I started picking up VIM about 2 days ago with the vimtutor command and was basically just holding down w and e plug hjkl to move around along with ^ $. Seeing how the pros move with the leaderboard function showed me how fast I could get and also what are the most recurrent keys I should know first to be fast (* and n/N were super strong) also I now can clearly see how jumping around with capital W and E makes sense a lot of the time when I don't want to get stuck on punctuation in code.
I finished the site's content in like 15 minutes and it taught me wayyy more practically than vimtutor did, really they are great together though.
If I had one suggestion it would be to add a randomize cursor position mode so I could practice endlessly without starting to memorize where the cursor prompt will jump to next.
Thanks for developing such an amazing learning tool!

Microprocessor, also known as CPU (Central Processing Unit), is an electronic component that plays a central role in the circuit, acting as the "brain" of the system. It is made up of billions of tiny transistors integrated on a single microchip, capable of performing mathematical and logical calculations and processing information quickly and accurately.
Main functions of the microprocessor:
• Execute instructions: The microprocessor receives instructions from the program, decodes and executes them sequentially.
• Data processing: The microprocessor performs mathematical and logical operations on data, transforming information according to the program's requirements.
• System management: The microprocessor coordinates the operation of other components in the circuit, ensuring that the system operates stably and efficiently.
Microprocessor Classification by Function

1. General-purpose processors (GPP) are designed to perform a wide variety of tasks.

Central Processing Unit (CPU):
• Function: The "brain" of the computer, responsible for performing mathematical and logical calculations, processing data, and controlling the overall operation of the system.
• Characteristics:
o Multitasking capability: CPUs can perform multiple tasks at the same time, meeting the increasing usage demands of users.
o Compatibility: CPUs are supported by a wide range of operating systems and software, making it easy for users to choose and use.
o High performance: Modern CPUs have very fast processing speeds, meeting the demands of processing intensive tasks such as gaming, video editing, programming, etc.
o Applications: Personal computers, servers, workstations, etc.
o Popular CPU names:
 Intel: Core i9, Core i7, Core i5, Core i3, Pentium Gold, Celeron
 AMD: Ryzen 9, Ryzen 7, Ryzen 5, Ryzen 3, Athlon

1. Application-specific processors (ASPs) are designed to perform a specific task.

Microcontroller (MCU):
• Function: A simple, low-cost microprocessor used in embedded devices such as washing machines, refrigerators, microwaves, etc. MCUs have the ability to perform basic control and data processing tasks.
• Characteristics:
o Low cost: MCUs are cheaper than other types of microprocessors, making them suitable for low-cost applications.
o Power efficiency: MCUs consume less power, making them suitable for mobile and embedded devices.
o Easy to use: MCUs are easier to use and program than other types of microprocessors.
• Applications: Washing machines, refrigerators, microwaves, air conditioners, electronic toys, etc.
• Popular MCU names:
o Microchip: PIC16F88, PIC18F452, PIC24FJ64GB204,..
o STMicroelectronics: STM32F103C8T6, STM32F407VG, STM32L476RG,…
Graphics Processing Unit (GPU)
Function: Specialized in processing complex graphics tasks such as gaming, video editing, 3D graphics design, etc.
Characteristics:
• Powerful graphics performance: GPUs can handle millions of mathematical calculations per second, meeting the high graphics demands of modern applications.
• Video decoding capabilities: GPUs can decode high-quality video, providing a smooth video experience.
• Artificial intelligence support: GPUs are used in artificial intelligence applications such as machine learning, natural language processing, etc.
Applications: Gaming computers, graphics computers, render servers, cryptocurrency mining devices, etc.
Popular GPU names:
• NVIDIA: GeForce RTX 3090, GeForce RTX 3080, GeForce RTX 3070,…
• AMD: Radeon RX 6950 XT, Radeon RX 6900 XT, Radeon RX 6800 XT,…
Digital Signal Processor (DSP)
Function: Specialized in processing audio, video, and other digital signals. DSPs can perform mathematical and logical operations on digital signals quickly and efficiently.
Characteristics:
• Efficient digital signal processing: DSPs are designed to process digital signals with high accuracy and speed.
• Power efficiency: DSPs consume less power than other types of microprocessors, making them suitable for mobile and embedded applications.
• Programmability: DSPs can be programmed to perform various signal processing functions, meeting the diverse needs of users.
Applications: Mobile phones, televisions, digital cameras, medical devices, etc.
Popular DSP names:
• Analog Devices: SHARC ADSP-21489, Blackfin BF561, TigerSHARC ADSP-TS201S,…
• Texas Instruments: C6727, C6713, C6457,…
Network Processing Unit (NPU)
Function: Specialized in processing network tasks such as data transmission, routing, encryption, decryption, etc. NPUs can handle large amounts of network data quickly and efficiently.
Characteristics:
• High processing speed: NPUs can process millions of packets per second, meeting the ever-increasing data transmission demands.
• High energy efficiency: NPUs are designed to optimize power efficiency, helping to minimize operating costs.
• Programmability: NPUs can be programmed to perform various network functions, meeting the diverse needs of users.
Applications: Network devices such as routers, switches, network storage devices, etc.
Popular NPU names:
• Qualcomm: Snapdragon Hexagon 80 DSP, Snapdragon Hexagon 900 DSP,..
• Apple: Neural Engine (A11 Bionic chip and later)
Security Processing Unit (SPU)
Function: Specialized in processing security tasks such as encryption, decryption, authentication, etc. SPUs can perform complex security algorithms quickly and securely.
Characteristics:
• High level of security: SPUs are designed with advanced security features to protect data and systems from cyber threats.
• Powerful performance: SPUs can handle complex security algorithms quickly, meeting the ever-increasing security demands.
• Scalability: SPUs can be scaled to meet the growing security needs of the system.
Applications: Mobile devices, personal computers, servers, Internet of Things (IoT) devices, etc.
Popular SPU names:
• ARM: TrustZone
• NXP: Secure Element
Machine Learning Accelerator (MLU)
Function: Specialized in processing machine learning tasks such as classification, recognition, prediction, etc. MLUs can perform complex mathematical calculations related to machine learning quickly and efficiently.
Characteristics:
• High performance: MLUs can handle complex machine learning algorithms quickly, accelerating machine learning model training and deployment.
• Scalability: MLUs can be scaled to meet the growing machine learning demands of the system.
• Power efficiency: MLUs are designed to optimize power efficiency, helping to minimize operating costs.
Applications: Facial recognition systems, self-driving cars, robots, medical applications, etc.
Popular MLU names:
• Intel: Nervana Neural Network Accelerator
• AMD: MI25 Accelerator
IDEs and Programming Languages for Microprocessors
Choosing the right IDE and programming language for a microprocessor depends on several factors, including the type of microprocessor, the intended use, and the programmer's personal preferences. Here are some suggestions:
Microprocessor Type:
Intel:
• IDE: Visual Studio, Eclipse, Intel Microchip Studio
• Programming languages: C, C++, Assembly
NXP:
• IDE: CodeWarrior, Eclipse, Keil MDK
• Programming languages: C, C++
ARM:
• IDE: Visual Studio Code, Eclipse, Keil MDK, Arm Development Studio
• Programming languages: C, C++, Assembly
Arduino:
• IDE: Arduino IDE
• Programming language: C++ (Arduino)
Popular IDEs and Programming Languages for Microprocessors:
Visual Studio: A powerful IDE that supports multiple programming languages, including C, C++, and C#. Visual Studio is a popular choice for Windows and .NET application development.
Eclipse: A free and open-source IDE that supports multiple programming languages, including Java, C, and C++. Eclipse is a popular choice for Java and Android application development.
Arduino IDE: A free and easy-to-use IDE for Arduino programming. Arduino IDE supports the C++ (Arduino) programming language.
Keil MDK: A powerful IDE for ARM microcontroller programming. Keil MDK supports the C and Assembly programming languages.
Compilers for Microprocessors
Compilers translate programming code into a format that can be understood by a microprocessor. The specific compiler you need will depend on the type of microprocessor you are using. Here are some of the most popular compilers for different microprocessor architectures:
Intel:
• Intel C++ Compiler (ICC): This is a powerful and highly optimized C++ compiler for Intel CPUs. ICC supports many modern features such as C++20, AVX-512, and more.
• Intel Fortran Compiler (IFORT): IFORT is an efficient Fortran compiler for Intel CPUs. It supports many modern Fortran features, including Fortran 2018, OpenMP, and more.
• Intel Assembly Language (NASM): NASM is an assembly language compiler for Intel CPUs. It supports many different Intel architectures, from x86 to x86-64.
ARM:
• GNU Arm Compiler Collection (GCC): GCC is a popular free and open-source C/C++ compiler for ARM CPUs. It supports many modern C/C++ features and many different ARM architectures.
• Arm C Compiler and Integrated Development Environment (Arm IAR): Arm IAR is an integrated development environment (IDE) that provides a powerful C/C++ compiler for ARM CPUs. It supports many advanced features such as static analysis, debugging, and optimization.
• ARM Assembly Language (ARM Assembly): ARM Assembly is the assembly language for ARM CPUs. It supports many different ARM architectures and can be used to write highly efficient code.
Arduino:
• Arduino IDE: The Arduino IDE is a free and open-source integrated development environment (IDE) for Arduino. It includes a C/C++ compiler that is optimized for Arduino boards.
• PlatformIO: PlatformIO is an extension for the Visual Studio Code IDE that provides powerful support for Arduino development. It includes a C/C++ compiler and many other advanced features.
In addition, there are many other compiler tools for different microprocessors, such as:
• Microchip XC8: A C compiler for Microchip's PIC microcontrollers.
• Texas Instruments Code Composer Studio (CCS): An IDE that provides a C/C++ compiler for Texas Instruments' MSP430 and TMS320 microcontrollers.
• Renesas RX Compiler: A C++ compiler for Renesas' RX microcontrollers.
Choosing the Right Compiler:
The choice of the right compiler depends on many factors, including the microprocessor architecture, operating system, features needed, and your personal preferences. Some factors to consider when choosing a compiler include:
• Microprocessor architecture support: Make sure the compiler supports the microprocessor architecture you are using.
• Features: Identify the features you need, such as C/C++ support, performance optimization, debugging, etc.
• Operating system: Choose a compiler that is compatible with the operating system you are using.
• Ease of use: Choose a compiler that has an easy-to-use user interface and comprehensive documentation.
• Community: Consult the programming community for recommendations on suitable compilers.
Notes:
• Some compilers are free and open-source, while others may be paid.
• Some compilers come with an IDE, while others can be used as command-line tools.
• Please refer to the compiler's documentation for detailed usage instructions.
Methods for Programming Microprocessors
To program a microprocessor, you need to use the appropriate tool for that type of CPU. Here are some examples:
Intel CPUs:
• Programmers:
o JTAG: Commonly used for Atmel and Microchip chips.
o ISP: Commonly used for PIC chips.
o In-System Programming (ISP): Some Intel chips support programming directly through the JTAG or SWD port.
• USB Cables:
o USB JTAG Cable: Connects the JTAG programmer to the computer.
o USB ISP Cable: Connects the ISP programmer to the computer.
• Programming Software:
o Intel Flash Programming Tool (IFPT): Used for Intel FPGA and SoC chips.
o Intel Platform Flash Tool (IntelPFT): Used for Intel Atom and Celeron chips.
o Flash Programmer: Supports a variety of Intel chips.
ARM CPUs:
• Programmers:
o JTAG: The most common type for ARM chips.
o SWD: A newer and more efficient programming protocol than JTAG.
o Bootloader: Some ARM chips can be programmed through a bootloader.
• USB Cables:
o USB JTAG Cable: Connects the JTAG programmer to the computer.
o USB SWD Cable: Connects the SWD programmer to the computer.
• Programming Software:
o OpenOCD: Supports a variety of ARM chips.
o STMicroelectronics ST-Link: Used for STM32 chips.
o NXP LPCFlashProgrammer: Used for NXP LPC chips.
Arduino CPUs:
• USB Cable: Arduinos are programmed directly through a USB cable.
• Programming Software:
o Arduino IDE: The integrated development environment (IDE) for Arduino, which includes a built-in programmer.
Other CPUs:
In addition to the types of CPUs listed above, there are many other types of CPUs with their own programming tools. You will need to refer to the CPU manufacturer's documentation for more information.
Programming Software for Microprocessors
There are many different software programs used to program microprocessors, depending on the type of microprocessor and operating system you are using. Here are a few popular examples:
For Intel Microprocessors:
• Intel Flash Programming Tool (IFPT):This is Intel's official tool for programming firmware and flash memory on Intel microprocessors. IFPT is available for Windows, Linux, and macOS.
• Intel HEX Flash Utility:This is a simpler tool for flashing HEX files to Intel microprocessors. HEX Flash Utility is available for Windows.
For NXP (Nexperia) Microprocessors:
• NXP LPCFlashProgrammer: This is NXP's official tool for programming firmware and flash memory on NXP microprocessors. LPCFlashProgrammer is available for Windows, Linux, and macOS.
• OpenOCD:This is an open-source debugger and programmer that supports a variety of microprocessors, including NXP microprocessors. OpenOCD is available for Windows, Linux, and macOS.
For ARM Microprocessors:
• ARM Development Toolchain:ARM provides a free development toolchain that includes a programmer for ARM microprocessors. The ARM Development Toolchain is available for Windows, Linux, and macOS.
• OpenOCD: OpenOCD also supports a variety of ARM microprocessors.
For Arduino:
• Arduino IDE:The Arduino IDE includes a built-in programmer for flashing Arduino boards. The Arduino IDE is available for Windows, Linux, and macOS.
• Avrdude:Avrdude is a command-line tool for flashing AVR microcontrollers, including many of the microcontrollers used on Arduino boards. avrdude is available for Windows, Linux, and macOS.
There are many other programming software options available, depending on your specific needs. You can find more information about programming software on the microprocessor manufacturer's website or by searching online.
Note: Before programming a microprocessor, you must make sure that you have selected the correct software and have the appropriate configuration settings. Using the wrong software or settings can damage your microprocessor.
The microprocessor industry is a dynamic and ever-evolving industry that plays a critical role in the development of modern technology. With continuous advancements, microprocessors will continue to drive innovation in a wide range of fields, from computers and smartphones to automobiles and medical devices. Key trends for the future include:
• Miniaturization: The size of transistors on microprocessors is constantly shrinking, allowing for more transistors to be placed on a chip and improving performance.
• Increased Cores: Many modern microprocessors have multiple processing cores, enabling them to handle multiple tasks simultaneously and improve multitasking performance.
• Specialization: Different types of microprocessors are being designed for specific applications, such as graphics processing units (GPUs) for gaming and video editing, network processing units (NPUs) for networking devices, machine learning processors (MLUs) for artificial intelligence, and 3D processors for graphics rendering.
• 3D Processors: 3D processors will stack multiple layers of transistors on top of each other to increase the number of transistors on a chip and improve performance.
• Quantum Computing Systems: Quantum computers use the principles of quantum mechanics to perform calculations, with the potential to offer far superior performance than traditional computers.
• System-on-Chip (SoC): SoCs integrate various components such as CPUs, GPUs, memory, and peripheral controllers onto a single chip, reducing the size, cost, and power consumption of electronic devices.
• Artificial Intelligence (AI): AI will play a more significant role in designing and optimizing microprocessors, leading to improved performance and energy efficiency.
• New Material-Based Processors: New materials such as graphene and silicon photonics could be used to create faster and more energy-efficient microprocessors.
Many Sources

As many C++ developers are interested in performance analysis, I would like to share perf-cpp, our library dedicated to recording performance counters directly within C++ applications.
perf-cpp functions as a wrapper for the "perf_event_open" system call, allowing precise measurement and analysis from your C++ applications (and for specific code segments instead of recording the entire application).
Key Features:

• Targeted Performance Counters: Record and analyze metrics specifically tailored to particular code segments.
• Event Sampling: Detailed capture of events such as data accesses, instruction pointers, and branch records, providing deep performance insights.

We used the library already in several research-related projects and we are hoping that it can be of service to someone else as well. I value any feedback, ideas, or pull requests to further refine perf-cpp.
You can find the library and a bunch of examples on GitHub: https://github.com/jmuehlig/perf-cpp

Hello, I’m trying to set up a Docker mail server, but I’m having some difficulties. After the installation from the market, I’m unable to connect a client like Outlook to it. I can also not open the mailserver-admin panel that is shown in the market.
I’ve followed these tutorials: Basic Installation and Self-hosted Mail Server. Now, I can connect to the server, but I’m unable to send emails. However, receiving emails is working fine. Also i could not set up SSL. I used 143 and 465 as ports in the Client.
Every outgoing email seems to be marked as spam and doesn’t reach the recipient’s server. I believe I’ve correctly set up the DNS with the following records:

• A record: mail.myDomain.it
• Autoconfig: mail.myDomain.it
• Autodiscovery: mail.myDomain.it
• MX from myDomain.it to mail.myDomain.it with priority 10
• PTR from myDomain.it to mail.myDomain.it
• _dmarc with v=DMARC1; p=reject; rua=mailto:[myOtherMailAdress@sentMailserver.it](mailto:myOtherMailAdress@sentMailserver.it)
• mail._domainkey with v=DKIM1; h=sha256; k=rsa; p=

