Linksight

linksight

Introduction I often want to add visually interesting elements to my projects and struggle with content. Let’s say you have a subpage that shows all the posts/any other information related to a city. It would be great to have a photo of said city in the header, wouldn’t it? You may think that the way to go is to generate something with AI. It may work, but there are many issues with this approach. There are great free stock photo sites like Pexels or Unsplash. But how to find the right picture? I stumbled upon this problem while working on one of my side projects. It turns out Unsplash has a great API. In Python, we need to call it manually but there are client packages for JS, Ruby, PHP, mobile, etc. Unsplash API The API basically allows you to find photos matching your search query. A few caveats: Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed.


You must attribute Unsplash and the author.


The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. More on usage guidelines: Unsplash. Getting started (in-depth guide): Sign up for an account: https://unsplash.com/oauth/applications


Go to your apps: https://unsplash.com/oauth/applications


Create a “New application.”


Go to your app and save Access key; we’ll need it in the following steps Fetching an Image Have a look at this sample function; you can adapt it to your needs. import requests
import os

# Load Unsplash API key from environment variables
UNSPLASH_ACCESS_KEY = os.getenv('UNSPLASH_ACCESS_KEY')

def fetch_image_info(query):
    url = "<https://api.unsplash.com/search/photos>"
    params = {
        "query": query,
        "client_id": UNSPLASH_ACCESS_KEY,
        "per_page": 1,
        "orientation": "landscape",
    }
    response = requests.get(url, params=params)

    if response.status_code == 200:
        data = response.json()
        if data['results']:
            return data['results'][0]
    elif response.status_code == 403:
		    raise ConnectionError("Rate limited or not authorized")

    return None When you call this function for a “New York” query, you should receive an object like that: {
   "id": "string",
   "slug": "string",
   "created_at": "string",
   "width": "integer",
   "height": "integer",
   "color": "string",
   "blur_hash": "string",
   "alt_description": "string",
   "breadcrumbs": "array",
   "urls": "object",
   "links": "object",
   "likes": "integer",
   "user": "object",
   "tags": "array",
   "...": "..."
} You should pay attention to three of them really. First, urls - this is what you will use in your app. "urls": {
    "raw": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3>",
    "full": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=srgb&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=85>",
    "regular": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=1080>",
    "small": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=400>",
    "thumb": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=200>",
    "small_s3": "<https://s3.us-west-2.amazonaws.com/images.unsplash.com/small/photo-1496442226666-8d4d0e62e6e9>",
 }, Second, user - you will use this info for proper attribution, namely: user.name : Author name


user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. Third, alt_description , make sure to add it to your final IMG tags for accessibility and SEO. This piece of code is really everything you need to get going. With one caveat. Watching for Rate Limit When you’re in initial demo mode, you will only have 50 requests per hour. When trying to fetch more images, hitting rate limit is a given. What to do? Simply wait for a bit if the function above throws ConnectionError. I wait for around 70 seconds. Also mind that if no results are found, you will receive None. In this case, I am using a simplified query (country instead of city). Once the website is online, you can request higher quota in the dashboard. Summary We've covered how to use Python and the Unsplash API to automatically fetch photos for your apps and websites. I hope you’ll build something cool with it. Send your projects my way in the comments or on Twitter. Subscribe to my profile by filling in your email address on the left, and be up-to-date with my articles! I will soon be releasing a crazy interesting project that uses this solution! Don't forget to follow me on Twitter @horosin, and subscribe to my blog’s newsletter for more tips and insights! Introduction I often want to add visually interesting elements to my projects and struggle with content. Let’s say you have a subpage that shows all the posts/any other information related to a city. It would be great to have a photo of said city in the header, wouldn’t it? You may think that the way to go is to generate something with AI. It may work, but there are many issues with this approach. There are great free stock photo sites like Pexels or Unsplash. But how to find the right picture? I stumbled upon this problem while working on one of my side projects. It turns out Unsplash has a great API. In Python, we need to call it manually but there are client packages for JS, Ruby, PHP, mobile, etc. Unsplash API The API basically allows you to find photos matching your search query. A few caveats: Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed. You must attribute Unsplash and the author. The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed. Make sure you use direct links from Unsplash. Downloading and re-hosting images is not allowed. You must attribute Unsplash and the author. You must attribute Unsplash and the author. The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. The API is rate limited to 50 requests per hour for unverified apps, 5k after approval and more if you need it. More on usage guidelines: Unsplash . Unsplash Getting started ( in-depth guide ): in-depth guide Sign up for an account: https://unsplash.com/oauth/applications Go to your apps: https://unsplash.com/oauth/applications Create a “New application.” Go to your app and save Access key; we’ll need it in the following steps Sign up for an account: https://unsplash.com/oauth/applications Sign up for an account: https://unsplash.com/oauth/applications https://unsplash.com/oauth/applications Go to your apps: https://unsplash.com/oauth/applications Go to your apps: https://unsplash.com/oauth/applications https://unsplash.com/oauth/applications Create a “New application.” Create a “New application.” Go to your app and save Access key; we’ll need it in the following steps Go to your app and save Access key; we’ll need it in the following steps Fetching an Image Have a look at this sample function; you can adapt it to your needs. import requests
import os

# Load Unsplash API key from environment variables
UNSPLASH_ACCESS_KEY = os.getenv('UNSPLASH_ACCESS_KEY')

def fetch_image_info(query):
    url = "<https://api.unsplash.com/search/photos>"
    params = {
        "query": query,
        "client_id": UNSPLASH_ACCESS_KEY,
        "per_page": 1,
        "orientation": "landscape",
    }
    response = requests.get(url, params=params)

    if response.status_code == 200:
        data = response.json()
        if data['results']:
            return data['results'][0]
    elif response.status_code == 403:
		    raise ConnectionError("Rate limited or not authorized")

    return None import requests
import os

# Load Unsplash API key from environment variables
UNSPLASH_ACCESS_KEY = os.getenv('UNSPLASH_ACCESS_KEY')

def fetch_image_info(query):
    url = "<https://api.unsplash.com/search/photos>"
    params = {
        "query": query,
        "client_id": UNSPLASH_ACCESS_KEY,
        "per_page": 1,
        "orientation": "landscape",
    }
    response = requests.get(url, params=params)

    if response.status_code == 200:
        data = response.json()
        if data['results']:
            return data['results'][0]
    elif response.status_code == 403:
		    raise ConnectionError("Rate limited or not authorized")

    return None When you call this function for a “New York” query, you should receive an object like that: {
   "id": "string",
   "slug": "string",
   "created_at": "string",
   "width": "integer",
   "height": "integer",
   "color": "string",
   "blur_hash": "string",
   "alt_description": "string",
   "breadcrumbs": "array",
   "urls": "object",
   "links": "object",
   "likes": "integer",
   "user": "object",
   "tags": "array",
   "...": "..."
} {
   "id": "string",
   "slug": "string",
   "created_at": "string",
   "width": "integer",
   "height": "integer",
   "color": "string",
   "blur_hash": "string",
   "alt_description": "string",
   "breadcrumbs": "array",
   "urls": "object",
   "links": "object",
   "likes": "integer",
   "user": "object",
   "tags": "array",
   "...": "..."
} You should pay attention to three of them really. First, urls - this is what you will use in your app. urls "urls": {
    "raw": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3>",
    "full": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=srgb&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=85>",
    "regular": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=1080>",
    "small": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=400>",
    "thumb": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=200>",
    "small_s3": "<https://s3.us-west-2.amazonaws.com/images.unsplash.com/small/photo-1496442226666-8d4d0e62e6e9>",
 }, "urls": {
    "raw": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3>",
    "full": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=srgb&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=85>",
    "regular": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=1080>",
    "small": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=400>",
    "thumb": "<https://images.unsplash.com/photo-1496442226666-8d4d0e62e6e9?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3w2MjA1MzR8MHwxfHNlYXJjaHwxfHxOZXclMjBZb3JrfGVufDB8MHx8fDE3MjA5MDAxMTd8MA&ixlib=rb-4.0.3&q=80&w=200>",
    "small_s3": "<https://s3.us-west-2.amazonaws.com/images.unsplash.com/small/photo-1496442226666-8d4d0e62e6e9>",
 }, Second, user - you will use this info for proper attribution, namely: user user.name : Author name user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. user.name : Author name user.name : Author name user.name user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. user.links.html : Link to their profile, you must use. Make sure to append utm_source=your_app_name&utm_medium=referral to it, per guidelines. user.links.html utm_source=your_app_name&utm_medium=referral Third, alt_description , make sure to add it to your final IMG tags for accessibility and SEO. alt_description This piece of code is really everything you need to get going. With one caveat. Watching for Rate Limit When you’re in initial demo mode, you will only have 50 requests per hour. When trying to fetch more images, hitting rate limit is a given. What to do? Simply wait for a bit if the function above throws ConnectionError. I wait for around 70 seconds. Also mind that if no results are found, you will receive None. In this case, I am using a simplified query (country instead of city). Once the website is online, you can request higher quota in the dashboard. Summary We've covered how to use Python and the Unsplash API to automatically fetch photos for your apps and websites. I hope you’ll build something cool with it. Send your projects my way in the comments or on Twitter. Subscribe to my profile by filling in your email address on the left, and be up-to-date with my articles! I will soon be releasing a crazy interesting project that uses this solution! Don't forget to follow me on Twitter @horosin , and subscribe to my blog’s newsletter for more tips and insights! @horosin newsletter

How to Programatically Get Photos Matching Content in Your Apps With Python and Unsplash API

