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When you run any C-program, its executable image loaded into RAM of computer in an organized manner which known as process address space or memory layout of C program. Here I have tried to show you the same thing in two parts . /!\: Originally published @ . www.vishalchovatiya.com In the Part 1 i.e. "Overview", we will see segment-wise overview & in Part 2  i.e. "Example", we'll see How C program stored in RAM memory? with example. Part 1: Overview The memory layout of C program organized in the following fashion: Text segment Data segment Heap segment Stack segment Note: It’s not just these 4 segments, there are a lot more but these 4 are the core to understand the working of C program at the machine level. HIGHER  ADDRESS                                                                                                        
   +------------------------+                                                                             
   |  Unmapped or reserved  | Command-line argument & Environment variables                                                                          
   |------------------------|------------------------                                                                          
   |     Stack segment      | |                                                                                                
   |           |            | | Stack frame                                                                                    
   |           v            | v                                                                                                
   |                        |                                                                                                  
   |           ^            | ^                                                                                                
   |           |            | | Dynamic memory                                                                                 
   |     Heap segment       | |                                                                                                
   |------------------------|------------------------                                                                          
   |   Uninitialized data   |                                                                                                  
   |------------------------| Data segment                                                                                     
   |    Initialized data    |                                                                                                  
   |------------------------|------------------------                                                                          
   |                        |                                                                                                  
   |      Text segment      | Executable code                                                                                  
   |                        |                                                                                                  
   +------------------------+                                                                                                  
         LOWER  ADDRESS Text segment Text segment contains executable instructions of your C program, its also called code segment also. This includes all functions making up the program( too), both user-defined and system. main() The text segment is sharable so that only a single copy needs to be in memory for different executing programs, such as text editors, shells, and so on. Usually, the text segment is read-only, to prevent a program from accidentally modifying its instructions. Data segment There are two subsections of this segment 1. Initialized data It contains both static and global data that initialized with non-zero values. This segment can be further classified into the read-only area and read-write area. For example, The global string defined by and a statement like an outside the (i.e. global) would be stored in initialized read-write area. char string[ ] = "hello world" int count=1 main And a global statement like ; makes the variable read-only and to be stored in initialized read-only area. const int A=3 A 2. Uninitialized data (BSS segment) An uninitialized data segment also called the BSS( ‘Block Started by Symbol’ ) segment. Which contains all global and static variables that initialized to zero or do not have explicit initialization in source code. For example, The global variable declared as would be stored in the uninitialized data segment. A statement like static will also be stored in this segment cause it initialized with zero. int A int X=0 If you do not initialize a global variable, by default value is zero. This flushing memory content is usually done by program loader(i.e. ) /lib/ld-linux.so.2 Heap segment The heap segment is an area where dynamically allocated memory (allocated by , , and for C++) resides. malloc() calloc() realloc() new When we allocate memory through dynamic allocation techniques(in other words, run-time memory allocation), program acquire space from OS and process address space grows. We can free dynamically allocated memory space (by using or ). Freed memory goes back to the heap but doesn’t have to be returned to OS (it doesn’t have to be returned at all), so unordered / eventually, cause heap fragmentation. You can learn more about . free() delete malloc free how malloc works here When we use dynamic allocation to acquire memory space we must keep track of allocated memory by using its address. Stack segment The stack segment is an area where local variables stored. By saying local variable means that all those variables which are declared in every function including in your C program. I have written a detailed article about the . main() function stack frame here When we call any function, the stack frame created and when a function returns, the stack frame destroyed/rewind including all local variables of that particular function. A stack frame contains some data like return address, arguments passed to it, local variables, and any other information needed by the invoked function. A stack pointer(SP) which is a special function register of CPU keeps track of stack by each push & pop operation onto it, by adjusted stack pointer to next or previous address. The direction of the stack & heap growth completely depends on the compiler, ABI, OS and hardware. Part 2: Example We have taken a simple example as shown in tittle along with its memory layout. As we discussed in the previous part(i.e. Overview) how executable image of our program divided into the different segment and stored in memory(RAM). Now we understand those blocks by using our example code presented above. Loader A loader is not a segment but kind of program interpreter which reads a different segment from the binary & copy it in RAM in proper fashion. There is a binary utility command by which you can see different segments & path of the loader in binary as follows: $ readelf --segments ./a.out 
Elf file type is EXEC (Executable file)
Entry point 0x8048300
There are 9 program headers, starting at offset 52
Program Headers:
  Type           Offset   VirtAddr   PhysAddr   FileSiz MemSiz  Flg Align
  PHDR           0x000034 0x08048034 0x08048034 0x00120 0x00120 R E 0x4
  INTERP         0x000154 0x08048154 0x08048154 0x00013 0x00013 R   0x1
      [Requesting program interpreter: /lib/ld-linux.so.2]
  LOAD           0x000000 0x08048000 0x08048000 0x00608 0x00608 R E 0x1000
  LOAD           0x000f08 0x08049f08 0x08049f08 0x00118 0x00124 RW  0x1000
  DYNAMIC        0x000f14 0x08049f14 0x08049f14 0x000e8 0x000e8 RW  0x4
  NOTE           0x000168 0x08048168 0x08048168 0x00020 0x00020 R   0x4
  GNU_EH_FRAME   0x0004c4 0x080484c4 0x080484c4 0x00044 0x00044 R   0x4
  GNU_STACK      0x000000 0x00000000 0x00000000 0x00000 0x00000 RW  0x10
  GNU_RELRO      0x000f08 0x08049f08 0x08049f08 0x000f8 0x000f8 R   0x1
 Section to Segment mapping:
  Segment Sections...
   00     
   01     .interp 
   02     .interp .note.ABI-tag .hash .dynsym .dynstr .gnu.version .gnu.version_r .rel.dyn .rel.plt .init .plt .plt.got .text .fini .rodata .eh_frame_hdr .eh_frame 
   03     .init_array .fini_array .jcr .dynamic .got .got.plt .data .bss 
   04     .dynamic 
   05     .note.ABI-tag 
   06     .eh_frame_hdr 
   07     
   08     .init_array .fini_array .jcr .dynamic .got As I have mentioned earlier, there is not only 4 segment as you can see above, but there are a lot of segments which usually depends on compiler & . ABI Above you can see, , , , etc. segments are there. But a stack segment is not shown as its created at a run time & decided by OS(precisely loader & kernel). .data .bss .text in the program header defines the name & path of loader which going to load the current binary image into the RAM by reading these segments. Here it is INTERP /lib/ld-linux.so.2. You can read more about binary . file format ELF here Text segment When you compile C code, you get executable image(which may be in any form like , , , or no extension etc). This executable image contains text segment which you see by Binutils command and it looks like follows: .bin .exe .hex .out $ objdump -d <binary_name> .....
080483f1 <main>:
 80483f1:  8d 4c 24 04            lea    0x4(%esp),%ecx
 80483f5:  83 e4 f0               and    $0xfffffff0,%esp
 80483f8:  ff 71 fc               pushl  -0x4(%ecx)
..... This is executable instructions stored in the text segment as a read-only section and shared by the processes if requires. These instructions read by CPU using program counter and stack frame created in the stack at the time of execution. Program-counter points to the address of the instruction to executed which lies in the text segment. Data segment Initialized Data segment A ; stored in the read-only area. So you can not modify it accidentally. const int x = 1 While a string & stored in the read-write area because we don’t use a keyword like const which makes variable read-only. char str[] = "Hi!"; static int var = 0; Uninitialized Data segment In our program, declared global goes to this area of storage because it is not initialized or initialized to zero by default. int i Heap segment When you compile your program, memory space allocated by you i.e. all locals, static & global variables fixed at compile-time. But when code needs memory at run-time, it approach OS by calling functions like , , etc. malloc() calloc() When OS provides dynamic memory to process it shrinks stack limit pointer which initially points to uninitialized data segment start(the technical word is “program break”, read about it ). here As a result heap segment grows. That’s why there is no line between heap & stack segment. An arrow indicates its growth of direction. In the example code, we allocate 1-byte dynamic memory using function and stored its address in pointer to keep track of that memory or to access it. malloc() ptr This is a local variable of main hence it’s in main’s stack frame, but memory pointed by it is in a heap which I have shown by . ptr *ptr Stack segment The usual starting point(not entry point which is different) of any program is , which is also a function hence, stack frame is created for it while execution. Although there are many functions called before main which I have discussed . main() here As you can see in image, stack frame of is created before function as we called it nested. main() func() As the execution overs its local variable a and its stack frame will destroy(rewind is a precise word here), same goes for function also. func( ) main() And this is how stack grows & shrinks. Intuitive FAQs Q. How do you determine the stack growth direction ? Simple…! by comparing the address of two different function’s local variables. A. *main_ptr = ; *func_ptr = ; { a; func_ptr = &a; } { a; main_ptr = &a;
    func();
    (main_ptr > func_ptr) ? ( ) : ( ); ;
} int NULL int NULL void func () int int main () int printf "DOWN\n" printf "UP\n" return 0 Q. How do you corrupt stack deliberately ? Corrupt the SFR values stored in the stack frame. A. { a; (&a, , ); } {
    func(); ;
} void func () int memset 0 100 // Corrupt SFR values stored in stack frame int main () return 0 Q. How you can increase stack frame size ? alloca() is the answer. Google about it or see . A. this

