Download extended brainfuck interpreter

Author: m | 2025-04-24

Download eXtended BrainFuck interpreter 1.0 - A tiny BrainFuck interpreter. eXtended BrainFuck interpreter is a command line utility that offers users an interpreter for the Brainfuck programming Download eXtended BrainFuck interpreter for free. eXtended BrainFuck interpreter (xbf) is basically a BrainFuck interpreter, just with more commands added, such as condition

GitHub - campbellcole/brainfuck-extended: A Brainfuck interpreter

--> Java BrainFuck解释器Brainfuck只由八个简单的命令和一个指令指针组成。虽然它是完全图灵完备的，但它并不是为了实际使用，而是为了挑战和娱乐程序员。BrainFuck只由8个字符命令组成，这使得它的使用即使对于简单的任务也非常具有挑战性 –>命令增加数据指针（指向右边的下一个单元）。+命令增加（增加1）数据指针的字节。命令”-“使数据指针上的字节减少（减少一个）。.命令输出数据指针上的字节。命令接受一个字节的输入，将其值存储在数据指针的字节中。[- 如果数据指针上的字节为0，那么不要将指令指针向前移动到下一条指令，而是将其向前跳到匹配的]指令之后。]- 如果数据指针上的字节为非零，那么与其将指令指针向前移动到下一条命令，不如跳回匹配的[ 命令后的命令。(另外，]命令可以被翻译成无条件跳转到相应的[]命令，反之亦然；程序的行为是一样的，但由于不必要的重复搜索，运行速度会更慢。)[和]的匹配通常与括号一样：每个[正好匹配一个]，反之亦然，[在前，两者之间不能有未匹配的[或]。由于BrainFuck只由这8个命令组成，为BrainFuck建立一个解释器是非常简单的。在这篇文章中，我们将建立一个简单的程序，将BrainFuck代码作为输入并产生所需的输出:我们将简单地接受BrainFuck代码作为一个字符串，并通过解析该字符串和检查每个字符的实际功能来产生输出:内存由一个字节类型的数组表示，模拟从0到65534的最大65535位的内存（65535是可以用无符号16位二进制数表示的最高数字）。变量ptr指的是内存数组的当前索引。在这篇文章中，我们不会讨论在BrainFuck中编写程序的细节。例子Input : Output : Hello World!Input : Output : GEEKS FOR GEEKSBrainFuck解释器的Java实现-import java.util.*; class BrainFuck{ private static Scanner ob = new Scanner(System.in); private static int ptr; // Data pointer // Max memory limit. It is the highest number which // can be represented by an unsigned 16-bit binary // number. Many computer programming environments // beside brainfuck may have predefined // constant values representing 65535. private static int length = 65535; // Array of byte type simulating memory of max // 65535 bits from 0 to 65534. private static byte memory[] = new byte[length]; // Interpreter function which accepts the code // a string parameter private static void interpret(String s) { int c = 0; // Parsing through each character of the code for (int i = 0; i moves the pointer to the right if (s.charAt(i) == '>') { if (ptr == length - 1)//If memory is full ptr = 0;//pointer is returned to zero else ptr ++; } // 0 || s.charAt(i) != ']') { if (s.charAt(i) == '[') c++; else if (s.charAt(i) == ']') c--; i ++; } } } // ] jumps back to the matching [ if the // cell under the pointer is nonzero else if (s.charAt(i) == ']') { if (memory[ptr] != 0) { i --; while (c > 0 || s.charAt(i) != '[') { if (s.charAt(i) == ']') c ++; else if (s.charAt(i) == '[') c --; i --; } } } } } // Driver code public static void main(String args[]) { System.out.println("Enter the code:"); String code = ob.nextLine(); System.out.println("Output:"); interpret(code); }}输出1Enter the code:--[+++++++>-->+>+>+---.>--..>+.->>.+++[.输出2Enter the code:++++++++++[>+++++++>++++++++>+++++..

eXtended BrainFuck interpreter 1.0 - Download

Here are 19 public repositories matching this topic... Code Issues Pull requests This project focuses on understanding the language ecosystem Updated Oct 7, 2023 Python Code Issues Pull requests Discussions Multi-client Cross-platform Python Backdoor/Reverse Shell/RAT with AES Encryption Updated Jul 20, 2024 Python Code Issues Pull requests Tiny calculator interpreter with supporting Mathematical functions using Python. Updated Jul 24, 2020 Python Code Issues Pull requests A Discord bot that can interpret python code. Updated Mar 12, 2022 Python Code Issues Pull requests This is a discord bot that allows you to quickly calculate and graph math. Updated May 10, 2023 Python Code Issues Pull requests The Natscript interpreter, a custom programming language, with a natural English-like syntax. Updated Mar 3, 2025 Python Code Issues Pull requests 🈶 An Interpreter of the language Befunge written in Python Updated Dec 5, 2021 Python Code Issues Pull requests Python interpreter based on expressions and functions. The input and output of this interpreter will be through a web interface, although a command line interface is also provided. Updated Jan 28, 2023 Python Code Issues Pull requests A Python Interpreter written purely in Python Updated Jun 8, 2022 Python Code Issues Pull requests Updated Dec 13, 2020 Python Code Issues Pull requests An interactive python interpreter for your command line. Updated Apr 10, 2019 Python Code Issues Pull requests 一个用 python 实现的简单python解释器，分版本逐步实现一个简单的python解释器功能，适合初学者了解解释器的工作原理。 Updated Mar 14, 2025 Python Code Issues Pull requests Just what it sounds like, decorator that patches function bytecode to allow GOTO's *ROFL* Updated Apr 3, 2023 Python Code Issues Pull requests About Python's multithreading and GIL Updated Feb 7, 2025 Python Code Issues Pull requests Discussions A cool bot named BOSA-bot that will do a lot of general commands, and will have many new commands added through active development. Updated May 18, 2022 Python Code Issues Pull requests Developed a new scripting language in which we can visualize the data structures like Arrays, Stacks, Queues etc., and their performed operations. Updated Nov 15, 2020 Python Code Issues Pull requests PYON ROBOT - UI Test Automation with Robot Framework Updated Jun 16, 2021 Python Code Issues Pull requests A simple Python Interpreter for the Brainfuck Esoteric Language. Updated Sep 27, 2021 Python Code Issues Pull requests Interpretador da Linguagem Interpretada de Manipulação de Pilha (LIMPIL) Updated Feb 28, 2024 Python Improve this page Add a description, image, and links to the python-interpreter topic page so that developers can more easily learn about it. Curate this topic Add this topic to your repo To associate your repository with the python-interpreter topic, visit your repo's landing page and select "manage topics." Learn more