What is Link Building? Link building is about the marketing efforts to get links from other websites to your website. Being a compulsory side of search engine optimization it’s seen as one of the most powerful tools to achieve higher rankings for your site in search engines. If a lot of high-quality links lead to a certain page, search engines will consider it a popular or meaningful article, and, therefore, they’ll rank it higher. Link-building is a noisy atmosphere. Plenty of remarks and completely free pieces of advice free of the foundation are chocking the internet. Looking at the link-building truths, we discover a ground with cluttered directions and ideas that can bite, scratch, and eat your website living. There is not any such thing as pure monochrome. But when it comes to link building, you've got to choose sides: snowy - or black hat link building. And here comes the threat contradicting information out there. It is simple to feel that you are on kaboom -- and the side employing this technique or this! The Google penalty strikes you. SEO is All About Backlinks One of the biggest backlinking myths is that SEO is all about these links. There is so much more to SEO than just creating backlinks; they are simply one component of this strategy. There are more essential things to focus on if you want to create a website that will rank well in Google. These include the following: A process of optimizing your website for the crawling and indexing stage. During this process, search engines will access, crawl, interpret, and index your site without complications. Technical SEO: On-page SEO is the method of optimizing web pages of your website to rank higher and earn more relevant traffic from search engines. On-page SEO relates to both the content and HTML source code of a page that can be optimized. On-Page SEO: Off-Page SEO is related to techniques you can use to improve a website’s position on SERPs (Search Engine Result Pages). Simply put, Off-Page SEO is the method of promoting your website to rank higher on a search results page Off-Page SEO: Lots of Backlinks Means Good You can easily build several backlinks, but this won’t help your website rank well if they aren’t relevant. It’s best to remove low-quality backlinks by submitting  disavow in Google, which asks Google to value those particular links or domain that is posting to your website. Examples: # Two pages to disavow http://spam.example.com/stuff/comments.html http://spam.example.com/stuff/paid-links.html # One domain to disavow domain:xyz.com By doing this, you can remove low-quality links, allowing your website to link better on Google SERPs. Links From Wikipedia Guarantee Top Rank People believe that when you get rankings from high-authority websites like Wikipedia, it will boost your website’s ranking quickly. But this couldn’t be further from the truth. Google ranks Wikipedia and other high-authority websites just like every other website does – they calculate the page ranking value of links to that website. even made this statement clear! Garry Illyes This statement clears that Wikipedia, just like other high-ranking websites, don’t receive special treatment from Google. Instead, it works on getting internal links to achieve higher success. High Domain Authority Compliments Good Backlinks There are still people who think high-quality backlinks have higher domain authority, but there is no rule for this. Other metrics, such as Trust Flow and Domain Authority, help you compare your website with your competitor’s sites. It won’t help you get a good ranking on the Google SERP, nor will it affect your backlinks. Here are a few points that will help you identify a good link: Contextual Anchor Text Relevant Content on Link High Traffic Value of the Linking Domain Link Should be Valuable for Users Image Links Are Bad for SEO Still, another myth about link-building regards image search engine optimization. Every component of search engine optimization may seem awful when abused. For example, for instance, image search engine optimization - at which plenty of crucial words in alt descriptions to get rankings and deceive Google's system. That span is gone, but a few folks look at that image links are not awful. The following issue that feeds this link-building myth is that the chance of reading pictures by crawlers. Several years ago, Google's robot could not go through the alt feature from an image very well to parse it, and it was better to know the HTML text. Nofollow Links implies Zero Link Profit A link credited using " " indicates Google never to abide by along. With a couple of popular websites deploying an automatic no trace, most of their outside links mechanism has caused it a useless metric at the search engine optimization arena. However, the facts are remote from that which sensed. The no-follow links are still out not; they nevertheless have Search Engine Optimization significance because: No-follow Even if they no-follow links, they are still getting traffic that can be a key ranking factor. Suspicious backlink profile can make Google overrun it with do-follow links. No-follow links still can be featured down there. Their existence was known to Google, i.e., there is still some links juice prevailing. Avoid Directory Links Paid directories are not the very best place to get backlinks. The sites which having directory links often penalized by Google. But the fact is there are high-quality resource directories, listings, and link lists, which won't get you penalized. There are some directories that you should wish to get listed. As an instance, consider directories which display, let us say, the very best pet stores. Guest Posting is done by SEO Spammers only The solution is ripe and straight. Guest Blogging/Posting continues to be a favorite tactic for building robust and authoritative backlinks. You ought to be selective about picking up the sites that accept guest articles and belongs to a relevant industry. Few aspects for an ideal guest posting strategy: Place it on a relevant website Loyal, reliable and established audience for the chosen website Write long-form and meaningful content that shares/add value Paid links will safe your time Avoid being part of a Private Blog Network (PBN) For a guest-blogging strategy to become active, incorporate most of the above mentioned should you wish to see extraordinary results. Just take out some time, identify the many essential websites, and who knows, you might rock it using an augmented search engine visibility. Conclusion As we know that content is the king in SEO promotion. Matured and experienced specialists are sure that link building still has a great value in providing SEO ranking. High-quality links will give a chance of better ranking in SERP. Try to check metrics with tool Ahrefs So do not go for quantity but focus on quality links to get higher ranking in search results. Origin source Gotreviewed.com

Search Engine Optimization: Link Building Myths and Reality

A series on best practices in large codebases for new engineers. This one focuses on how to think about large codebases via an introduction to the practice of commenting. Some variable names have been changed to protect the innocent. The first thing I advise new software engineers fresh out of school is this: . Trust no one It is certainly absurdly harsh, but I’ve found it serves a dual purpose: it gives engineers the confidence to share their own ideas (since existing assumptions are always suspect), and it helps motivate them to write robust, readable, and maintainable code. Most engineers at the beginning of their career have coded primarily for themselves or their TAs or at most a small group. Much of that code is temporary and used only until you hand in your assignment or scrape and analyze your data. Habits that work very well for academic or personal code will bite you in the ass in a professional environment. Imagine an environment where dozens (maybe hundreds) are contributing alongside you. Your code will live on for years, and people will be trying to read it long after you’ve forgotten what you’ve written and maybe long after you’ve left the project or company. In this things will break. Code that once worked will regress. It will regress in a thousand tiny and unpredictable ways. Your nice clean elegant API will have crap added to it, parameters slapped in and deprecated, and semantics inverted until it ends up like this real actual function from Windows XP used to create a symlink: codebase BOOL br= ::DeviceIoControl(hDir, FSCTL_GET_REPARSE_POINT, NULL, 0,&ReparseBuffer, MAXIMUM_REPARSE_DATA_BUFFER_SIZE, &dwRet, NULL); Source: http://www.flexhex.com/docs/articles/hard-links.phtml Part 1: Comments to the rescue? In this article, I’m going to discuss comments as a way to illustrate how code can evolve badly and how you can try to protect against future mistakes. Often people turn to comments to combat the uncontrolled growth of codebases. But comments are ripe for abuse. As with all code, there are good ways and bad ways to write them. Comments can do terrible damage Let’s look at a practical example. Everybody knows it’s good to comment your code to “make it readable”. Some people also know it’s bad to comment your code “too much”. Consider this comment: def user_history(days=90): for i in xrange(days):… # Do 90 days of history by default Let’s say you add this comment to your fresh new function. In a large codebase with many contributors, hundreds of people could end up reading this comment. They don’t learn anything new from reading it that they didn’t learn from the default parameter above it. That means it’s wasting engineers’ time. But it’s not just a time waster. Over the course of a year, hundreds of programmers will make changes to your codebase. Let’s say some new code is added: def user_history(days=90): … prep_user_history_processing()do_some_more_stuff() Do 90 days of history by default for i in xrange(days):… There are now many lines of code separating the function’s declaration from your comment. They might not even fit on the screen together at the same time. Now let’s say that in the intervening year, the user history processing has changed so that now we often read 1000 days of user history. Eventually one of the many programmers using this function is sick of writing user_history(days=1000). They go ahead and change the default. def user_history( ):prep_user_history_processing()do_some_more_stuff() days=1000 … Do of history by default 90 days for i in xrange(days):… Boom. Tests pass. Code pushed. A month later, another coder comes along and skims this function. They’re skipping through the file, and they see the comment but don’t actually look at the function declaration. They call the function with the default argument assuming it’s 90 days. Thus a bug is born. This is how large code bases actually work. This is how many bugs are written. Learn to program defensively Comments that don’t add anything, like the one above, aren’t useful and are potentially dangerous. But more than that . To write truly robust code you must ensure it’s not only correct (which itself can be challenging) but also resistant to skimming, misreading, carelessness, and growth. in a large codebase the evolution of your code is simply beyond your control When the author added the “90 days” comment above it was the second line of the function just below the default argument. They may have reasoned that if somebody were to change the number they would likely change the comment. They didn’t take into account that in the intervening year enough code could be added that the two would no longer even fit on the screen together at the same time. It might seem lazy to you that someone would just skip to this part of the code without looking at the top of it. But coders are people and people make mistakes. Reading code is hard. With enough commits and enough time, mistakes are inevitable. To be a great programmer, you must take into account these future mistakes when you write your code today. Writing useful comments Thoughtful code that that keeps in mind and takes care of its many hapless readers can help turn the tide toward order in a large codebase. In our user history case, the coder can fix things by simply not having the comment in the first place. It’s not needed. In the more general case the coder should consider for comments and for all code: How will this be useful over time? What misinterpretations and misconfigurations will this cause? Is this simple enough to be understood even in the face of a barrage of commits, our penchant for finding the easiest way, and our inexhaustible capacity to forget? Who is this code for? To answer the last question it’s useful to distinguish whether a comment is for someone this function or for somebody the function. calling modifying Public comments If your function is public-facing in a large codebase then the caller shouldn’t have to read the function body to understand how to call it. A clear docstring should obviate the need to read (or likely misread) the code. def user_history(days=90):"""Remotely fetch user events for the specified number of days. Args:days (int): the number of days to fetch for, rounded down byday to 12am Returns:list(UserEvent): all events found, empty list if none are found Raises:AccessException: raised if caller does not have permissionto access this data"""for i in xrange(days):… This comment has useful information upfront. The reader doesn’t have to trawl through many lines of python to figure out (possibly incorrectly) something like the return type. ( Note that static typing or something like Python’s type hints can help with this.) Take care with your docstring. Take time to consider whether it’s are really useful, whether and how it can be misinterpreted, and whether it’s likely to still be useful a month or a year from now, after many more commits. The comment above is written in . If your large codebase doesn’t have a standardized format for docstrings, you should invest time in starting one. Google’s python args standard Standards help protect against laziness and mistakes by providing a correct and easy habit. Because it’s the standard used by every function and it’s parsed for use on the docs page, everybody is accustomed to updating it. You could even deploy a linter that warns you if you’ve changed a function without updating its description. Public comments do not address the implementation of your function. People only calling the function don’t need to know about that. That’s where internal comments come in. Internal comments In theory good code should speak for itself. Variables and functions should be clearly named and control flow should be obvious. But there will always be places when the code can’t speak for itself because of something very subtle or very complex. Internal comments are written inline with the code and won’t be parsed to show in public docs. They’re intended for somebody intimate enough with the code that they’re actually going to go through and read it line by line. Well, let’s be honest; they’ll probably skip a lot of lines. Here’s an example of an internal comment: # DO NOT CHANGE THIS TO A SET!!! IT WILL BREAK number_list = [number for number in xrange(10)]do_something(number_list) 1 This comment is pretty useful — it says something that’s very hard to glean from the code, and if other coders read it then it could prevent bugs. But as we know, because they’ll be skimming or they’ll make a mistake. This comment would be even more useful as an assert: many coders won’t read it number_list = [number for number in xrange(10)] do_something(number_list) assert type(number_list) is list Now we have the same information, but even if the coder is lazy and skims the comments, they still can’t cause a bug. Their testing will fail. (Yes, there might not be adequate testing. That’s a subject for .) another time Still, we didn’t explain why, and it’s really not obvious from reading the code why we have to have a list there. Somebody might come in and try to clean up the code by removing this seemingly superfluous assert. Here you can put in a useful comment to explain what’s going on. number_list = [number for number in xrange(10)]# As of version 1.2.1, do_something has a bug that causes it to# crash if non-list collections are used for certain inputs.assert type(number_list) is listdo_something(number_list) Now, we have a precise internal comment that tells the reader something useful that they can’t easily glean from the code concerns the internals of the function that callers of this function don’t care about it explains why it’s there and why you shouldn’t remove it This by no means covers all places you should use comments, but the general rules hold. Your code should be concise and explicit, and when it can’t easily speak for itself you can write a comment that tells the reader something useful. Often people won’t bother to read comments, so you shouldn’t rely on them too heavily. Compassion without trust The truth is that reading and understanding somebody else’s code is hard. Given enough people with enough code and enough time, mistakes and carelessness are guaranteed to happen regularly. Trust no one to be mistake-free. When producing code for others you should keep these traits of your fellow coders (and yourself) in mind and defend against them as consistently as possible. By being thoughtful and concise and using well-understood standards, and by generally being aware of our own faults as we write code, we can begin to guard against our own mistakes. In future articles I’ll discuss how this applies to other aspects of software including testing and global state. engineering Article 2: A Beginner’s Guide to Automated Testing Article 3: Quick Tips for Gitting on a Team is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Trust No One: An Introduction to Large Codebases for New Engineers