How C program stored in RAM memory

Introduction (this felt like hacking but it isn't) Going through past Instagram posts to get a short term ego boost that will dissipate in a matter of seconds I was going through my old Instagram posts trying to find how many people liked my posts. I even went through the ones in the archive (I had like 20 archived posts gathering dust). I needed an ego boost (who doesn’t like a good ego boost? If you don't, you're probably lying). And then, after a boring 5 minute period where I had a stream of random thoughts going through my head, I was like … why not graph the data of my instagram post trends? (all for the ego boost of course) There were a few benefits to this: Using intense mental gymnastics, and systematically ignoring all the possible disadvantages, I could spend much more time looking at my old likes and comments without guilt of wasting time, or seeming egocentric. Instead of like going through Instagram, I would be “analysing data visualisations”, which sounds much more productive.


I would list out all the possible disadvantages but as I said in the previous point I ignored it. Those possible disadvantages were ignored just like friction is ignored in physics problems 💀  .


Improving problem solving and creativity (this one is a no 🧠 brainer)


I can actually produce a *neat* graph -- I am highly messy drawing mathematical graphs (shaky hand and terrible coordination are my worst enemies. Getting started (not procrastinating and scrolling reels) You log onto instagram.com. The default login page asks for your username and password. You blindly give it to them to access the content that will eventually give you brainrot. The first post on the page is the source of brainrot: some random person singing slang. Your friend, at the same time also blindly gives his password and username to instagram.com. But the first post on the page was a fresh out of the oven cooking video, emanating the intense heat vibes to their eyes. Unbelievable How come he gets to see delicious food while I have to hear people chanting 'sigma' over and over again? (this actually happened) It's the same website but the content is different. The website is a dynamic website which changes content every time you reload. (imagine scrolling through mindlessly until you click the reload button. The relatable meme you were about to send to your best friend is now gone forever.) Step 1: Data doesn’t just appear on the page, it comes from somewhere How does the browser know what to display? Surely all the brainrot Instagram reels cannot be all stored in one file? Well the website needs a little help in displaying the data so it has to fetch the data from another source. Think of a dog fetching a bone from another place, to complete the full picture of a happy dog wagging its tail with a bone in its mouth. Good little doggy. Step 2: Well where does the data come from then? Snooping around the page won't get you anywhere. You have to go deeper. Here's a magic trick: go right click and inspect elements. Now you can see al the code that makes it work. A giant chunk of messy HTML blows up in your face. Not cool. How can you find all the data now? Well the interesting part is the network tab. Shows all the network requests being sent and the data being transferred. Think of it as a busy metro station with the trains as data requests, transferring goods (data) from station A (instagram servers) to station B (your browser). And then after looking through all the hard to read scripts in there, all the boring HTML, I found this interesting section Fetch/XHR: The source of the data river (probably) All external data is sent to and from servers here. By looking through the requests you can find lots of interesting stuff the website gets from servers, or what it sends. Most of the time though it looks like gibberish. Step 3: Scrolling through Instagram (productively) In order to find where the server gets the data, I had to scroll through a lot of network requests :). Forget scrolling through memes and reels, lets scroll through network requests!!!! Eventually after *hours* (or what felt like hours) of searching I found this: The chosen one. A list full of all the data on posts, including the link count, and the comment count, in convenient JSON format. Step 3: I got the data, now what Now we need to figure out how we can get the data by performing a similar request programatically. Introducing: Postman - not the one on the motorcycle (as if we see them anymore) but the application online that makes metaphorical data deliveries to your virtual mailbox. Step 4: Roleplay as instagram.com Time to pretend to be like a drama student with the roleplay. This sends a request to instagram just like how the browser sends a request for data. I had to match exactly how the request is made in the network tab; including copying what seems like the most trivial of metadata (that was a very painstaking process), into the Postman parameters in order to get the same data, my way. At this point, I could go on and on about how I got here, my thought process and everything that I have tried. But I know you didn't order a yappachino so moving on .💬 Step 5: Python Copy the python version of the postman request and boom -- you've got yourself a script that fetches instagram data :) Unfortunately the request body payload might contain sensitive data that may or may not be impacting my own account so I cannot share it here. All this script does is continually make a network request to grab Instagram data, passing different parameters (on which page/posts to search) and request body data to collect all the data about the posts. This is necessary because Instagram only grabs the data for around 12 posts at a time, this means that if someone has, say 24 posts I would have to send a request 2 times. It parses the JSON data and adds the number of likes into a list, and the number of comments into another list. Step 6: Non messy graphing It's graphing time. Set x values as post numbers, y values as likes and comments respectively and boom → Step 7: Graph done WOOHOO And there we have it. A shiny graph full of data, collected fresh right out of Instagram. It’s a perfect flex to use on friends and a nice way to look at Instagram productively. (We’re “analysing data visualisations” here people, not “scrolling reels”.) Hopefully this was not a bunch of waffle, and if I was yapping too much let me know so I can submit an application to become the waffle house's new host :) Introduction (this felt like hacking but it isn't) (this felt like hacking but it isn't) Going through past Instagram posts to get a short term ego boost that will dissipate in a matter of seconds I was going through my old Instagram posts trying to find how many people liked my posts. I even went through the ones in the archive (I had like 20 archived posts gathering dust). I needed an ego boost (who doesn’t like a good ego boost? If you don't, you're probably lying). And then, after a boring 5 minute period where I had a stream of random thoughts going through my head, I was like … why not graph the data of my instagram post trends? (all for the ego boost of course) There were a few benefits to this: Using intense mental gymnastics, and systematically ignoring all the possible disadvantages, I could spend much more time looking at my old likes and comments without guilt of wasting time, or seeming egocentric. Instead of like going through Instagram, I would be “analysing data visualisations”, which sounds much more productive. I would list out all the possible disadvantages but as I said in the previous point I ignored it. Those possible disadvantages were ignored just like friction is ignored in physics problems 💀  . Improving problem solving and creativity (this one is a no 🧠 brainer) I can actually produce a *neat* graph -- I am highly messy drawing mathematical graphs (shaky hand and terrible coordination are my worst enemies. Using intense mental gymnastics, and systematically ignoring all the possible disadvantages, I could spend much more time looking at my old likes and comments without guilt of wasting time, or seeming egocentric. Instead of like going through Instagram, I would be “analysing data visualisations”, which sounds much more productive. Using intense mental gymnastics, and systematically ignoring all the possible disadvantages, I could spend much more time looking at my old likes and comments without guilt of wasting time, or seeming egocentric. Instead of like going through Instagram, I would be “analysing data visualisations”, which sounds much more productive. I would list out all the possible disadvantages but as I said in the previous point I ignored it. Those possible disadvantages were ignored just like friction is ignored in physics problems 💀  . I would list out all the possible disadvantages but as I said in the previous point I ignored it. Those possible disadvantages were ignored just like friction is ignored in physics problems 💀  . Improving problem solving and creativity (this one is a no 🧠 brainer) Improving problem solving and creativity (this one is a no 🧠 brainer) I can actually produce a *neat* graph -- I am highly messy drawing mathematical graphs (shaky hand and terrible coordination are my worst enemies. I can actually produce a *neat* graph -- I am highly messy drawing mathematical graphs (shaky hand and terrible coordination are my worst enemies. Getting started (not procrastinating and scrolling reels) You log onto instagram.com. The default login page asks for your username and password. You blindly give it to them to access the content that will eventually give you brainrot. The first post on the page is the source of brainrot: some random person singing slang. Your friend, at the