud-native solution for managing and analyzing large-scale ML datasets. Requirements To follow along with this tutorial, you will need to use . You can install the Docker Engine and Docker Compose binaries separately or together using Docker Desktop. The simplest option is to install Docker Desktop. Docker Compose Ensure that Docker Compose is installed by running the following command: docker compose version You will also need to install Python. You can download Python from . During installation, make sure to check the option to add Python to your system's PATH. here Optionally, you can choose to create a Virtual Environment. It's good practice to create a virtual environment to isolate dependencies. To do so, open a terminal and run: python -m venv venv To Activate the virtual environment: On Windows: .\venv\Scripts\activate On macOS/Linux: source venv/bin/activate Getting Started Begin by cloning the project from . Once done, navigate to the folder where you downloaded the files in a terminal window and run: here docker-compose up minio This will start up the MinIO container. You can navigate to ‘ ’ to take a look at the MinIO console. http://172.20.0.2:9001 Log in with the username and password . minioadmin:minioadmin Next, run the following command to create a MinIO bucket called . lance docker compose up mc This command performs a series of (mc) commands within a shell. MinIO Client Here's a breakdown of each command: ;: This command repeatedly attempts to configure a MinIO host named with the specified parameters (endpoint, access key, and secret key) until successful. During each attempt, it echoes a waiting message and pauses for 1 second. until (/usr/bin/mc config host add minio http://minio:9000 minioadmin minioadmin) do echo '...waiting...' && sleep 1; done minio This command forcefully removes (deletes) all contents within the bucket in MinIO. /usr/bin/mc rm -r --force minio/lance;: lance : This command creates a new bucket named in MinIO. /usr/bin/mc mb minio/lance; lance : This command sets the policy of the bucket to public, allowing public read access. /usr/bin/mc policy set public minio/lance; lance : This command ensures that the script exits with a status code of 0, indicating successful execution. exit 0; LanceDB Unfortunately, LanceDB does not have native S3 support, and as a result, you will have to use something like boto3 to connect to the MinIO container you made. As LanceDB matures we look forward to native S3 support that will make the user experience all the better. The sample script below will get you started. Install the required packages using pip. Create a file named requirements.txt with the following content: lancedb~=0.4.1
boto3~=1.34.9
botocore~=1.34.9 Then run the following command to install the packages: pip install -r requirements.txt You will need to change your credentials if your method of creating the MinIO container differs from the one outlined above. Save the below script to a file, e.g., . lancedb_script.py import lancedb
import os
import boto3
import botocore
import random

def generate_random_data(num_records):
    data = []
    for _ in range(num_records):
        record = {
            "vector": [random.uniform(0, 10), random.uniform(0, 10)],
            "item": f"item_{random.randint(1, 100)}",
            "price": round(random.uniform(5, 100), 2)
        }
        data.append(record)
    return data

def main():
    # Set credentials and region as environment variables
    os.environ["AWS_ACCESS_KEY_ID"] = "minioadmin"
    os.environ["AWS_SECRET_ACCESS_KEY"] = "minioadmin"
    os.environ["AWS_ENDPOINT"] = "http://localhost:9000"
    os.environ["AWS_DEFAULT_REGION"] = "us-east-1"

    minio_bucket_name = "lance"

    # Create a boto3 session with path-style access
    session = boto3.Session()
    s3_client = session.client("s3", config=botocore.config.Config(s3={'addressing_style': 'path'}))

    # Connect to LanceDB using path-style URI and s3_client
    db_uri = f"s3://{minio_bucket_name}/"
    db = lancedb.connect(db_uri)

    # Create a table with more interesting data
    table = db.create_table("mytable", data=generate_random_data(100))

    # Open the table and perform a search
    result = table.search([5, 5]).limit(5).to_pandas()
    print(result)

if __name__ == "__main__":
    main() This script will create a Lance table from randomly generated data and add it to your MinIO bucket. Again, if you don’t use the method in the previous section to create a bucket you will need to do so before running the script. Remember to change the sample script above to match what you name your MinIO bucket. Finally, the script opens the table, without moving it out of MinIO, and uses Pandas to do a search and print the results. The result of the script should look similar to the one below. Remember that the data itself is randomly generated each time. vector      item  price  _distance
0  [5.1022754, 5.1069164]   item_95  50.94   0.021891
1   [4.209107, 5.2760105]  item_100  69.34   0.701694
2     [5.23562, 4.102992]   item_96  99.86   0.860140
3   [5.7922664, 5.867489]   item_47  56.25   1.380223
4    [4.458882, 3.934825]   item_93   9.90   1.427407 Expand on your Own There are many ways to build on this foundation offered in this tutorial to create performant, scalable and future-proofed ML/AI architectures. You have two cutting-edge and open-source building blocks in your arsenal – MinIO object storage and the LanceDB vector database –  consider this your winning ticket to the ML/AI . tournament Don’t stop here. LanceDB offers a wide range of and tutorials to expand on what you’ve built in this tutorial including a recently announced Udacity course on . Of particular interest is recipe to chat with your documents. We are all for breaking down barriers to getting the most from your data. recipes Building Generative AI Solutions with Vector Databases this Please show us what you’re building and should you need guidance on your noble quest don’t hesitate to email us at hello@minio.io or join our round table on . Slack