I am pleased to invite Brilliantcrypto's Chief Executive Officer, Harai Yoshiaki, to this exclusive interview. Brilliantcrypto has grown remarkably since its June 2024 launch, hitting vital benchmarks in transaction volume and user engagement. Additionally, the platform has captured the attention of players all across the world with its unique combination of strategy, adventure, and real-world rewards. Harai Yoshiaki will go over the main elements of Brilliantcrypto's quick growth in this interview, including its innovative gaming features, long-term play-to-earn strategy, and future development plans. Let's dive in. — Ade: Could you tell us about the motivation for Brilliantcrypto and how it embodies the spirit of exploration and gem mining, similar to historical events like the Gold Rush? Harai Yoshiaki: Of course. Originally, the idea for Brilliantcrypto came from the founder’s interest in two particular forms of value: gemstones and Bitcoin. He is both a big gemstone collector and has also been watching and investing in Bitcoin for a long time, so he wanted to try to combine the two and create digital gemstones on the blockchain. The game is the vehicle for generating these new assets. In a sense, it is a digital gemstone rush. Once the project becomes mainstream, it will surely cause a big stir in the gaming community. Ade: What distinguishes Brilliantcrypto from other play-to-earn games, and could you explain how users go from being casual players to becoming profitable miners? Harai Yoshiaki: Brilliantcrypto is different in that it aims to be sustainable. Most play-to-earn games, or at least all of the games that made the genre popular, are known to have been similar to “ponzi” schemes. In order to earn the token, you must purchase the token, and the earnings of the early investors are powered by new investors - when new investors cease joining, the value collapses. \ Brilliantcrypto does this differently. The “earning” aspect exists through the sale of the NFT gemstones mined in the game, and the token, a separate entity, is the entry to this possibility (through the purchase of NFT pickaxes). Meanwhile, the gemstones are positioned for purchase from gemstone enthusiasts, NFT collectors, and those who intend to use them in digital worlds as NFT fashion in the future. Therefore, the value of the gemstones (the asset to be earned) is subject to outside investment, breaking the closed-circle economy and promoting sustainability. Ade: In what ways does Brilliantcrypto's decentralized gameplay model promote sustainability, fair play, and transparency? Additionally, what effect does the "Proof of Gaming" concept have on the game's economy? Harai Yoshiaki: The main decentralized concept of Brilliantcrypto is that the team is not responsible for the generation of the NFT gemstones - the players are. If the gemstones are not mined, then they will not be generated. Further, The aspects of each individual gemstone (Color, Clarity, Cut, and Carat Weight) are decided by an innovative open-source algorithm that uses the future unpredictability of blockchain transaction hashes as a key to randomize these factors. The team cannot predict if, when or where an extremely rare gemstone will appear. The team also has a strong relationship with its community and aims to be timely and transparent about updates or new developments surrounding the project. Ade: Could you provide more details on the BRIL token's benefits and mechanics, such as its listing on international cryptocurrency exchanges like Bit2Me, its utility within the game, and its potential impact on the game's user base expansion? Harai Yoshiaki: The BRIL token broke records in Japan, with its IEO reaching $212 million USD in application funds, the largest ever on the Coincheck IEO platform and, therefore, likely the largest in Japan. The token can be used for purchasing pickaxes and gemstones, repairing and upgrading pickaxes, and incentivizing scholars. It is typically stable as the token is the target of “earning” in the game, which is a benefit. It also trades in over 60 countries on the Bit2Me platform, which will promote the expansion of the game around the world. We plan to list the token on more and more exchanges to make it more available for potential players. Ade: How does the scholar system work in detail? What are the typical arrangements between mine owners and scholars, and how are rewards split? Harai Yoshiaki: The scholarship system is fully inbuilt, and pickaxe owners can choose how much BRIL is distributed to those who mine “Brilliantstones” in the game on their behalf. Gemstones and Treasure Capsules (containing items such as dynamite or costumes) are sent to the owner, while the Brilliantstones are split between the owner and the scholars who are mining in the mine. Ade: What enhancements or new features will be added to Brilliantcrypto Version 1.1.6, and are there any further releases planned for iOS? Harai Yoshiaki: The update will feature updates to the scholarship U.I, as well as marketplace features, such as a new “bulk sale” feature and the ability to change prices for items while they are on sale. Also, some other minor fixes will ship with the update. We will make announcements about future updates as soon as the information becomes available. Ade: Can you tell us more about Brilliantcrypto's experience at NFT Brazil 2024, including the reception of the Proof of Gaming concept? Are there any plans for similar events or expansions in other regions? Harai Yoshiaki: Brilliantcrypto has been at events around the world on many continents and has received a great response every time from the passionate web3 gamers that visit the booth. We have also been able to meet some great business partners through participating in these events. NFT Brazil 2024, in particular, was a great event with a greater ratio of gamers than usual, and we were able to connect with many community leaders to help us distribute the game in the country. Ade: Could you perhaps share more details about Brilliantcrypto's roadmap? What new features, collaborations, or expansions are planned for Q4 20224 and the foreseeable future? Harai Yoshiaki: We plan to start work on phase 2 of our roadmap, which is the feature that allows players to create NFT jewelry using their NFT gemstones. Following this, we hope to propagate this jewelry throughout the digital world through collaborations with large-scale metaverses and fashion brands alike. I am pleased to invite Brilliantcrypto's Chief Executive Officer, Harai Yoshiaki, to this exclusive interview. I am pleased to invite Brilliantcrypto's Chief Executive Officer, Harai Yoshiaki, to this exclusive interview. Brilliantcrypto has grown remarkably since its June 2024 launch, hitting vital benchmarks in transaction volume and user engagement. Additionally, the platform has captured the attention of players all across the world with its unique combination of strategy, adventure, and real-world rewards. Brilliantcrypto has grown remarkably since its June 2024 launch, hitting vital benchmarks in transaction volume and user engagement. Additionally, the platform has captured the attention of players all across the world with its unique combination of strategy, adventure, and real-world rewards. Brilliantcrypto Brilliantcrypto Harai Yoshiaki will go over the main elements of Brilliantcrypto's quick growth in this interview, including its innovative gaming features, long-term play-to-earn strategy, and future development plans. Let's dive in. — Ade: Could you tell us about the motivation for Brilliantcrypto and how it embodies the spirit of exploration and gem mining, similar to historical events like the Gold Rush? Ade: Could you tell us about the motivation for Brilliantcrypto and how it embodies the spirit of exploration and gem mining, similar to historical events like the Gold Rush? Could you tell us about the motivation for Brilliantcrypto and how it embodies the spirit of exploration and gem mining, similar to historical events like the Gold Rush? Harai Yoshiaki: Of course. Originally, the idea for Brilliantcrypto came from the founder’s interest in two particular forms of value: gemstones and Bitcoin. He is both a big gemstone collector and has also been watching and investing in Bitcoin for a long time, so he wanted to try to combine the two and create digital gemstones on the blockchain. The game is the vehicle for generating these new assets. Harai Yoshiaki: In a sense, it is a digital gemstone rush. Once the project becomes mainstream, it will surely cause a big stir in the gaming community. Ade: What distinguishes Brilliantcrypto from other play-to-earn games, and could you explain how users go from being casual players to becoming profitable miners? Ade: What distinguishes Brilliantcrypto from other play-to-earn games, and could you explain how users go from being casual players to becoming profitable miners? What distinguishes Brilliantcrypto from other play-to-earn games, and could you explain how users go from being casual players to becoming profitable miners? Harai Yoshiaki: Brilliantcrypto is different in that it aims to be sustainable. Most play-to-earn games, or at least all of the games that made the genre popular, are known to have been similar to “ponzi” schemes. In order to earn the token, you must purchase the token, and the earnings of the early investors are powered by new investors - when new investors cease joining, the value collapses. \ Harai Yoshiaki: Brilliantcrypto does this differently. The “earning” aspect exists through the sale of the NFT gemstones mined in the game, and the token, a separate entity, is the entry to this possibility (through the purchase of NFT pickaxes). Meanwhile, the gemstones are positioned for purchase from gemstone enthusiasts, NFT collectors, and those who intend to use them in digital worlds as NFT fashion in the future. Therefore, the value of the gemstones (the asset to be earned) is subject to outside investment, breaking the closed-circle economy and promoting sustainability. Ade: In what ways does Brilliantcrypto's decentralized gameplay model promote sustainability, fair play, and transparency? Additionally, what effect does the "Proof of Gaming" concept have on the game's economy? Ade: In what ways does Brilliantcrypto's decentralized gameplay model promote sustainability, fair play, and transparency? Additionally, what effect does the "Proof of Gaming" concept have on the game's economy? Ade: In what ways does Brilliantcrypto's decentralized gameplay model promote sustainability, fair play, and transparency? Additionally, what effect does the "Proof of Gaming" concept have on the game's economy? Harai Yoshiaki: The main decentralized concept of Brilliantcrypto is that the team is not responsible for the generation of the NFT gemstones - the players are. If the gemstones are not mined, then they will not be generated. Harai Yoshiaki: Further, The aspects of each individual gemstone (Color, Clarity, Cut, and Carat Weight) are decided by an innovative open-source algorithm that uses the future unpredictability of blockchain transaction hashes as a key to randomize these factors. The team cannot predict if, when or where an extremely rare gemstone will appear. The team also has a strong relationship with its community and aims to be timely and transparent about updates or new developments surrounding the project. Ade: Could you provide more details on the BRIL token's benefits and mechanics, such as its listing on international cryptocurrency exchanges like Bit2Me, its utility within the game, and its potential impact on the game's user base expansion? Ade: Could you provide more details on the BRIL token's benefits and mechanics, such as its listing on international cryptocurrency exchanges like Bit2Me, its utility within the game, and its potential impact on the game's user base expansion? Could you provide more details on the BRIL token's benefits and mechanics, such as its listing on international cryptocurrency exchanges like Bit2Me, its utility within the game, and its potential impact on the game's user base expansion? Harai Yoshiaki: The BRIL token broke records in Japan, with its IEO reaching $212 million USD in application funds, the largest ever on the Coincheck IEO platform and, therefore, likely the largest in Japan. Harai Yoshiaki: The token can be used for purchasing pickaxes and gemstones, repairing and upgrading pickaxes, and incentivizing scholars. It is typically stable as the token is the target of “earning” in the game, which is a benefit. It also trades in over 60 countries on the Bit2Me platform, which will promote the expansion of the game around the world. We plan to list the token on more and more exchanges to make it more available for potential players. Ade: How does the scholar system work in detail? What are the typical arrangements between mine owners and scholars, and how are rewards split? Ade: How does the scholar system work in detail? What are the typical arrangements between mine owners and scholars, and how are rewards split? How does the scholar system work in detail? What are the typical arrangements between mine owners and scholars, and how are rewards split? Harai Yoshiaki: The scholarship system is fully inbuilt, and pickaxe owners can choose how much BRIL is distributed to those who mine “Brilliantstones” in the game on their behalf. Gemstones and Treasure Capsules (containing items such as dynamite or costumes) are sent to the owner, while the Brilliantstones are split between the owner and the scholars who are mining in the mine. Harai Yoshiaki: Ade: What enhancements or new features will be added to Brilliantcrypto Version 1.1.6, and are there any further releases planned for iOS? Ade: What enhancements or new features will be added to Brilliantcrypto Version 1.1.6, and are there any further releases planned for iOS? Ade: What enhancements or new features will be added to Brilliantcrypto Version 1.1.6, and are there any further releases planned for iOS? Harai Yoshiaki: The update will feature updates to the scholarship U.I, as well as marketplace features, such as a new “bulk sale” feature and the ability to change prices for items while they are on sale. Also, some other minor fixes will ship with the update. Harai Yoshiaki: We will make announcements about future updates as soon as the information becomes available. Ade: Can you tell us more about Brilliantcrypto's experience at NFT Brazil 2024, including the reception of the Proof of Gaming concept? Are there any plans for similar events or expansions in other regions? Ade: Can you tell us more about Brilliantcrypto's experience at NFT Brazil 2024, including the reception of the Proof of Gaming concept? Are there any plans for similar events or expansions in other regions? Can you tell us more about Brilliantcrypto's experience at NFT Brazil 2024, including the reception of the Proof of Gaming concept? Are there any plans for similar events or expansions in other regions? Harai Yoshiaki: Brilliantcrypto has been at events around the world on many continents and has received a great response every time from the passionate web3 gamers that visit the booth. We have also been able to meet some great business partners through participating in these events. Harai Yoshiaki: NFT Brazil 2024, in particular, was a great event with a greater ratio of gamers than usual, and we were able to connect with many community leaders to help us distribute the game in the country. Ade: Could you perhaps share more details about Brilliantcrypto's roadmap? What new features, collaborations, or expansions are planned for Q4 20224 and the foreseeable future? Ade: Could you perhaps share more details about Brilliantcrypto's roadmap? What new features, collaborations, or expansions are planned for Q4 20224 and the foreseeable future? Could you perhaps share more details about Brilliantcrypto's roadmap? What new features, collaborations, or expansions are planned for Q4 20224 and the foreseeable future? Harai Yoshiaki: We plan to start work on phase 2 of our roadmap, which is the feature that allows players to create NFT jewelry using their NFT gemstones. Following this, we hope to propagate this jewelry throughout the digital world through collaborations with large-scale metaverses and fashion brands alike. Harai Yoshiaki:

Interview with Brilliantcrypto's CEO: Insights on Innovation, Growth, and What's Next

Web3 gaming is here to stay and is growing into a significant market within the web3 industry. This emergence is evolving beyond a game into a new ecology and economy in which players are active stakeholders rather than passive participants. In this exciting time of development, we are honoured to have Faizy Ahmed, a real pioneer and visionary, to share his views and skills. As a seasoned crypto veteran, blockchain thought leader, Web3 VC partner and driving force behind Astra Nova, as well as a Blockchain Game Alliance member, Faizy has played a key role in developing the MENA region's Web3 gaming environment. Now, let's get started. Ade: You've had an impressive journey in the blockchain and crypto space since 2015. Can you share some experiences and how they've shaped your approach to Web3 gaming? Faizy: My journey started in 2015 as a crypto miner, and it didn’t take long for me to see the incredible potential blockchain holds, especially for gaming. I’ve always been a gamer, but the idea that you could actually own your in-game assets and have a say in how the game world evolves fascinated me. Over the years, I moved from mining to trading, participating in ICOs, and contributing to DAOs. This broad exposure to different aspects of blockchain helped shape my approach to Web3 gaming. What excites me about this space is the ability to give players real ownership and build along with the users, which is lacking in traditional games. That’s what we’re bringing to Astra Nova, an ecosystem where players are stakeholders, not just consumers. Ade: How has blockchain's value proposition in gaming evolved in MENA, and what emerging trends or models show promise? Faizy: The MENA region is one of the fastest-growing gaming markets in the world. In countries like Saudi Arabia, 8 out of 10 people identify themselves as gamers, and the region as a whole has over 400 million people with a strong mobile-first culture. Blockchain adds immense value here by offering digital ownership and decentralized economies. Gamers in MENA are already spending heavily on in-game purchases, so the transition to owning these digital assets is a natural next step. One of the most promising trends is the play-to-earn model, which is gaining traction as it blends entertainment with financial incentives. Players want to be rewarded for their time, and that’s what we’re doing with Astra Nova by offering real ownership and opportunities to earn from their gameplay. Ade: Your work with Astra Nova aims to bridge the gap between traditional gamers and Web3 gaming in the MENA region. Can you share insight about your offerings and products? Faizy: At Astra Nova, we’re not just creating a game; we’re building a Web3 gaming ecosystem that’s designed to bring the benefits of blockchain to traditional gamers. Our game is an action RPG, where players can own in-game assets through NFTs and participate in UGC (User generated content) contests, which actually shape the world they’re playing in. One of our recent milestones was securing Saudi royal family backing from Prince Faisal Turki Al Saud, which has given us a strong foundation to expand. We’ve raised over $2.5M across fundraising rounds, showing that our community has real buying power and investor interest despite challenging market conditions. We’re also proud of selling out our SuperNova NFT series with OKX in record time and having onboarded 200k+ users through our Black Pass Loyalty & Questing Platform. We’ve also strengthened our team by onboarding top talent, including AAA gaming experienced senior executives for Creative Director, Technical Director, Executive Producer and Art Director roles who’ve worked on renowned blockbuster games. Looking forward, we’re excited to be selected for the NTDP and Outlier Ventures Riyadh Base Camp Acceleration Program, along with launching our Telegram Mini-Game through the NOTCOIN TON Accelerator to a potential audience of 40M+ users. These achievements demonstrate the strong appetite for Web3 gaming in the region. Ade: As a member of the Blockchain Game Alliance and Forbes Web3 Council, how do you envision the future of blockchain gaming? Will Giant traditional game Leaders take the lead in the blockchain space in the long run? Faizy: I believe blockchain gaming will be shaped by early innovators before traditional gaming giants fully enter the space. Right now, smaller, more agile projects like Astra Nova are pushing the boundaries of what’s possible. While big companies like Ubisoft are starting to experiment with blockchain, the real innovation is happening in projects that are built from the ground up on Web3 principles. The community-driven aspect of blockchain gaming, where players own assets and have a say in governance is something that large traditional companies will struggle to fully integrate. However, as blockchain gaming gains traction, I do expect traditional gaming leaders to adopt some elements of it, but Web3-first projects like ours will be leading the charge. Ade: What advice would you give to entrepreneurs and developers entering Web3 gaming, and how can they scale up and on? Faizy: My biggest advice is to build around your community from day one. In Web3, the community isn’t just important; they are your co-creators and investors. Engage them early, listen to their feedback, and let them help shape your project. Secondly, make sure your game has real utility. NFTs and tokens are great, but they need to have value beyond speculation, whether that’s in gameplay or governance. In terms of scaling, partnerships are crucial. At Astra Nova, our growth has been fueled by collaborations with top-tier partners like Aethir, OKX, Gate.io, Base, TON, Google Web3 Accelerator, and Outlier Ventures, which have helped us expand our reach and credibility. Don’t be afraid to network and build strategic alliances early on. Ade: What factors will drive blockchain gaming's mainstream adoption in MENA, and how might AI and blockchain technologies converge to create new gaming possibilities? Faizy: The MENA region is ripe for blockchain gaming adoption. With gaming being such a major part of the culture, especially in countries like Saudi Arabia, where 80% of the population identifies as gamers—blockchain gaming’s offer of true ownership and earning potential is incredibly appealing. The region is tech-savvy and hungry for innovative experiences. As for the convergence of AI and blockchain, I see it creating more dynamic, personalized gaming experiences. AI can enhance how games respond to players, while blockchain ensures that these personalized experiences are truly owned by the player. At Astra Nova, we’re already exploring how these technologies can intersect to create a more immersive, player-driven game world. Web3 gaming is here to stay and is growing into a significant market within the web3 industry. This emergence is evolving beyond a game into a new ecology and economy in which players are active stakeholders rather than passive participants. In this exciting time of development, we are honoured to have Faizy Ahmed, a real pioneer and visionary, to share his views and skills. As a seasoned crypto veteran, blockchain thought leader, Web3 VC partner and driving force behind Astra Nova, as well as a Blockchain Game Alliance member, Faizy has played a key role in developing the MENA region's Web3 gaming environment. Now, let's get started. Ade: You've had an impressive journey in the blockchain and crypto space since 2015. Can you share some experiences and how they've shaped your approach to Web3 gaming? Ade: You've had an impressive journey in the blockchain and crypto space since 2015. Can you share some experiences and how they've shaped your approach to Web3 gaming? Faizy: My journey started in 2015 as a crypto miner, and it didn’t take long for me to see the incredible potential blockchain holds, especially for gaming. I’ve always been a gamer, but the idea that you could actually own your in-game assets and have a say in how the game world evolves fascinated me. Over the years, I moved from mining to trading, participating in ICOs, and contributing to DAOs. This broad exposure to different aspects of blockchain helped shape my approach to Web3 gaming. What excites me about this space is the ability to give players real ownership and build along with the users, which is lacking in traditional games. That’s what we’re bringing to Astra Nova, an ecosystem where players are stakeholders, not just consumers. Faizy: Ade: How has blockchain's value proposition in gaming evolved in MENA, and what emerging trends or models show promise? Ade: How has blockchain's value proposition in gaming evolved in MENA, and what emerging trends or models show promise? Faizy: The MENA region is one of the fastest-growing gaming markets in the world. In countries like Saudi Arabia, 8 out of 10 people identify themselves as gamers, and the region as a whole has over 400 million people with a strong mobile-first culture. Blockchain adds immense value here by offering digital ownership and decentralized economies. Gamers in MENA are already spending heavily on in-game purchases, so the transition to owning these digital assets is a natural next step. One of the most promising trends is the play-to-earn model, which is gaining traction as it blends entertainment with financial incentives. Players want to be rewarded for their time, and that’s what we’re doing with Astra Nova by offering real ownership and opportunities to earn from their gameplay. Ade: Your work with Astra Nova aims to bridge the gap between traditional gamers and Web3 gaming in the MENA region. Can you share insight about your offerings and products? Ade: Your work with Astra Nova aims to bridge the gap between traditional gamers and Web3 gaming in the MENA region. Can you share insight about your offerings and products? Faizy: At Astra Nova, we’re not just creating a game; we’re building a Web3 gaming ecosystem that’s designed to bring the benefits of blockchain to traditional gamers. Our game is an action RPG, where players can own in-game assets through NFTs and participate in UGC (User generated content) contests, which actually shape the world they’re playing in. One of our recent milestones was securing Saudi royal family backing from Prince Faisal Turki Al Saud, which has given us a strong foundation to expand. We’ve raised over $2.5M across fundraising rounds, showing that our community has real buying power and investor interest despite challenging market conditions. Faizy: We’re also proud of selling out our SuperNova NFT series with OKX in record time and having onboarded 200k+ users through our Black Pass Loyalty & Questing Platform. We’ve also strengthened our team by onboarding top talent, including AAA gaming experienced senior executives for Creative Director, Technical Director, Executive Producer and Art Director roles who’ve worked on renowned blockbuster games. Looking forward, we’re excited to be selected for the NTDP and Outlier Ventures Riyadh Base Camp Acceleration Program, along with launching our Telegram Mini-Game through the NOTCOIN TON Accelerator to a potential audience of 40M+ users. These achievements demonstrate the strong appetite for Web3 gaming in the region. Ade: As a member of the Blockchain Game Alliance and Forbes Web3 Council, how do you envision the future of blockchain gaming? Will Giant traditional game Leaders take the lead in the blockchain space in the long run? Ade: As a member of the Blockchain Game Alliance and Forbes Web3 Council, how do you envision the future of blockchain gaming? Will Giant traditional game Leaders take the lead in the blockchain space in the long run? Faizy: I believe blockchain gaming will be shaped by early innovators before traditional gaming giants fully enter the space. Right now, smaller, more agile projects like Astra Nova are pushing the boundaries of what’s possible. While big companies like Ubisoft are starting to experiment with blockchain, the real innovation is happening in projects that are built from the ground up on Web3 principles. The community-driven aspect of blockchain gaming, where players own assets and have a say in governance is something that large traditional companies will struggle to fully integrate. However, as blockchain gaming gains traction, I do expect traditional gaming leaders to adopt some elements of it, but Web3-first projects like ours will be leading the charge. Faizy: Ade: What advice would you give to entrepreneurs and developers entering Web3 gaming, and how can they scale up and on? Ade: What advice would you give to entrepreneurs and developers entering Web3 gaming, and how can they scale up and on? Faizy: My biggest advice is to build around your community from day one. In Web3, the community isn’t just important; they are your co-creators and investors. Engage them early, listen to their feedback, and let them help shape your project. Secondly, make sure your game has real utility. NFTs and tokens are great, but they need to have value beyond speculation, whether that’s in gameplay or governance. In terms of scaling, partnerships are crucial. At Astra Nova, our growth has been fueled by collaborations with top-tier partners like Aethir, OKX, Gate.io, Base, TON, Google Web3 Accelerator, and Outlier Ventures, which have helped us expand our reach and credibility. Don’t be afraid to network and build strategic alliances early on. Faizy: Ade: What factors will drive blockchain gaming's mainstream adoption in MENA, and how might AI and blockchain technologies converge to create new gaming possibilities? Ade: What factors will drive blockchain gaming's mainstream adoption in MENA, and how might AI and blockchain technologies converge to create new gaming possibilities? Faizy: The MENA region is ripe for blockchain gaming adoption. With gaming being such a major part of the culture, especially in countries like Saudi Arabia, where 80% of the population identifies as gamers—blockchain gaming’s offer of true ownership and earning potential is incredibly appealing. The region is tech-savvy and hungry for innovative experiences. As for the convergence of AI and blockchain, I see it creating more dynamic, personalized gaming experiences. AI can enhance how games respond to players, while blockchain ensures that these personalized experiences are truly owned by the player. At Astra Nova, we’re already exploring how these technologies can intersect to create a more immersive, player-driven game world. Faizy:

Leveling Up MENA's Web3 Gaming Scene: Insights from Faizy Ahmed, Astra Nova CEO & Co-Founder