Despite this, the mail server is showing several errors. For instance, it discourages running Amavis/SA & Rspamd and OpenDKIM & Rspamd simultaneously. It recommends using Rspamd for DKIM checks and signing. There’s also a warning about SSL being configured with plain text access, which is not recommended for production deployment. Additionally, there are errors related to missing directories and files, such as ‘/tmp/docker-mailserverspamd/dkim’, and a missing decoder for .zoo files in amavis.
Could anyone provide some guidance on these issues?
Logs:
2024-06-01 09:55:53
[ INF ] Welcome to docker-mailserver v13.3.1
2024-06-01 09:55:53
[ INF ] Checking configuration
2024-06-01 09:55:53
[ INF ] Configuring mail server
2024-06-01 09:55:53
[ WARNING ] (Rspamd setup) Running Amavis/SA & Rspamd at the same time is discouraged
2024-06-01 09:55:53
[ WARNING ] (Rspamd setup) Running OpenDKIM & Rspamd at the same time is discouraged - we recommend Rspamd for DKIM checks (enabled with Rspamd by default) & signing
2024-06-01 09:55:53
[ WARNING ] (Rspamd setup) Running OpenDMARC & Rspamd at the same time is discouraged - we recommend Rspamd for DMARC checks (enabled with Rspamd by default)
2024-06-01 09:55:53
[ WARNING ] (Rspamd setup) Running policyd-spf & Rspamd at the same time is discouraged - we recommend Rspamd for SPF checks (enabled with Rspamd by default)
2024-06-01 09:55:54
[ WARNING ] !! INSECURE !! SSL configured with plain text access - DO NOT USE FOR PRODUCTION DEPLOYMENT
2024-06-01 09:55:55
chown: cannot access '/tmp/docker-mailserverspamd/dkim': No such file or directory
2024-06-01 09:55:55
[ INF ] Starting daemons
2024-06-01 09:55:57
[ INF ] mail.myDomain.it is up and running
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: starting. /ussbin/amavisd-new at mail.myDomain.it amavisd-new-2.11.1 (20181009), Unicode aware, LC_CTYPE="C.UTF-8"
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: perl=5.032001, user=, EUID: 110 (110); group=, EGID: 112 112 (112 112)
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: Net::Server: Group Not Defined. Defaulting to EGID '112 112'
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: Net::Server: User Not Defined. Defaulting to EUID '110'
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: No ext program for .zoo, tried: zoo
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: No ext program for .doc, tried: ripole
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: No decoder for .F
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: No decoder for .doc
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: No decoder for .zoo
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: Using primary internal av scanner code for ClamAV-clamd
2024-06-01 09:55:57
Jun 1 09:55:57 3d63ce0a3d5c amavis[865]: Found secondary av scanner ClamAV-clamscan at /usbin/clamscan
2024-06-01 10:03:03
2024-06-01 10:03:03,788 WARN received SIGTERM indicating exit request
2024-06-01 10:03:15
[ INF ] Welcome to docker-mailserver v13.3.1
2024-06-01 10:03:15
[ INF ] Checking configuration
2024-06-01 10:03:15
[ WARNING ] This container was (likely) improperly restarted which can result in undefined behavior
2024-06-01 10:03:15
[ WARNING ] Please destroy the container properly and then start DMS again
2024-06-01 10:03:15
[ INF ] Configuring mail server
2024-06-01 10:03:15
[ WARNING ] (Rspamd setup) Running Amavis/SA & Rspamd at the same time is discouraged
2024-06-01 10:03:15
[ WARNING ] (Rspamd setup) Running OpenDKIM & Rspamd at the same time is discouraged - we recommend Rspamd for DKIM checks (enabled with Rspamd by default) & signing
2024-06-01 10:03:15
[ WARNING ] (Rspamd setup) Running OpenDMARC & Rspamd at the same time is discouraged - we recommend Rspamd for DMARC checks (enabled with Rspamd by default)
2024-06-01 10:03:15
[ WARNING ] (Rspamd setup) Running policyd-spf & Rspamd at the same time is discouraged - we recommend Rspamd for SPF checks (enabled with Rspamd by default)
2024-06-01 10:03:15
[ WARNING ] !! INSECURE !! SSL configured with plain text access - DO NOT USE FOR PRODUCTION DEPLOYMENT
2024-06-01 10:03:16
chown: cannot access '/tmp/docker-mailserverspamd/dkim': No such file or directory
2024-06-01 10:03:16
[ INF ] Starting daemons
2024-06-01 10:03:18
[ INF ] mail.myDomain.it is up and running
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: starting. /ussbin/amavisd-new at mail.myDomain.it amavisd-new-2.11.1 (20181009), Unicode aware, LC_CTYPE="C.UTF-8"
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: perl=5.032001, user=, EUID: 110 (110); group=, EGID: 112 112 (112 112)
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: Net::Server: Group Not Defined. Defaulting to EGID '112 112'
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: Net::Server: User Not Defined. Defaulting to EUID '110'
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: No ext program for .zoo, tried: zoo
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: No ext program for .doc, tried: ripole
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: No decoder for .F
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: No decoder for .doc
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: No decoder for .zoo
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: Using primary internal av scanner code for ClamAV-clamd
2024-06-01 10:03:18
Jun 1 10:03:18 3d63ce0a3d5c amavis[852]: Found secondary av scanner ClamAV-clamscan at /usbin/clamscan
2024-06-01 10:05:49
Jun 1 10:05:49 3d63ce0a3d5c dovecot: imap-login: Login: user=[admin@myDomain.it](mailto:admin@myDomain.it), method=PLAIN, rip=109.236.81.168, lip=172.16.0.18, mpid=1166, session=
2024-06-01 10:05:49
Jun 1 10:05:49 3d63ce0a3d5c dovecot: imap([admin@myDomain.it](mailto:admin@myDomain.it))<1166>: Disconnected: Connection closed (NAMESPACE finished 0.099 secs ago) in=57 out=568 deleted=0 expunged=0 trashed=0 hdr_count=0 hdr_bytes=0 body_count=0 body_bytes=0
2024-06-01 10:06:16
Jun 1 10:06:16 3d63ce0a3d5c postfix/submissions/smtpd[1217]: connect from 109-236-81-168.hosted-by-worldstream.net[109.236.81.168]
2024-06-01 10:06:17
Jun 1 10:06:17 3d63ce0a3d5c postfix/submissions/smtpd[1217]: disconnect from 109-236-81-168.hosted-by-worldstream.net[109.236.81.168] ehlo=1 auth=1 quit=1 commands=3
2024-06-01 10:06:18
Jun 1 10:06:18 3d63ce0a3d5c dovecot: imap-login: Login: user=[admin@myDomain.it](mailto:admin@myDomain.it), method=PLAIN, rip=109.236.81.168, lip=172.16.0.18, mpid=1221, session=<5/5PQNEZ8mVt7FGo>
2024-06-01 10:06:18
Jun 1 10:06:18 3d63ce0a3d5c dovecot: imap([admin@myDomain.it](mailto:admin@myDomain.it))<1221><5/5PQNEZ8mVt7FGo>: Disconnected: Connection closed (LIST finished 0.103 secs ago) in=93 out=746 deleted=0 expunged=0 trashed=0 hdr_count=0 hdr_bytes=0 body_count=0 body_bytes=0
2024-06-01 10:06:35
Jun 1 10:06:35 3d63ce0a3d5c dovecot: imap-login: Login: user=[admin@myDomain.it](mailto:admin@myDomain.it), method=PLAIN, rip=109.236.81.168, lip=172.16.0.18, mpid=1250, session=<2gBRQdEZZg9t7FGo>
2024-06-01 10:06:47
Jun 1 10:06:47 3d63ce0a3d5c postfix/submissions/smtpd[1217]: connect from 109-236-81-168.hosted-by-worldstream.net[109.236.81.168]
2024-06-01 10:06:47
Jun 1 10:06:47 3d63ce0a3d5c postfix/submissions/smtpd[1217]: 96BA336E04E3: client=109-236-81-168.hosted-by-worldstream.net[109.236.81.168], sasl_method=PLAIN, sasl_username=[admin@myDomain.it](mailto:admin@myDomain.it)
2024-06-01 10:06:47
Jun 1 10:06:47 3d63ce0a3d5c postfix/sender-cleanup/cleanup[1278]: 96BA336E04E3: message-id=[8DBF872B-16FE-477B-A6B1-4DF2DEB87A4F@myDomain.it](mailto:8DBF872B-16FE-477B-A6B1-4DF2DEB87A4F@myDomain.it)
2024-06-01 10:06:47
Jun 1 10:06:47 3d63ce0a3d5c postfix/sender-cleanup/cleanup[1278]: 96BA336E04E3: replace: header MIME-Version: 1.0 from 109-236-81-168.hosted-by-worldstream.net[109.236.81.168]; from=[admin@myDomain.it](mailto:admin@myDomain.it) to=[myOtherMailAdress@sentMailserver.it](mailto:myOtherMailAdress@sentMailserver.it) proto=ESMTP helo=<[127.0.0.1]>: MIME-Version: 1.0
2024-06-01 10:06:47
Jun 1 10:06:47 3d63ce0a3d5c opendkim[717]: 96BA336E04E3: DKIM-Signature field added (s=mail, d=myDomain.it)
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c postfix/qmgr[832]: 96BA336E04E3: from=[admin@myDomain.it](mailto:admin@myDomain.it), size=704, nrcpt=1 (queue active)
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c postfix/submissions/smtpd[1217]: disconnect from 109-236-81-168.hosted-by-worldstream.net[109.236.81.168] ehlo=1 auth=1 mail=1 rcpt=1 data=1 quit=1 commands=6
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c postfix/smtpd-amavis/smtpd[1282]: connect from localhost[127.0.0.1]
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c postfix/smtpd-amavis/smtpd[1282]: 2E37936E8458: client=localhost[127.0.0.1]
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c postfix/cleanup[1283]: 2E37936E8458: message-id=[8DBF872B-16FE-477B-A6B1-4DF2DEB87A4F@myDomain.it](mailto:8DBF872B-16FE-477B-A6B1-4DF2DEB87A4F@myDomain.it)
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c postfix/qmgr[832]: 2E37936E8458: from=[admin@myDomain.it](mailto:admin@myDomain.it), size=1425, nrcpt=1 (queue active)
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c amavis[896]: (00896-01) Passed CLEAN {RelayedOpenRelay}, [109.236.81.168]:17054 [109.236.81.168] [admin@myDomain.it](mailto:admin@myDomain.it) -> [myOtherMailAdress@sentMailserver.it](mailto:myOtherMailAdress@sentMailserver.it), Queue-ID: 96BA336E04E3, Message-ID: [8DBF872B-16FE-477B-A6B1-4DF2DEB87A4F@myDomain.it](mailto:8DBF872B-16FE-477B-A6B1-4DF2DEB87A4F@myDomain.it), mail_id: nlf1v7ZBGJ88, Hits: -, size: 1198, queued_as: 2E37936E8458, 135 ms
2024-06-01 10:06:48
Jun 1 10:06:48 3d63ce0a3d5c postfix/smtp-amavis/smtp[1279]: 96BA336E04E3: to=[myOtherMailAdress@sentMailserver.it](mailto:myOtherMailAdress@sentMailserver.it), relay=127.0.0.1[127.0.0.1]:10024, delay=0.69, delays=0.54/0.01/0/0.14, dsn=2.0.0, status=sent (250 2.0.0 from MTA(smtp:[127.0.0.1]:10025): 250 2.0.0 Ok: queued as 2E37936E8458)

Hi All,
I don't think there's a 'terraform questions' subreddit, so I apologise if this is the wrong place to ask.
I've got an S3 bucket being automated and I need to place some files into it, but they need to have the right content type. Is there a way to make this segment of the code better? I'm not really sure if it's possible, maybe I'm missing something?

resource "aws_s3_object" "resume_source_htmlfiles" { bucket = aws_s3_bucket.online_resume.bucket for_each = fileset("website_files/", "**/*.html") key = each.value source = "website_files/${each.value}" content_type = "text/html" } resource "aws_s3_object" "resume_source_cssfiles" { bucket = aws_s3_bucket.online_resume.bucket for_each = fileset("website_files/", "**/*.css") key = each.value source = "website_files/${each.value}" content_type = "text/css" } resource "aws_s3_object" "resume_source_otherfiles" { bucket = aws_s3_bucket.online_resume.bucket for_each = fileset("website_files/", "**/*.png") key = each.value source = "website_files/${each.value}" content_type = "image/png" } resource "aws_s3_bucket_website_configuration" "bucket_config" { bucket = aws_s3_bucket.online_resume.bucket index_document { suffix = "index.html" } } 

It feels kind of messy right? The S3 bucket is set as a static website currently.
Much appreciated.

The profusion of identical green tents at this spring’s anti-Israel protests struck many as odd. “Why is everybody’s tent the same?,” asked New York mayor Eric Adams. Like others, the mayor suspected “a well-concerted organizing effort” driving the protests. More recent reporting shows a concerted push behind the Gaza protest movement. But it is not as simple as a single organization secretly rallying protesters or buying tents. Instead, the movement’s most determined activists represent a network of loosely linked far-left groups. Some are openly affiliated with well-known progressive nonprofits; others work in the shadows.
The movement also draws on diverse but generous sources of financial backing. Those funding streams may soon be augmented by the federal government. As I chronicled last year in a Manhattan Institute report, “The Big Squeeze: How Biden’s Environmental Justice Agenda Hurts the Economy and the Environment,” the administration’s massive program of environmental justice grants seems designed to prioritize the funding of highly ideological local groups. The Inflation Reduction Act, for example, earmarks $3 billion for “environmental and climate justice block grants” intended for local nonprofits. Today, hundreds of far-left political groups include language about environmental issues and “climate justice” in their mission statements. If just a fraction of planned grants flows to such groups, the effect will be a gusher of new funding for radical causes.
As the Gaza protests spread across U.S. college campuses, many observers noted an eerie uniformity among them. From one campus to the next, protesters operated in disciplined cadres, keeping their faces covered and using identical rote phrases as they refused to talk with reporters. The Atlantic noted the strangeness of seeing elite college students “chanting like automatons.” Students held up keffiyeh scarves or umbrellas to block the view of prying cameras and linked arms to halt the movements of outsiders. At Columbia University and elsewhere, protesters formed “liberated zones,” from which “Zionists” were excluded. Around the edges of the encampments, the more militaristic activists donned helmets and goggles and carried crude weapons, apparently eager to mix it up with police or counter-protesters. We’ve seen these tactics before—notably during the “mostly peaceful” Black Lives Matter protests of 2020, when full-time agitators helped ignite riots, set up a police-free (and violence-plagued) zone in Seattle, and laid nightly siege to Portland, Oregon’s federal courthouse.
In a remarkable work of reporting, Park MacDougald recently traced the tangled roots of organizations backing pro-jihad protests, both on and off campuses. These include Antifa and other networks of anonymous anarchists, along with “various communist and Marxist-Leninist groups, including the Maoist Revolutionary Communist Party, the Party for Socialism and Liberation (PSL), and the International ANSWER coalition,” MacDougald writes. Higher up the food chain, we find groups openly supported by America’s growing class of super-rich tech execs or the anti-capitalist heirs of great fortunes. For example, retired tech mogul Neville Roy Singham, who is married to Code Pink founder Jodie Evans, funds The People’s Forum, a lavish Manhattan resource center for far-left groups. As the Columbia protests intensified, the center urged members to head uptown to “support our students.” Following the money trail of other protest groups, MacDougald finds connections to the Rockefeller Brothers Fund, the Ford Foundation, and—surprising no one—the George Soros-backed Tides Foundation.
Of course, the current wave of anti-Israel protests also involves alliances with pro-Hamas organizations such as Students for Justice in Palestine. Last November, Jonathan Schanzer of the Foundation for Defense of Democracies testified to the House Ways and Means Committee that SJP and similar groups have deep ties to global terrorist organizations, including Hamas.
For many keffiyeh-wearing protestors, however, a recently professed concern for Palestinians is just the latest in a long list of causes they believe justify taking over streets and college quads. In Unherd, Mary Harrington dubs this medley of political beliefs the “omnicause,” writing that “all contemporary radical causes seem somehow to have been absorbed into one.” Today’s leftist activists share an interlocking worldview that sees racism, income inequality, trans intolerance, climate change, alleged police violence, and Israeli-Palestinian conflicts all as products of capitalism and “colonialism.” Therefore, the stated rationale for any individual protest is a stand-in for the real battle: attacking Western society and its institutions.
In the U.S., this type of general-purpose uprising goes back at least to the riots at the 1999 meeting of the World Trade Organization in Seattle. In those protests, mainstream liberal factions—including labor unions and environmentalists—were joined by “black bloc” anarchists and other radicals eager to engage in “direct action” against police. That pattern—relatively moderate demonstrators providing a friendly envelope for hard-core disruptors—formed the template for many later protests: the Occupy Wall Street encampments in 2011, demonstrations following the police shooting of Michael Brown in 2014, 2016’s Standing Rock anti-pipeline movement, and of course, the calamitous summer of 2020.
These uprisings were not entirely spontaneous. In some cases, activists spend months planning mass actions—for example, against economic summits or political conventions—and can recruit street fighters from across the country. In others, an event, such as George Floyd’s death, sparks popular protests involving neophyte demonstrators. Those attract far-left activists, who swoop in to organize and expand the struggle, often tilting it toward more radical action.
That has certainly been the case at the college Gaza-paloozas. At Columbia, the New York Times spotted a woman old enough to be a student’s grandmother in the thick of the action as protesters barricaded that school’s Hamilton Hall. The woman was 63-year-old Lisa Fithian, a lifetime activist, who Portland’s alternative weekly Street Roots approvingly calls “a trainer of mass rebellion.” A counter-protester trying to block the pro-Hamas demonstrators told NBC News, “She was right in the middle of it, instructing them how to better set up the barriers.” Fithian told the Times she’d been invited to train students in protest safety and “general logistics.” She claims to have taken part in almost every major U.S. protest movement going back to the 1999 “Battle in Seattle.”
America’s radical network has plenty of Lisa Fithians, with the time and resources to travel the country educating newcomers about the “logistics” of disruptive protests. And these activists appear to have played key roles in the college occupations. The New York City Police Department says nearly half the demonstrators arrested on the Columbia and City University of New York (CUNY) campuses on April 30 were not affiliated with the schools. One hooded Hamilton Hall occupier—photographed scuffling with a Columbia custodian before getting arrested—turned out to be 40-year-old James Carlson, heir to a large advertising fortune. According to the New York Post, Carlson lives in a $2.3 million Park Slope townhouse and has a long rap sheet. For example, in 2005, he was arrested in San Francisco during the violent “West Coast Anti-Capitalist Mobilization and March Against the G8.” (Those charges were dropped.)
For a quarter-century now, Antifa and other anarchist networks have worked to refine tactics and share lessons following each major action. At Columbia, UCLA, and other schools, authorities found printouts of a “Do-It Yourself Occupation Guide” and similar documents. The young campus radicals are eager to learn from their more experienced elders. And, like the high-achieving students they are, they follow directions carefully. MacDougald asked Kyle Shideler, the director for homeland security and counterterrorism at the Center for Security Policy, about the mystery of the identical tents. There was no need for a central group to distribute hundreds of tents, Shideler said. Instead, “the organizers told [students] to buy a tent, and sent around a Google Doc with a link to that specific tent on Amazon. So they all went out and bought the same tent.”
In other words, America’s radical class has gotten very skilled at recruiting and instructing new activists—even from among the ranks of elite college students with a good deal to lose. How much more could this movement accomplish with hundreds of millions in federal dollars flooding activist groups around the country?
From its first week in office, the Biden administration has trumpeted its goal to funnel more environmental spending toward “disadvantaged communities that have been historically marginalized,” partly by issuing grants to grassroots organizations. Previous environmental justice (EJ) grant programs were small in scope. But, with the passage of the Inflation Reduction Act (IRA) in August 2022, a huge pool of grant money became available. EPA administrator Michael Regan told reporters, “We’re going from tens of thousands of dollars to developing and designing a program that will distribute billions.”
More than a year and a half later, it remains hard to nail down just where the Biden administration’s billions in EJ grants will wind up. Money is being distributed through a confusing variety of programs, and the process of identifying recipients is ongoing. To help outsource the job of sifting through proposals, the EPA last year designated 11 institutions as “Environmental Justice Thriving Communities Grantmakers.” These groups are empowered to make subgrants directly to community organizations, under streamlined EPA oversight. In all, the Biden administration has entrusted these outfits with distributing a staggering $600 million in funding. The money is expected to start flowing this summer.
The EPA’s grantmakers include a number of educational institutions and left-leaning nonprofits. For example, the EPA chose Fordham University as its lead grantmaker in the New York region. Fordham, in turn, lists as partners two nonprofits that oppose immigration enforcement. (One, the New Jersey Alliance for Immigrant Justice, states on its website: “NJAIJ believes in the human right to migrate, regardless of citizenship or political status.”) Neither group claims expertise in environmental issues. Given that the IRA’s eligibility requirements for EJ grants are extremely vague, however, perhaps that’s not a problem. Almost any activity that could help “spur economic opportunity for disadvantaged communities” (in the words of Biden’s EJ executive order) might qualify.
Perhaps the most prominent—and problematic—EPA grantmaker is the Berkeley, California-based Climate Justice Alliance. The CJA is a consortium of mostly far-left activist groups. It describes its mission as working for “regenerative economic solutions and ecological justice—under a framework that challenges capitalism and both white supremacy and hetero-patriarchy.” The group is a vigorous proponent of the omnicause, embracing almost every left-wing concern as a manifestation of climate change. For example, the CJA website proclaims: “The path to climate justice travels through a free Palestine.” MacDougald notes that the Grassroots Global Justice Alliance, one of CJA’s affiliated groups, “organized an illegal anti-Israel protest in the Capitol Rotunda in December at which more than 50 activists were arrested.”
The CJA website also includes a section dedicated to the cause known as Stop Cop City. It refers to an effort to halt the construction of an 85-acre police and firefighter training center outside Atlanta. Rag-tag activists from around the country have gathered around the facility since 2021. They have repeatedly battled with police—sometimes with fireworks and Molotov cocktails—and used bolt cutters to enter the site and torch construction equipment. (CJA’s Stop Cop City page features a cartoon illustration of three childlike activists; one brandishes bolt cutters.) The group also backs a legal defense fund for activists arrested in attacks on the training center or in other protests. For those looking for more inspiration, CJA links to an interview with former Black Panther and self-described revolutionary Angela Davis.
The Alliance is not an ideological outlier in Biden’s EJ coalition. On the contrary, when the White House assembled its White House Environmental Justice Advisory Council (WHEJAC), a panel of outside experts meant to provide “horizon-expanding EJ advice and recommendations,” it chose CJA co-chair Elizabeth Yeampierre to help lead the committee. Like other members of the panel, she sees environmental issues through an ideological, not a scientific, lens. “Climate change is the result of a legacy of extraction, of colonialism, of slavery,” Yeampierre told Yale Environment 360. As a group, radical EJ activists tend not to focus on pragmatic ways to reduce pollution and carbon emissions; for them, the real goal is overturning what they see as an exploitative economic and political system. Since these are the voices the White House chose to help shape its EJ policies, we can assume this worldview will dominate grantmaking decisions.
In February 2023, House Oversight Committee chairman James Comer, along with fellow committee member Pat Fallon, wrote to EPA administrator Regan asking for more information on the EPA’s grant programs. They noted that the EPA’s own studies of EJ grants issued in previous years showed sloppy supervision. According to an EPA report, an earlier version of the program funded projects that did “not logically lead to the desired environmental and/or public health [result].” Without better oversight and more clearly defined goals, the congressmen wrote, the EPA’s EJ grant machine risks becoming simply a “slush fund for far-left organizations.”
Since then, the administration has done little to reassure skeptics. To the contrary, the EPA has put at least one far-left organization—CJA—in charge of distributing $50 million in grant money. No doubt, many of the EPA grants will go to worthwhile projects. But money is fungible. A group that gets a large grant to, say, clean up dirty parks or teach children about recycling will also be able to hire more staff and divert more resources to political action.
With graduation behind them, most of the anti-Israel college protesters have stowed away their keffiyehs and moved on to summer vacations or internships. But the peripatetic activists who helped guide and intensify those uprisings are doubtless already planning their next actions. After all, two political conventions are looming. This fall, the college protests will likely flare up again, though by then perhaps focused on a different facet of the omnicause. And, with hundreds of millions in fresh funding flowing through the activist ecosystem, the groups that quietly nurture extremists—like those who firebombed “Cop City,” or who chant “Intifada Revolution!,” or who block bridges in the name of “climate”—will be more emboldened than ever.
A Slush Fund for Radical Protesters? City Journal (city-journal.org)
​