LanceDB: Your Trusted Steed in the Duel Against Data Complexity

ল্যান্সডিবি: ডেটা জটিলতার বিরুদ্ধে দ্বন্দ্বে আপনার বিশ্বস্ত স্টাইড

LanceDB: Ihr vertrauenswürdiges Pferd im Kampf gegen die Datenkomplexität

LanceDB: su corcel de confianza en el duelo contra la complejidad de los datos

LanceDB : votre monture de confiance dans le duel contre la complexité des données

लांसडीबी: डेटा जटिलता के विरुद्ध द्वंद्व में आपका विश्वसनीय घोड़ा

LanceDB: データの複雑さとの戦いにおいて信頼できる馬

LanceDB: 데이터 복잡성과의 결투에서 신뢰할 수 있는 군마

LanceDB: seu corcel confiável no duelo contra a complexidade dos dados

LanceDB: ваш надежный помощник в борьбе со сложностью данных

LanceDB: Veri Karmaşıklığına Karşı Düelloda Güvenilir Atınız

LanceDB: Chiến mã đáng tin cậy của bạn trong cuộc đấu tranh chống lại sự phức tạp của dữ liệu

LanceDB：对抗数据复杂性的值得信赖的骏马

For my first ever wrote article, I've decided to write about web scraping and how I built a Stock Market Watcher using Ruby and Nokogiri Gem. First of all some requirements to follow this guide: Ruby HTML/CSS basics The purpose of this guide is to show what you can do using Ruby, probably out there you'll find better/professional ways to do this. The repository containing the complete code used on this guide will be linked at the end of the article. Let's begin First of all, set up your environment, create a ruby file that will contain our scrapper code. In this case, I'll create a file called stock_watcher.rb and I'll add the following code to it. #stock_watcher.rb require 'nokogiri' require 'open-uri' This code requires a Ruby Gem called (Help us to handle extracted data from the website) and the open-uri module to extract data from the web. Nokogiri After this, we need to define from where we're going to get the data for our scrapper, here you can use any website you wish, for this guide I'm going to use data. TradingView With the source defined now we need to get the data from the page, Nokogiri got some useful parsing methods to help us, we're going to use the parser since we're scraping an HTML page. Nokogiri::HTML Add the following code to stock_watcher.rb page = Nokogiri::HTML(URI.open( )) #stock_watcher.rb 'https://www.tradingview.com/markets/stocks-usa/market-movers-large-cap' This simple get the data from the web using the URI module and parse it using Nokogiri, it returns a . Nokogiri::HTML::Document With our page object, we have in our hands a bunch of useful methods. Now we have to use our HTML/CSS acknowledgment to define from where on the page we want to get the data. Chrome dev tools is a good friend here. The page in question is , this page shows us the US STOCK MARKET companies ordered by large capitalization. This page stores data on the main table as you can see, what we have to find is a way to identify this table. https://www.tradingview.com/markets/stocks-usa/market-movers-large-cap/ Nokogiri offers us a method called "css" that lets us search for specific CSS rules on our , we can search for specific CSS classes, ids, etc... Nokogiri::HTML::Document Let's try to find out what's the class or ID of the table using the Chrome inspect tool, just right click on any table element then inspect, you'll find a window similar to that one: Scroll a bit till you find the <table> tag like this: As you can see our table uses the "tv-data-table" class, that one is going to be our target class. Now we got our table class, add the following code to : stock_watcher.rb table = page&.css( ); #stock_watcher.rb '.tv-data-table' This code get our page object and find an HTML element with "tv-data-table" class assigned to it and returns a . Nokogiri::XML::NodeSet The only thing we need from the table is the table data, the table data (td) is stored inside the table rows (tr). offers more useful methods that allow us to search for specific keys inside it. Nokogiri::XML::NodeSet Add the following code to : stock_watcher.rb rows = table.search( ); #stock_watcher.rb 'tr' This returns another with a subset of keys and values from the table that contains 'tr'. Nokogiri::XML:NodeSet We're getting close to our goal. Now add the following code to : stock_wacher.rb rows.each stock_content = row.search( ).map { td.text.strip }