Messy government data has been part of the reason we've been the COVID-19 pandemic. If federal organizations can't decode big data, what hope do small businesses have? unable to understand We’ve never had this much data on our hands. But as the material we’re dealing with grows, we need to ensure the skeleton that supports it is evolving sturdily alongside it. That’s no small feat, given the breakneck speed at which our data load . The more data we have at our fingertips, the more unbridled its potential is - but the harder it also becomes to harness it, especially for companies with less of a tech background or smaller R&D capabilities. A lack of accessible and affordable tooling would leave smaller players behind, making access to big data analytics an increasingly exclusive privilege. increases every day Nevertheless, the past decade has shown us that data analytics and tools have the capacity to speedily advance to accommodate the growing wave of information. And has a big role to play in that. As all companies are also becoming tech companies, engineering teams need to develop software that captures big data and uses machine learning so they can stay in the game. Development Analytics tools like Waydev are there to help engineering managers fully understand and improve the development process of these analytics projects. So it’s a bit of an analytics inception if you will. Development Analytics In today’s landscape, three essential pillars have emerged in data analytics - visualization, open source, and AI. Here's how the future of data analytics and tools can make this sphere more democratic, and what this means for software development. Data visualization tools will make big data digestible Startups are no stranger to big data. They can get huge swaths of it relatively straightforward, but that doesn't mean they hold the keys to understanding it. One of the first data visualization tools, the Gantt chart, was invented in 1910 to visually track data across large teams. Big data is typically unstructured, making it difficult to draw conclusions from the figures. But if companies can't accurately interpret data, its value is wasted. Raw data doesn't always tell a story; to do that it has to be processed and curated in a more visually appealing form. The next generation of big data tools will prioritize simple data visualization for all users. Graphics, maps, tables, and patterns are far more intuitive than text and can stitch seemingly abstract stats together - which is especially useful for small business leaders who don't have a tech background. In fact, we're already seeing the shift with tools like Visme and Tableau, which transform big data into engaging charts and infographics. Visualization becomes all the more powerful when coupled with relevant data sources to provide business insights and help leaders gain a better understanding of their team’s work. Gantt charts may have been an innovation for its time but in the Agile software development environment, they are of no use. Development Analytics tools like Waydev and Pluralsight Flow use user-friendly dashboards, heatmaps, color-coding, charts, and diagrams to signal progress, issues, and steer the user’s attention to the important values. Open source will improve data tools' transparency and quality The popularization of data tools has paved the way for open source software like Android (the operating system used by Google phones), which can be freely used or modified by anyone. Whereas data tools used to be exclusively reserved for paying customers (read: deep-pocketed corporations), open source tooling is free and made for the public, by the public. Many tools that were once closed source have made the switch to open source in recent years. For example, Apache Hadoop, the framework that uses programming models for distributed processing of large data sets, quickly became supported by NoSQL databases and other open source infrastructure when it started to be widely adopted. Today, the majority of infrastructure used to store and stream large volumes of data is open source, including databases like MongoDB and tools like TensorFlow. Even the notoriously secretive Apple has hopped on the bandwagon, releasing its programming language, Swift, as open source in 2014. Data science wouldn't be as large in scope, nor as efficient, without open source tooling. As data grows, small businesses need more sophisticated data processing tools, which will benefit from open contributions. The more people that contribute to code, the higher the quality of the tool. And, as more people own a cell phone, are active online, and share information digitally, data sets will continue to grow exponentially and push demand for data-processing tools. In order to maximize the functionality and interoperability of these tools, businesses should start embracing open source from the get-go. AI data tools will become mainstream (at last) Despite finding its roots in the 1950s, AI didn't really until the 1990s. In 1997, the world chess champion was beaten by IBM's AI chess-playing program, Deep Blue. In the same year, Dragon System developed and implemented its speech-recognition software on Windows. The two creations were a big step for AI, and for many people, signaled its impending uptake across industries. get started The reality though, is that AI is yet to be adopted on a mass scale. A 2020 survey revealed that only of US businesses use AI, and the ones that do are mostly big companies that have the money to invest in the tech: not surprising when companies pay between for AI software. 8.9% $6,000 and $300,000 Although the uptake is relatively slow, early signs of more financially flexible AI exist, and they go a long way toward democratizing data tools for all companies, regardless of their size. Takeaway Data and analytics tools won't be fizzling out any time soon. Both have progressed considerably in recent years, and with expanding data sets at the heart of these tools, they'll not only get better, they’ll also get smarter. In software development, in particular, we’ll be seeing widespread adoption of development analytics in the following years. Engineering leaders will keep investing in Development Analytics tools so they can meet the growing demands of their organizations, and remain ahead of the innovation curve. This only means we’re getting one step closer to normalizing a data-driven culture in software engineering.

How to Democratize Access to Data Insights for Businesses of All Sizes

JobQuest Assistant: How I Built a Smart Companion for Stress-Free Job Hunting With Coze

Asistente de JobQuest: cómo creé un compañero inteligente para buscar trabajo sin estrés con Coze

JobQuest Assistant: nola sortu nuen Estresik gabeko Lan Ehizarako Lagun Adimendun bat Coze-rekin

JobQuest Assistant: איך בניתי בן לוויה חכם לציד עבודה נטול מתחים עם Coze

JobQuest Assistant: Coze を使ってストレスフリーな就職活動のためのスマートなコンパニオンを構築する方法

ជំនួយការ JobQuest: របៀបដែលខ្ញុំបង្កើតដៃគូឆ្លាតវៃសម្រាប់ការស្វែងរកការងារដោយគ្មានភាពតានតឹងជាមួយ Coze

JobQuest Assistant: Coze로 스트레스 없는 취업 활동을 위한 스마트한 동반자를 만든 방법

JobQuest asistents: kā es izveidoju gudru kompanjonu darba meklējumiem bez stresa ar mājīgumu

JobQuest Yanapakuq: Imaynatataq huk Smart Compañerota ruwarqani Cozewan Mana Estrés Llamkana Cazapaq

JobQuest Assistant: Coze සමඟ ආතතියෙන් තොර රැකියා දඩයම් කිරීම සඳහා මම ස්මාර්ට් සහකාරියක් ගොඩනඟා ගත් ආකාරය

JobQuest kömekçisi: Coze bilen stresiz iş awlamak üçin akylly ýoldaş nädip gurdum

JobQuest Assistant: як я створив розумного компаньйона для пошуку роботи без стресу з Coze

Trợ lý JobQuest: Cách tôi xây dựng một người bạn đồng hành thông minh để tìm việc không căng thẳng với Coze