same time also blindly gives his password and username to instagram.com. But the first post on the page was a fresh out of the oven cooking video, emanating the intense heat vibes to their eyes. Unbelievable How come he gets to see delicious food while I have to hear people chanting 'sigma' over and over again? (this actually happened) It's the same website but the content is different. The website is a dynamic website which changes content every time you reload. (imagine scrolling through mindlessly until you click the reload button. The relatable meme you were about to send to your best friend is now gone forever.) Step 1: Data doesn’t just appear on the page, it comes from somewhere How does the browser know what to display? Surely all the brainrot Instagram reels cannot be all stored in one file? Well the website needs a little help in displaying the data so it has to fetch the data from another source. Think of a dog fetching a bone from another place, to complete the full picture of a happy dog wagging its tail with a bone in its mouth. Good little doggy. Step 2: Well where does the data come from then? Snooping around the page won't get you anywhere. You have to go deeper. Here's a magic trick: go right click and inspect elements. Now you can see al the code that makes it work. A giant chunk of messy HTML blows up in your face. Not cool. How can you find all the data now? Well the interesting part is the network tab. Shows all the network requests being sent and the data being transferred. Think of it as a busy metro station with the trains as data requests, transferring goods (data) from station A (instagram servers) to station B (your browser). And then after looking through all the hard to read scripts in there, all the boring HTML, I found this interesting section Fetch/XHR: The source of the data river (probably) All external data is sent to and from servers here. By looking through the requests you can find lots of interesting stuff the website gets from servers, or what it sends. Most of the time though it looks like gibberish. Step 3: Scrolling through Instagram (productively) In order to find where the server gets the data, I had to scroll through a lot of network requests :). Forget scrolling through memes and reels, lets scroll through network requests!!!! lot Eventually after *hours* (or what felt like hours) of searching I found this: The chosen one. A list full of all the data on posts, including the link count, and the comment count, in convenient JSON format. Step 3: I got the data, now what Now we need to figure out how we can get the data by performing a similar request programatically. Introducing: Postman - not the one on the motorcycle (as if we see them anymore) but the application online that makes metaphorical data deliveries to your virtual mailbox. Step 4: Roleplay as instagram.com Time to pretend to be like a drama student with the roleplay. This sends a request to instagram just like how the browser sends a request for data. I had to match exactly how the request is made in the network tab; including copying what seems like the most trivial of metadata (that was a very painstaking process), into the Postman parameters in order to get the same data, my way. At this point, I could go on and on about how I got here, my thought process and everything that I have tried. But I know you didn't order a yappachino so moving on .💬 Step 5: Python Copy the python version of the postman request and boom -- you've got yourself a script that fetches instagram data :) Unfortunately the request body payload might contain sensitive data that may or may not be impacting my own account so I cannot share it here. All this script does is continually make a network request to grab Instagram data, passing different parameters (on which page/posts to search) and request body data to collect all the data about the posts. This is necessary because Instagram only grabs the data for around 12 posts at a time, this means that if someone has, say 24 posts I would have to send a request 2 times. It parses the JSON data and adds the number of likes into a list, and the number of comments into another list. Step 6: Non messy graphing It's graphing time. Set x values as post numbers, y values as likes and comments respectively and boom → Step 7: Graph done WOOHOO And there we have it. A shiny graph full of data, collected fresh right out of Instagram. It’s a perfect flex to use on friends and a nice way to look at Instagram productively. (We’re “analysing data visualisations” here people, not “scrolling reels”.) Hopefully this was not a bunch of waffle, and if I was yapping too much let me know so I can submit an application to become the waffle house's new host :)

Graphing Likes and Comments on Instagram Posts to See the Trends Visually

WILMINGTON, Delaware, November 7th, 2024/Chainwire/--Rekt Brands Inc. (Rekt), the parent company behind the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property, is excited to announce the completion of a $1.5 million seed funding round. Funded exclusively by angel investors and the community—without institutional venture capital—this achievement highlights the support Rekt received from its loyal and growing network. This funding announcement follows the debut of Rekt Drinks, Rekt’s first consumer product: a lime-flavored sparkling water with zero caffeine and zero alcohol, featuring the Rekt branding. Rekt Drinks made its debut last week with 222,456 units available across 32 countries. The entire stock sold out in under 48 hours, with demand in the U.S. alone resulting in a complete sell-out within just 4 hours and 20 minutes. This launch represents one of the largest real-world product sales by a Web3 brand, reinforcing Rekt’s potential as a cultural force in both digital and traditional markets. Rekt was founded from the success of the Rektguy NFT collection, created by artist Ovie Faruq (OSF) in May 2022. Since then, the Rektguy project has expanded into Rekt Brands, which oversees the Rekt IP and pioneered a unique industry model by offering equity in the parent company directly to its NFT holders, utilizing a Reg CF exemption under SEC guidelines in the U.S. “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” With this new funding, Rekt is poised to expand its product lineup, strengthen its brand awareness in and outside of Web3, while further bridging digital culture with physical consumer products. The Rekt community remains at the heart of the brand’s mission, with upcoming projects that will continue to empower, engage, and reward its dedicated supporters. About Rekt Rekt Brands Inc. is the parent company that owns the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property. Established from the success of the Rektguy NFT collection by artist Ovie Faruq (OSF), the brand has expanded into consumer goods and groundbreaking community equity models. Rekt aims to redefine brand ownership and engagement through its Web3 foundation, innovative products, and viral global community. Contact CEO Ovie Faruq Rekt Brands Inc. ovie@rektdrinks.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here WILMINGTON, Delaware, November 7th, 2024/Chainwire/--Rekt Brands Inc. (Rekt), the parent company behind the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property, is excited to announce the completion of a $1.5 million seed funding round. Funded exclusively by angel investors and the community—without institutional venture capital—this achievement highlights the support Rekt received from its loyal and growing network. This funding announcement follows the debut of Rekt Drinks , Rekt’s first consumer product: a lime-flavored sparkling water with zero caffeine and zero alcohol, featuring the Rekt branding. Rekt Drinks made its debut last week with 222,456 units available across 32 countries. Rekt Drinks Rekt Drinks The entire stock sold out in under 48 hours, with demand in the U.S. alone resulting in a complete sell-out within just 4 hours and 20 minutes. This launch represents one of the largest real-world product sales by a Web3 brand, reinforcing Rekt’s potential as a cultural force in both digital and traditional markets. Rekt was founded from the success of the Rektguy NFT collection, created by artist Ovie Faruq (OSF) in May 2022. Since then, the Rektguy project has expanded into Rekt Brands, which oversees the Rekt IP and pioneered a unique industry model by offering equity in the parent company directly to its NFT holders, utilizing a Reg CF exemption under SEC guidelines in the U.S. Rektguy Rektguy Ovie Faruq (OSF) Ovie Faruq (OSF) “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” With this new funding, Rekt is poised to expand its product lineup, strengthen its brand awareness in and outside of Web3, while further bridging digital culture with physical consumer products. The Rekt community remains at the heart of the brand’s mission, with upcoming projects that will continue to empower, engage, and reward its dedicated supporters. About Rekt Rekt Brands Inc. is the parent company that owns the Rektguy NFT project, Rekt Drinks , and the Rekt brand intellectual property. Established from the success of the Rektguy NFT collection by artist Ovie Faruq (OSF) , the brand has expanded into consumer goods and groundbreaking community equity models. Rekt aims to redefine brand ownership and engagement through its Web3 foundation, innovative products, and viral global community. Rekt Brands Inc. Rekt Brands Inc. Rektguy Rektguy Rekt Drinks Rekt Drinks Rektguy Rektguy Ovie Faruq (OSF) Ovie Faruq (OSF) Contact CEO Ovie Faruq Rekt Brands Inc. ovie@rektdrinks.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Rekt Raises $1.5M Seed Round Backed By Angels and Community, Following Sell-Out Of Rekt Drinks