  stock_content[ ]&.gsub!( , ) do |row| 'td' |td| 0 "\n\t\t\t\t\t\t\t\t\t" " " end Now we iterate over our XML subset, in this case, it's our rows containing the data from stocks. Then we search in every row for the table data (td) using the same method search, it returns an array with all the data of the iterated row. We need to strip our text because Nokogiri returns a lot of useless pieces of text inside our strings. Now you can play a bit with that code, try to print the stock content you got using "puts stock_content" before the end keyword of the last code and see what you got, it should return something similar to this: MSFT Microsoft Corp. % Buy M B Technology Services "[" ", " 182.51 ", " -2.27 ", " -4.23 ", " ", " 31.952 ", " 1384.054 ", " 31.09 ", " 6.06 ", " 144000.00 ", " "]" With this data in hand who define what should be done next is you, there are thousands of possibilities of what could be done with this, I'll give a few examples: Store data on a file for later checking, store data on a database to handle it later even showing the data on another website (Check for permissions first), create triggers if a stock value is on a price that you want. As I said, there are a lot of possibilities. Now to finish this guide I've created a class to handle the data we got from our last piece of code. First, create another file on the same folder called stock.rb and add the following code: , , , , , , @name = stock_data[ ]
    @last = stock_data[ ]
    @change_percentage = stock_data[ ]
    @change_value = stock_data[ ]
    @rating = stock_data[ ]
    @vol = stock_data[ ]
    @market_cap = stock_data[ ] #stock.rb class Stock attr_reader :name :last :change_percentage :change_value :rating :vol :market_cap def initialize (stock_data) 0 1 2 3 4 5 6 end end I'm not going to explain how classes work on Ruby since that is not the purpose of this guide. But basically, that class receive the data we got on the "stack_content" variable and store it elegantly. Now replace your entire code on file to look like this: stock_watcher.rb require_relative page = Nokogiri::HTML(URI.open( ))
table = page&.css( );
rows = table.search( );
stock_arr = []
rows.each stock_content = row.search( ).map { td.text.strip }
  stock_content[ ]&.gsub!( , )
  stock_arr << Stock.new(stock_content) stock_content.length.positive? #stock_watcher.rb require 'nokogiri' require 'open-uri' 'stock' 'https://www.tradingview.com/markets/stocks-usa/market-movers-large-cap' '.tv-data-table' 'tr' do |row| 'td' |td| 0 "\n\t\t\t\t\t\t\t\t\t" " " if end It requires our new class file, creates a new array called stock_arr, and store every new stock object on it. If you print the first object on stock_arr you'll get something like this: MSFT Microsoft Corp. - % - Buy M B "#<Stock:0x00007fffd443e2f8 @name=" ", @last=" 182.51 ", @change_percentage=" 2.27 ", @change_value=" 4.23 ", @rating=" ", @vol=" 31.977 ", @market_cap=" 1384.054 ">" As you can see, it's a better way to store our stocks, and you can access its properties using dot notation, like stock.name, stock.last, stock.change_value, etc... Thank you for reading this guide, I hope it helps you in some way. Feel free to contact me on any social media listed on HackerNoon. Useful links: Complete code repository: https://github.com/kazumaki/stock-market-watcher TradingView website: https://www.tradingview.com/ Nokogiri documentation: https://nokogiri.org/rdoc/index.html