Authors:
(1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.;
(2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com;
(3) Aliaksandr Siarohin, Snap Inc.;
(4) Ivan Skorokhodov, Snap Inc.;
(5) Yanyu Li, Snap Inc.;
(6) Dahua Lin, CUHK;
(7) Xihui Liu, HKU;
(8) Ziwei Liu, NTU;
(9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 4 Human Verse Dataset 5 Experiments 5.1 Main Results 5.2 Ablation Study 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses ABSTRACT Despite significant advances in large-scale text-to-image models, achieving hyperrealistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL·E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to the fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a largescale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface-normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface-normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios 1 INTRODUCTION Generating hyper-realistic human images from user conditions, e.g., text and pose, is of great importance to various applications, such as image animation (Liu et al., 2019) and virtual try-on (Wang et al., 2018). To this end, many efforts explore the task of controllable human image generation. Early methods either resort to variational auto-encoders (VAEs) in a reconstruction manner (Ren et al., 2020), or improve the realism by generative adversarial networks (GANs) (Siarohin et al., 2019). Though some of them create high-quality images (Zhang et al., 2022; Jiang et al., 2022), the unstable training and limited model capacity confine them to small datasets of low diversity. Recent emergence of diffusion models (DMs) (Ho et al., 2020) has set a new paradigm for realistic synthesis and become the predominant architecture in Generative AI (Dhariwal & Nichol, 2021). Nevertheless, the exemplar text-to-image (T2I) models like Stable Diffusion (Rombach et al., 2022) and DALL·E 2 (Ramesh et al., 2022) still struggle to create human images with coherent anatomy, e.g., arms and legs, and natural poses. The main reason lies in that human is articulated with nonrigid deformations, requiring structural information that can hardly be depicted by text prompts. To enable structural control for image generation, recent works like ControlNet (Zhang & Agrawala, 2023) and T2I-Adapter (Mou et al., 2023) introduce a learnable branch to modulate the pre-trained DMs, e.g., Stable Diffusion, in a plug-and-play manner. However, these approaches suffer from the feature discrepancy between the main and auxiliary branches, leading to inconsistency between the control signals (e.g., pose maps) and the generated images. To address the issue, HumanSD (Ju et al., 2023b) proposes to directly input body skeleton into the diffusion U-Net by channel-wise concatenation. However, it is confined to generating artistic style images of limited diversity. Besides, human images are synthesized only with pose control, while other structural information like depth maps and surface-normal maps are not considered. In a nutshell, previous studies either take a singular control signal as input condition, or treat different control signals separately as independent guidance, instead of modeling the multi-level correlations between human appearance and different types of structural information. Realistic human generation with coherent structure remains unsolved. In this paper, we propose a unified framework HyperHuman to generate in-the-wild human images of high realism and diverse layouts. The key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. Specifically, we first establish a large-scale human-centric dataset called HumanVerse that contains 340M in-the-wild human images of high quality and diversity. It has comprehensive annotations, such as the coarse-level body skeletons, the fine-grained depth and surface-normal maps, and the high-level image captions and attributes. Based on this, two modules are designed for hyper-realistic controllable human image generation. In Latent Structural Diffusion Model, we augment the pre-trained diffusion backbone to simultaneously denoise the RGB, depth, and normal. Appropriate network layers are chosen to be replicated as structural expert branches, so that the model can both handle input/output of different domains, and guarantee the spatial alignment among the denoised textures and structures. Thanks to such dedicated design, the image appearance, spatial relationship, and geometry are jointly modeled within a unified network, where each branch is complementary to each other with both structural awareness and textural richness. To generate monotonous depth and surface-normal that have similar values in local regions, we utilize an improved noise schedule to eliminate low-frequency information leakage. The same timestep is sampled for each branch to achieve better learning and feature fusion. With the spatially-aligned structure maps, in Structure-Guided Refiner, we compose the predicted conditions for detailed generation of high resolution. Moreover, we design a robust conditioning scheme to mitigate the effect of error accumulation in our two-stage generation pipeline. To summarize, our main contributions are three-fold: 1) We propose a novel HyperHuman framework for in-the-wild controllable human image generation of high realism. A large-scale humancentric dataset HumanVerse is curated with comprehensive annotations like human pose, depth, and surface normal. As one of the earliest attempts in human generation foundation model, we hope to benefit future research. 2) We propose the Latent Structural Diffusion Model to jointly capture the image appearance, spatial relationship, and geometry in a unified framework. The Structure-Guided Refiner is further devised to compose the predicted conditions for generation of better visual quality and higher resolution. 3) Extensive experiments demonstrate that our HyperHuman yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Authors: Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: jren@snapchat.com; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem Setting 3 Our Approach and 3.1 Preliminaries and Problem Setting 3.2 Latent Structural Diffusion Model 3.2 Latent Structural Diffusion Model 3.3 Structure-Guided Refiner 3.3 Structure-Guided Refiner 4 Human Verse Dataset 4 Human Verse Dataset 5 Experiments 5 Experiments 5.1 Main Results 5.1 Main Results 5.2 Ablation Study 5.2 Ablation Study 6 Discussion and References 6 Discussion and References A Appendix and A.1 Additional Quantitative Results A Appendix and A.1 Additional Quantitative Results A.2 More Implementation Details and A.3 More Ablation Study Results A.2 More Implementation Details and A.3 More Ablation Study Results A.4 More User Study Details A.4 More User Study Details A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.5 Impact of Random Seed and Model Robustness and A.6 Boarder Impact and Ethical Consideration A.7 More Comparison Results and A.8 Additional Qualitative Results A.7 More Comparison Results and A.8 Additional Qualitative Results A.9 Licenses A.9 Licenses ABSTRACT Despite significant advances in large-scale text-to-image models, achieving hyperrealistic human image generation remains a desirable yet unsolved task. Existing models like Stable Diffusion and DALL·E 2 tend to generate human images with incoherent parts or unnatural poses. To tackle these challenges, our key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to the fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. To this end, we propose a unified framework, HyperHuman, that generates in-the-wild human images of high realism and diverse layouts. Specifically, 1) we first build a largescale human-centric dataset, named HumanVerse, which consists of 340M images with comprehensive annotations like human pose, depth, and surface-normal. 2) Next, we propose a Latent Structural Diffusion Model that simultaneously denoises the depth and surface-normal along with the synthesized RGB image. Our model enforces the joint learning of image appearance, spatial relationship, and geometry in a unified network, where each branch in the model complements to each other with both structural awareness and textural richness. 3) Finally, to further boost the visual quality, we propose a Structure-Guided Refiner to compose the predicted conditions for more detailed generation of higher resolution. Extensive experiments demonstrate that our framework yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios 1 INTRODUCTION Generating hyper-realistic human images from user conditions, e.g., text and pose, is of great importance to various applications, such as image animation (Liu et al., 2019) and virtual try-on (Wang et al., 2018). To this end, many efforts explore the task of controllable human image generation. Early methods either resort to variational auto-encoders (VAEs) in a reconstruction manner (Ren et al., 2020), or improve the realism by generative adversarial networks (GANs) (Siarohin et al., 2019). Though some of them create high-quality images (Zhang et al., 2022; Jiang et al., 2022), the unstable training and limited model capacity confine them to small datasets of low diversity. Recent emergence of diffusion models (DMs) (Ho et al., 2020) has set a new paradigm for realistic synthesis and become the predominant architecture in Generative AI (Dhariwal & Nichol, 2021). Nevertheless, the exemplar text-to-image (T2I) models like Stable Diffusion (Rombach et al., 2022) and DALL·E 2 (Ramesh et al., 2022) still struggle to create human images with coherent anatomy, e.g., arms and legs, and natural poses. The main reason lies in that human is articulated with nonrigid deformations, requiring structural information that can hardly be depicted by text prompts. To enable structural control for image generation, recent works like ControlNet (Zhang & Agrawala, 2023) and T2I-Adapter (Mou et al., 2023) introduce a learnable branch to modulate the pre-trained DMs, e.g., Stable Diffusion, in a plug-and-play manner. However, these approaches suffer from the feature discrepancy between the main and auxiliary branches, leading to inconsistency between the control signals (e.g., pose maps) and the generated images. To address the issue, HumanSD (Ju et al., 2023b) proposes to directly input body skeleton into the diffusion U-Net by channel-wise concatenation. However, it is confined to generating artistic style images of limited diversity. Besides, human images are synthesized only with pose control, while other structural information like depth maps and surface-normal maps are not considered. In a nutshell, previous studies either take a singular control signal as input condition, or treat different control signals separately as independent guidance, instead of modeling the multi-level correlations between human appearance and different types of structural information. Realistic human generation with coherent structure remains unsolved. In this paper, we propose a unified framework HyperHuman to generate in-the-wild human images of high realism and diverse layouts. The key insight is that human image is inherently structural over multiple granularities, from the coarse-level body skeleton to fine-grained spatial geometry. Therefore, capturing such correlations between the explicit appearance and latent structure in one model is essential to generate coherent and natural human images. Specifically, we first establish a large-scale human-centric dataset called HumanVerse that contains 340M in-the-wild human images of high quality and diversity. It has comprehensive annotations, such as the coarse-level body skeletons, the fine-grained depth and surface-normal maps, and the high-level image captions and attributes. Based on this, two modules are designed for hyper-realistic controllable human image generation. In Latent Structural Diffusion Model, we augment the pre-trained diffusion backbone to simultaneously denoise the RGB, depth, and normal. Appropriate network layers are chosen to be replicated as structural expert branches, so that the model can both handle input/output of different domains, and guarantee the spatial alignment among the denoised textures and structures. Thanks to such dedicated design, the image appearance, spatial relationship, and geometry are jointly modeled within a unified network, where each branch is complementary to each other with both structural awareness and textural richness. To generate monotonous depth and surface-normal that have similar values in local regions, we utilize an improved noise schedule to eliminate low-frequency information leakage. The same timestep is sampled for each branch to achieve better learning and feature fusion. With the spatially-aligned structure maps, in Structure-Guided Refiner, we compose the predicted conditions for detailed generation of high resolution. Moreover, we design a robust conditioning scheme to mitigate the effect of error accumulation in our two-stage generation pipeline. To summarize, our main contributions are three-fold: 1) We propose a novel HyperHuman framework for in-the-wild controllable human image generation of high realism. A large-scale humancentric dataset HumanVerse is curated with comprehensive annotations like human pose, depth, and surface normal. As one of the earliest attempts in human generation foundation model, we hope to benefit future research. 