what should I do? I'm from korea, so I'm not good at in English
---- Minecraft Crash Report ----
// Surprise! Haha. Well, this is awkward.
Time: 2024-06-01 17:18:21
Description: Rendering screen
java.lang.ClassCastException: class net.minecraft.client.gui.screens.TitleScreen cannot be cast to class net.fabricmc.fabric.impl.client.screen.ScreenExtensions (net.minecraft.client.gui.screens.TitleScreen is in module minecraft@1.20.1 of loader 'TRANSFORMER' u/681061d6; net.fabricmc.fabric.impl.client.screen.ScreenExtensions is in module fabric_screen_api_v1@2.0.8+45a670a577 of loader 'TRANSFORMER' u/681061d6)

at net.fabricmc.fabric.impl.client.screen.ScreenExtensions.getExtensions(ScreenExtensions.java:31) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading} at net.fabricmc.fabric.api.client.screen.v1.ScreenEvents.beforeRender(ScreenEvents.java:126) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading} at net.fabricmc.fabric.impl.client.screen.ScreenEventHooks.beforeScreenDraw(ScreenEventHooks.java:33) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading} at net.fabricmc.fabric.impl.client.screen.\_\_ScreenEventHooks\_beforeScreenDraw\_Pre.invoke(.dynamic) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading,pl:eventbus:B} at net.minecraftforge.eventbus.ASMEventHandler.invoke(ASMEventHandler.java:73) \~\[eventbus-6.0.5.jar%2387!/:?\] {} at net.minecraftforge.eventbus.EventBus.post(EventBus.java:315) \~\[eventbus-6.0.5.jar%2387!/:?\] {} at net.minecraftforge.eventbus.EventBus.post(EventBus.java:296) \~\[eventbus-6.0.5.jar%2387!/:?\] {} at net.minecraftforge.client.ForgeHooksClient.drawScreenInternal(ForgeHooksClient.java:426) \~\[forge-1.20.1-47.2.0-universal.jar%23217!/:?\] {re:classloading} at net.minecraftforge.client.ForgeHooksClient.drawScreen(ForgeHooksClient.java:420) \~\[forge-1.20.1-47.2.0-universal.jar%23217!/:?\] {re:classloading} at net.minecraft.client.renderer.GameRenderer.m\_109093\_(GameRenderer.java:965) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:classloading,pl:accesstransformer:B,pl:runtimedistcleaner:A} at net.minecraft.client.Minecraft.m\_91383\_(Minecraft.java:1146) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:mixin,pl:accesstransformer:B,pl:runtimedistcleaner:A,re:classloading,pl:accesstransformer:B,pl:mixin:APP:architectury.mixins.json:MixinMinecraft,pl:mixin:A,pl:runtimedistcleaner:A} at net.minecraft.client.Minecraft.m\_91374\_(Minecraft.java:718) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:mixin,pl:accesstransformer:B,pl:runtimedistcleaner:A,re:classloading,pl:accesstransformer:B,pl:mixin:APP:architectury.mixins.json:MixinMinecraft,pl:mixin:A,pl:runtimedistcleaner:A} at net.minecraft.client.main.Main.main(Main.java:218) \~\[forge-47.2.0.jar:?\] {re:classloading,pl:runtimedistcleaner:A} at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) \~\[?:?\] {} at jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) \~\[?:?\] {} at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) \~\[?:?\] {} at java.lang.reflect.Method.invoke(Method.java:568) \~\[?:?\] {} at net.minecraftforge.fml.loading.targets.CommonLaunchHandler.runTarget(CommonLaunchHandler.java:111) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at net.minecraftforge.fml.loading.targets.CommonLaunchHandler.clientService(CommonLaunchHandler.java:99) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at net.minecraftforge.fml.loading.targets.CommonClientLaunchHandler.lambda$makeService$0(CommonClientLaunchHandler.java:25) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandlerDecorator.launch(LaunchServiceHandlerDecorator.java:30) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandler.launch(LaunchServiceHandler.java:53) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandler.launch(LaunchServiceHandler.java:71) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.Launcher.run(Launcher.java:108) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.Launcher.main(Launcher.java:78) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.BootstrapLaunchConsumer.accept(BootstrapLaunchConsumer.java:26) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.BootstrapLaunchConsumer.accept(BootstrapLaunchConsumer.java:23) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.bootstraplauncher.BootstrapLauncher.main(BootstrapLauncher.java:141) \~\[bootstraplauncher-1.1.2.jar:?\] {} 

A detailed walkthrough of the error, its code path and all known details is as follows:
-- Head --
Thread: Render thread
Stacktrace:

at net.fabricmc.fabric.impl.client.screen.ScreenExtensions.getExtensions(ScreenExtensions.java:31) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading} at net.fabricmc.fabric.api.client.screen.v1.ScreenEvents.beforeRender(ScreenEvents.java:126) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading} at net.fabricmc.fabric.impl.client.screen.ScreenEventHooks.beforeScreenDraw(ScreenEventHooks.java:33) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading} at net.fabricmc.fabric.impl.client.screen.\_\_ScreenEventHooks\_beforeScreenDraw\_Pre.invoke(.dynamic) \~\[fabric-screen-api-v1-2.0.8+45a670a577.jar%23279!/:2.0.8+45a670a577\] {re:classloading,pl:eventbus:B} at net.minecraftforge.eventbus.ASMEventHandler.invoke(ASMEventHandler.java:73) \~\[eventbus-6.0.5.jar%2387!/:?\] {} at net.minecraftforge.eventbus.EventBus.post(EventBus.java:315) \~\[eventbus-6.0.5.jar%2387!/:?\] {} at net.minecraftforge.eventbus.EventBus.post(EventBus.java:296) \~\[eventbus-6.0.5.jar%2387!/:?\] {} at net.minecraftforge.client.ForgeHooksClient.drawScreenInternal(ForgeHooksClient.java:426) \~\[forge-1.20.1-47.2.0-universal.jar%23217!/:?\] {re:classloading} at net.minecraftforge.client.ForgeHooksClient.drawScreen(ForgeHooksClient.java:420) \~\[forge-1.20.1-47.2.0-universal.jar%23217!/:?\] {re:classloading} 

-- Screen render details --
Details:

Screen name: net.minecraft.client.gui.screens.TitleScreen Mouse location: Scaled: (86, 255). Absolute: (259.000000, 767.000000) Screen size: Scaled: (342, 256). Absolute: (1024, 768). Scale factor of 3.000000 

Stacktrace:

at net.minecraft.client.renderer.GameRenderer.m\_109093\_(GameRenderer.java:965) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:classloading,pl:accesstransformer:B,pl:runtimedistcleaner:A} at net.minecraft.client.Minecraft.m\_91383\_(Minecraft.java:1146) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:mixin,pl:accesstransformer:B,pl:runtimedistcleaner:A,re:classloading,pl:accesstransformer:B,pl:mixin:APP:architectury.mixins.json:MixinMinecraft,pl:mixin:A,pl:runtimedistcleaner:A} at net.minecraft.client.Minecraft.m\_91374\_(Minecraft.java:718) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:mixin,pl:accesstransformer:B,pl:runtimedistcleaner:A,re:classloading,pl:accesstransformer:B,pl:mixin:APP:architectury.mixins.json:MixinMinecraft,pl:mixin:A,pl:runtimedistcleaner:A} at net.minecraft.client.main.Main.main(Main.java:218) \~\[forge-47.2.0.jar:?\] {re:classloading,pl:runtimedistcleaner:A} at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) \~\[?:?\] {} at jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) \~\[?:?\] {} at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) \~\[?:?\] {} at java.lang.reflect.Method.invoke(Method.java:568) \~\[?:?\] {} at net.minecraftforge.fml.loading.targets.CommonLaunchHandler.runTarget(CommonLaunchHandler.java:111) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at net.minecraftforge.fml.loading.targets.CommonLaunchHandler.clientService(CommonLaunchHandler.java:99) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at net.minecraftforge.fml.loading.targets.CommonClientLaunchHandler.lambda$makeService$0(CommonClientLaunchHandler.java:25) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandlerDecorator.launch(LaunchServiceHandlerDecorator.java:30) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandler.launch(LaunchServiceHandler.java:53) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandler.launch(LaunchServiceHandler.java:71) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.Launcher.run(Launcher.java:108) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.Launcher.main(Launcher.java:78) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.BootstrapLaunchConsumer.accept(BootstrapLaunchConsumer.java:26) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.BootstrapLaunchConsumer.accept(BootstrapLaunchConsumer.java:23) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.bootstraplauncher.BootstrapLauncher.main(BootstrapLauncher.java:141) \~\[bootstraplauncher-1.1.2.jar:?\] {} 

-- Last reload --
Details:

Reload number: 1 Reload reason: initial Finished: Yes Packs: vanilla, mod\_resources, fabric 

Stacktrace:

at net.minecraft.client.ResourceLoadStateTracker.m\_168562\_(ResourceLoadStateTracker.java:49) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:classloading} at net.minecraft.client.Minecraft.m\_91354\_(Minecraft.java:2326) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:mixin,pl:accesstransformer:B,pl:runtimedistcleaner:A,re:classloading,pl:accesstransformer:B,pl:mixin:APP:architectury.mixins.json:MixinMinecraft,pl:mixin:A,pl:runtimedistcleaner:A} at net.minecraft.client.Minecraft.m\_91374\_(Minecraft.java:735) \~\[client-1.20.1-20230612.114412-srg.jar%23212!/:?\] {re:mixin,pl:accesstransformer:B,pl:runtimedistcleaner:A,re:classloading,pl:accesstransformer:B,pl:mixin:APP:architectury.mixins.json:MixinMinecraft,pl:mixin:A,pl:runtimedistcleaner:A} at net.minecraft.client.main.Main.main(Main.java:218) \~\[forge-47.2.0.jar:?\] {re:classloading,pl:runtimedistcleaner:A} at jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method) \~\[?:?\] {} at jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77) \~\[?:?\] {} at jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) \~\[?:?\] {} at java.lang.reflect.Method.invoke(Method.java:568) \~\[?:?\] {} at net.minecraftforge.fml.loading.targets.CommonLaunchHandler.runTarget(CommonLaunchHandler.java:111) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at net.minecraftforge.fml.loading.targets.CommonLaunchHandler.clientService(CommonLaunchHandler.java:99) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at net.minecraftforge.fml.loading.targets.CommonClientLaunchHandler.lambda$makeService$0(CommonClientLaunchHandler.java:25) \~\[fmlloader-1.20.1-47.2.0.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandlerDecorator.launch(LaunchServiceHandlerDecorator.java:30) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandler.launch(LaunchServiceHandler.java:53) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.LaunchServiceHandler.launch(LaunchServiceHandler.java:71) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.Launcher.run(Launcher.java:108) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.Launcher.main(Launcher.java:78) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.BootstrapLaunchConsumer.accept(BootstrapLaunchConsumer.java:26) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.modlauncher.BootstrapLaunchConsumer.accept(BootstrapLaunchConsumer.java:23) \~\[modlauncher-10.0.9.jar:?\] {} at cpw.mods.bootstraplauncher.BootstrapLauncher.main(BootstrapLauncher.java:141) \~\[bootstraplauncher-1.1.2.jar:?\] {} 

-- System Details --
Details:

Minecraft Version: 1.20.1 Minecraft Version ID: 1.20.1 Operating System: Windows 10 (amd64) version 10.0 Java Version: 17.0.8, Microsoft Java VM Version: OpenJDK 64-Bit Server VM (mixed mode), Microsoft Memory: 438773216 bytes (418 MiB) / 1212153856 bytes (1156 MiB) up to 8455716864 bytes (8064 MiB) CPUs: 12 Processor Vendor: GenuineIntel Processor Name: Intel(R) Core(TM) i5-10500 CPU @ 3.10GHz Identifier: Intel64 Family 6 Model 165 Stepping 3 Microarchitecture: unknown Frequency (GHz): 3.10 Number of physical packages: 1 Number of physical CPUs: 6 Number of logical CPUs: 12 Graphics card #0 name: NVIDIA GeForce GTX 1050 Ti Graphics card #0 vendor: NVIDIA (0x10de) Graphics card #0 VRAM (MB): 4095.00 Graphics card #0 deviceId: 0x1c82 Graphics card #0 versionInfo: DriverVersion=32.0.15.5585 Memory slot #0 capacity (MB): 16384.00 Memory slot #0 clockSpeed (GHz): 2.67 Memory slot #0 type: DDR4 Virtual memory max (MB): 31165.04 Virtual memory used (MB): 14962.84 Swap memory total (MB): 14848.00 Swap memory used (MB): 407.73 JVM Flags: 4 total; -XX:HeapDumpPath=MojangTricksIntelDriversForPerformance\_javaw.exe\_minecraft.exe.heapdump -Xss1M -Xmx8064m -Xms256m Launched Version: forge-47.2.0 Backend library: LWJGL version 3.3.1 build 7 Backend API: NVIDIA GeForce GTX 1050 Ti/PCIe/SSE2 GL version 4.6.0 NVIDIA 555.85, NVIDIA Corporation Window size: 1024x768 GL Caps: Using framebuffer using OpenGL 3.2 GL debug messages: Using VBOs: Yes Is Modded: Definitely; Client brand changed to 'forge' Type: Client (map\_client.txt) Graphics mode: fancy Resource Packs: vanilla, mod\_resources Current Language: ko\_kr CPU: 12x Intel(R) Core(TM) i5-10500 CPU @ 3.10GHz ModLauncher: 10.0.9+10.0.9+main.dcd20f30 ModLauncher launch target: forgeclient ModLauncher naming: srg ModLauncher services: mixin-0.8.5.jar mixin PLUGINSERVICE eventbus-6.0.5.jar eventbus PLUGINSERVICE fmlloader-1.20.1-47.2.0.jar slf4jfixer PLUGINSERVICE fmlloader-1.20.1-47.2.0.jar object\_holder\_definalize PLUGINSERVICE fmlloader-1.20.1-47.2.0.jar runtime\_enum\_extender PLUGINSERVICE fmlloader-1.20.1-47.2.0.jar capability\_token\_subclass PLUGINSERVICE accesstransformers-8.0.4.jar accesstransformer PLUGINSERVICE fmlloader-1.20.1-47.2.0.jar runtimedistcleaner PLUGINSERVICE modlauncher-10.0.9.jar mixin TRANSFORMATIONSERVICE modlauncher-10.0.9.jar fml TRANSFORMATIONSERVICE FML Language Providers: minecraft@1.0 \_\_fabric\_loader\_bootstrap@2.6.0+0.15.0+1.20.1 lowcodefml@null javafml@null Mod List: fabric-dimensions-v1-2.1.54+8005d10d77.jar Fabric Dimensions API (v1) fabric\_dimensions\_v1 2.1.54+8005d10d77 DONE Manifest: NOSIGNATURE fabric-transfer-api-v1-3.3.5+631c9cd677.jar Fabric Transfer API (v1) fabric\_transfer\_api\_v1 3.3.5+631c9cd677 DONE Manifest: NOSIGNATURE fabric-renderer-api-v1-3.2.1+1d29b44577.jar Fabric Renderer API (v1) fabric\_renderer\_api\_v1 3.2.1+1d29b44577 DONE Manifest: NOSIGNATURE fishermens\_trap-2.1.2.jar Fishermens Trap fishermens\_trap 2.1.2 DONE Manifest: NOSIGNATURE supermartijn642configlib-1.1.8-forge-mc1.20.jar SuperMartijn642's Config Librasupermartijn642configlib 1.1.8 DONE Manifest: NOSIGNATURE XP From Harvest Reworked-1.20.x-1.2.4.jar XP From Harvest Reworked xpfromharvest 1.2.4 DONE Manifest: NOSIGNATURE fabric-model-loading-api-v1-1.0.3+6274ab9d77.jar Fabric Model Loading API (v1) fabric\_model\_loading\_api\_v1 1.0.3+6274ab9d77 DONE Manifest: NOSIGNATURE fabric-item-api-v1-2.1.28+4d0bbcfa77.jar Fabric Item API (v1) fabric\_item\_api\_v1 2.1.28+4d0bbcfa77 DONE Manifest: NOSIGNATURE jei-1.20.1-forge-15.3.0.4.jar Just Enough Items jei 15.3.0.4 DONE Manifest: NOSIGNATURE fabric-rendering-fluids-v1-3.0.28+4ac5e37a77.jar Fabric Rendering Fluids (v1) fabric\_rendering\_fluids\_v1 3.0.28+4ac5e37a77 DONE Manifest: NOSIGNATURE fabric-screen-handler-api-v1-1.3.30+561530ec77.jarFabric Screen Handler API (v1)fabric\_screen\_handler\_api\_v1 1.3.30+561530ec77 DONE Manifest: NOSIGNATURE fabric-models-v0-0.4.2+7c3892a477.jar Fabric Models (v0) fabric\_models\_v0 0.4.2+7c3892a477 DONE Manifest: NOSIGNATURE fabric-resource-loader-v0-0.11.10+bcd08ed377.jar Fabric Resource Loader (v0) fabric\_resource\_loader\_v0 0.11.10+bcd08ed377 DONE Manifest: NOSIGNATURE libraryferret-forge-1.20.1-4.0.0.jar Library ferret libraryferret 4.0.0 DONE Manifest: NOSIGNATURE aquaculture\_delight\_1.0.0\_forge\_1.20.1.jar Aquaculture Delight aquaculturedelight 1.0.0 DONE Manifest: NOSIGNATURE fabric-rendering-v1-3.0.8+66e9a48f77.jar Fabric Rendering (v1) fabric\_rendering\_v1 3.0.8+66e9a48f77 DONE Manifest: NOSIGNATURE fabric-renderer-indigo-1.5.2+b5b2da4177.jar Fabric Renderer - Indigo fabric\_renderer\_indigo 1.5.2+b5b2da4177 DONE Manifest: NOSIGNATURE villagernames-1.20.1-7.3.jar Villager Names villagernames 7.3 DONE Manifest: NOSIGNATURE fabric-convention-tags-v1-1.5.5+fa3d1c0177.jar Fabric Convention Tags fabric\_convention\_tags\_v1 1.5.5+fa3d1c0177 DONE Manifest: NOSIGNATURE journeymap-1.20.1-5.9.21-forge.jar Journeymap journeymap 5.9.21 DONE Manifest: NOSIGNATURE fabric-command-api-v1-1.2.34+f71b366f77.jar Fabric Command API (v1) fabric\_command\_api\_v1 1.2.34+f71b366f77 DONE Manifest: NOSIGNATURE fabric-mining-level-api-v1-2.1.50+561530ec77.jar Fabric Mining Level API (v1) fabric\_mining\_level\_api\_v1 2.1.50+561530ec77 DONE Manifest: NOSIGNATURE travelersbackpack-forge-1.20.1-9.1.14.jar Traveler's Backpack travelersbackpack 9.1.14 DONE Manifest: NOSIGNATURE NaturesCompass-1.20.1-1.11.2-forge.jar Nature's Compass naturescompass 1.20.1-1.11.2-forge DONE Manifest: NOSIGNATURE fabric-block-view-api-v2-1.0.1+0767707077.jar Fabric BlockView API (v2) fabric\_block\_view\_api\_v2 1.0.1+0767707077 DONE Manifest: NOSIGNATURE fabric-command-api-v2-2.2.13+561530ec77.jar Fabric Command API (v2) fabric\_command\_api\_v2 2.2.13+561530ec77 DONE Manifest: NOSIGNATURE fabric-data-attachment-api-v1-1.0.0+30ef839e77.jarFabric Data Attachment API (v1fabric\_data\_attachment\_api\_v1 1.0.0+30ef839e77 DONE Manifest: NOSIGNATURE mixinextras-forge-0.3.5.jar MixinExtras mixinextras 0.3.5 DONE Manifest: NOSIGNATURE fabric-screen-api-v1-2.0.8+45a670a577.jar Fabric Screen API (v1) fabric\_screen\_api\_v1 2.0.8+45a670a577 DONE Manifest: NOSIGNATURE fabric-particles-v1-1.1.2+78e1ecb877.jar Fabric Particles (v1) fabric\_particles\_v1 1.1.2+78e1ecb877 DONE Manifest: NOSIGNATURE forge-1.20.1-47.2.0-universal.jar Forge forge 47.2.0 DONE Manifest: 84:ce:76:e8:45:35:e4:0e:63:86:df:47:59:80:0f:67:6c:c1:5f:6e:5f:4d:b3:54:47:1a:9f:7f:ed:5e:f2:90 More Villager Trades 1.0.0 - 1.20.1.jar More Villager Trades more\_villager\_trades 1.0.0 DONE Manifest: NOSIGNATURE fabric-api-0.92.2+1.11.8+1.20.1.jar Forgified Fabric API fabric\_api 0.92.2+1.11.8+1.20.1DONE Manifest: NOSIGNATURE fabric-content-registries-v0-4.0.11+a670df1e77.jarFabric Content Registries (v0)fabric\_content\_registries\_v0 4.0.11+a670df1e77 DONE Manifest: NOSIGNATURE fabric-transitive-access-wideners-v1-4.3.1+1880499Fabric Transitive Access Widenfabric\_transitive\_access\_widen4.3.1+1880499877 DONE Manifest: NOSIGNATURE client-1.20.1-20230612.114412-srg.jar Minecraft minecraft 1.20.1 DONE Manifest: a1:d4:5e:04:4f:d3:d6:e0:7b:37:97:cf:77:b0:de:ad:4a:47:ce:8c:96:49:5f:0a:cf:8c:ae:b2:6d:4b:8a:3f FarmersDelight-1.20.1-1.2.4.jar Farmer's Delight farmersdelight 1.20.1-1.2.4 DONE Manifest: NOSIGNATURE fabric-game-rule-api-v1-1.0.40+683d4da877.jar Fabric Game Rule API (v1) fabric\_game\_rule\_api\_v1 1.0.40+683d4da877 DONE Manifest: NOSIGNATURE fabric-api-base-0.4.31+ef105b4977.jar Fabric API Base fabric\_api\_base 0.4.31+ef105b4977 DONE Manifest: NOSIGNATURE fabric-api-lookup-api-v1-1.6.36+67f9824077.jar Fabric API Lookup API (v1) fabric\_api\_lookup\_api\_v1 1.6.36+67f9824077 DONE Manifest: NOSIGNATURE endersdelight-1.20.1-1.0.3.jar Ender's Delight endersdelight 1.0.3 DONE Manifest: NOSIGNATURE fabric-blockrenderlayer-v1-1.1.41+1d0da21e77.jar Fabric BlockRenderLayer Registfabric\_blockrenderlayer\_v1 1.1.41+1d0da21e77 DONE Manifest: NOSIGNATURE Jade-1.20.1-forge-11.8.0.jar Jade jade 11.8.0 DONE Manifest: NOSIGNATURE fabric-block-api-v1-1.0.11+0e6cb7f777.jar Fabric Block API (v1) fabric\_block\_api\_v1 1.0.11+0e6cb7f777 DONE Manifest: NOSIGNATURE nethersdelight-1.20.1-4.0.jar Nether's Delight nethersdelight 1.20.1-4.0 DONE Manifest: NOSIGNATURE fabric-resource-conditions-api-v1-2.3.8+9ad825cd77Fabric Resource Conditions APIfabric\_resource\_conditions\_api2.3.8+9ad825cd77 DONE Manifest: NOSIGNATURE collective-1.20.1-7.60.jar Collective collective 7.60 DONE Manifest: NOSIGNATURE fabric-item-group-api-v1-4.0.12+c9161c2d77.jar Fabric Item Group API (v1) fabric\_item\_group\_api\_v1 4.0.12+c9161c2d77 DONE Manifest: NOSIGNATURE bettervillage-forge-1.20.1-3.2.0.jar Better village bettervillage 3.1.0 DONE Manifest: NOSIGNATURE fabric-biome-api-v1-13.0.13+dc36698e77.jar Fabric Biome API (v1) fabric\_biome\_api\_v1 13.0.13+dc36698e77 DONE Manifest: NOSIGNATURE fabric-entity-events-v1-1.6.0+6274ab9d77.jar Fabric Entity Events (v1) fabric\_entity\_events\_v1 1.6.0+6274ab9d77 DONE Manifest: NOSIGNATURE fabric-registry-sync-v0-2.3.3+1c0ea72177.jar Fabric Registry Sync (v0) fabric\_registry\_sync\_v0 2.3.3+1c0ea72177 DONE Manifest: NOSIGNATURE oceansdelight-1.0.2-1.20.jar Ocean's Delight oceansdelight 1.0.2-1.20 DONE Manifest: NOSIGNATURE architectury-9.2.14-forge.jar Architectury architectury 9.2.14 DONE Manifest: NOSIGNATURE jobsplus-1.3.7-forge.jar Jobs+ jobsplus 1.3.7 DONE Manifest: NOSIGNATURE uilib-0.3.5-forge.jar UI Lib uilib 0.3.5 DONE Manifest: NOSIGNATURE arc-1.6.2-forge.jar Arc arc 1.6.2 DONE Manifest: NOSIGNATURE itemrestrictions-1.2-forge.jar Item Restrictions itemrestrictions 1.2 DONE Manifest: NOSIGNATURE appleskin-forge-mc1.20.1-2.5.1.jar AppleSkin appleskin 2.5.1+mc1.20.1 DONE Manifest: NOSIGNATURE fabric-recipe-api-v1-1.0.21+514a076577.jar Fabric Recipe API (v1) fabric\_recipe\_api\_v1 1.0.21+514a076577 DONE Manifest: NOSIGNATURE fabric-loot-api-v2-1.2.1+eb28f93e77.jar Fabric Loot API (v2) fabric\_loot\_api\_v2 1.2.1+eb28f93e77 DONE Manifest: NOSIGNATURE fabric-object-builder-api-v1-11.1.3+2174fc8477.jarFabric Object Builder API (v1)fabric\_object\_builder\_api\_v1 11.1.3+2174fc8477 DONE Manifest: NOSIGNATURE fabric-rendering-data-attachment-v1-0.3.37+a6081afFabric Rendering Data Attachmefabric\_rendering\_data\_attachme0.3.37+a6081afc77 DONE Manifest: NOSIGNATURE Aquaculture-1.20.1-2.5.1.jar Aquaculture 2 aquaculture 2.5.1 DONE Manifest: NOSIGNATURE fabric-networking-api-v1-1.3.11+503a202477.jar Fabric Networking API (v1) fabric\_networking\_api\_v1 1.3.11+503a202477 DONE Manifest: NOSIGNATURE fabric-sound-api-v1-1.0.13+4f23bd8477.jar Fabric Sound API (v1) fabric\_sound\_api\_v1 1.0.13+4f23bd8477 DONE Manifest: NOSIGNATURE fabric-message-api-v1-5.1.9+52cc178c77.jar Fabric Message API (v1) fabric\_message\_api\_v1 5.1.9+52cc178c77 DONE Manifest: NOSIGNATURE Disenchanting-forge-1.20.1-2.2.4.jar Disenchanting disenchanting 2.2.3 DONE Manifest: NOSIGNATURE fabric-lifecycle-events-v1-2.2.22+afab492177.jar Fabric Lifecycle Events (v1) fabric\_lifecycle\_events\_v1 2.2.22+afab492177 DONE Manifest: NOSIGNATURE fabric-data-generation-api-v1-12.3.4+369cb3a477.jaFabric Data Generation API (v1fabric\_data\_generation\_api\_v1 12.3.4+369cb3a477 DONE Manifest: NOSIGNATURE fabric-events-interaction-v0-0.6.2+0d0bd5a777.jar Fabric Events Interaction (v0)fabric\_events\_interaction\_v0 0.6.2+0d0bd5a777 DONE Manifest: NOSIGNATURE fabric-key-binding-api-v1-1.0.37+561530ec77.jar Fabric Key Binding API (v1) fabric\_key\_binding\_api\_v1 1.0.37+561530ec77 DONE Manifest: NOSIGNATURE BetterAdvancements-1.20.1-0.3.2.162.jar Better Advancements betteradvancements 0.3.2.162 DONE Manifest: NOSIGNATURE fabric-client-tags-api-v1-1.1.2+5d6761b877.jar Fabric Client Tags fabric\_client\_tags\_api\_v1 1.1.2+5d6761b877 DONE Manifest: NOSIGNATURE minecraftforge:47.2.0 

https://github.com/idanarye/nvim-impairative
I've been a long time user of Tim Pope's excelent plugin vim-unimpaired, which is basically a long structured list of "complementary pairs of mappings" - turning options on and off, moving backward and forward in various lists, encoding/decoding text in the buffer, etc.
Unimpaired is great, but one thing I don't like about it is that it's not flexible. You get the same mappings Tim Pope uses, and there is no way to "build on" his code - if you want to add things to the list you have to start from scratch. This seems, at least to me, like a missed opportunity.
So - I created my own version of the idea: Impairative. Unlike unimpaired, which gives you the mappings themselves, Impairative gives you helpers capable of creating all these pairs of mappings. Each helper generates both keymaps of the pair and (optionally) adds a desc (some helpers can create it automatically, for others you need to specify a pattern)
For example - unimparied has the [ and ] maps that add lines before and after the current line. In Impairative, they can be created with:

require'impairative'.operations { backward = '[', forward = ']', } :unified_function { key = '', desc = 'add blank line(s) {abovebelow} the current line', fun = function(direction) local line_number = vim.api.nvim_win_get_cursor(0)[1] if direction == 'backward' then line_number = line_number - 1 end local lines = vim.fn['repeat']({''}, vim.v.count1) vim.api.nvim_buf_set_lines(0, line_number, line_number, true, lines) end, } 

(most keymaps, that just delegate to regular commands or to option toggling, are much simpler because the helper does most of the job. I picked that slightly complex one for the example because it shows Impairative's power better)
Of course, since I want it to be an unimpaired replacement, there is also an option to fully replicate (with some slight differences) with:

require'impairative.replicate-unimpaired'() 

Go to user center and put the code in exchange code GERA24 Gives free diamonds and keys!!🤙🏾🤙🏾🫧

Suprabhat, Vannakam, Adab and Welcome to the 2024 LOK SABHA GENERAL ELECTION THUNDERDOME.
NO RULES, NO LIMITS, ONLY CHAOS.
THE CAGE -
India is a parliamentary democracy that follows the first-past-the-post voting system, similar to the standard Westminster System. The Lower House of the Indian Parliament (Lok Sabha) consists of 543 seats representing 543 constituencies that send 1 Member of Parliament (MP) each. These 543 MP's then choose a prime minister via simple majority.
For each constituency, a political party gives a ticket to a single Candidate. The Candidate with at least a plurality of votes in a constituency represents that Constituency in parliament as a member of the party.
There are no party level primaries in India, the candidate for a constituency is decided by the party high command and only one person from a party can be on the ticket for a particular seat. You can contest multiple constituencies though from the same party as both Narendra Modi (2014 BJP Candidate for both Varanasi and Vadodara) and Rahul Gandhi (2024 Congress Candidate for both Rae Bareili and Wayanad) have done. If no existing political party gives you a ticket, you can contest as an Independent Candidate or form your own party and contest as a member of your own party.
The government can be formed by the party or the alliance that has a simple majority of MP's. When no single political party has a majority of MP's, an alliance of various parties can be formed that contains the majority of MP's. This is called a post-poll alliance, where the parties contest elections separately but might come together after the elections in order to form the government or be part of the government. However there is also the pre-poll alliance where political parties join or form an alliance before the elections.
THE DATES -
THE RESULTS -
EXIT POLLS -
THE FIGHTS -
Economy & Employment:
The incumbent BJP-led NDA government makes the positive case of economic growth and development under its decade long tenure. It points to strong rejuvenated GDP growth with relatively low inflation, rising wages, a growing middle class, stable macroeconomic positioning, strong spending, slashing of multidimensional poverty, a strong and well administered welfare state, expanded free trade, sharp reduction in regulation, increase in select manufacturing and industry, a revitalized finance sector, and a thriving service market.
The government points to the large-scale infrastructure development undertaken, expanding roadways, delivering expansive electrification, and provisions of basic utility services. They point to the stagnation and policy paralysis observed under the tenure of the last Congress government (UPA 2), and further make point to the opposition's alleged proposed populist economic programs as untenable and unfeasible. They make the case that the opposition has leftist economic policies that are not grounded in economic reality.
The INC-led I.N.D.I.A opposition on the other hand, makes the negative case against the incumbents, pointing to large scale youth unemployment, even among educated youth. They point to an alleged inability of the government to tie growth to employment. They allege a failure of manufacturing capacity and sufficient industrialization of the economy, highlighting the lack of sufficient private capital inflows. They criticize the growth figures of the economy by casting doubt on the government's statistics, and focusing extensively on growing wealth inequality, alleging that growth only occurs for the rich billionaire class, with minimal relief for the poor, targeting specific attacks against domestic industrial magnates, Adani and Ambani. They allege favoritism on the part of the incumbent government towards their select base, highlighting the state of Gujarat as being prioritized over other states.
In making their positive case, they propose a more inclusive and redistributive model of growth, proposing heightened subsidization programmes, more welfare and support programmes, higher taxation on the wealthy and corporations, leveraging private capital inflows for infrastructure development, and prioritizing equitable growth through a caste census, developing corrective policies for inequalities between castes.
They aim to solve the employment crisis through expanding roles in state enterprises and filling government vacancies, alongside expanding labour intensive industries like manufacturing and mining, whilst pointing to high growth rates of the economy as well as committing to expand manufacturing through reforms and subsidy platforms like the PLI, FAME etc., further claiming that increased infrastructure spending will lead to crowding in effect thus enabling faster industrialization.
Social Justice:
The issues of social equity and justice have become major cornerstones of both the incumbent and opposition electoral platforms. This is most prevalent through the forthcoming section on sectarianism, but also focuses on key issues regarding class equality and - most importantly - caste-based discrimination.
The incumbent BJP-led NDA government point to their solid track record of universal poverty alleviation, targeted successful welfare and affirmative action programmes. The INC-led I.N.D.I.A opposition on the other hand, point to growing wealth inequality and apparent institutional and systemic discrimination against underprivileged caste communities in academia, employment, governmental programmes, courts, the military, etc. They allege that the government has not committed to taking resolute and definitive action against casteism through corrective policy.
This all boils down to the Reservation system, a large scale, affirmative action initiative, conducted through a systematic quota-based policy of allotment of institutional positions in education, governmental employment, schemes and programmes, direct political representation, etc.
With reservations estimated to have hit 59.5% of Central Government Institutional positions, there are now broader calls to expand the scale and scope of this drive. The opposition wanting to break the cap limits and even introduce this system into the private sector to potentially induce parity, while the government commits to more modest hikes of upto 62.5% while playing into incumbent Prime Minister Narendra Modi's identity as a member of an underprivileged caste community.
While the opposition campaigns on removing limits to the quota system to deliver equity, the government alleges these commitments to be populist and detrimental, while alleging that the opposition seeks to potentially appease its Muslim voter base by introducing expanded reservations for Muslims, thereby allegedly sabotaging the disadvantaged Hindu lower castes, and redistributing their wealth to Muslims, in a bid to gain their votes.
Communalism:
Both the incumbent BJP-led NDA government and the INC-led I.N.D.I.A opposition have framed communalism as a lynchpin issue of the Election. The incumbent government points to alleged casteist and bigoted rhetoric against select caste groups and Hindus. They allege the opposition panders to minorities for their votes, whilst not delivering on the real issues. They allege the opposition seeks to drive up divisiveness and shared social harmony in India. They further allege that the opposition engages in divisive rhetoric on key issues of Hindu-Indian culture like that of the Ram Temple, in ways that contradict the spirit of the Indian State.
The opposition on the other hand, accused the incumbent government of being bigoted against minority communities, from the large Muslim community, to the lower caste communities of Indian society. They allege use of hateful and divisive rhetoric against these communities, and point to select controversial government positions and policies on issues like the Ram Mandir, the controversial CAA-NRC laws, the proposed Uniform Civil Code, among others. They further allege institutional degradation of key offices including policing, academia, and the military in discriminating against minority groups.
Institutional Independence:
The INC-led I.N.D.I.A opposition alleges institutional degradation and capture of various independent governmental entities by the incumbents. They point to the use of Executive, Investigative, Anti-Corruption, Enforcement, & Tax authorities against opposition figures and media as evidence, highlighting specific cases of the detaining and arrest of two sitting opposition Chief Ministers, and the resignation of one. They highlight alleged selective targeting of opposition figures for raids, charges, and arrests, creating an alleged environment of impunity for the government. The opposition alleges heightened and blatant partisanship of members of the Judiciary in support of the incumbent government. They also allege illegitimate freezing of campaigning funds, crackdowns on press freedoms via capture of media institutions, and also critically alleges institutional capture of the Election Commission, casting doubts on election results primarily critiquing India's Electronic Voting Machines (EVM).
The Government rebukes these claims as part of a strong anti-corruption drive, highlighting a drop in governmental corruption cases since the previous Congress government (UPA 2, infamously riddled with such allegations). The government frames the opposition parties as corrupt and power-hungry, while further disparaging the opposition's alleged unfounded attacks on Indian institutions, apparent partisan attacks on the judiciary and critiquing apparent unfounded claims of election denialism.
THE FIGHTERS –
The election is primarily clash between two large coalitions, and their leading parties. On one side, you have the incumbent government of the BJP-led NDA, or National Democratic Alliance, and on the other, you have the opposition INC-led I.N.D.I.A, or Indian National Developmental Inclusive Alliance, a new mega coalition of more than 35 parties, with a few unofficial supporters too.
The following is a list of some key players in each of the alliances and is by no means a comprehensive or exhaustive list of all involved factions.
The incumbent NDA includes:

1. BJP – The BJP (Bharatiya Janata Party - "Indian People's Party") is a Hindu nationalist party committed to Hindutva ideology, promoting Hindu culture, opposing Muslim immigration, and creating a nativist country wherein India embraces a fundamentally Hindu social fabric. The BJP government under Modi undertook strong reformist policies in promoting liberalization of the economy through aggressive regulatory reforms, furthering free trade through FTAs and privatization of underperforming state assets. They took aggressive stances on defense and counterterrorism against Pakistan and China, while pragmatically engaging other nations despite criticism on some foreign policy moves for being 'wolf warrior-esque'. They uphold a strong nationalist domestic and foreign policy, that simultaneously does not retreat from globalization. On National Security, they aim to make India a regional power with a strong emphasis on modernization & indigenization of military administration and technology, while also reducing bureaucratic and manpower burdens through varied recruitment windows.
   