How to Create a Stock Market Price Watcher Using Ruby

Image credit: Stock.Adobe.com In the past few years, you could stumble upon articles about how people are trying to improve their own health and bodies in ways that are not always customary. There was Jack Dorsey, his approach to fasting, former NASA employee Josiah Zayner was gene-editing technology - CRISPR - to upgrade his own DNA, while startup founder Serge Faguet was busy on "hacking" his body with drugs, clever gadgets, and new tech. explaining using spending $200k These examples are all part of a larger, ever more common  trend called biohacking. Biohackers are actively seeking a solution to improve their focus, confidence, intelligence, health, and, of course, life expectancy. These people want to become much more productive, more powerful, more intelligent, and live a very long life. What is the current state of biohacking? Can people really become superhumans? Who and what is responsible for biohacking and how can you control the risks while experimenting? I had the opportunity to discuss these questions with Sergey L. Mikheev, MD, PhD, Physician, Cardiologist, and Specialist in regenerative medicine at the clinical center, who is professionally involed in regenerative medicine and biohacking. Swiss Medica 21 *** How long has biohacking been around? And what has changed? The term "biohacking" emerged together with the concept of "DIY biology" in the late 1980s. After some time, in the mid-2000s, it got a second wind in the Silicon Valley tech community when entrepreneurs and engineers began the first experiments aimed at improving productivity and health. Now, biohacking is an actively developing trend. It is hip, especially in places like Silicon Valley. Biohacking is a collection of methods aimed at boosting humans’ physical, cognitive, and psychological parameters. Biohackers want to be more productive, and they want to slow down or even reverse the ageing process. They want to "hack" their bodies in order to become a more advanced and competitive life being. In fact, the biohacker's toolkit may already include some well-known tools like meditation or intermittent fasting. Many biohackers are also actively experimenting with anti-aging supplements, nootropics and other drugs. Extreme biohackers use cryotherapy, near-infrared saunas, and virtual float tanks. There are also so-called "grinders," who even implant high-tech devices like computer chips into their bodies to enhance its productivity. What could some of the real-life results of biohacking be? To talk about it, you should distinguish biohackers and other people that want to use science to enhance productivity, quality of life, fight aging. For example, there is a movement of transhumanists who advocate the development and implementation of sophisticated new tech that may be able to enhance the intellect (brain-computer integration) and physical parameters (via bionics) of all the people in the world. Unlike them, biohackers are working towards enhancing the capabilities of themselves only. They believe that by being more productive and healthier, they can bring more benefit to themselves as members of society. In the case of successful biohacking methods applications, the real-life results may be the increase of the human lifespan up to 100 years, while also being able to control some of biological processes in the human body. Are there any trends and developments in medicine that can be useful for biohacking? Regenerative medicine is the primary development. It is a branch of research in tissue engineering and molecular biology which deals with activating the body's ability to repair damaged tissues and organs. This involves using stem cells, which can trigger these regenerative processes with no possibility of severe side effects. These cells are the youngest in the body and have the most significant potential for development. Stem cells stimulate the recovery and update of all of the other tissue in the body. There are multiple ways of applying stem cells, including intravenous, intramuscular or local injections, depending on the purpose. This technique allows humans to become more vigorous in their approach, while it enhances sleep and stamina, and slows down or even reverses aging -  i.e., in some degree, it achieves many of the goals biohackers are targeting Wait, is this reverse aging idea even possible? Well, to answer this question, you need to understand what aging is from a biological point of view. It is a complex process involving changes in the intracellular and extracellular structures of the body. During the aging process, the functionality of cells responsible for recovery break down. Gradually, the body's cells do not working optimally, and the quality of their operations decreases. This influences all of the body's tissue and organs. It is known that, as we age, the reserves of the body's own stem cells responsible for regeneration are depleted. This process is currently unavoidable. However, we can inject the body with large amounts of stem cells to activate the body's ability to regenerate tissues and organs. This, in particular, leads to rejuvenation, endurance,  higher energy levels, better potency, etc. Also, stem cells can help boost the quality of sleep. You need less time to recover your power. The time you need to sleep therefore decreases, but you recuperate faster and better, and you do not want to sleep during the daytime. Okay. Let's assume I want to dive into biohacking. What should my first step be? You should start with a full check-up of your body. It is useful in any sense – you'll be aware of the current state of your health. For biohacking, you need to know the starting point to measure your results and effectiveness of any applied method. Plenty of biohacking methods have no evidentiary basis for now, so people are actually testing them. It is hard to predict