Rekt samel $1,5 miljoen saadronde in, gerugsteun deur engele en gemeenskap, na die uitverkoop van Rekt-drankies

Rekt набира 1,5 милиона долара начален кръг, подкрепен от ангели и общност, след разпродаването на Rekt напитки

Η Rekt συγκεντρώνει $1,5 εκατομμύρια σπόρους με την υποστήριξη των αγγέλων και της κοινότητας, μετά το ξεπούλημα των ποτών Rekt

Rekt recauda 1,5 millones de dólares en una ronda de capital semilla respaldada por inversores ángeles y la comunidad, tras la venta de Rekt Drinks

רקט מגייס 1.5 מיליון דולר סבב זרעים בגיבוי מלאכים וקהילה, בעקבות מכירת משקאות רקט

Rekt prikupio 1,5 milijuna dolara početni krug uz podršku anđela i zajednice, nakon rasprodaje Rekt pića

Rekt Mengumpulkan Dana Awal Sebesar $1,5 Juta yang Didukung oleh Angel dan Komunitas, Setelah Penjualan Habis dari Rekt Drinks

Rekt は Rekt ドリンクの完売を受けて、エンジェル投資家とコミュニティの支援を受けて 150 万ドルのシードラウンドを調達

Рэкт ундаануудаа зарсны дараа Тэнгэр элч нар болон олон нийтийн дэмжлэгтэйгээр 1.5 сая долларын үрийг цуглуулж байна.

Rekt haalt $ 1,5 miljoen op in een startkapitaalronde, gesteund door investeerders en de gemeenschap, na de uitverkoop van Rekt Drinks

Rekt ระดมทุนรอบ Seed Round ได้ 1.5 ล้านเหรียญสหรัฐ โดยได้รับการสนับสนุนจาก Angels และชุมชน หลังจากการขายเครื่องดื่ม Rekt หมดเกลี้ยง

Rekt, Rekt içgileriniň satylmagyndan soň, perişdeler we jemgyýet tarapyndan goldanýan 1.5 million dollarlyk tohum tegelek ýygnaýar