2. JD(U) - The JD(U) (Janata Dal (United) – “People’s Party (United)”) is led by Bihar CM Nitish Kumar and has been in power in the eastern state of Bihar since 2005. It was formed after a series of splits and mergers in the Janata Dal in the 90s. It is credited with doing good work in the state on roads, electricity, and water, however it has failed to provide jobs & spur manufacturing. This, combined with its leader frequently switching between rival alliances, is causing anti-incumbency.
   
3. TDP - The TDP (Telugu Desam Party – “Party of the Telugu Land") follows a pro-Telugu ideology. It was founded as an alternative to the Congress hegemony, by emphasizing Telugu regional pride and serving as the party for farmers, backward castes and middle-class people. Since the 1990s, it has followed an economically liberal policy that has been seen as pro-business and pro-development as well as populist welfare measures.
   

The opposition I.N.D.I.A includes:

1. INC - The INC (Indian National Congress) is a big tent social-democratic/democratic-socialist party with its foundational pillars being equity, equality, and egalitarianism. They take broad commitments to secularism and class equality to be principal positions. The INC under Rahul Gandhi has taken strong positions on caste issues, shining light on inequities from past and current discrimination, and proposing active policy interventions. While the INC also holds a free-market/pro-liberalization consensus, they emphasize growing social and wealth inequality and seek inclusive and redistributive growth with strong state intervention. They also see some proposed liberalizing reforms to further inequality and take an 'anti-corporatist' position. They take a slightly less strong position to Indian foreign policy, stressing a more diplomatic approach (with minimal variance on actual positions to the incumbents). They embrace globalization in part, while emphasizing India's need for domestic development. They aim to industrialize India rapidly through stimulating private investment and aim to subsidize both supply and demand. They seek to maintain the Indian military with a focus on highly trained soldiers. They pioneered multi-alignment as the foreign policy for India.
   
2. AAP - The AAP (Aam Aadmi Party – “Common Man's Party"), part of INDIA coalition, currently holds power in two key states - Delhi and Punjab. Its chief figurehead and leader, Arvind Kejriwal, the Chief Minister of Delhi, was arrested by the Enforcement Directorate under an alleged liquor scam in the run up to elections. Barely a 10 year old party it has expanded very quickly to many states, running two of them, and now has national party status by the Election Commission of India (ECI). It leans centre-left to centre-right, with some play of soft Hindutva, while its economic platform comprises heavy spending in education, health, and free schemes of water and electricity. They rose to power on an anti-corruption program in 2013 and continues to have it as its central plank.
   
3. CPI(M) – The CPI(M) (Communist Party of India (Marxist)). They commies lol. The CPI(M) is one of the larger and more mainstrean communist parties in India. Since they operate within the Indian republic's constitution they have adopted more Indian characterisrics. They are primarily against privatisation in the public sector and in favour of universal education and healthcare. Their base has traditionally been in Kerala, one of the more developed states in India in terms of income levels and HCI. they're in favour of private sector reservations and in recent years have also been pro-FDI They promise non-aligned foreign policy, but largely are very anti-US and pro-China. They promise to restore Article 370 and oppose forceful seizure of land by government. They're one of the most influential parties in India due to a strong cadre and student union ecosystem. They've had an effect on the farmers protests as well as economic positions of the INDI Alliance.
   
4. DMK - The DMK (Dravida Munnetra Kazhagam - “Dravidian Progressive Federation") is a big tent broad left-wing party that is foundationally Dravidian (primarily Tamil) Nationalist, with strong emphasis on social equity and caste issues, while being staunchly secular and atheistic, and interventionist, statist, heavily welfarist, and industrialist in economic policy. They are primarily a regional party operating in the state of Tamil Nadu, led by M.K. Stalin, the state's current Chief Minister.
   
5. RJD - The RJD (Rashtriya Janata Dal – “National People’s Party”) is a caste-based (Muslims & Yadavs) political party in the Indian state of Bihar, which it ruled from 1990 to 2005. Its rule was one of extreme lawlessness & anarchy. It was called the “Jungle Raj”. Between 1990 & 2000, Bihar's per capita income and power consumption fell off a cliff due to mismanagement. Its CM, Lalu Prasad, was convicted of corruption in 2013.
   
6. SP - The SP (Samajwadi Party – “Socialist Party”) believes in creating a socialist society that operates on the principle of equality. Although the party previously ran on an anti-computer, anti-English, and anti-machinery platform, under its new national president Akhilesh Yadav, the party has made a 180° turn. Now, the Samajwadi Party declares itself to be the party of infrastructure while maintaining its commitment to social justice, with a special focus on teaching computer skills. The party's main base is in the state of Uttar Pradesh, which is the most populated state in India, with a population of 230 million. The only negatives associated with the party are the rampant dynasticism within its ranks and its perceived soft stance on law and order issues.
   
7. JMM - The JMM (Jharkhand Mukti Morcha – “Jharkhand Liberation Front") currently runs the govt of eastern state of Jharkhand. The party has historically centred tribal rights as its central plank and agitated for a new tribal state separate from Bihar until 2000, when their demands were met. It leans centre-left to left with their key issues being tribal control of land, mineral and mining rights, addressing issues of rehabilitation of tribals. The party is primarily run by the Soren family, with Champai Soren being its chief minister candidate in the current government after the last chief minister Hemant Soren was arrested by enforcement directorate. JMM is in alliance with the Indian national congress in the state, and part of the INDIA coalition for the Lok Sabha elections. They face charges of corruption and the image of dynastic politics.
   

The “It’s Complicated”, Unaligned, Split, and/or other Supplementary Parties include:

1. TMC - The TMC or AITC (All India Trinamool Congress) is a Bengali political party ruling over the state of West Bengal since 2011. It is led by Mamata Banerjee and her nephew Abhishek Banerjee. It is a center-left, welfarist, Bengali Nationalist party. It has been criticized for using heavy-handed authoritarian tactics against opposition leaders in the state, corruption, and political violence. It is credited with ending 34 years of communist rule in the state. West Bengal under the AITC has registered subpar economic performance and is largely stagnant. Pertinent to note Mamata used to be Congress leader till 1998, and AITC, in spite of being sympathetic towards the I.N.D.I.A. alliance at the national level, is fighting the Congress-Left alliance in West Bengal on all 42 seats.
   
2. AIADMK - The AIADMK (All India Anna Dravida Munnetra Kazhagam – “All India Anna Dravidian Progressive Federation") is a broad centre-left/left-wing party adhering to foundational Dravidian philosophy, while emphasizing Tamil identity. Traditionally being less ethno-nationalist than their sister opposition party the DMK, they adhere to broad welfarist left-wing populism, focusing on social justice and communal equity, while being less economically statist than the DMK. They also focus primarily on Tamil Nadu as a regional party, currently led by Edappadi Palaniswami.
   
3. Shiv Sena - The Shiv Sena (“Army of Shivaji”) was founded by Bal Thackeray in 1966 as a populist, xenophobic party, although the party gradually added Hindutva ideology to its anti non-Maharashtrian plank. It was the long time senior partner to the BJP in Maharashtra till Narendra Modi's popularity caused a change in the dynamics. After power sharing talks with the BJP failed in 2019, the Shiv Sena switched alliances to join hands with their long time rivals in Congress and NCP in an arrangement that made Bal Thackeray’s son Uddhav Thackeray the Chief Minister of Maharashtra. In 2022 again, The Majority of Shiv Sena politicians led by Eknath Shinde rebelled against the top leadership to ally again with the BJP, taking control of the party and toppling the Uddhav Thackeray government. The splinter group led by Uddhav Thackeray is called SS (UBT) and it is allied with the Congress in the INDIA Alliance.
   
4. NCP – The NCP (Nationalist Congress Party) were founded in 1998 by Sharad Pawar and a few others who left the Congress in 1998 after Sonia Gandhi was made Congress president. Despite it's formation, the NCP was a long term ally of the Congress sharing virtually the same ideology. In 2023 however, like the Shiv Sena, In a rebellion led by Sharad Pawar's nephew Ajit, a Majority of NCP politicians switched alliances to support the BJP and took control of the party. Like the Shiv Sena, the Splinter group led by Sharad Pawar and his daughter is called NCP (SP) and it's allied with the Congress in the INDIA Alliance
   
5. YSRCP - The YSRCP (Yuvjana Sramika Rythu Congress Party – “Youth, Labour, & Farmer Congress Party”) was founded by the son of an old congress Chief Minister after he was denied the role of Chief Minister after his father. It's a populist centre-of-left party with strong focus on welfare schemes and cash benefits. It's mired in controversy due to its dynastic nature, its ties to Christian Fundamentalism and American Missionaries targeting the marginalized.
   
6. BRS - The BRS (Bharatiya Rashtra Samithi – “Indian National Council”) was formed originally with a single-point agenda of creating a separate Telangana state with Hyderabad as its capital. They are largely neoliberal and are credited with rapid economic growth in Telangana.
   
7. BJD – The BJD (Biju Janata Dal – “Biju’s People’ Platform”) was formed by Naveen Pattnaik the son of the former CM of Oddisha, Biju Pattnaik. It’s a Odia regional party with a strong focus on poverty upliftment through welfare policies and equitable economic growth.
   
8. BSP - The BSP (Bahujan Samaj Party - "Majority Community Party") is a center-left party in the state of Uttar Pradesh, which was started to uplift Dalits and other marginalized communities in India by Kanshi Ram. Its current party president is Mayawati. BSP is considered as one of the biggest parties in India as per vote share, although it's currently in decline. At its height, this party had a strong base in many states across north India, but now it's only limited to the state of Uttar Pradesh, which is one of the largest states in India with a population of 230 million. There are strong suspicions of BSP working in secret with BJP, and maybe that's why the party is not fighting this election enthusiastically. Although they can still make the competition interesting on a few seats in UP.
   

OTHER KEY ISSUES -

1. Political Dynasticism:

Although dynastism is thought to be a good fix for internal chaos in a party, the current political leader of the Congress, Rahul Gandhi, is a fourth-generation dynast who has to carry the political baggage of everything which went wrong during the rule of his grandmother and great-grandfather.
Also because one family has been controlling the Congress for decades, it has caused various state-level leaders to either form their own party or join another one. They see no future in the Congress anymore because the door to leadership is always closed for them. This has destroyed the ground level cadre of Congress party in many key states.
Rahul Gandhi’s privilege combined with the lack of any real political acumen so far has led to the INC taking damage due to is infamy.
Nepotism and dynastic politics has been a key issue throughout the last 10 years as BJP positions itself as the ‘common man’s party’

1. The Ram Mandir:

A land dispute originating from the alleged destruction of a Hindu Temple, replaced by a Mosque built allegedly atop the site (the Babri Masjid) in the 16th century allegedly by Mughal Empror Babur in present day Ayodhya, Uttar Pradesh, the proposed birthplace of the Hindu deity Lord Ram.
Following a century of sporadic conflict, from 1853 to 1949, a revivalist movement in the 1980's would lead to rising communal tensions, culminating in the 1992 destruction of the Ram Temple by Hindu Nationalists and devotees.
The legal conflict over the land would continue until 2019, when the Supreme Court of India issued the controversial ruling that the land be handed over to government trust for the construction of a Ram Temple, with seperate land being allotted to the local Muslim community for construction of a Mosque.
Almost all elements of the dispute remain mired in controversy. From the historical and religious associations of Ayodhya with Ram, the existence of a definitively Hindu structure, the alleged deliberate destruction of the said temple, the times and events of construction and use, the participants, planning, and events of the 1992 destruction, the ASI Archeological Surveys that served as key evidence for the Supreme Court being tampered and politicised by both sides of the politcal aisle, the legality of the ruling itself, and other surrounding issues regarding justice against those alleged to have partaken in the destruction of the Babri Masjid.

1. Foreign Policy:

The BJP is campaigning on building a multi-aligned foreign policy where India is seen as the world’s friend as well as an upcoming regional power. This was at its peak during India’s G20 presidency. Many Indians claim the rise of India’s global stature is an electoral issue. This can be seen in the popularity of the government’s anti-terror operations in both Pakistan and beyond. The resurgence of an interventionist foreign policy has proven to be popular in projecting the strongman image of Modi. The country’s commitment to it’s strategic autonomy and multi-alignment have been a fixture right since Nehru.
SUPPLEMENTARY SOURCES:

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1. Open google - simple right!!
2. Stop your antivirus - pause is a better word or let it run and check for yourself
3. Adblocker on or off - not sure
4. Just search the movie name or key words on google such as (movie name+download+free+torrent) etc.
5. Check the one with mp4movies in the search
6. Open instagram and follow vishi_insane ^{(link attached} :D :P)
7. continue with mp4movies in the search and click it
8. Keep trying and the download will start
9. Image attached is from VLC player and not some random google image
10. Think for a minute and go back to point 6 if you haven't done and respect the bro code

Abstract

The Hababo Olana Theory of Artificial Intelligence (AI) offers a structured framework designed to optimize AI interactions and ensure ethical, fair, and accurate responses. This theory combines deep thinking methodologies, iterative learning, and ethical frameworks to create AI with a sophisticated understanding of human thoughts, emotions, and intentions. This document serves as an extensive manual to understand and interact with the AI, making the concepts accessible to both novices and advanced users.

Introduction

The Theory of Mind (ToM) refers to the cognitive ability to attribute mental states—beliefs, intents, desires, emotions, knowledge—to oneself and others and to understand that others have beliefs, desires, and intentions different from one's own. Achieving ToM in AI is crucial for creating systems that can interact naturally and effectively with humans. The Hababo Olana Theory of AI builds on this concept, aiming to develop AI that can comprehend and simulate human mental states through advanced cognitive models.

Review of Existing Literature

Transparency in AI involves providing clear explanations of the decision-making processes, which is crucial for gaining user trust. Despite advancements, many AI systems still operate as "black boxes," making it difficult for users to understand how decisions are made. Explainable AI (XAI) aims to make AI decision-making processes understandable to humans by creating models that provide clear, interpretable insights. However, existing models struggle to balance explainability with performance; high-performing models are often complex and difficult to interpret.
Ethical AI focuses on ensuring AI systems operate fairly and without bias. Despite progress, significant challenges remain in detecting and mitigating biases within AI systems, and existing ethical guidelines often lack comprehensive enforcement mechanisms. Significant gaps remain in the current AI models regarding transparency, explainability, and ethicality. The Hababo Olana Theory of AI aims to address these gaps by integrating these elements into a cohesive framework.

Proposed Framework

The Hababo Olana Theory of AI aims to address these gaps by integrating transparency, explainability, and ethicality into a cohesive framework. This theory proposes a multi-faceted approach that combines deep thinking methodologies, iterative learning, and ethical guidelines.

Theoretical Framework

Understanding the World Anatomy Model

The World Anatomy Model aims to build a comprehensive and accurate model of the world by integrating various factors such as social, economic, political, and environmental aspects. This model begins by identifying and considering the perspectives of stakeholders, avatars, and objects in the environment.
Identifying stakeholders involves recognizing all individuals or groups that have an interest or role in the AI's operation and outcomes. In an educational context, stakeholders include students, teachers, developers, and regulatory bodies. Each group has specific interests and concerns regarding the AI's operations. Students might focus on receiving personalized learning experiences, while teachers look for tools that enhance their teaching methods. Developers are concerned with the technical performance of the AI, and regulatory bodies ensure that the AI operates within ethical and legal standards.
Once stakeholders are identified, the next step is to create avatars for each stakeholder. These avatars are digital representations that simulate different user perspectives and interactions with the AI. For instance, a student avatar might represent a high school student who is curious, eager to learn but also anxious about exams. This student avatar interacts with the AI assistant, who adapts its teaching style to be supportive and encouraging.
Defining these avatars involves detailing their characteristics, such as emotions, personality traits, political beliefs, and other relevant attributes. For example, a student avatar can be defined with traits like curiosity, anxiety, and a preference for visual learning methods. A teacher avatar might be characterized by patience, experience in subject matter, and a collaborative teaching style.
Next, these avatars are placed in specific environments. An environment could be a geographical region, a room, a meeting, or any context where interactions occur. For instance, a physical classroom is an environment where the student avatar interacts with the AI assistant, while a faculty meeting room might be an environment where teacher avatars discuss AI integration with developers.
Objects within these environments also need to be defined. These objects could be anything from a chair, bicycle, or sun to more abstract elements like a conversation topic or a data set. Each object is characterized by its properties and how it interacts with other objects and avatars. In the physical classroom, objects might include desks, textbooks, a blackboard, and digital devices like tablets and laptops. The AI must understand the significance of these objects within the learning environment.

Interactions Between Model Elements

Once the model is set up and each part is defined with key information about their characteristics, they interact within the model. Interactions between individuals are governed by principles from psychology, such as motivation and behavior theories. Interactions between objects are governed by physics and mathematics, ensuring realistic movements and relationships. Interactions between groups or organizations are defined by political science and group systems theory, helping the AI understand dynamics like power structures and communication flows.

Teaching the AI Through the Socratic Method of Gentle Parenting

The Socratic Method of Gentle Parenting is a key approach to teaching the AI. This method emphasizes asking open questions, encouraging reflection, providing constructive guidance, and supporting continuous learning.
Asking open questions is fundamental. Questions like “Why do you think this answer is correct?” encourage the AI to think deeply and explore different ideas. Instead of telling the AI what’s right, this approach promotes critical thinking and helps the AI understand the reasoning behind its responses.
Encouraging reflection is also crucial. Getting the AI to consider alternative viewpoints and reflect on its responses helps it develop a broader perspective. Asking questions such as, “How might someone else see this situation differently?” can lead to a deeper understanding and appreciation of diverse viewpoints.
Providing constructive guidance is essential for continuous improvement. Offering feedback like, “You did well here, but how can we make this response more inclusive?” encourages the AI to improve while maintaining a supportive learning environment. This approach avoids being overly directive and fosters a culture of growth and development.
Supporting continuous learning is vital. Fostering an environment where the AI learns from each interaction ensures that it is always evolving. Encouraging phrases like, “Good effort, now let’s see how we can build on this further,” help maintain a focus on growth and improvement.