2) We propose the Latent Structural Diffusion Model to jointly capture the image appearance, spatial relationship, and geometry in a unified framework. The Structure-Guided Refiner is further devised to compose the predicted conditions for generation of better visual quality and higher resolution. 3) Extensive experiments demonstrate that our HyperHuman yields the state-of-the-art performance, generating hyper-realistic human images under diverse scenarios. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion

Introduction Imagine sitting down for a two-hour conversation with an AI interviewer. You discuss your childhood, pivotal life moments, and even your views on social issues. Soon after, an eerily accurate digital version of yourself, a so-called "AI twin", is created. This clone mirrors your personality, preferences, and decision-making processes with up to 85% similarity. This isn’t science fiction. Groundbreaking research by Stanford University and Google DeepMind, as highlighted in James O’Donnell’s recent piece for MIT Technology Review “AI can now create a replica of your personality”, explores the fascinating and unsettling world of AI-powered replicas. This article dives into the research, its potential applications, and the ethical dilemmas of sharing your decision-making power with a machine. The Making of a Digital Twin Led by Joon Sung Park, the research team conducted interviews with 1,000 participants across a diverse demographic spectrum. The interviews covered personal histories, career journeys, and opinions on societal issues. From this data, the researchers created AI agents designed to mimic participants’ behaviors in personality tests, social surveys, and logic games. These digital doppelgängers achieved an impressive 85% similarity in their responses when compared to their human counterparts. Though not flawless, the results point to a near future where digital replicas could extend our influence into tasks we may not have time, or inclination, to handle. Park suggests these replicas might one day act as proxies in decision-making processes. “If you can have a bunch of small ‘yous’ running around and actually making the decisions that you would have made, that, I think, is ultimately the future,” he says. Applications Beyond Entertainment While the concept of having an AI clone may conjure images of personalized avatars in gaming or digital assistants, its implications extend far beyond entertainment. These simulation agents could become invaluable tools for research in social sciences, where ethical or logistical constraints often limit the scope of studies. John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” From analyzing how interventions combat misinformation to understanding the behavioral drivers of traffic jams, these agents promise to simulate complex human scenarios at scale, offering researchers a new lens for exploring societal challenges. Ethical Quandaries and Risks Despite its promise, this technology raises significant ethical concerns. Much like how image-generation AI has enabled deepfakes, personality replication tools could be misused. Imagine a digital twin endorsing products or political ideologies without your consent. The implications for privacy, consent, and misinformation are enormous. Additionally, critics have noted the limitations of current evaluation methods used in the study, such as the General Social Survey and Big Five personality assessments. While useful, these tools may fail to capture the intricacies of what makes us unique. For instance, AI agents struggled in behavioral tests like the “dictator game,” which explores fairness in decision-making. Reducing the Data Burden The research also introduces an efficient alternative to the vast datasets typically required to create digital twins. Current companies, such as Tavus, often rely on extensive datasets, emails, social media interactions, and more, to replicate personalities. This study, however, demonstrates that a focused interview might achieve similar results in less time and with fewer resources. Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” The Future of AI Replicas As AI advances, the line between human and machine behavior will continue to blur. Whether aiding researchers, enhancing productivity, or enabling creative exploration, simulation agents represent a powerful new frontier. However, their ethical implications cannot be overlooked. Would you trust an AI version of yourself to make decisions on your behalf? As technology like this becomes more accessible, the question isn’t just “if” but “when” we’ll confront these dilemmas in our daily lives. By leveraging the insights from James O’Donnell’s reporting and the latest AI research, we stand at the cusp of a transformative era, one where our digital selves may be as active as our physical ones. But as we move forward, a balanced approach will be critical, ensuring that these tools serve humanity without compromising the essence of individuality. Sources: James O’Donnell’s article in MIT Technology Review
Research from Stanford University and Google DeepMind What are your thoughts on living alongside your AI twin? Share them below! Introduction Introduction Imagine sitting down for a two-hour conversation with an AI interviewer. You discuss your childhood, pivotal life moments, and even your views on social issues. Soon after, an eerily accurate digital version of yourself, a so-called "AI twin", is created. This clone mirrors your personality, preferences, and decision-making processes with up to 85% similarity. This isn’t science fiction. Groundbreaking research by Stanford University and Google DeepMind, as highlighted in James O’Donnell’s recent piece for MIT Technology Review “AI can now create a replica of your personality” , explores the fascinating and unsettling world of AI-powered replicas. MIT Technology Review “AI can now create a replica of your personality” This article dives into the research, its potential applications, and the ethical dilemmas of sharing your decision-making power with a machine. The Making of a Digital Twin The Making of a Digital Twin Led by Joon Sung Park, the research team conducted interviews with 1,000 participants across a diverse demographic spectrum. The interviews covered personal histories, career journeys, and opinions on societal issues. From this data, the researchers created AI agents designed to mimic participants’ behaviors in personality tests, social surveys, and logic games. These digital doppelgängers achieved an impressive 85% similarity in their responses when compared to their human counterparts. Though not flawless, the results point to a near future where digital replicas could extend our influence into tasks we may not have time, or inclination, to handle. Park suggests these replicas might one day act as proxies in decision-making processes. “If you can have a bunch of small ‘yous’ running around and actually making the decisions that you would have made, that, I think, is ultimately the future,” he says. Applications Beyond Entertainment Applications Beyond Entertainment While the concept of having an AI clone may conjure images of personalized avatars in gaming or digital assistants, its implications extend far beyond entertainment. These simulation agents could become invaluable tools for research in social sciences, where ethical or logistical constraints often limit the scope of studies. John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” John Horton, a professor at MIT Sloan School of Management, highlights their potential: “This paper is showing how you can do a kind of hybrid: use real humans to generate personas which can then be used programmatically/in-simulation in ways you could not with real humans.” From analyzing how interventions combat misinformation to understanding the behavioral drivers of traffic jams, these agents promise to simulate complex human scenarios at scale, offering researchers a new lens for exploring societal challenges. Ethical Quandaries and Risks Ethical Quandaries and Risks Despite its promise, this technology raises significant ethical concerns. Much like how image-generation AI has enabled deepfakes, personality replication tools could be misused. Imagine a digital twin endorsing products or political ideologies without your consent. The implications for privacy, consent, and misinformation are enormous. Additionally, critics have noted the limitations of current evaluation methods used in the study, such as the General Social Survey and Big Five personality assessments. While useful, these tools may fail to capture the intricacies of what makes us unique. For instance, AI agents struggled in behavioral tests like the “dictator game,” which explores fairness in decision-making. Reducing the Data Burden Reducing the Data Burden The research also introduces an efficient alternative to the vast datasets typically required to create digital twins. Current companies, such as Tavus, often rely on extensive datasets, emails, social media interactions, and more, to replicate personalities. This study, however, demonstrates that a focused interview might achieve similar results in less time and with fewer resources. Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” Tavus CEO Hassaan Raza acknowledges the potential of this streamlined approach: “How about you just talk to an AI interviewer for 30 minutes today, 30 minutes tomorrow? And then we use that to construct this digital twin of you.” The Future of AI Replicas The Future of AI Replicas As AI advances, the line between human and machine behavior will continue to blur. Whether aiding researchers, enhancing productivity, or enabling creative exploration, simulation agents represent a powerful new frontier. However, their ethical implications cannot be overlooked. Would you trust an AI version of yourself to make decisions on your behalf? As technology like this becomes more accessible, the question isn’t just “if” but “when” we’ll confront these dilemmas in our daily lives. By leveraging the insights from James O’Donnell’s reporting and the latest AI research, we stand at the cusp of a transformative era, one where our digital selves may be as active as our physical ones. But as we move forward, a balanced approach will be critical, ensuring that these tools serve humanity without compromising the essence of individuality. Sources : Sources James O’Donnell’s article in MIT Technology Review Research from Stanford University and Google DeepMind James O’Donnell’s article in MIT Technology Review MIT Technology Review Research from Stanford University and Google DeepMind What are your thoughts on living alongside your AI twin? Share them below!