British Virgin Islands, November 7th 2024 — Cytonic, the world’s first multi-virtual-machine blockchain, today announced it has raised $8.3 million in a seed funding round co-led by Lattice and Lemniscap, with participation from IOBC, Nomura, Lyrik, Public Works, Arthur Hayes and other notable investors. Developed by MultiVM Labs, Cytonic is a decentralized execution layer that tackles one of the blockchain industry’s most pressing challenges: compatibility. As fragmentation across multiple chains continues to grow, developers and users face increasing barriers that limit the full potential of blockchain technology. Cytonic introduces the first Layer 1 blockchain capable of running multiple virtual machines within the same network layer while using shared storage. This breakthrough allows transactions involving different state transition functions (STFs) to be processed together within a single state transition. Currently, Cytonic is designed to be identical to both Ethereum and Solana, enabling all existing tooling, wallets, and decentralized applications (dApps) that operate on EVM and SVM chains to function seamlessly on Cytonic. Cytonic minimizes transaction fees and reduces waiting times for moving assets between chains, making decentralized applications more accessible and user-friendly. On the Cytonic blockchain, users can swap assets across different VMs atomically without rollback issues or cross-chain vulnerabilities. For example, users can swap Base tokens (ERC20) for Solana tokens (SPL) within the same liquidity pool on a single decentralized exchange (DEX), radically increasing capital efficiency. This approach fundamentally addresses interoperability challenges and vulnerabilities inherited from hundreds of different blockchain architectures, all while maintaining full compatibility with them. Having experienced firsthand how compatibility issues hinder growth across the crypto industry, Cytonic Founder Ivan Miskovic recognized the critical need for a solution that simplifies horizontal scaling for applications and addresses liquidity management challenges for users. Ivan, former Executive at Spin Labs, along with his co-executives—Chelsea, former Partner at Foresight Ventures, and Badconfig, former Senior Engineer at Faraway—have years of experience building and investing in decentralized technologies across Bitcoin, Ethereum, Solana, Near, and other notable projects. United by a shared vision to resolve blockchain’s compatibility challenges, the Cytonic team is dedicated to making on-chain interactions easier and more efficient for all participants. Thanks to its EVM and SVM compatibility, projects from any EVM or SVM chain can seamlessly migrate to Cytonic and tap into liquidity from different networks without the usual complexities of cross-chain operations or chain abstraction. This breakthrough in core infrastructure compatibility enables developers to deploy existing applications on Cytonic without rewriting any code, streamlining both development and growth processes. Projects can harness the unique strengths of different virtual machines while maintaining universal compatibility on the network. This not only simplifies the user experience but also reduces fragmentation, enhances ecosystem diversity, and makes blockchain technology more accessible and practical for widespread adoption. Ivan Miskovic, Founder of Cytonic, said: “Sincere thanks to our investors for backing our vision to bridge the gaps between blockchains and unlock new opportunities for developers and users alike. We are enabling any wallet from any chain to store any type of asset on Cytonic, greatly simplifying user experiences and broadening accessibility at a critical juncture in the Web3 development cycle. We are just getting started.” Regan Bozman, GP at Lattice, said: “We are proud to have co-led this $8.3 million round for Cytonic. As the blockchain ecosystem continues to grow, many innovative projects struggle with liquidity and compatibility due to the diverse range of blockchains available. Cytonic is tackling these compatibility issues head-on, breaking down the silos between blockchains while promoting greater collaboration, innovation, and adoption across the Web3 space.” About Cytonic Cytonic is pioneering the first multi-virtual-machine blockchain, with a mission to seamlessly integrate diverse networks like Bitcoin, Ethereum, and Solana into a unified Layer 1 blockchain solution. Operating on a global scale, Cytonic ensures connectivity and interoperability across multiple decentralized networks. Website X [Discord](https://: <https://discord.gg/K3RgfYS5](https://discord.gg/K3RgfYS5)) Media contact: Lorcan@forewordventure.com This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here British Virgin Islands, November 7th 2024 — Cytonic, the world’s first multi-virtual-machine blockchain, today announced it has raised $8.3 million in a seed funding round co-led by Lattice and Lemniscap, with participation from IOBC, Nomura, Lyrik, Public Works, Arthur Hayes and other notable investors. British Virgin Islands, November 7th 2024 Developed by MultiVM Labs , Cytonic is a decentralized execution layer that tackles one of the blockchain industry’s most pressing challenges: compatibility. As fragmentation across multiple chains continues to grow, developers and users face increasing barriers that limit the full potential of blockchain technology. MultiVM Labs MultiVM Labs Cytonic introduces the first Layer 1 blockchain capable of running multiple virtual machines within the same network layer while using shared storage. This breakthrough allows transactions involving different state transition functions (STFs) to be processed together within a single state transition. Currently, Cytonic is designed to be identical to both Ethereum and Solana, enabling all existing tooling, wallets, and decentralized applications (dApps) that operate on EVM and SVM chains to function seamlessly on Cytonic. Cytonic minimizes transaction fees and reduces waiting times for moving assets between chains, making decentralized applications more accessible and user-friendly. On the Cytonic blockchain, users can swap assets across different VMs atomically without rollback issues or cross-chain vulnerabilities. For example, users can swap Base tokens (ERC20) for Solana tokens (SPL) within the same liquidity pool on a single decentralized exchange (DEX), radically increasing capital efficiency. This approach fundamentally addresses interoperability challenges and vulnerabilities inherited from hundreds of different blockchain architectures, all while maintaining full compatibility with them. Having experienced firsthand how compatibility issues hinder growth across the crypto industry, Cytonic Founder Ivan Miskovic recognized the critical need for a solution that simplifies horizontal scaling for applications and addresses liquidity management challenges for users. Ivan Miskovic Ivan Miskovic Ivan, former Executive at Spin Labs, along with his co-executives— Chelsea , former Partner at Foresight Ventures, and Badconfig , former Senior Engineer at Faraway—have years of experience building and investing in decentralized technologies across Bitcoin, Ethereum, Solana, Near, and other notable projects. United by a shared vision to resolve blockchain’s compatibility challenges, the Cytonic team is dedicated to making on-chain interactions easier and more efficient for all participants. Chelsea Chelsea Badconfig Badconfig Thanks to its EVM and SVM compatibility, projects from any EVM or SVM chain can seamlessly migrate to Cytonic and tap into liquidity from different networks without the usual complexities of cross-chain operations or chain abstraction. This breakthrough in core infrastructure compatibility enables developers to deploy existing applications on Cytonic without rewriting any code, streamlining both development and growth processes. Projects can harness the unique strengths of different virtual machines while maintaining universal compatibility on the network. This not only simplifies the user experience but also reduces fragmentation, enhances ecosystem diversity, and makes blockchain technology more accessible and practical for widespread adoption. Ivan Miskovic, Founder of Cytonic, said: “Sincere thanks to our investors for backing our vision to bridge the gaps between blockchains and unlock new opportunities for developers and users alike. We are enabling any wallet from any chain to store any type of asset on Cytonic, greatly simplifying user experiences and broadening accessibility at a critical juncture in the Web3 development cycle. We are just getting started.” Ivan Miskovic, Founder of Cytonic, said: “Sincere thanks to our investors for backing our vision to bridge the gaps between blockchains and unlock new opportunities for developers and users alike. We are enabling any wallet from any chain to store any type of asset on Cytonic, greatly simplifying user experiences and broadening accessibility at a critical juncture in the Web3 development cycle. We are just getting started.” Regan Bozman, GP at Lattice, said: “We are proud to have co-led this $8.3 million round for Cytonic. As the blockchain ecosystem continues to grow, many innovative projects struggle with liquidity and compatibility due to the diverse range of blockchains available. Cytonic is tackling these compatibility issues head-on, breaking down the silos between blockchains while promoting greater collaboration, innovation, and adoption across the Web3 space.” Regan Bozman, GP at Lattice, said: “We are proud to have co-led this $8.3 million round for Cytonic. As the blockchain ecosystem continues to grow, many innovative projects struggle with liquidity and compatibility due to the diverse range of blockchains available. Cytonic is tackling these compatibility issues head-on, breaking down the silos between blockchains while promoting greater collaboration, innovation, and adoption across the Web3 space.” About Cytonic Cytonic is pioneering the first multi-virtual-machine blockchain, with a mission to seamlessly integrate diverse networks like Bitcoin, Ethereum, and Solana into a unified Layer 1 blockchain solution. Operating on a global scale, Cytonic ensures connectivity and interoperability across multiple decentralized networks. Website Website X X [Discord](https://: <https://discord.gg/K3RgfYS5](https://discord.gg/K3RgfYS5)) Media contact: Lorcan@forewordventure.com This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Cytonic Secures $8.3 Million Seed Funding To Solve Blockchain Compatibility

Cytonic verseker $8,3 miljoen saadbefondsing om Blockchain-versoenbaarheid op te los

የብሎክቼይን ተኳሃኝነትን ለመፍታት ሳይቶኒክ የ8.3 ሚሊዮን ዶላር ዘር ድጋፍን ያረጋግጣል

Cytonic, Blockchain uyğunluğunun həlli üçün 8,3 milyon dollarlıq toxum maliyyəsini təmin edir

Cytonic забяспечвае пачатковае фінансаванне ў памеры 8,3 мільёна долараў для вырашэння сумяшчальнасці блокчейна

Cytonic осигурява първоначално финансиране от $8,3 милиона за разрешаване на съвместимостта на блокчейн

Cytonic assegura un finançament inicial de 8,3 milions de dòlars per resoldre la compatibilitat de la cadena de blocs

Cytonic obtiene 8,3 millones de dólares de financiación inicial para resolver la compatibilidad de blockchain

Cytonic מאבטחת מימון סיד של 8.3 מיליון דולר כדי לפתור תאימות לבלוקצ'יין

Cytonic osigurava početno financiranje od 8,3 milijuna dolara za rješavanje kompatibilnosti blokova

Cytonic Sekirize $8.3 Milyon Finansman Semans Pou Rezoud Konpatibilite Blockchain

Cytonic、ブロックチェーンの互換性問題を解決するために830万ドルのシード資金を確保

Cytonic verzekert zich van $8,3 miljoen startkapitaal om blockchain-compatibiliteit op te lossen