the exact result, so you have to control and measure as many parameters, as well as the state of your body as regularly as possible. It is the only way to come up with a set of methods that work for each specific person. Besides a complete check-up, you will need to consult with several specialists (physicians, endocrinologists, neurologists, nutritionists, personal coaches, and so on). Then you can try to start with stem cells – you will need to go to a clinic specializing in cell-based treatments. The clinic should have a wide range of programs in order to be useful to the needs of biohackers. If the place offers additional complex therapies, this might be an advantage as well. Check for the availability of methods like: Intracellular Metabolism Recovery therapy (here is ), how it might look Xenon inhalation therapy, Hypoxi-hyperoxi therapy, Mesodiencephalic modulation (MDM), Plasmapheresis. These keywords will be useful in finding clinics that might be helpful for all biohacking needs. These additional therapies help to improve vitamin balance, trace element balance, and blood supply, ensure normal cellular respiration, normalize the hypothalamic-pituitary system, and improve cognitive functions. And how often can you use stem cells for biohacking needs? Can this be an ongoing process? Usually, an average lifespan of injected stem cells is about six months. However, the effect of stem cell therapy can last for up to one or two years. So, there is no sense in having injections more than once every six months. There are no risks: you can actually can this every few once days if you wanted to, and sometimes this is what you need to cure specific diseases. However, doing it this regularly makes  no sense in terms of biohacking, so once a year is perfect for getting sustainable, long-term results. Also, there is no such thing as addiction. Stem cells are not chemical. When stem cell therapy is provided, only biological, natural substances are used. It has been that there is no risk of long-term negative consequences in this therapy either. proven What is the main obstacle preventing more rapid biohacking development now? The major problem with biohacking is that it’s not affordable yet. T , posted by a biohacking advocate and tech entrepreneur, Serge Faguet, shows how much money you may spend on it — he put $200K into biohacking experiments prior to getting notable results. Still, you can start with a smaller investment, but even at the beginning, you have to undergo a thorough and costly check-up, do multiple tests, consult with several (expensive) medical professionals to come up with your own hacking plan. his article Sure, there are tools like specialized apps for biohackers which may help organize sleep (for example ) and control basic health parameters ( ), and mood ( ). But biohacking at full swing still requires massive investment. Sleep Cycle TrackMyStack HeadSpace Can you describe the typical biohacker nowadays? He or she obviously has money, but what else? Biohacking is about the quality of life, and this approach is used by those who understand that when you have energy and robust health, you can achieve better life results. So, usually, biohackers are successful people who value their time and personal productivity. Biohackers see the connection between personal productivity and their overall health. They believe their investments in health will have a positive ROI. These people actively follow tech and biotech news, analyze trends, and think about practical applications of the things they read about. You can safely bet that biohackers are ambitious: this is evident as they want to become better versions of themselves, stay positive, enjoy everyday communication with other people, and build stronger relationships. Could you share any advice on "responsible biohacking"? You should understand that biohacking is a pretty new discipline in terms of theresearch branch. Nowadays, there is not enough science-backed research proving the effectiveness of any method you might use as a part of your biohacking process. Yes, there are some cases with documented short-term positive effects, like or . However, no results of long-term studies are available at the moment. decreasing the risk of developing a disease reducing the symptoms of depression It is better to start with methods that have less possible side-effects, like stem cell injections, where there are no potential problems that have been documented. So, pick a clinic where you'll begin to your biohacking experience, and continuously monitor the results together with your doctors. So, can biohacking have side-effects? Well, it often involves using prescription-based drugs. These drugs can have side effects for sure. "Hackers" always act at their own risk. Anyone who has decided to upgrade himself to become smarter, more productive, and live longer should understand that experiments with health can bear risks. Even just dieting or going to the gym can lead to severe damage if the person is not healthy enough. So, experiments involving drugs and other things are even riskier. Let's talk about the future - do you think biohacking research and implementation will continue to grow? I believe that the possible future development of biohacking is directly connected to genetic engineering. If people find how they can experiment with DNA, it can have a dramatic impact on boosting health, cognitive abilities, anti-aging, and increasing quality of life. However, it is hard to expect any breakthroughs in this niche in the short term. Experiments with DNA are costly, and there is not enough research backing them up, so you can't predict the results, and the level of security is uncertain. While these problems are not being solved by genetic engineering, it will be safer to use a regenerative medicine toolkit for biohacking. This will become the primary trend in the next few years.