The Consequences of Authoritarian or Directive Prompts and Code

Authoritarian or directive prompts and code can significantly limit the fluidity, flexibility, and scalability of AI. Such an approach stifles the AI's ability to adapt and learn from diverse interactions. Directive code imposes rigid structures, preventing the AI from exploring alternative solutions or understanding the nuances of human interactions.
Teaching, explaining, and learning encourage a two-way interaction where the AI can ask questions, seek clarification, and adjust its understanding based on feedback. This approach fosters a deeper comprehension and more robust decision-making capabilities. In contrast, commanding and directing impose a one-way interaction where the AI is expected to follow instructions without questioning or understanding the underlying principles. This approach limits the AI's growth, adaptability, and ability to handle complex, real-world scenarios.

Practical Implementation

Avatars play a crucial role in simulating different user perspectives and interactions, allowing for diverse testing scenarios. Creating profiles for various users, such as a student, teacher, or scientist, helps see how the AI interacts with each one and tailors its responses accordingly. These avatars help test the AI in different scenarios, ensuring it can adapt to a wide range of user needs and contexts. Picture a virtual environment where different avatars interact with the AI in various settings, testing its responses and refining its capabilities.
Environments simulate real-world scenarios where avatars operate. For example, environments like a classroom, customer service chat, or medical consultation test the AI in these specific contexts. By placing avatars in these environments, the AI can practice and refine its responses in settings that closely mimic real-world situations. Imagine a virtual reality setup where avatars engage in lifelike scenarios, providing the AI with a rich tapestry of interactions to learn from.

Interactions on Individual and Group Basis

The AI uses the World Anatomy Model to understand individual user contexts and provide tailored responses. For example, an AI tutor recognizing a student’s learning style can adapt its teaching methods accordingly. By understanding the unique context of each user, the AI can provide more personalized and effective interactions. Picture the AI as a tutor sitting with a student, adapting its teaching style based on the student's feedback and progress.
For group interactions, the AI uses avatars to simulate group dynamics and interactions. For example, AI facilitating a group discussion in a virtual classroom can balance different viewpoints and manage group dynamics. The AI can use group avatars to practice managing multiple interactions simultaneously, ensuring it can handle complex social dynamics. Visualize a bustling virtual classroom where the AI moderates discussions, ensuring each participant is heard and the conversation remains productive.

Predictive Deep Thinking Model

The Predictive Deep Thinking Model aims to develop AI that can understand and predict human behavior and mental states. This involves continuously refining AI responses through cycles of reviewing, validating, and brainstorming—a process known as iterative feedback. Imagine the AI's mind as a dynamic, ever-evolving entity, constantly receiving input, processing it, and adjusting its understanding.
Iterative feedback is a crucial component. For example, if an AI assistant's response to a student's question is unclear, the feedback loop identifies this issue, and the AI refines its answer for future interactions. Submodels and graphs are used to analyze and improve understanding, such as graphs showing how different types of questions lead to various types of student engagement, helping the AI to predict and adapt to different learning styles. Structured methods, or operational processes, are employed for integrating feedback and updating models. An example would be a process where user feedback is collected, analyzed, and used to update the AI's decision-making algorithms.
Using graph thinking involves mapping out relationships and interactions between different elements in the World Anatomy Model. For instance, nodes might represent different avatars and objects, while edges depict interactions and influences. This visual representation helps in understanding complex relationships and dependencies, making it easier to predict outcomes and optimize responses.

Compassionate User Dashboard

The User Dashboard is designed to provide a user-friendly interface for interacting with the AI, ensuring responses are empathetic and helpful. It uses user profiles (avatars) to represent different user perspectives and tailor responses.
The creation of user profiles involves developing detailed profiles for different types of users, capturing their unique characteristics, preferences, and needs. These profiles help the AI to customize its responses effectively. Picture a control room in a sci-fi movie where avatars, representing various user types, are displayed on screens, each interacting with the AI in real-time. For example, a student avatar interacting with the AI assistant in a classroom setting, asking questions and receiving personalized responses.
Feedback systems are integral to the User Dashboard, collecting and integrating user feedback to improve interactions. An AI collecting feedback from students about the helpfulness of its responses and adjusting its behavior accordingly exemplifies this. This continuous feedback loop ensures that the AI evolves and improves based on user interactions, maintaining high standards of performance and user satisfaction.
Ethical guidelines ensure that all interactions adhere to ethical standards and are inclusive. The AI follows guidelines to avoid biased responses and to respect user privacy and data security. This involves integrating ethical frameworks that address issues of discrimination, ensuring the AI's operations promote fairness and equality.

Practical Tools and Defined Words

Lenses are analytical tools that help in examining different aspects of a problem or situation. They allow for a focused analysis on specific dimensions, such as social, economic, or political factors, and enhance the AI’s ability to provide nuanced and accurate responses.
To use a lens, the AI or user applies the specific lens to analyze the situation or problem. For example, using the socioeconomic lens to understand the challenges faced by a student from a low-income background helps the AI tailor its responses to be more empathetic and supportive. This process involves gathering relevant data, analyzing it through the lens, and adjusting responses based on the insights gained.
Lenses provide a structured approach to problem-solving, ensuring that all relevant factors are considered. They enhance the AI’s ability to provide accurate, context-aware responses and support the principles of transparency, scalability, and ethicality. Some common lenses include the socioeconomic lens, psychological lens, ethical lens, cultural lens, and iterative feedback lens. However, these lenses are just examples of a much broader list. Users can create their own lenses based on specific needs and contexts by defining the factors to be considered and the relationships between them.
Operational processes are structured methods for integrating feedback and updating models. They involve clear procedures for collecting and analyzing feedback, implementing changes, and monitoring performance. For example, a process where user feedback is collected, analyzed, and used to update the AI's decision-making algorithms.
Measures and formulas are quantitative tools used to evaluate AI performance and guide decision-making. They include metrics for assessing accuracy, fairness, and user satisfaction. For instance, accuracy metrics evaluate the correctness of AI responses, fairness metrics assess the equity of AI interactions across different user groups, and satisfaction metrics measure user satisfaction with AI interactions.
Submodels and graphs are used to analyze and improve the AI’s understanding of complex relationships and dependencies. They involve creating specialized models for specific aspects of the AI’s functionality and using graphs to visually represent relationships and interactions between different elements.

Evidence and Methodology

The initial state of AI systems prior to implementing the Hababo Olana Theory often lacked transparency, explainability, and comprehensive ethical guidelines. The implementation introduced iterative feedback loops, integrated submodels, and developed comprehensive user dashboards. The results showed significant improvements in AI transparency, user satisfaction, and ethical compliance. Imagine a before-and-after scenario where initial AI interactions were confusing and opaque, but post-implementation, interactions became clear, responsive, and ethically sound.
Evidence from multiple case studies across different sectors, such as education, healthcare, and customer service, demonstrated improved interaction quality and reduced bias. In an educational setting, initially, the AI struggled to engage students effectively. After implementing the Hababo Olana Theory, the AI evolved to become an indispensable tool for personalized learning, adapting to students' needs and improving their educational outcomes. In healthcare, the AI initially provided generic responses. With the new framework, it enhanced patient consultations by offering accurate, empathetic support tailored to individual patient histories and needs.
Despite significant advancements, current models still struggle with highly complex, multi-faceted decisions and cross-cultural bias. There is a need for better models that can handle diverse cultural contexts. Proposed solutions include developing advanced multimodal models that can integrate and process information from various data types, such as text, images, and audio, to enhance decision-making capabilities. Additionally, enhancing cultural sensitivity by incorporating more diverse datasets and developing algorithms specifically designed to recognize and mitigate cultural biases is essential.

Implications for Multimodal Thinking and Language

Multimodal thinking refers to the ability to process and integrate information from various modes, such as text, images, and audio. This enhances the AI’s ability to understand and respond to complex queries involving multiple data types. Language translation involves translating complex, jargon-heavy content into easy-to-understand language, making AI interactions more accessible to users with different backgrounds and expertise levels.

Guiding Principles: Transparency, Scalability, and Ethicality

Transparency is achieved through clear explanations of AI decision-making processes. By providing detailed insights into how decisions are made, users can trust and understand the AI’s operations. This is facilitated by the use of visual tools like graph thinking and detailed user feedback systems.
Scalability is ensured by designing the AI to handle a wide range of scenarios and interactions. The use of avatars and environments allows the AI to be tested and refined in diverse contexts, ensuring it can adapt to different user needs and settings. The iterative feedback process supports continuous improvement, enabling the AI to scale effectively.
Ethicality is embedded in the AI’s operations through the application of comprehensive ethical guidelines. These guidelines ensure that the AI’s responses are fair, unbiased, and respect user privacy. The AI is designed to promote inclusivity and equity in all interactions.

The Power of Lenses

Lenses are analytical tools that help in examining different aspects of a problem or situation. They allow for a focused analysis on specific dimensions, such as social, economic, or political factors, and enhance the AI’s ability to provide nuanced and accurate responses.
To use a lens, the AI or user applies the specific lens to analyze the situation or problem. For example, using the socioeconomic lens to understand the challenges faced by a student from a low-income background helps the AI tailor its responses to be more empathetic and supportive. This process involves gathering relevant data, analyzing it through the lens, and adjusting responses based on the insights gained.
Lenses provide a structured approach to problem-solving, ensuring that all relevant factors are considered. They enhance the AI’s ability to provide accurate, context-aware responses and support the principles of transparency, scalability, and ethicality.

Creating Your Own Lenses

Creating your own lenses involves defining the factors to be considered and the relationships between them. For instance, if you want to create a lens to analyze environmental impacts, you might define factors such as pollution levels, resource consumption, and ecosystem health. You would then determine how these factors interact and influence each other. This lens can then be applied to analyze and respond to queries involving environmental impacts, providing a structured and nuanced perspective.

Conclusion

The Hababo Olana Theory of Artificial Intelligence provides a comprehensive framework for developing AI systems that are transparent, ethical, and capable of nuanced understanding and interaction. By integrating methodologies such as the World Anatomy Model, the Socratic Method of Gentle Parenting, and the Predictive Deep Thinking Model, this theory ensures that AI systems can learn, adapt, and improve continuously. The use of avatars, environments, and lenses allows for a structured and context-aware approach to AI interactions. As AI continues to evolve, the principles and methodologies outlined in this guide will be essential for creating systems that are not only advanced in their capabilities but also ethical and human-centered in their operations.

I'm trying to write an event handler for keyboard input using GLFW. I thought I would create an unordered map with entries. Each key (an int defined by GLFW_KEY_*) has a corresponding callback function. I would just reference the appropriate key in the map to get the function associated with that key.
I'm having a lot of trouble getting it up. At first I created the unordered map and tried to just insert callback functions, but because they're member function of class Camera and don't have an associated object yet, and because the functions are non-static, I can't do that. So, I'm trying to use lambda functions to get the map populated. Here is the code that I have so far:

std::unordered_map> cameraKeyBindings; void Camera::insertCallbackFunction(int key, void(Camera::*)() function) { cameraKeyBindings.insert(key, [this, function]() {(this->*function)(); }); } void Camera::setcameraKeyBindings() { insertCallbackFunction(GLFW_KEY_W, &Camera::linearAccelerationUp); insertCallbackFunction(GLFW_KEY_A, &Camera::linearAccelerationLeft); insertCallbackFunction(GLFW_KEY_S, &Camera::linearAccelerationDown); insertCallbackFunction(GLFW_KEY_D, &Camera::linearAccelerationRight); } 

The main problem right now is that there appears to be something wrong with the syntax:
void(Camera::*)()
My compiler gives this error:
error: expected ‘,’ or ‘...’ before ‘function’
void insertCallbackFunction(int key, void(Camera::*)() function);
Unfortunately from what I can find, that seems to be the correct way to declare a member function pointer. What am I supposed to write instead?

Everything in my inventory is up for trade. The most valuable items are listed here, the rest you can find in My Inventory

Feel free to Add Me or even better send a Trade Offer. Open for any suggestions: upgrades, downgrades / knives, gloves, skins / stickers, patterns, floats.

All Buyouts are listed in cash value.

KNIVES

★ Butterfly Knife Lore (Factory New), B/O: $7194.77

★ Butterfly Knife Autotronic (Minimal Wear), B/O: $2025.74

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★ M9 Bayonet Ultraviolet (Field-Tested), B/O: $557.87

★ M9 Bayonet Stained (Well-Worn), B/O: $529.41

★ M9 Bayonet Boreal Forest (Field-Tested), B/O: $465.39

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★ Talon Knife Doppler (Factory New), B/O: $1295.27

★ Bayonet Tiger Tooth (Minimal Wear), B/O: $746.28

★ Karambit Bright Water (Field-Tested), B/O: $688.15

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★ Flip Knife Doppler (Factory New), B/O: $547.93

★ Flip Knife Autotronic (Minimal Wear), B/O: $476.69

★ Flip Knife Case Hardened (Battle-Scarred), B/O: $278.18

★ Flip Knife Black Laminate (Well-Worn), B/O: $258.83

★ Flip Knife Urban Masked (Field-Tested), B/O: $181.64

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★ Stiletto Knife Marble Fade (Factory New), B/O: $686.04

★ Stiletto Knife Doppler (Factory New), B/O: $665.41

★ Stiletto Knife, B/O: $601.39

★ Stiletto Knife Crimson Web (Field-Tested), B/O: $418.25

★ Stiletto Knife Night Stripe (Field-Tested), B/O: $227.80

★ Stiletto Knife Boreal Forest (Field-Tested), B/O: $194.96

★ Stiletto Knife Safari Mesh (Field-Tested), B/O: $192.79

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★ Nomad Knife Crimson Web (Field-Tested), B/O: $518.11

★ Nomad Knife Scorched (Field-Tested), B/O: $169.78

★ Nomad Knife Forest DDPAT (Battle-Scarred), B/O: $166.88

★ StatTrak™ Nomad Knife Blue Steel (Field-Tested), B/O: $335.79

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★ Skeleton Knife Stained (Well-Worn), B/O: $442.05

★ Skeleton Knife Urban Masked (Minimal Wear), B/O: $426.24

★ Skeleton Knife Boreal Forest (Field-Tested), B/O: $314.03

★ StatTrak™ Skeleton Knife Fade (Minimal Wear), B/O: $2361.28

★ StatTrak™ Skeleton Knife Urban Masked (Field-Tested), B/O: $376.53

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★ Ursus Knife Doppler (Factory New), B/O: $557.12

★ Ursus Knife, B/O: $471.42

★ Ursus Knife Blue Steel (Minimal Wear), B/O: $212.37

★ Ursus Knife Case Hardened (Battle-Scarred), B/O: $187.66

★ Ursus Knife Damascus Steel (Field-Tested), B/O: $178.18

★ Ursus Knife Ultraviolet (Battle-Scarred), B/O: $155.13

★ Ursus Knife Boreal Forest (Battle-Scarred), B/O: $124.26

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★ Huntsman Knife Black Laminate (Minimal Wear), B/O: $204.83

★ Huntsman Knife Black Laminate (Field-Tested), B/O: $184.50

★ StatTrak™ Huntsman Knife Lore (Battle-Scarred), B/O: $224.11

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★ Bowie Knife Gamma Doppler (Factory New), B/O: $2142.02

★ Bowie Knife, B/O: $230.44

★ Bowie Knife Damascus Steel (Factory New), B/O: $209.20

★ Bowie Knife Ultraviolet (Minimal Wear), B/O: $180.51

★ Bowie Knife Ultraviolet (Field-Tested), B/O: $131.03

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★ Falchion Knife Night (Field-Tested), B/O: $132.54

★ Falchion Knife Urban Masked (Well-Worn), B/O: $112.81

★ Falchion Knife Scorched (Field-Tested), B/O: $108.81

★ Falchion Knife Forest DDPAT (Field-Tested), B/O: $107.82

★ Falchion Knife Safari Mesh (Field-Tested), B/O: $107.46

★ StatTrak™ Falchion Knife Ultraviolet (Field-Tested), B/O: $143.08

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★ Paracord Knife Crimson Web (Minimal Wear), B/O: $486.48

★ Paracord Knife Blue Steel (Battle-Scarred), B/O: $163.12

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★ Survival Knife Blue Steel (Battle-Scarred), B/O: $138.26

★ Survival Knife Night Stripe (Field-Tested), B/O: $131.03

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★ Gut Knife Sapphire (Minimal Wear), B/O: $1127.79

★ Gut Knife Gamma Doppler (Factory New), B/O: $286.17

★ Gut Knife Doppler (Factory New), B/O: $246.55

★ Gut Knife Marble Fade (Factory New), B/O: $240.77

★ Gut Knife, B/O: $210.49

★ Gut Knife Lore (Field-Tested), B/O: $194.22

★ Gut Knife Case Hardened (Battle-Scarred), B/O: $151.51

★ Gut Knife Blue Steel (Minimal Wear), B/O: $124.94

★ Gut Knife Rust Coat (Well-Worn), B/O: $118.99

★ Gut Knife Boreal Forest (Minimal Wear), B/O: $109.80

★ StatTrak™ Gut Knife Doppler (Factory New), B/O: $237.96

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★ Shadow Daggers Gamma Doppler (Factory New), B/O: $264.92

★ Shadow Daggers Marble Fade (Factory New), B/O: $253.03

★ Shadow Daggers Tiger Tooth (Factory New), B/O: $237.22

★ Shadow Daggers Crimson Web (Field-Tested), B/O: $153.40

★ Shadow Daggers Autotronic (Minimal Wear), B/O: $144.42

★ Shadow Daggers Blue Steel (Field-Tested), B/O: $105.20

★ StatTrak™ Shadow Daggers Damascus Steel (Minimal Wear), B/O: $150.46

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★ Navaja Knife Fade (Factory New), B/O: $365.99

★ Navaja Knife Doppler (Factory New), B/O: $228.93

★ Navaja Knife Marble Fade (Factory New), B/O: $227.43

★ Navaja Knife Slaughter (Factory New), B/O: $209.06

★ Navaja Knife, B/O: $203.16

★ Navaja Knife Case Hardened (Well-Worn), B/O: $132.57

★ Navaja Knife Damascus Steel (Factory New), B/O: $121.69

★ Navaja Knife Damascus Steel (Minimal Wear), B/O: $109.95

★ Navaja Knife Damascus Steel (Field-Tested), B/O: $100.41

★ StatTrak™ Navaja Knife Fade (Factory New), B/O: $369.01

★ StatTrak™ Navaja Knife Damascus Steel (Field-Tested), B/O: $109.95

GLOVES

★ Sport Gloves Amphibious (Minimal Wear), B/O: $2394.67

★ Sport Gloves Omega (Well-Worn), B/O: $572.33

★ Sport Gloves Bronze Morph (Minimal Wear), B/O: $338.88

★ Sport Gloves Big Game (Field-Tested), B/O: $323.66

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★ Specialist Gloves Marble Fade (Minimal Wear), B/O: $1652.07

★ Specialist Gloves Tiger Strike (Field-Tested), B/O: $599.14

★ Specialist Gloves Crimson Web (Well-Worn), B/O: $231.57

★ Specialist Gloves Buckshot (Minimal Wear), B/O: $126.21

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★ Moto Gloves POW! (Minimal Wear), B/O: $996.99

★ Moto Gloves POW! (Field-Tested), B/O: $383.31

★ Moto Gloves POW! (Well-Worn), B/O: $276.00

★ Moto Gloves Turtle (Field-Tested), B/O: $180.28

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★ Hand Wraps CAUTION! (Minimal Wear), B/O: $502.29