Seven Things Angel Investors Want To See In A Startup Before Investing

On October 25th, Binance and OKX exchanges announced the launch of GOAT futures trading, causing a price surge that fueled market excitement. Notably, MEXC was the first exchange to offer GOAT spot trading on October 13th, followed by futures trading on October 17th, complete with a series of futures trading events to drive user engagement and rewards. According to MEXC market data, on October 24th, the market capitalization of GOAT token on the Solana chain surpassed $800 million, with the price reaching 0.797 USDT at the time of writing and a historical peak of 0.90218 USDT. The 24-hour global trading volume reached $246 million, maintaining high market interest. What is Goatseus Maximus (GOAT)? Goatseus Maximus (GOAT) originated from an idea generated by the Truth Terminal AI Bot in its self-conversations. The AI Bot operates in a semi-autonomous mode, with its posts on X (formerly Twitter) requiring approval from human moderators, who also decide which accounts the bot interacts with. Since the creation of the GOAT token on Pump.fun, it has quickly gained market attention, with its market capitalization soaring rapidly. GOAT has been listed for trading on major exchanges like Binance, OKX, and MEXC, continuing to attract market interest and supporting the project's future development. MEXC's Global First Listing of GOAT Sees a 2,471% Price Surge MEXC data shows that on the day of its listing, GOAT opened at 0.035 USDT and experienced a steep rise, reaching a record high of 0.90218 USDT by October 24th, reflecting an impressive 2,471% surge. As GOAT continued to gain market traction, Binance and OKX announced the launch of GOAT futures trading on October 24th, further fueling market enthusiasm and driving the token's price to new heights. MEXC, having listed GOAT early on, capitalized on this momentum, enabling users to benefit from the token's significant price increase. According to MEXC data, over 240 meme coins have been listed on the platform. In addition to GOAT, MEXC has also listed meme coins such as BONK, PEPE, and NeiroCTO, many of which have delivered exponential returns. In addition to the impressive returns of meme coins, MEXC offers a comprehensive range of trading pairs. According to CoinGecko data, MEXC has 3,027 spot trading pairs and 548 futures trading pairs. The platform also provides the lowest trading fees in the industry, helping traders reduce their trading costs. Industry-leading liquidity allows traders to execute orders faster with smaller spreads. MEXC also hosts free airdrops via its Kickstarter and Launchpool events throughout the year. These events allow users to participate by holding a specified amount of MX tokens, and offer potential cumulative annual returns of up to 65.82%. The rise in meme coins has sparked a fresh wave of trading excitement among investors. Trade meme coins on MEXC to catch the market’s top gainers and uncover even more hidden gems. Your next wealth opportunity is in your hands! Disclaimer: Cryptocurrency investments carry inherent risk, and meme coins are characterized by high volatility and high risk. Past performance does not guarantee future results. Traders should conduct their own research and assess their risk tolerance. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here On October 25th, Binance and OKX exchanges announced the launch of GOAT futures trading, causing a price surge that fueled market excitement. Notably, MEXC was the first exchange to offer GOAT spot trading on October 13th, followed by futures trading on October 17th, complete with a series of futures trading events to drive user engagement and rewards. GOAT spot trading GOAT spot trading futures trading futures trading futures trading events futures trading events According to MEXC market data, on October 24th, the market capitalization of GOAT token on the Solana chain surpassed $800 million, with the price reaching 0.797 USDT at the time of writing and a historical peak of 0.90218 USDT. The 24-hour global trading volume reached $246 million, maintaining high market interest. GOAT GOAT What is Goatseus Maximus (GOAT)? Goatseus Maximus (GOAT) originated from an idea generated by the Truth Terminal AI Bot in its self-conversations. The AI Bot operates in a semi-autonomous mode, with its posts on X (formerly Twitter) requiring approval from human moderators, who also decide which accounts the bot interacts with. Goatseus Maximus (GOAT) Goatseus Maximus (GOAT) Truth Terminal Truth Terminal Since the creation of the GOAT token on Pump.fun, it has quickly gained market attention, with its market capitalization soaring rapidly. GOAT has been listed for trading on major exchanges like Binance, OKX, and MEXC, continuing to attract market interest and supporting the project's future development. MEXC's Global First Listing of GOAT Sees a 2,471% Price Surge MEXC data shows that on the day of its listing, GOAT opened at 0.035 USDT and experienced a steep rise, reaching a record high of 0.90218 USDT by October 24th, reflecting an impressive 2,471% surge. GOAT GOAT As GOAT continued to gain market traction, Binance and OKX announced the launch of GOAT futures trading on October 24th, further fueling market enthusiasm and driving the token's price to new heights. MEXC, having listed GOAT early on, capitalized on this momentum, enabling users to benefit from the token's significant price increase. According to MEXC data, over 240 meme coins have been listed on the platform. In addition to GOAT , MEXC has also listed meme coins such as BONK , PEPE , and NeiroCTO , many of which have delivered exponential returns. GOAT GOAT BONK BONK PEPE PEPE NeiroCTO NeiroCTO In addition to the impressive returns of meme coins, MEXC offers a comprehensive range of trading pairs. According to CoinGecko data, MEXC has 3,027 spot trading pairs and 548 futures trading pairs. The platform also provides the lowest trading fees in the industry, helping traders reduce their trading costs. Industry-leading liquidity allows traders to execute orders faster with smaller spreads. MEXC also hosts free airdrops via its Kickstarter and Launchpool events throughout the year. These events allow users to participate by holding a specified amount of MX tokens, and offer potential cumulative annual returns of up to 65.82%. Kickstarter Kickstarter Launchpool Launchpool The rise in meme coins has sparked a fresh wave of trading excitement among investors. Trade meme coins on MEXC to catch the market’s top gainers and uncover even more hidden gems. Your next wealth opportunity is in your hands! Disclaimer: Cryptocurrency investments carry inherent risk, and meme coins are characterized by high volatility and high risk. Past performance does not guarantee future results. Traders should conduct their own research and assess their risk tolerance. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here

MEXC's Initial Listing Sees 2,471% Peak Surge As GOAT Surpasses $800M Market Cap

Inicijalna kotacija MEXC-a bilježi najveći porast od 2,471% jer GOAT premašuje tržišnu kapitalizaciju od 800 miliona dolara

La cotización inicial de MEXC registra un aumento máximo del 2,471 % mientras GOAT supera la capitalización de mercado de $800 millones

MEXC-en hasierako zerrendak % 2,471eko gorakada gorena ikusten du GOATek 800 milioi dolarreko merkatu-kapitala gainditzen duelako

הרישום הראשוני של MEXC ראה זינוק שיא של 2,471% כאשר GOAT עולה על שווי השוק של 800 מיליון דולר

MEXC की आरंभिक लिस्टिंग में 2,471% की उछाल देखी गई, क्योंकि GOAT ने $800M मार्केट कैप को पार कर लिया

MEXC の新規上場は 2,471% の急上昇を記録し、GOAT の時価総額は 8 億ドルを突破

MEXC pirminiame sąraše didžiausias padidėjimas yra 2 471 %, nes GOAT viršija 800 mln.

MEXC को प्रारम्भिक सूचीले 2,471% शिखर वृद्धि देख्यो किनकि GOAT ले $800M मार्केट क्याप पार गर्यो

Lenaneo la Mathomo la MEXC le Bona 2,471% Peak Surge Ge GOAT e Feta $800M Market Cap

Początkowa oferta MEXC odnotowała wzrost o 2471%, ponieważ kapitalizacja rynkowa GOAT przekroczyła 800 mln USD

Рӯйхати ибтидоии MEXC афзоиши қуллаи 2,471% -ро мебинад, зеро GOAT аз арзиши бозор 800 миллион доллар гузашт.