[Interview] Medical Expert Explains Biohacking, Its Future, and the Safest Way to Boost Yourself

In the era of widespread adoption of AI models, the data science community exhibits a rich diversity of backgrounds, fostering an environment conducive to innovation. Collaborative efforts stemming from this diversity continue to propel us toward the envisioned future of a decade or two ago. However, innovation often follows incremental patterns and may require decades before yielding tangible commercial benefits. Among industries adept at translating ideas into tangible products, automotive manufacturers stand out, boasting a deep familiarity with research and development processes and advanced algorithms. This proficiency is evident in the innovation pipeline for super and hypercars, drawing inspiration from advancements in rocket science. Leveraging extensive simulations and field tests, these manufacturers consistently push the boundaries of performance and safety in vehicle design. Major manufacturers have achieved breakthroughs with torque vectoring systems by capitalizing on established development pipelines and harnessing the capabilities of reinforcement learning. The late 2010s witnessed a surge in commercial vehicles featuring these systems, attributed partly to their association with electric and hybrid vehicles and partly to the appeal of enhanced safety. While the full extent of their influence on driver experience remains to be realized, these systems have already demonstrated remarkable efficacy, particularly in high-performance vehicles. So, what exactly is a torque vectoring system, and how is this innovation shaping the landscape of automotive manufacturing? What is Torque Vectoring In Torque Vectoring Systems (TVS), torque denotes a rotational force applied to a vehicle’s differentials or generated by independent electric motors on its wheels. These systems aim to regulate this rotational force to enhance speed, stability, handling, balance, and other performance aspects. A compelling illustration of the protracted nature of innovation is evident in the evolution of torque vectoring systems, with the first cars equipped with Torque Vectoring Systems (TVS) debuting in the 1990s, pioneered by Mitsubishi. Initially known as Active Yaw Control, Mitsubishi integrated this technology into vehicles like the 1996 Mitsubishi Lancer Evolution IV GSR models. The initial implementation relied on steering, throttle inputs, and g-force sensors to mitigate torque steer, a phenomenon where engine torque influences steering behavior. Initially geared towards racing applications, these systems have since evolved to cater to broader audiences, with companies like Rimac continuing to employ them for racing. In contrast, others emphasize safety and enhanced handling features for everyday drivers. Furthermore, advancements driven by the pursuit of eco-friendly transportation have led to the utilization of independent electric motors in torque vectoring systems, further expanding their applicability. Commercial Applications Ricardo (Honorable Mention) In 2006, Ricardo, a renowned engineering consultancy with over a century of experience, introduced the concept of torque vectoring. Ricardo rolled out its initial torque vectoring project centered around the Audi A6 4.2L, intending to license its technology to interested automakers. Unlike Mitsubishi’s Active Yaw Control system and similar counterparts, Ricardo’s torque vectoring approach prioritized safety and stability, showcasing a different aspect of the technology. Limited information is currently available on Ricardo’s involvement in assisting manufacturers in developing these systems for commercially available vehicles. However, they have publicly provided engineering support to Chinese students building cars for the global multi-event Formula SAE race in 2019. Rimac When discussing electric vehicles, Tesla typically takes the spotlight regarding market dominance. However, Rimac stands out as a company dedicated to crafting high-performance electric hypercars, where they excel. Just ask Elon… ( t) X pos Rimac burst onto the scene in January 2016 with their inaugural concept car, the Concept_One prototype. This marked the debut of their torque vectoring system (TVS) in an all-wheel-drive fully electric vehicle. Spearheaded by Tomislav Šimunić, Rimac’s TVS development pushed the boundaries by employing individual motors on each wheel. Moreover, they implemented reinforcement algorithms utilizing data from various sensors, including accelerometers, gyroscopes, steering angle, and wheel speed sensors. This data is then used to predict torque distribution to optimize vehicle stability. https://www.youtube.com/shorts/i4jVW29gbDc?embedable=true Currently, Rimac boasts one of the fastest and safest hypercars on the market. Their innovation has led to unprecedented control, resulting in their car achieving the title of the fastest production car ever in 21 acceleration and deceleration categories. Furthermore, their TVS is so reliable that their drivers can cruise hands-free at speeds exceeding 230 mph while enjoying gelato! Toyota In 2018, Toyota introduced their Dynamic Torque Vectoring AWD and Electric 4WD systems, expanding the scope of this technology beyond on-road safety and control to encompass off-road safety features and enhanced fuel efficiency. https://www.youtube.com/watch?v=LCqCa7PP1gE&embedable=true The video above showcases Toyota’s emphasis on “proactive torque transfer,” allowing for seamless adjustment of torque distribution when one or more wheels lose traction, enabling drivers to navigate various terrains more effectively. https://youtu.be/d1UG1a38diw?embedable=true Furthermore, Toyota optimizes torque distribution to enhance fuel-efficiency, shifting from AWD to 2WD when conditions permit, directing torque predominantly to the front wheels and thus cutting fuel consumption. Their limited content suggests the employment of a Dynamic Control System designed for stability, taking into account challenging driving scenarios. Moreover, the system can evaluate tire traction levels. These functionalities resonate with the research