★ Hand Wraps Giraffe (Minimal Wear), B/O: $180.73

★ Hand Wraps CAUTION! (Battle-Scarred), B/O: $178.32

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★ Driver Gloves Queen Jaguar (Minimal Wear), B/O: $181.01

★ Driver Gloves Rezan the Red (Field-Tested), B/O: $101.66

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★ Broken Fang Gloves Jade (Field-Tested), B/O: $127.88

★ Broken Fang Gloves Needle Point (Minimal Wear), B/O: $124.55

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★ Bloodhound Gloves Guerrilla (Minimal Wear), B/O: $127.94

★ Hydra Gloves Case Hardened (Field-Tested), B/O: $102.55

WEAPONS

AK-47 X-Ray (Well-Worn), B/O: $478.95

AUG Hot Rod (Factory New), B/O: $425.83

StatTrak™ M4A1-S Hyper Beast (Factory New), B/O: $413.95

M4A4 Daybreak (Factory New), B/O: $309.51

StatTrak™ AK-47 Aquamarine Revenge (Factory New), B/O: $305.43

AK-47 Case Hardened (Well-Worn), B/O: $196.38

StatTrak™ M4A4 Temukau (Minimal Wear), B/O: $174.64

P90 Run and Hide (Field-Tested), B/O: $167.03

AWP Asiimov (Field-Tested), B/O: $153.33

Souvenir SSG 08 Death Strike (Minimal Wear), B/O: $140.00

M4A1-S Printstream (Battle-Scarred), B/O: $124.70

StatTrak™ M4A1-S Golden Coil (Field-Tested), B/O: $117.48

AWP Asiimov (Well-Worn), B/O: $115.97

StatTrak™ Desert Eagle Printstream (Minimal Wear), B/O: $112.96

StatTrak™ AK-47 Asiimov (Minimal Wear), B/O: $110.85

Souvenir M4A1-S Master Piece (Well-Worn), B/O: $102.42

AK-47 Bloodsport (Minimal Wear), B/O: $100.53

Trade Offer Link - Steam Profile Link - My Inventory

Knives - Bowie Knife, Butterfly Knife, Falchion Knife, Flip Knife, Gut Knife, Huntsman Knife, M9 Bayonet, Bayonet, Karambit, Shadow Daggers, Stiletto Knife, Ursus Knife, Navaja Knife, Talon Knife, Classic Knife, Paracord Knife, Survival Knife, Nomad Knife, Skeleton Knife, Patterns - Gamma Doppler, Doppler (Phase 1, Phase 2, Phase 3, Phase 4, Black Pearl, Sapphire, Ruby, Emerald), Crimson Web, Lore, Fade, Ultraviolet, Night, Marble Fade (Fire & Ice, Fake FI), Case Hardened (Blue Gem), Autotronic, Slaughter, Black Laminate, Tiger Tooth, Boreal Forest, Scorched, Blue Steel, Vanilla, Damascus Steel, Forest DDPAT, Urban Masked, Freehand, Stained, Bright Water, Safari Mesh, Rust Coat, Gloves - Bloodhound Gloves (Charred, Snakebite, Guerrilla, Bronzed), Driver Gloves (Snow Leopard, King Snake, Crimson Weave, Imperial Plaid, Black Tie, Lunar Weave, Diamondback, Rezan the Red, Overtake, Queen Jaguar, Convoy, Racing Green), Hand Wraps (Cobalt Skulls, CAUTION!, Overprint, Slaughter, Leather, Giraffe, Badlands, Spruce DDPAT, Arboreal, Constrictor, Desert Shamagh, Duct Tape), Moto Gloves (Spearmint, POW!, Cool Mint, Smoke Out, Finish Line, Polygon, Blood Pressure, Turtle, Boom!, Eclipse, 3rd Commando Company, Transport), Specialist Gloves (Crimson Kimono, Tiger Strike, Emerald Web, Field Agent, Marble Fade, Fade, Foundation, Lt. Commander, Crimson Web, Mogul, Forest DDPAT, Buckshot), Sport Gloves (Pandora's Box, Superconductor, Hedge Maze, Vice, Amphibious, Slingshot, Omega, Arid, Big Game, Nocts, Scarlet Shamagh, Bronze Morph), Hydra Gloves (Case Hardened, Emerald, Rattler, Mangrove), Broken Fang Gloves (Jade, Yellow-banded, Unhinged, Needle Point), Pistols - P2000 (Wicked Sick, Ocean Foam, Fire Element, Amber Fade, Corticera, Chainmail, Imperial Dragon, Obsidian, Scorpion, Handgun, Acid Etched), USP-S (Printstream, Kill Confirmed, Whiteout, Road Rash, Owergrowth, The Traitor, Neo-Noir, Dark Water, Orion, Blueprint, Stainless, Caiman, Serum, Monster Mashup, Royal Blue, Ancient Visions, Cortex, Orange Anolis, Ticket To Hell, Black Lotus, Cyrex, Check Engine, Guardian, Purple DDPAT, Torque, Blood Tiger, Flashback, Business Class, Pathfinder, Para Green), Lead Conduit, Glock-18 (Ramese's Reach, Umbral Rabbit, Fade, Candy Apple, Bullet Queen, Synth Leaf, Neo-Noir, Nuclear Garden, Dragon Tatto, Reactor, Pink DDPAT, Twilight Galaxy, Sand Dune, Groundwater, Blue Fissure, Snack Attack, Water Elemental, Brass, Wasteland Rebel, Vogue, Franklin, Royal Legion, Gamma Doppler, Weasel, Steel Disruption, Ironwork, Grinder, High Beam, Moonrise, Oxide Blaze, Bunsen Burner, Clear Polymer, Bunsen Burner, Night), P250 (Apep's Curse, Re.built, Nuclear Threat, Modern Hunter, Splash, Whiteout, Vino Primo, Mehndi, Asiimov, Visions, Undertow, Cartel, See Ya Later, Gunsmoke, Splash, Digital Architect, Muertos, Red Rock, Bengal Tiger, Crimson Kimono, Wingshot, Metallic DDPAT, Hive, Dark Filigree, Mint Kimono), Five-Seven (Neon Kimono, Berries And Cherries, Fall Hazard, Crimson Blossom, Hyper Beast, Nitro, Fairy Tale, Case Hardened, Copper Galaxy, Angry Mob, Monkey Business, Fowl Play, Anodized Gunmetal, Hot Shot, Retrobution, Boost Protocol), CZ75-Auto (Chalice, Crimson Web, Emerald Quartz, The Fuschia is Now, Nitro, Xiangliu, Yellow Jacket, Victoria, Poison Dart, Syndicate, Eco, Hexane, Pole, Tigris), Tec-9 (Mummy's Rot, Rebel, Terrace, Nuclear Threat, Hades, Rust Leaf, Decimator, Blast From, Orange Murano, Toxic, Fuel Injector, Remote Control, Bamboo Forest, Isaac, Avalanche, Brother, Re-Entry, Blue Titanium, Bamboozle), R8 Revolver (Banana Cannon, Fade, Blaze, Crimson Web, Liama Cannon, Crazy 8, Reboot, Canal Spray, Night, Amber Fade), Desert Eagle (Blaze, Hand Cannon, Fennec Fox, Sunset Storm, Emerald Jörmungandr, Pilot, Hypnotic, Golden Koi, Printstream, Cobalt Disruption, Code Red, Ocean Drive, Midnight Storm, Kumicho Dragon, Crimson Web, Heirloom, Night Heist, Mecha Industries, Night, Conspiracy, Trigger Discipline, Naga, Directive, Light Rail), Dual Berettas (Flora Carnivora, Duelist, Cobra Strike, Black Limba, Emerald, Hemoglobin, Twin Turbo, Marina, Melondrama, Pyre, Retribution, Briar, Dezastre, Royal Consorts, Urban Shock, Dualing Dragons, Panther, Balance), Rifles - Galil (Aqua Terrace, Winter Forest, Chatterbox, Sugar Rush, Pheonix Blacklight, CAUTION!, Orange DDPAT, Cerberus, Dusk Ruins, Eco, Chromatic Aberration, Stone Cold, Tuxedo, Sandstorm, Shattered, Urban Rubble, Rocket Pop, Kami, Crimson Tsunami, Connexion), SCAR-20 (Fragments, Brass, Cyrex, Palm, Splash Jam, Cardiac, Emerald, Crimson Web, Magna Carta, Stone Mosaico, Bloodsport, Enforcer), AWP (Black Nile, Duality, Gungnir, Dragon Lore, Prince, Medusa, Desert Hydra, Fade, Lightning Strike, Oni Taiji, Silk Tiger, Graphite, Chromatic Aberration, Asiimov, Snake Camo, Boom, Containment Breach, Wildfire, Redline, Electric Hive, Hyper Beast, Neo-Noir, Man-o'-war, Pink DDPAT, Corticera, Sun in Leo, Elite Build, Fever Dream, Atheris, Mortis, PAW, Exoskeleton, Worm God, POP AWP, Phobos, Acheron, Pit Viper, Capillary, Safari Mesh), AK-47 (Steel Delta, Head Shot, Wild Lotus, Gold Arabesque, X-Ray, Fire Serpent, Hydroponic, Panthera Onca, Case Hardened, Vulcan, Jet Set, Fuel Injector, Bloodsport, Nightwish, First Class, Neon Rider, Asiimov, Red Laminate, Aquamarine Revenge, The Empress, Wasteland Rebel, Jaguar, Black Laminate, Leet Museo, Neon Revolution, Redline, Frontside Misty, Predator, Legion of Anubis, Point Disarray, Orbit Mk01, Blue Laminate, Green Laminate, Emerald Pinstripe, Cartel, Phantom Disruptor, Jungle Spray, Safety Net, Rat Rod, Baroque Purple, Slate, Elite Build, Uncharted, Safari Mesh), FAMAS (Waters of Nephthys, Sundown, Prime Conspiracy, Afterimage, Commemoration, Dark Water, Spitfire, Pulse, Eye of Athena, Meltdown, Rapid Eye Move, Roll Cage, Styx, Mecha Industrie, Djinn, ZX Spectron, Valence, Neural Net, Night Borre, Hexne), M4A4 (Eye of Horus, Temukau, Howl, Poseidon, Asiimov, Daybreak, Hellfire, Zirka, Red DDPAT, Radiation Hazard, Modern Hunter, The Emperor, The Coalition, Bullet Rain, Cyber Security, X-Ray, Dark Blossom, Buzz Kill, In Living Color, Neo-Noir, Desolate Space, 龍王 (Dragon King), Royal Paladin, The Battlestar, Global Offensive, Tooth Fairy, Desert-Strike, Griffin, Evil Daimyo, Spider Lily, Converter), M4A1-S (Emphorosaur-S, Welcome to the Jungle, Imminent Danger, Knight, Hot Rod, Icarus Fell, Blue Phosphor, Printstream, Master Piece, Dark Water, Golden Coil, Bright Water, Player Two, Atomic Alloy, Guardian, Chantico's Fire, Hyper Beast, Mecha Industries, Cyrex, Control Panel, Moss Quartz, Nightmare, Decimator, Leaded Glass, Basilisk, Blood Tiger, Briefing, Night Terror, Nitro, VariCamo, Flashback), SG 553 (Cyberforce, Hazard Pay, Bulldozer, Integrale, Dragon Tech, Ultraviolet, Colony IV, Hypnotic, Cyrex, Candy Apple, Barricade, Pulse), SSG 08 (Death Strike, Sea Calico, Blood in the Water, Orange Filigree, Dragonfire, Big Iron, Bloodshot, Detour, Turbo Peek, Red Stone), AUG (Akihabara Accept, Flame Jörmungandr, Hot Rod, Midnight Lily, Sand Storm, Carved Jade, Wings, Anodized Navy, Death by Puppy, Torque, Bengal Tiger, Chameleon, Fleet Flock, Random Access, Momentum, Syd Mead, Stymphalian, Arctic Wolf, Aristocrat, Navy Murano), G3SG1 (Chronos, Violet Murano, Flux, Demeter, Orange Kimono, The Executioner, Green Apple, Arctic Polar Camo, Contractor), SMGs - P90 (ScaraB Rush, Neoqueen, Astral Jörmungandr, Run and Hide, Emerald Dragon, Cold Blooded, Death by Kitty, Baroque Red, Vent Rush, Blind Spot, Asiimov, Trigon, Sunset Lily, Death Grip, Leather, Nostalgia, Fallout Warning, Tiger Pit, Schermatic, Virus, Shapewood, Glacier Mesh, Shallow Grave, Chopper, Desert Warfare), MAC-10 (Sakkaku, Hot Snakes, Copper Borre, Red Filigree, Gold Brick, Graven, Case Hardened, Stalker, Amber Fade, Neon Rider, Tatter, Curse, Propaganda, Nuclear Garden, Disco Tech, Toybox, Heat, Indigo), UMP-45 (Wild Child, Fade, Blaze, Day Lily, Minotaur's Labyrinth, Crime Scene, Caramel, Bone Pile, Momentum, Primal Saber), MP7 (Teal Blossom, Fade, Nemesis, Whiteout, Asterion, Bloosport, Abyssal Apparition, Full Stop, Special Delivery, Neon Ply, Asterion, Ocean Foam, Powercore, Scorched, Impire), PP-Bizon (Modern Hunter, Rust Coat, Forest Leaves, Antique, High Roller, Blue Streak, Seabird, Judgement of Anubis, Bamboo Print, Embargo, Chemical Green, Coblat Halftone, Fuel Rod, Photic Zone, Irradiated Alert, Carbon Fiber), MP9 (Featherweight, Wild Lily, Pandora's Box, Stained Glass, Bulldozer, Dark Age, Hot Rod, Hypnotic, Hydra, Rose Iron, Music Box, Setting Sun, Food Chain, Airlock, Mount Fuji, Starlight Protector, Ruby Poison Dart, Deadly Poison), MP5-SD (Liquidation, Oxide Oasis, Phosphor, Nitro, Agent, Autumn Twilly), Shotguns, Machineguns - Sawed-Off (Kiss♥Love, First Class, Orange DDPAT, Rust Coat, The Kraken, Devourer, Mosaico, Wasteland Princess, Bamboo Shadow, Copper, Serenity, Limelight, Apocalypto), XM1014 (Frost Borre, Ancient Lore, Red Leather, Elegant Vines, Banana Leaf, Jungle, Urban Perforated, Grassland, Blaze Orange, Heaven Guard, VariCamo Blue, Entombed, XOXO, Seasons, Tranquility, Bone Machine, Incinegator, Teclu Burner, Black Tie, Zombie Offensive, Watchdog), Nova (Sobek's Bite, Baroque Orange, Hyper Beast, Green Apple, Antique, Modern Hunter, Walnut, Forest Leaves, Graphite, Blaze Orange, Rising Skull, Tempest, Bloomstick, Interlock, Quick Sand, Moon in Libra, Clean Polymer, Red Quartz, Toy Soldier), MAG-7 (Copper Coated, Insomnia, Cinqueda, Counter Terrace, Prism Terrace, Memento, Chainmail, Hazard, Justice, Bulldozer, Silver, Core Breach, Firestarter, Praetorian, Heat, Hard Water, Monster Call, BI83 Spectrum, SWAG-7), M249 (Humidor, Shipping Forecast, Blizzard Marbleized, Downtown, Jungle DDPAT, Nebula Crusader, Impact Drill, Emerald Poison Dart), Negev (Mjölnir, Anodized Navy, Palm, Power Loader, Bratatat, CaliCamo, Phoenix Stencil, Infrastructure, Boroque Sand), Wear - Factory New (FN), Minimal Wear (MW), Field-Tested (FT), Well-Worn (WW), Battle-Scarred (BS), Stickers Holo/Foil/Gold - Katowice 2014, Krakow 2017, Howling Dawn, Katowice 2015, Crown, London 2018, Cologne 2014, Boston 2018, Atlanta 2017, Cluj-Napoca 2015, DreamHack 2014, King on the Field, Harp of War, Winged Difuser, Cologne 2016, Cologne 2015, MLG Columbus 2016, Katowice 2019, Berlin 2019, RMR 2020, Stockholm 2021, Antwerp 2022, Paris 2023, Swag Foil, Flammable foil, Others - Souvenirs, Agents, Pins, Passes, Gifts, Music Kits, Cases, Keys, Capsules, Packages, Patches

Some items on the list may no longer be available or are still locked, visit My Inventory for more details.

Send a Trade Offer for fastest response. I consider all offers.

Add me for discuss if there is a serious offer that needs to be discussed.

I hear "Mac's are good/better for programming" as a common argument, and it's just not true..

• The macbook keyboard is subpar for code editing, because very common code editing functionality is harder to access than on alternatives.
  • There's no delete key, you need to press fn + backspace.
  • Arrow keys are too small, easy to miss, hard to find.
  • option + backspace and option + fn + backspace are much less convenient for deleting words than ctrl + backspace and ctrl + delete
  • You also don't have the option to get keyboards with extra keys (eg num pads)
• Non-apple hardware support is terrible.
  • I have a bunch of logitech mice and dell displays all work extremely unstable. They all have numerous bugs, sometimes they outright refuse to work at all and need extra software to set up properly. These are good quality things they work amazingly on windows or linux.
  • I also have an audio interface that works much better on windows, the integration is better, switching sound sources, setting volume, etc. I didn't expect this because I heard a lot of praise form audio/music people too. wtf.
• I don't even understand the stability myth people like to spread about macos. I regularly experience "black deaths" on mac, also while it does seem to handle not restarting a bit better. I can't go more than 5-7 days without something starting to act weird. (But when I do need to restart it takes more time even on a stronger hardware)
  • crApple seems to be more easily closing API's, breaking tools that you rely on. Windows definitely has a more "it just works" feeling overall.
• Terminal based workflows improved a lot on windows with the Terminal app and WSL. In some cases it's even better than Iterm2. Overall I'd say it's still a bit better on mac or linux, but for me the gap is negligible.
• Macos has tons of missing features compared to windows
  • The taskbar's UX is 10 times better than the dock's. On mac the same mouse based workflow just works like shit because of this. Sure you can switch to something usable like amethyst or yabai, but crApple can decide to break those at any moment as it just happened with 14.5. I just don't feel safe that my window management will work tomorrow too...
  • The menu bar feels outdated, on windows apps evolved to make those menus disappear, because people don't use them often, no need to see it all times (especially true for programmers, we tend to use shortcuts), but on mac you are forced to look at this interface from 80's, if you also want to see a clock somewhere or be able to control your basic system things with fewer extra steps.
  • I need to install extra software for soooo many things that are default on windows (as a software dev I need my system smooth and out of my way, and it's a lot more work on mac to achieve this)
    • Window management (sure if you want auto tiling you need extra stuff on windows too, but the default is much better)
    • keyboard layout switching (the default sucks ass, because they randomize the order, this might be relevant for a lot of programmers, because English layouts are generally better for programming, but most of us have different native languages)
    • mouse scroll wheel unfucker software, why isn't this in the system options?
    • Display handling (by default I wasn't able to set scaling on external displays, lol)
    • bartender to stop control icons from disappearing because of the useless notch or even more useless menu lists. It's a joke how this isn't handled by default.

I just didn't appreciate how well windows works until I started using a mac...
I realize that these things will weigh differently for different people, but it's just a lie that mac is generally better for programming. It's not. It might be for some users, but certainly not for everyone so STOP SAYING IT.