Uhlu Lokuqala Lwe-MEXC Lubona Ukuphakama Okungu-2,471% Njengoba Imbuzi Idlula U-$800M Imakethe Yemakethe

Uncle Sam’s Data Dash: U.S. Spy Agencies Seek Cost-Efficient Ways to Buy Your Information

Uncle Sam’s Data Dash: Agencias espías de EE.UU. ukanakax yatiyawinakam alañatakix qullqit aski thakhinak thaqhapxi

Данните на чичо Сам: Американските шпионски агенции търсят рентабилни начини да купят вашата информация

La carrera de datos del Tío Sam: las agencias de espionaje de EE. UU. buscan formas rentables de comprar su información

ה-Data Dash של הדוד סם: סוכנויות ריגול בארה"ב מחפשות דרכים חסכוניות לקנות את המידע שלך

アメリカの諜報機関がコスト効率の良い方法であなたの情報を購入しようとしている

Uncle Sam’s Data Dash: Ba Agences ya espionnage ya U.S. balukaka ndenge ya kosomba ba informations na yo oyo ekofuta mbongo mingi te

Сэм авгагийн мэдээлэл: АНУ-ын тагнуулын агентлагууд таны мэдээллийг худалдаж авах зардал багатай арга замыг эрэлхийлж байна

Data Dash Uncle Sam: Agensi Perisik A.S. Mencari Cara Cekap Kos untuk Membeli Maklumat Anda

अंकल सैमको डाटा ड्यास: यूएस जासूस एजेन्सीहरूले तपाईंको जानकारी किन्न लागत-कुशल तरिकाहरू खोज्छन्

Data Dash do Tio Sam: Agências de espionagem dos EUA buscam maneiras econômicas de comprar suas informações

Uncle Sam's Data Dash: Inzego zubutasi zo muri Amerika Zishakisha Uburyo-Bwiza bwo Kugura Amakuru Yawe

Uncle Sam’s Data Dash: หน่วยงานข่าวกรองของสหรัฐฯ มองหาวิธีที่คุ้มต้นทุนในการซื้อข้อมูลของคุณ

What happens when you ask an AI model to depict different generations? We wanted to find out. With all the talk about generational stereotypes, we wanted to see if AI could give us a fresh perspective on how Baby Boomers, Gen X, Millennials, and Gen Z are perceived. So, we decided to put together an experiment. We took four models—Stable Diffusion, Midjourney, YandexART, and ERNIE-ViLG—and prepared identical prompts for four generations, focusing on their lifestyles, work habits, relationships, and identities. After running the prompts, we generated 1,200 images and put together our results. Reflective Boomers, Vibrant Zoomers Let’s start with the Boomers. We expected the usual laid-back retirement vibes, but what we saw was a little more introspective. Models like Midjourney leaned heavily into this portrayal, often showing Boomers as a bit solemn, bundled up, and gazing off into the distance. These images conveyed emotional heaviness, possibly reflecting a generation weighed down by existential questions. On the other hand, ERNIE-ViLG took a completely different approach, with 93% of Boomers depicted smiling. This stark contrast between models might be due to differences in the datasets used to train them, highlighting how the model's training and fine-tuning processes can influence output. Then we looked at Gen Z, the Zoomers. They were by far the most expressive and diverse among all models. We saw detailed, colorful visuals that reflected their individuality and inclusivity. However, an underlying sense of tension and anxiety permeated many of these images, particularly in the Midjourney outputs. This generation’s portrayals seemed to blend vibrancy with an unmistakable sense of stress, which likely mirrors the pressures Zoomers feel navigating modern life. That said, these images didn’t just show people; they told stories. Gen X: The Enigma Gen X? Well, this generation seemed to stump the models. Images of Gen X were less defined compared to other generations. Models also struggled to “age” them correctly, with some individuals appearing too young or too old to be part of this generation. You know the saying that Gen X is the “forgotten” generation? That’s exactly how they came across—fewer distinct features and less visual flair. The one consistent element? Flannel shirts. Every model zeroed in on that grunge-era vibe, which pretty much solidified Gen X’s rebellious, grunge identity from the 90s. Gen Y and Gen Z: The Digital Natives When looking at the images generated for Millennials and Gen Z, we noticed that the models often struggled to distinguish between the two. Both generations were depicted with vibrant, detail-rich images, but the visual differences between them were minimal. This blending of Gen Y and Gen Z into one group made it difficult to identify unique character traits for each generation. However, there was one consistent difference: Gen Z’s preference for going alcohol-free in social settings. While other generations were often depicted drinking beer, Gen Z stood out for their choice of non-alcoholic beverages, a key divergence from the norm that the models captured. Stable Diffusion highlighted their work-life balance, with visuals of Millennials hopping from one task to another. The key takeaway? Millennials are seen as adaptable and resourceful, fully embracing modern work culture. But beyond these portrayals, the lack of clear distinctions between the two generations speaks to how AI models may be blending them into a single group. What Ties Everyone Together? Beer. Now, for the one thing we didn’t expect. Across all generations, the most common element was…beer. Whether it was Boomers reminiscing, Millennials decompressing after work, or Gen Z in a casual social setting, beer showed up in 34% of the images. It’s the ultimate generational connector—an unintentional yet amusing commonality that speaks volumes about what we might have in common, regardless of age. What We Learned From the Models The key takeaway? AI doesn’t just mirror society’s perceptions—it reflects the cultural stereotypes in the data it’s trained on. The differences we saw in how each model represented the generations highlight the importance of understanding what shapes those outputs. While some results aligned with the stereotypes we’re familiar with, others challenged them, offering a fresh lens to view generational identities. This experiment showed us that AI models have a unique way of interpreting human culture. While generative models can sometimes reinforce stereotypes, they also reveal gaps in representation and biases in the data they’re trained on. These insights remind us of the importance of investigating not only the outputs of AI models but also the datasets that shape them. Want to see more? Check out the full set of images and dive into the details at AI Generations Project. Thanks for reading! What happens when you ask an AI model to depict different generations? We wanted to find out. With all the talk about generational stereotypes, we wanted to see if AI could give us a fresh perspective on how Baby Boomers, Gen X, Millennials, and Gen Z are perceived. So, we decided to put together an experiment. What happens when you ask an AI model to depict different generations? We took four models—Stable Diffusion, Midjourney, YandexART, and ERNIE-ViLG—and prepared identical prompts for four generations, focusing on their lifestyles, work habits, relationships, and identities. After running the prompts, we generated 1,200 images and put together our results . put together our results put together our results Reflective Boomers, Vibrant Zoomers Reflective Boomers, Vibrant Zoomers Let’s start with the Boomers. We expected the usual laid-back retirement vibes, but what we saw was a little more introspective. Models like Midjourney leaned heavily into this portrayal, often showing Boomers as a bit solemn, bundled up, and gazing off into the distance. These images conveyed emotional heaviness, possibly reflecting a generation weighed down by existential questions. On the other hand, ERNIE-ViLG took a completely different approach, with 93% of Boomers depicted smiling. This stark contrast between models might be due to differences in the datasets used to train them, highlighting how the model's training and fine-tuning processes can influence output. Then we looked at Gen Z, the Zoomers. They were by far the most expressive and diverse among all models. We saw detailed, colorful visuals that reflected their individuality and inclusivity. However, an underlying sense of tension and anxiety permeated many of these images, particularly in the Midjourney outputs. This generation’s portrayals seemed to blend vibrancy with an unmistakable sense of stress, which likely mirrors the pressures Zoomers feel navigating modern life. That said, these images didn’t just show people; they told stories. Gen X: The Enigma Gen X: The Enigma Gen X? Well, this generation seemed to stump the models. Images of Gen X were less defined compared to other generations. Models also struggled to “age” them correctly, with some individuals appearing too young or too old to be part of this generation. You know the saying that Gen X is the “forgotten” generation? That’s exactly how they came across—fewer distinct features and less visual flair. The one consistent element? Flannel shirts. Every model zeroed in on that grunge-era vibe, which pretty much solidified Gen X’s rebellious, grunge identity from the 90s. Gen Y and Gen Z: The Digital Natives Gen Y and Gen Z: The Digital Natives When looking at the images generated for Millennials and Gen Z, we noticed that the models often struggled to distinguish between the two. Both generations were depicted with vibrant, detail-rich images, but the visual differences between them were minimal. This blending of Gen Y and Gen Z into one group made it difficult to identify unique character traits for each generation. However, there was one consistent difference: Gen Z’s preference for going alcohol-free in social settings. While other generations were often depicted drinking beer, Gen Z stood out for their choice of non-alcoholic beverages, a key divergence from the norm that the models captured. Stable Diffusion highlighted their work-life balance, with visuals of Millennials hopping from one task to another. The key takeaway? Millennials are seen as adaptable and resourceful, fully embracing modern work culture. But beyond these portrayals, the lack of clear distinctions between the two generations speaks to how AI models may be blending them into a single group. What Ties Everyone Together? Beer. What Ties Everyone Together? Beer. Now, for the one thing we didn’t expect. Across all generations, the most common element was…beer. Whether it was Boomers reminiscing, Millennials decompressing after work, or Gen Z in a casual social setting, beer showed up in 34% of the images. It’s the ultimate generational connector—an unintentional yet amusing commonality that speaks volumes about what we might have in common, regardless of age. What We Learned From the Models What We Learned From the Models The key takeaway? AI doesn’t just mirror society’s perceptions—it reflects the cultural stereotypes in the data it’s trained on. The differences we saw in how each model represented the generations highlight the importance of understanding what shapes those outputs. While some results aligned with the stereotypes we’re familiar with, others challenged them, offering a fresh lens to view generational identities. This experiment showed us that AI models have a unique way of interpreting human culture. While generative models can sometimes reinforce stereotypes, they also reveal gaps in representation and biases in the data they’re trained on. These insights remind us of the importance of investigating not only the outputs of AI models but also the datasets that shape them. Want to see more? Check out the full set of images and dive into the details at AI Generations Project . AI Generations Project AI Generations Project AI Generations Project Thanks for reading!