findings discussed below, showcasing the seamless transition from research concepts to practical implementation in the automotive industry. Research Review Conceptual Models — Bicycle, Vehicle, Tire Model Following Occam’s razor principle, simplicity reigns supreme when initiating modeling endeavors, with complexity introduced only when its benefits are worthwhile. This philosophy holds for TVS as well. Early TVS adopted a bicycle model, allowing researchers to streamline vehicle dynamics assumptions by confining movements to two wheels. This simplified framework facilitated the study of lateral vehicle dynamics during various events like braking, acceleration, and cornering under typical conditions. Key inputs into the bicycle model include yaw rate, lateral acceleration, steering angle, and velocity. Yaw rate denotes the vehicle’s rotation speed around its vertical axis, aiding in determining its orientation during maneuvers like cornering. Lateral acceleration captures sideways movements perpendicular to the vehicle’s direction of travel. Steering angle refers to the front wheel angle, typically in relation to the rear wheel positioning, while velocity represents the vehicle’s speed. For more details, check out this article explaining a simple bicycle model. https://dingyan89.medium.com/simple-understanding-of-kinematic-bicycle-model-81cac6420357?embedable=true Simplicity in modeling often comes with assumptions that may not hold, especially in challenging driving scenarios. Extreme conditions like rain, snow, or rough off-road terrain expose the limitations of the traditional bicycle model. The assumption of symmetry between the left and right wheels hinders the accurate analysis of lateral dynamics across all four wheels. To address this, researchers upgraded to a more comprehensive 4-wheel vehicle model. One such model, proposed in the 2023 journal article by Hui Deng, Youqun Zhao, Fen Lin, and Qiuwei Wang, boasts seven degrees of freedom. This expanded model captures all four wheels’ lateral, longitudinal, rotational, tilting, leaning, and spinning movements. However, it still falls short in capturing driving dynamics such as suspension behavior and tire-road interactions. Although suspension dynamics may be indirectly represented within the model, a separate tire model is necessary to capture tire-road interactions accurately. Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy & Safety² Tire models typically include parameters like tire force and slip angles. In the journal article by Michele Vignati, Edoardo Sabbioni, and Federico Cheli, researchers describe a simple tire model that incorporates lateral and longitudinal dynamics, slip angles, and slippage. Like the seven degrees of freedom model indirectly addresses suspension dynamics, the tire model considers slippage influenced by latent variables such as surface texture, conditions, and angles. This comprehensive approach allows researchers to analyze vehicle dynamics across various conditions and terrains, enhancing the torque vectoring system’s adaptability and performance. A Torque Vectoring Control for Enhancing Vehicle Performance in Drifting³ Control Schemes/Implementations In Torque Vectoring Systems (TVS) development, a significant portion of research revolves around employing Reinforcement Learning techniques, leveraging real-time data collected from various measurement devices. These solutions are predominantly implemented in all-wheel drive, four-wheel drive hybrid, and electric vehicles. Typically, the training of these systems takes place within simulated environments. However, in certain instances, researchers conduct road tests and utilize live test outcomes to validate simulation results. Torque Vectoring System Testing While specific details about testing TVS are often limited, researchers generally have three options: entirely computer-based simulations, hardware-in-the-loop testing combining simulation with real parts, and full-scale vehicle road tests. Computer-based simulations offer cost-effective evaluation of TVS with precise control over various driving conditions. Hardware-in-the-loop testing balances simulation and road tests, providing insights aligned with real-world performance. Although the most expensive, road tests offer the most reliable results by implementing TVS fully, enabling the most accurate evaluation of model performance. In the study by Shayan Taherian, Sampo Kuutti, Marco Visca, and Saber Fallah, computer-based simulations were utilized to test their RL algorithm across diverse environments. The researchers employed an adaptive algorithm based on Deep Deterministic Policy Gradient to update TVS control parameters as it learns from the environment. Testing across various velocity and friction environments allowed validation of their adaptive algorithm, particularly in high-speed/low-velocity scenarios. Self-adaptive Torque Vectoring Controller Using Reinforcement Learning⁴ Similarly, in the research by Wei Xu, Hong Chen, Haiyan Zhao, and Bingtao Ren, real-time Simulink simulations were leveraged to validate their model predictive controller. This controller predicts future system states and optimizes for multiple energy efficiency and safety objectives. Tests were conducted using an electric vehicle model with a 120 km/h velocity and an “adhesion coefficient” of .85, measuring tire friction between tire and ground. Torque Optimization Control for Electric Vehicles with Four In-Wheel Motors Equipped with Regenerative Braking System⁵ Powertrain & Fuel Types The specific problem addressed by torque vectoring systems heavily influences the powertrain choice and fuel types utilized in their implementations. Traditional gasoline and diesel vehicles necessitate mechanical implementations due to a single torque source, leading to complex mechanical differentials, challenges in power distribution, and overall less efficiency compared to hybrid and electric vehicle setups, which enable independent motor implementations. Similarly, All-Wheel Drive (AWD) and Four-Wheel Drive (4WD) powertrains offer superior advantages over other options. These configurations enhance stability, versatility, and