Can You Trust Your AI Twin to Think Like You?

Episode Four, a New York-based advertising agency, has introduced RYA (Radical Yet Acceptable), an AI tool designed to generate marketing campaign ideas using consumer behavior data. The platform processes 84 million data points weekly to analyze U.S. demographic preferences and spending patterns. The agency, projecting $20 million in revenue this year, developed RYA using proprietary data collected over a decade. The system analyzes where Americans spend their discretionary time and money, providing insights across 180 genres and 20 action categories. Mark Himmelsbach, Founding Partner of RYA and Episode Four, explained the tool's development: "What our agency used to do in six weeks we can now do in one day. The system generates insights and ideas in reduced time frames." The platform's technical capabilities include: Analysis of U.S. population samples


Data processing for demographic segments


Integration with client databases


Custom survey incorporation


Data clean room compatibility


Processing of unstructured data sources Current clients including Adobe, Charles Schwab, and Invesco QQQ have received early access to the platform. The system allows brands to: Access demographic data down to 0.5% of the U.S. population


Analyze consumer entertainment preferences


Track spending behavior patterns


Generate campaign concepts


Integrate proprietary data


Monitor audience engagement metrics Teddy Lynn, Founding Partner of RYA and Episode Four, noted the platform's role in campaign development: "RYA prompts broader thinking. With 180 genres, we can create concepts that align with audience preferences." The platform's development comes amid industry discussions about AI's impact on marketing roles. Episode Four positions RYA as an enhancement tool for human creativity rather than a replacement for marketing professionals. Technical features include: Consumer behavior analysis


Demographic segmentation


Data integration capabilities


Campaign concept generation


Performance tracking


Market trend analysis Future developments include plans for a marketplace connecting agencies, production companies, and marketing specialists. This expansion aims to facilitate campaign execution across various genres and platforms. Industry analysts note that AI-powered marketing tools represent a growing trend in advertising technology. The success of such platforms could influence how agencies approach campaign development and audience analysis. The tool's release reflects broader industry movements toward data-driven marketing decisions. Market observers suggest that integration of AI tools in creative processes could impact traditional agency workflows and campaign development timeframes. For the marketing industry, RYA's launch represents continued evolution in creative technology applications. The platform's performance could influence future developments in AI-assisted marketing tools and creative processes. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Episode Four , a New York-based advertising agency, has introduced RYA (Radical Yet Acceptable), an AI tool designed to generate marketing campaign ideas using consumer behavior data. The platform processes 84 million data points weekly to analyze U.S. demographic preferences and spending patterns. Episode Four RYA The agency, projecting $20 million in revenue this year, developed RYA using proprietary data collected over a decade. The system analyzes where Americans spend their discretionary time and money, providing insights across 180 genres and 20 action categories. Mark Himmelsbach, Founding Partner of RYA and Episode Four, explained the tool's development: "What our agency used to do in six weeks we can now do in one day. The system generates insights and ideas in reduced time frames." The platform's technical capabilities include: Analysis of U.S. population samples Data processing for demographic segments Integration with client databases Custom survey incorporation Data clean room compatibility Processing of unstructured data sources Analysis of U.S. population samples Analysis of U.S. population samples Data processing for demographic segments Data processing for demographic segments Integration with client databases Integration with client databases Custom survey incorporation Custom survey incorporation Data clean room compatibility Data clean room compatibility Processing of unstructured data sources Processing of unstructured data sources Current clients including Adobe, Charles Schwab, and Invesco QQQ have received early access to the platform. The system allows brands to: Access demographic data down to 0.5% of the U.S. population Analyze consumer entertainment preferences Track spending behavior patterns Generate campaign concepts Integrate proprietary data Monitor audience engagement metrics Access demographic data down to 0.5% of the U.S. population Access demographic data down to 0.5% of the U.S. population Analyze consumer entertainment preferences Analyze consumer entertainment preferences Track spending behavior patterns Track spending behavior patterns Generate campaign concepts Generate campaign concepts Integrate proprietary data Integrate proprietary data Monitor audience engagement metrics Monitor audience engagement metrics Teddy Lynn, Founding Partner of RYA and Episode Four, noted the platform's role in campaign development: "RYA prompts broader thinking. With 180 genres, we can create concepts that align with audience preferences." The platform's development comes amid industry discussions about AI's impact on marketing roles. Episode Four positions RYA as an enhancement tool for human creativity rather than a replacement for marketing professionals. Technical features include: Consumer behavior analysis Demographic segmentation Data integration capabilities Campaign concept generation Performance tracking Market trend analysis Consumer behavior analysis Consumer behavior analysis Demographic segmentation Demographic segmentation Data integration capabilities Data integration capabilities Campaign concept generation Campaign concept generation Performance tracking Performance tracking Market trend analysis Market trend analysis Future developments include plans for a marketplace connecting agencies, production companies, and marketing specialists. This expansion aims to facilitate campaign execution across various genres and platforms. Industry analysts note that AI-powered marketing tools represent a growing trend in advertising technology. The success of such platforms could influence how agencies approach campaign development and audience analysis. The tool's release reflects broader industry movements toward data-driven marketing decisions. Market observers suggest that integration of AI tools in creative processes could impact traditional agency workflows and campaign development timeframes. For the marketing industry, RYA's launch represents continued evolution in creative technology applications. The platform's performance could influence future developments in AI-assisted marketing tools and creative processes. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Episode Four Launches RYA AI Tool for Marketing Campaign Generation

Episode vier stel RYA AI-instrument bekend vir die generering van bemarkingsveldtogte

Afsnit fire lancerer RYA AI-værktøj til generering af marketingkampagner

Episode Four lanza la herramienta de inteligencia artificial RYA para la generación de campañas de marketing

Չորրորդ դրվագը գործարկում է RYA AI գործիքը մարքեթինգային արշավի ստեղծման համար

エピソード 4 がマーケティング キャンペーン生成用の RYA AI ツールをリリース

მეოთხე ეპიზოდი იწყებს RYA AI ხელსაწყოს მარკეტინგული კამპანიის თაობისთვის

Episode ya minei ebandi RYA AI Tool pona génération ya campagne ya marketing

Fizarana efatra dia namoaka fitaovana RYA AI ho an'ny famoronana fanentanana ara-barotra

Episodi i katërt lëshon Mjetin RYA AI për gjenerimin e fushatës së marketingut

Avsnitt fyra lanserar RYA AI-verktyg för generering av marknadsföringskampanjer

Dördüncü Bölüm, Pazarlama Kampanyası Oluşturma için RYA AI Aracını Başlatıyor

Xiphemu xa Vumune Xi Sungula Xitirhisiwa xa RYA AI xa ku Tumbuluxa Pfhumba ro Xavisa

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