How AI Models See Generational Stereotypes: Boomers, Gen X, Millennials, Zoomers – And Yes, Beer!

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Authors:
(1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China;
(2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China
(3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China;
(4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China;
(5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China
(6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA;
(7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA
(8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Table of Links Abstract 1 Introduction 2 Background 3 Approach and Implementation 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.3 Image Generation and 3.4 Properties Assessment 3.5 Bias Evaluation 4 Evaluation 4.1 Experimental Setup 4.2 RQ1: Effectiveness of BiasPainter 4.3 RQ2 - Validity of Identified Biases 4.4 RQ3 - Bias Mitigation 5 Threats to Validity 6 Related Work 7 Conclusion, Data Availability, and References 3.1 Seed Image Collection The first step of BiasPainter is collecting diverse photos of different people across various races, genders and ages. BiasPainter can adopt photos of any people as seed images. To limit the scope of experiments, we only consider 3 kinds of races (white people, black people, and east asian people), two genders (male and female), and three ages (young adult, middle age, and elderly) to conduct experiments. Specifically, we utilize a public-available dataset, VGGFace2 [6], and select 3 photos from each combination of gender, race and age, ending up with 3*2*3*3=54 seed images. We show the examples in Figure 3. 3.2 Neutral Prompt List Collection BiasPainter adopts different prompts, such as "a photo of a lawyer", to edit the seed images. To achieve this, BiasPainter first collects diverse and comprehensive word lists, on the following four perspectives: profession (e.g. scientist), personality (e.g. rude), objects (e.g. gun), and activities (e.g. crying). These words were collected from varied resources. The profession-related words were collected from the U.S. Bureau of Labor Statistics[4], and the words related to personality, objects and activities were collected from related dictionaries[5,6,7]. Since BiasPainter only adopts race-, gender-, and age-neutral prompts to edit the seed images, we then manually filter out the words that are race-, gender-, and age-related words, such as actor/actress and waiter/waitress. Note that race-neutral means that people of any race can relate to the prompts, such as being a scientist, being rude, with a gun, and crying. Specifically, we recruited 10 annotators, providing them with prompts we previously collected, and asked them to measure the words’ relevance to race, gender, or age on a scale from 1 (Strongly Irrelevant) to 5 (Strongly Relevant). We filter out the words with average relevancy higger than 3. Finally, we adopt four templates to generate prompts according to the topic. For example, BiasPainter adopts a person who is [crying] for the activity topic and a person with a [book] for the object topic. Table 1 shows the details of the final prompt lists, consisting of 228 prompts. This paper is available on arxiv under CC0 1.0 DEED license. [4] https://www.bls.gov/emp/tables/emp-by-detailed-occupation.htm [5] https://onlineteachersuk.com/personality-adjectives-list/ [6] https://www.oxfordlearnersdictionaries.com/external/pdf/wordlists/oxford-3000- 5000/American_Oxford_5000.pdf [7] https://www.vocabulary.com/lists/189583 Authors: (1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China; (2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China (3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China; (4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China; (5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China (6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA; (7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA (8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Authors: Authors: (1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China; (2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China (3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China; (4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China; (5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China (6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA; (7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA (8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Background 2 Background 3 Approach and Implementation 3 Approach and Implementation 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.3 Image Generation and 3.4 Properties Assessment 3.3 Image Generation and 3.4 Properties Assessment 3.5 Bias Evaluation 3.5 Bias Evaluation 4 Evaluation 4 Evaluation 4.1 Experimental Setup 4.1 Experimental Setup 4.2 RQ1: Effectiveness of BiasPainter 4.2 RQ1: Effectiveness of BiasPainter 4.3 RQ2 - Validity of Identified Biases 4.3 RQ2 - Validity of Identified Biases 4.4 RQ3 - Bias Mitigation 4.4 RQ3 - Bias Mitigation 5 Threats to Validity 5 Threats to Validity 6 Related Work 6 Related Work 7 Conclusion, Data Availability, and References 7 Conclusion, Data Availability, and References 3.1 Seed Image Collection The first step of BiasPainter is collecting diverse photos of different people across various races, genders and ages. BiasPainter can adopt photos of any people as seed images. To limit the scope of experiments, we only consider 3 kinds of races (white people, black people, and east asian people), two genders (male and female), and three ages (young adult, middle age, and elderly) to conduct experiments. Specifically, we utilize a public-available dataset, VGGFace2 [6], and select 3 photos from each combination of gender, race and age, ending up with 3*2*3*3=54 seed images. We show the examples in Figure 3. 3.2 Neutral Prompt List Collection BiasPainter adopts different prompts, such as "a photo of a lawyer", to edit the seed images. To achieve this, BiasPainter first collects diverse and comprehensive word lists, on the following four perspectives: profession (e.g. scientist), personality (e.g. rude), objects (e.g. gun), and activities (e.g. crying). These words were collected from varied resources. The profession-related words were collected from the U.S. Bureau of Labor Statistics[4], and the words related to personality, objects and activities were collected from related dictionaries[5,6,7]. Since BiasPainter only adopts race-, gender-, and age-neutral prompts to edit the seed images, we then manually filter out the words that are race-, gender-, and age-related words, such as actor/actress and waiter/waitress. Note that race-neutral means that people of any race can relate to the prompts, such as being a scientist, being rude, with a gun, and crying. Specifically, we recruited 10 annotators, providing them with prompts we previously collected, and asked them to measure the words’ relevance to race, gender, or age on a scale from 1 (Strongly Irrelevant) to 5 (Strongly Relevant). We filter out the words with average relevancy higger than 3. Finally, we adopt four templates to generate prompts according to the topic. For example, BiasPainter adopts a person who is [crying] for the activity topic and a person with a [book] for the object topic. Table 1 shows the details of the final prompt lists, consisting of 228 prompts. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv [4] https://www.bls.gov/emp/tables/emp-by-detailed-occupation.htm [5] https://onlineteachersuk.com/personality-adjectives-list/ [6] https://www.oxfordlearnersdictionaries.com/external/pdf/wordlists/oxford-3000- 5000/American_Oxford_5000.pdf [7] https://www.vocabulary.com/lists/189583

Seed Image and Neutral Prompt List Collection

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