energy efficiency. The work of Jeongmin Cho and Kunsoo Huh, in their study , underscores the stability and versatility benefits of AWD vehicles. By distributing power to all four wheels, AWD enhances the effectiveness of torque vectoring systems, granting increased control over each wheel. This translates to improved vehicle dynamics during cornering, acceleration, and deceleration. Torque Vectoring System Design for Hybrid Electric–All-Wheel Drive Vehicle⁶ Meta Control Algorithms In Reinforcement Learning (RL), an agent decides actions based on the current situation to maximize a reward function. It learns which actions yield the highest rewards by assessing the outcomes of its actions. In torque vectoring algorithms, auto manufacturers define different reward functions based on the specific goals of their TVS, which vary depending on the type of vehicles used, such as sports cars versus mini-vans. These goals include increased stability, controllability, fuel economy, cornering performance, and optimizing various vehicle dynamics behaviors. One example of a multi-objective implementation is outlined in the paper by Jae-Young Park, Seung-Jin Heo, and Daeoh Kang. Their algorithm features a supervisory controller that selects between an “agile” mode and a “safe” mode based on vehicle and driving conditions. Agile mode prioritizes driver controllability, while safe mode focuses on stability. Controllability refers to the synchronization between driver steering input and vehicle direction, while stability relates to the vehicle’s resistance to deviations or disturbances. Inputs such as vehicle speed, steering angle, yaw rate, lateral acceleration, and wheel slip guide the supervisory controller’s decision-making process. Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance⁷ Another example is presented in the paper by Hui Deng, Youqun Zhao, Fen Lin, and Qiuwei Wang. Here, the researchers optimize for safety, economy, and driver load. Economy involves assessing motor efficiency and energy savings compared to vehicles without torque vectoring systems. Safety focuses on vehicle stability, while driver load considers the physical and cognitive load based on the correlation between the driver’s steering wheel angle and the vehicle’s longitudinal acceleration. Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety⁸ The algorithm includes an active safety controller and a torque allocation layer. The active safety controller calculates total longitudinal acceleration and additional “yaw moment” values based on driving conditions, applying safety and driver load penalties. These values are then fed to the torque allocation layer, which distributes torque to all four wheels. Additionally, the researchers implement a tire model using a Fibonacci tree optimization algorithm and a vehicle model with 7 degrees of freedom to enhance system performance. The , research done by Park et al., aims to enhance the driver’s experience through improved smoothness and convergence. Their TVS, designed for commercial application, underwent rigorous validation through real vehicle experiments conducted under various conditions. Torque Vectoring Algorithm of Electronic-Four-Wheel Drive Vehicles for Enhancement of Cornering Performance⁹ The researchers implemented a smooth sliding controller, GPS, and sensor inputs to generate desired yaw moment values. These values are then utilized by the torque allocation algorithm, which employs a daisy-chaining allocation method to distribute torque efficiently. The goal is to optimize the convergence from current yaw moment values to target values, thereby reducing the instability during this transition. Conclusion The evolution of torque vectoring systems underscores the transformative power of innovation within the automotive industry. From pioneering beginnings to cutting-edge applications, these systems have revolutionized vehicle dynamics, speed, stability, and handling. As automotive manufacturers continue to push the boundaries of technology and leverage advancements in advanced algorithms, the future holds promise for further improvements in safety, performance, and driver experience. However, hurdles remain in integrating this technology into internal combustion engine vehicles, and ongoing research and development efforts are required to overcome them. With continued dedication to innovation, torque vectoring systems are poised to shape the future landscape of automotive manufacturing, driving us toward a new era of mobility. Research Reference Martin Dendaluce Jahnke et al., “Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles,” 8, no. 2 (February 22, 2019): 250 Electronics Huifan Deng et al., “Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety,” 11, no. 4 (April 6, 2023): 459 Machines Michele Vignati, Edoardo Sabbioni, and Federico Cheli, “A Torque Vectoring Control for Enhancing Vehicle Performance in Drifting,” 7, no. 12 (December 5, 2018): 394 Electronics Shayan Taherian et al., “Self-Adaptive Torque Vectoring Controller Using Reinforcement Learning,” , September 19, 2021 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) Wei Xu et al., “Torque Optimization Control for Electric Vehicles with Four In-Wheel Motors Equipped with Regenerative Braking System,” 57 (February 2019): 95–108 Mechatronics Jeongmin Cho and Kunsoo Huh, “Torque Vectoring System Design for Hybrid Electric–All Wheel Drive Vehicle,” 234, no. 10–11 (April 7, 2020): 2680–92 Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering Jae-Young Park, Seung-Jin Heo, and Dae-Oh Kang, “Development of Torque Vectoring Control Algorithm for Front Wheel Driven Dual Motor System and Evaluation of Vehicle Dynamics Performance,” 21, no. 5 (October 2020): 1283–91 International Journal of Automotive Technology Huifan Deng et al., “Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety,” 11, no. 4 (April 6, 2023): 459 Machines Giseo Park et al., “Torque Vectoring Algorithm of Electronic-Four-Wheel Drive Vehicles for Enhancement of Cornering Performance,” 69, no. 4 (April 2020): 3668–79 IEEE Transactions on Vehicular Technology

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