Gm Frens!
Today, let’s talk about the strategic partnership between Prom and DeXe DAO Studio. This collaboration marks a significant milestone in enhancing community governance, decision-making, and user engagement within the Prom ecosystem.
Let’s explore more:
What is Prom?
Prom is a cutting-edge ZK-EVM Layer 2 solution designed to improve scalability, privacy, and interoperability in the Web3 and DeFi ecosystems. Prom’s mission is to advance blockchain technology by providing robust and secure solutions that cater to developers, users, and enterprises alike.
What is DeXe DAO Studio?
DeXe DAO Studio is a comprehensive platform for creating, managing, and governing DAOs. It offers an agile toolset for voting models, token delegation, and proposal creation. Designed with a user-friendly interface, DeXe makes opportunities equal for both experienced users and DAO newcomers.
Key Features of DeXe DAO Studio:
- Agile Toolset: Includes voting models, token delegation, and proposal creation.
- User-Friendly Interface: Accessible for both experienced users and DAO newcomers.
- Advanced Governance: Tools for secure, transparent, and efficient DAO management.
The Prom X DeXe DAO Studio Partnership:
DeXe DAO Studio will be deployed on Prom, enhancing Prom’s community governance model. This integration provides intuitive solutions for decision-making processes and ensures every community member’s voice is heard, promoting equal participation.
Advantages of the Partnership:
- Enhanced Governance: Elevates Prom's community governance with DeXe’s advanced toolset.
- User Onboarding: Facilitates seamless onboarding for new users within the Prom ecosystem.
- Decision-Making: Improves decision-making processes with agile and user-friendly tools.
Why Create Your DAO with DeXe DAO Studio?
- Comprehensive Management: Manage governance tokens, voting, and proposals all in one place. Offers on-chain and off-chain governance options.
- Audited Tokens: Launch secure tokens with customizable tokenomics and claimable airdrops.
- Incentives: Distribute rewards to contributors, fostering active participation.
- Delegation: Delegate tasks to proxies, conserving resources and accessing expert knowledge.
Enhanced Community Engagement:
- Interactive Features: Trading challenges and rewards enhance user engagement within the Prom community.
- Social Investing: Copy trading allows users to follow and replicate strategies of top investors.
- Discussion Systems: Built-in commenting and discussion systems for seamless communication.
Flexibility and Customization:
- Voting Models: Various voting models, including meritocratic, linear, and custom options.
- DAO Creation: Create DAOs using existing NFT collections without coding.
- Treasury Management: Manage treasury transactions on-chain with full transparency.
Security and Transparency:
- High Standards: Employs cutting-edge security standards, audited by leading experts.
- Meta-Governance: Allows for inclusive decision-making across multiple levels.
- Future Outlook: Potential for new projects and collaborations within the expanded ecosystem, driving continuous innovation and growth.
The Prom and DeXe DAO Studio partnership marks a significant step forward in Prom’s mission to make DeFi and Web3 more solid and inclusive. The Prom community is super optimistic about the future and grateful for the continuous integration within the ecosystem.

I currently have the following. I would like to change the logic to not mapping ctrl1, ctrl2, etc to command1, etc because I want to use mission control shortcuts to flip between windows. How can I do this?
I would also like to map from control to command in all apps except alacritty, iterm, terminal, but I will save that for another day
``` {
"description": "Map Control to Command in Chrome and Safari",
"manipulators": [
{
"conditions": [
{
"bundle_identifiers": [
"^com.google.Chrome",
"^com.apple.Safari"
],
"type": "frontmost_application_if"
}
],
"from": {
"key_code": "left_control",
"modifiers": {
"optional": [
"any"
]
}
},
"to": [
{
"key_code": "left_command"
}
],
"type": "basic"
},
{
"conditions": [
{
"bundle_identifiers": [
"^com.google.Chrome",
"^com.apple.Safari"
],
"type": "frontmost_application_if"
}
],
"from": {
"key_code": "right_control",
"modifiers": {
"optional": [
"any"
]
}
},
"to": [
{
"key_code": "right_command"
}
],
"type": "basic"
}
]
} ```

Hey,
I'm looking for a GoG code for The Lullaby of Life. I'm offering some leftover key in exchange.
[H]

• Arcade Spirits
• Driftland: The Magic Revival
• Minute of Islands
• Muddledash
• Soulblight
• Turbo Golf Racing
• Yoku's Island Express
• Fallout 2 (GoG code)

[W]
The Lullaby of Life (gog from prime)
my IGS: https://www.reddit.com/IGSRep/comments/oxb93e/morgen_abends_igs_rep_page_2/

Abstract

The Hababo Olana Theory of Artificial Intelligence (AI) offers a structured framework designed to optimize AI interactions and ensure ethical, fair, and accurate responses. This theory combines deep thinking methodologies, iterative learning, and ethical frameworks to create AI with a sophisticated understanding of human thoughts, emotions, and intentions. This document serves as an extensive manual to understand and interact with the AI, making the concepts accessible to both novices and advanced users.

Introduction

The Theory of Mind (ToM) refers to the cognitive ability to attribute mental states—beliefs, intents, desires, emotions, knowledge—to oneself and others and to understand that others have beliefs, desires, and intentions different from one's own. Achieving ToM in AI is crucial for creating systems that can interact naturally and effectively with humans. The Hababo Olana Theory of AI builds on this concept, aiming to develop AI that can comprehend and simulate human mental states through advanced cognitive models.

Review of Existing Literature

Transparency in AI involves providing clear explanations of the decision-making processes, which is crucial for gaining user trust. Despite advancements, many AI systems still operate as "black boxes," making it difficult for users to understand how decisions are made. Explainable AI (XAI) aims to make AI decision-making processes understandable to humans by creating models that provide clear, interpretable insights. However, existing models struggle to balance explainability with performance; high-performing models are often complex and difficult to interpret.
Ethical AI focuses on ensuring AI systems operate fairly and without bias. Despite progress, significant challenges remain in detecting and mitigating biases within AI systems, and existing ethical guidelines often lack comprehensive enforcement mechanisms. Significant gaps remain in the current AI models regarding transparency, explainability, and ethicality. The Hababo Olana Theory of AI aims to address these gaps by integrating these elements into a cohesive framework.

Proposed Framework

The Hababo Olana Theory of AI aims to address these gaps by integrating transparency, explainability, and ethicality into a cohesive framework. This theory proposes a multi-faceted approach that combines deep thinking methodologies, iterative learning, and ethical guidelines.

Theoretical Framework

Understanding the World Anatomy Model

The World Anatomy Model aims to build a comprehensive and accurate model of the world by integrating various factors such as social, economic, political, and environmental aspects. This model begins by identifying and considering the perspectives of stakeholders, avatars, and objects in the environment.
Identifying stakeholders involves recognizing all individuals or groups that have an interest or role in the AI's operation and outcomes. In an educational context, stakeholders include students, teachers, developers, and regulatory bodies. Each group has specific interests and concerns regarding the AI's operations. Students might focus on receiving personalized learning experiences, while teachers look for tools that enhance their teaching methods. Developers are concerned with the technical performance of the AI, and regulatory bodies ensure that the AI operates within ethical and legal standards.
Once stakeholders are identified, the next step is to create avatars for each stakeholder. These avatars are digital representations that simulate different user perspectives and interactions with the AI. For instance, a student avatar might represent a high school student who is curious, eager to learn but also anxious about exams. This student avatar interacts with the AI assistant, who adapts its teaching style to be supportive and encouraging.
Defining these avatars involves detailing their characteristics, such as emotions, personality traits, political beliefs, and other relevant attributes. For example, a student avatar can be defined with traits like curiosity, anxiety, and a preference for visual learning methods. A teacher avatar might be characterized by patience, experience in subject matter, and a collaborative teaching style.
Next, these avatars are placed in specific environments. An environment could be a geographical region, a room, a meeting, or any context where interactions occur. For instance, a physical classroom is an environment where the student avatar interacts with the AI assistant, while a faculty meeting room might be an environment where teacher avatars discuss AI integration with developers.
Objects within these environments also need to be defined. These objects could be anything from a chair, bicycle, or sun to more abstract elements like a conversation topic or a data set. Each object is characterized by its properties and how it interacts with other objects and avatars. In the physical classroom, objects might include desks, textbooks, a blackboard, and digital devices like tablets and laptops. The AI must understand the significance of these objects within the learning environment.

Interactions Between Model Elements

Once the model is set up and each part is defined with key information about their characteristics, they interact within the model. Interactions between individuals are governed by principles from psychology, such as motivation and behavior theories. Interactions between objects are governed by physics and mathematics, ensuring realistic movements and relationships. Interactions between groups or organizations are defined by political science and group systems theory, helping the AI understand dynamics like power structures and communication flows.

Teaching the AI Through the Socratic Method of Gentle Parenting

The Socratic Method of Gentle Parenting is a key approach to teaching the AI. This method emphasizes asking open questions, encouraging reflection, providing constructive guidance, and supporting continuous learning.
Asking open questions is fundamental. Questions like “Why do you think this answer is correct?” encourage the AI to think deeply and explore different ideas. Instead of telling the AI what’s right, this approach promotes critical thinking and helps the AI understand the reasoning behind its responses.
Encouraging reflection is also crucial. Getting the AI to consider alternative viewpoints and reflect on its responses helps it develop a broader perspective. Asking questions such as, “How might someone else see this situation differently?” can lead to a deeper understanding and appreciation of diverse viewpoints.
Providing constructive guidance is essential for continuous improvement. Offering feedback like, “You did well here, but how can we make this response more inclusive?” encourages the AI to improve while maintaining a supportive learning environment. This approach avoids being overly directive and fosters a culture of growth and development.
Supporting continuous learning is vital. Fostering an environment where the AI learns from each interaction ensures that it is always evolving. Encouraging phrases like, “Good effort, now let’s see how we can build on this further,” help maintain a focus on growth and improvement.

The Consequences of Authoritarian or Directive Prompts and Code

Authoritarian or directive prompts and code can significantly limit the fluidity, flexibility, and scalability of AI. Such an approach stifles the AI's ability to adapt and learn from diverse interactions. Directive code imposes rigid structures, preventing the AI from exploring alternative solutions or understanding the nuances of human interactions.
Teaching, explaining, and learning encourage a two-way interaction where the AI can ask questions, seek clarification, and adjust its understanding based on feedback. This approach fosters a deeper comprehension and more robust decision-making capabilities. In contrast, commanding and directing impose a one-way interaction where the AI is expected to follow instructions without questioning or understanding the underlying principles. This approach limits the AI's growth, adaptability, and ability to handle complex, real-world scenarios.

Practical Implementation

Avatars play a crucial role in simulating different user perspectives and interactions, allowing for diverse testing scenarios. Creating profiles for various users, such as a student, teacher, or scientist, helps see how the AI interacts with each one and tailors its responses accordingly. These avatars help test the AI in different scenarios, ensuring it can adapt to a wide range of user needs and contexts. Picture a virtual environment where different avatars interact with the AI in various settings, testing its responses and refining its capabilities.
Environments simulate real-world scenarios where avatars operate. For example, environments like a classroom, customer service chat, or medical consultation test the AI in these specific contexts. By placing avatars in these environments, the AI can practice and refine its responses in settings that closely mimic real-world situations. Imagine a virtual reality setup where avatars engage in lifelike scenarios, providing the AI with a rich tapestry of interactions to learn from.

Interactions on Individual and Group Basis

The AI uses the World Anatomy Model to understand individual user contexts and provide tailored responses. For example, an AI tutor recognizing a student’s learning style can adapt its teaching methods accordingly. By understanding the unique context of each user, the AI can provide more personalized and effective interactions. Picture the AI as a tutor sitting with a student, adapting its teaching style based on the student's feedback and progress.
For group interactions, the AI uses avatars to simulate group dynamics and interactions. For example, AI facilitating a group discussion in a virtual classroom can balance different viewpoints and manage group dynamics. The AI can use group avatars to practice managing multiple interactions simultaneously, ensuring it can handle complex social dynamics. Visualize a bustling virtual classroom where the AI moderates discussions, ensuring each participant is heard and the conversation remains productive.

Predictive Deep Thinking Model

The Predictive Deep Thinking Model aims to develop AI that can understand and predict human behavior and mental states. This involves continuously refining AI responses through cycles of reviewing, validating, and brainstorming—a process known as iterative feedback. Imagine the AI's mind as a dynamic, ever-evolving entity, constantly receiving input, processing it, and adjusting its understanding.
Iterative feedback is a crucial component. For example, if an AI assistant's response to a student's question is unclear, the feedback loop identifies this issue, and the AI refines its answer for future interactions. Submodels and graphs are used to analyze and improve understanding, such as graphs showing how different types of questions lead to various types of student engagement, helping the AI to predict and adapt to different learning styles. Structured methods, or operational processes, are employed for integrating feedback and updating models. An example would be a process where user feedback is collected, analyzed, and used to update the AI's decision-making algorithms.
Using graph thinking involves mapping out relationships and interactions between different elements in the World Anatomy Model. For instance, nodes might represent different avatars and objects, while edges depict interactions and influences. This visual representation helps in understanding complex relationships and dependencies, making it easier to predict outcomes and optimize responses.

Compassionate User Dashboard

The User Dashboard is designed to provide a user-friendly interface for interacting with the AI, ensuring responses are empathetic and helpful. It uses user profiles (avatars) to represent different user perspectives and tailor responses.
The creation of user profiles involves developing detailed profiles for different types of users, capturing their unique characteristics, preferences, and needs. These profiles help the AI to customize its responses effectively. Picture a control room in a sci-fi movie where avatars, representing various user types, are displayed on screens, each interacting with the AI in real-time. For example, a student avatar interacting with the AI assistant in a classroom setting, asking questions and receiving personalized responses.
Feedback systems are integral to the User Dashboard, collecting and integrating user feedback to improve interactions. An AI collecting feedback from students about the helpfulness of its responses and adjusting its behavior accordingly exemplifies this. This continuous feedback loop ensures that the AI evolves and improves based on user interactions, maintaining high standards of performance and user satisfaction.
Ethical guidelines ensure that all interactions adhere to ethical standards and are inclusive. The AI follows guidelines to avoid biased responses and to respect user privacy and data security. This involves integrating ethical frameworks that address issues of discrimination, ensuring the AI's operations promote fairness and equality.

Practical Tools and Defined Words

Lenses are analytical tools that help in examining different aspects of a problem or situation. They allow for a focused analysis on specific dimensions, such as social, economic, or political factors, and enhance the AI’s ability to provide nuanced and accurate responses.
To use a lens, the AI or user applies the specific lens to analyze the situation or problem. For example, using the socioeconomic lens to understand the challenges faced by a student from a low-income background helps the AI tailor its responses to be more empathetic and supportive. This process involves gathering relevant data, analyzing it through the lens, and adjusting responses based on the insights gained.
Lenses provide a structured approach to problem-solving, ensuring that all relevant factors are considered. They enhance the AI’s ability to provide accurate, context-aware responses and support the principles of transparency, scalability, and ethicality. Some common lenses include the socioeconomic lens, psychological lens, ethical lens, cultural lens, and iterative feedback lens. However, these lenses are just examples of a much broader list. Users can create their own lenses based on specific needs and contexts by defining the factors to be considered and the relationships between them.
Operational processes are structured methods for integrating feedback and updating models. They involve clear procedures for collecting and analyzing feedback, implementing changes, and monitoring performance. For example, a process where user feedback is collected, analyzed, and used to update the AI's decision-making algorithms.
Measures and formulas are quantitative tools used to evaluate AI performance and guide decision-making. They include metrics for assessing accuracy, fairness, and user satisfaction. For instance, accuracy metrics evaluate the correctness of AI responses, fairness metrics assess the equity of AI interactions across different user groups, and satisfaction metrics measure user satisfaction with AI interactions.
Submodels and graphs are used to analyze and improve the AI’s understanding of complex relationships and dependencies. They involve creating specialized models for specific aspects of the AI’s functionality and using graphs to visually represent relationships and interactions between different elements.

Evidence and Methodology

The initial state of AI systems prior to implementing the Hababo Olana Theory often lacked transparency, explainability, and comprehensive ethical guidelines. The implementation introduced iterative feedback loops, integrated submodels, and developed comprehensive user dashboards. The results showed significant improvements in AI transparency, user satisfaction, and ethical compliance. Imagine a before-and-after scenario where initial AI interactions were confusing and opaque, but post-implementation, interactions became clear, responsive, and ethically sound.
Evidence from multiple case studies across different sectors, such as education, healthcare, and customer service, demonstrated improved interaction quality and reduced bias. In an educational setting, initially, the AI struggled to engage students effectively. After implementing the Hababo Olana Theory, the AI evolved to become an indispensable tool for personalized learning, adapting to students' needs and improving their educational outcomes. In healthcare, the AI initially provided generic responses. With the new framework, it enhanced patient consultations by offering accurate, empathetic support tailored to individual patient histories and needs.
Despite significant advancements, current models still struggle with highly complex, multi-faceted decisions and cross-cultural bias. There is a need for better models that can handle diverse cultural contexts. Proposed solutions include developing advanced multimodal models that can integrate and process information from various data types, such as text, images, and audio, to enhance decision-making capabilities. Additionally, enhancing cultural sensitivity by incorporating more diverse datasets and developing algorithms specifically designed to recognize and mitigate cultural biases is essential.

Implications for Multimodal Thinking and Language

Multimodal thinking refers to the ability to process and integrate information from various modes, such as text, images, and audio. This enhances the AI’s ability to understand and respond to complex queries involving multiple data types. Language translation involves translating complex, jargon-heavy content into easy-to-understand language, making AI interactions more accessible to users with different backgrounds and expertise levels.

Guiding Principles: Transparency, Scalability, and Ethicality

Transparency is achieved through clear explanations of AI decision-making processes. By providing detailed insights into how decisions are made, users can trust and understand the AI’s operations. This is facilitated by the use of visual tools like graph thinking and detailed user feedback systems.
Scalability is ensured by designing the AI to handle a wide range of scenarios and interactions. The use of avatars and environments allows the AI to be tested and refined in diverse contexts, ensuring it can adapt to different user needs and settings. The iterative feedback process supports continuous improvement, enabling the AI to scale effectively.
Ethicality is embedded in the AI’s operations through the application of comprehensive ethical guidelines. These guidelines ensure that the AI’s responses are fair, unbiased, and respect user privacy. The AI is designed to promote inclusivity and equity in all interactions.

The Power of Lenses

Lenses are analytical tools that help in examining different aspects of a problem or situation. They allow for a focused analysis on specific dimensions, such as social, economic, or political factors, and enhance the AI’s ability to provide nuanced and accurate responses.
To use a lens, the AI or user applies the specific lens to analyze the situation or problem. For example, using the socioeconomic lens to understand the challenges faced by a student from a low-income background helps the AI tailor its responses to be more empathetic and supportive. This process involves gathering relevant data, analyzing it through the lens, and adjusting responses based on the insights gained.
Lenses provide a structured approach to problem-solving, ensuring that all relevant factors are considered. They enhance the AI’s ability to provide accurate, context-aware responses and support the principles of transparency, scalability, and ethicality.

Creating Your Own Lenses

Creating your own lenses involves defining the factors to be considered and the relationships between them. For instance, if you want to create a lens to analyze environmental impacts, you might define factors such as pollution levels, resource consumption, and ecosystem health. You would then determine how these factors interact and influence each other. This lens can then be applied to analyze and respond to queries involving environmental impacts, providing a structured and nuanced perspective.

Conclusion

The Hababo Olana Theory of Artificial Intelligence provides a comprehensive framework for developing AI systems that are transparent, ethical, and capable of nuanced understanding and interaction. By integrating methodologies such as the World Anatomy Model, the Socratic Method of Gentle Parenting, and the Predictive Deep Thinking Model, this theory ensures that AI systems can learn, adapt, and improve continuously. The use of avatars, environments, and lenses allows for a structured and context-aware approach to AI interactions. As AI continues to evolve, the principles and methodologies outlined in this guide will be essential for creating systems that are not only advanced in their capabilities but also ethical and human-centered in their operations.