eXtended BrainFuck interpreter 1.0 - Download, Screenshots

Installs and launches the iOS app on the IOS simulator. Setting up Xcode for iOS Simulator Support Download and install Xcode from or download from Mac App Store. Launch Animate. Create or open an existing AIR for iOS document. On the Properties panel, set the Player target to the latest AIR version. Click the button, beside the Target drop-down to bring up the AIR for iOS Settings dialog. On the General tab, provide the fully qualified path to the iOS Simulator SDK manually or browse to location. For example: Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator6.0.sdk On the Deployment tab, provide the Certificate and Password. Optionally, provide the Provisioning Profile for the AIR application. Click OK to finish. You can now use the iOS Simulator to test and debug your application. For information, see Testing AIR applications using the Natives iOS Simulator and Debugging AIR applications using the Native iOS Simulator. Testing AIR applications using the Native iOS Simulator Ensure that you have set up Xcode and set the path to iPhone SDK before testing your AIR application. In Animate, Control > Test Movie > on iOS Simulator to bring up the iOS Simulator. However, if you have not set the iOS Simulator SDK path in AIR for iOS Settings dialog, an error is thrown to indicate the same. Browse to your application on the iOS Simulator, and click to launch. Debugging AIR applications using the Native iOS Simulator Ensure that you have set up Xcode and set the path to iPhone SDK path before debugging your AIR application. In Animate, select Debug > Debug Movie > on iOS Simulator to bring up the iOS Simulator. However, if you have not set the iOS Simulator SDK path in AIR for iOS Settings dialog, an error is thrown to indicate the same. In Animate, select Debug > Begin Remote Debugging Session > ActionScript 3.0. Browse to your application on iOS Simulator, and click to launch. Publishing AIR applications for devices with High Resolution Retina Display support Animate allows you to create rich AIR applications for iOS with an extended support for High-resolution Retina display. You can choose to select High-resolution Retina Display when publishing AIR applications. In Animate, create or open an existing AIR for iOS document. On the Properties panel, click the button beside the Target drop-down to bring up the AIR for iOS Settings dialog. On the General tab, set the Resolution as High. Click Publish. Test and debug using Interpreter Mode The Interpreter Mode allows you to quickly debug or test your AIR applications written for iOS. When the Interpreter Mode is selected, AIR applications are installed without converting to ARM code. To enable the Interpreter Mode, do the following: On the Properties panel,. Download eXtended BrainFuck interpreter 1.0 - A tiny BrainFuck interpreter. eXtended BrainFuck interpreter is a command line utility that offers users an interpreter for the Brainfuck programming Download eXtended BrainFuck interpreter for free. eXtended BrainFuck interpreter (xbf) is basically a BrainFuck interpreter, just with more commands added, such as condition

eXtended BrainFuck interpreter Files - SourceForge

128 Architectures: ["x86_64"] Handler: bootstrap Runtime: provided.al2 Timeout: 5 CodeUri: . Events: Brainfuck: Type: Api # More info about API Event Source: Properties: Path: /brainfuck Method: postOutputs: RestApi: Description: "API Gateway endpoint URL for Prod stage for Brainfuck function" Value: !Sub " BrainfuckFunction: Description: "Brainfuck World Lambda Function ARN" Value: !GetAtt BrainfuckFunction.Arn BrainfuckFunctionIamRole: Description: "Implicit IAM Role created for Brainfuck World function" Value: !GetAtt BrainfuckFunctionRole.ArnMakefileWe need to create a makefile file to simplify our work. We can run make in the console, and it will compile our application and prepare it to be deployed to AWS Lambda.Note that we are building an application with x86_64-unknown-linux-musl architecture. It’s because AWS Lambda requires us to use it.The process of building is the following:Add x86_64-unknown-linux-musl target to rustup.Compile application with musl architecture.sam build will execute build-BrainfuckFunction internally.Copy compiled binary into the output directory.Discards symbols from compiled binary to reduce size.ARCH = x86_64-unknown-linux-muslROOT_DIR = $(shell dirname $(realpath $(firstword $(MAKEFILE_LIST))))build: rustup target add $(ARCH) cargo build --target $(ARCH) --release --target-dir ../target sam buildbuild-BrainfuckFunction: cp $(ROOT_DIR)/../target/$(ARCH)/release/brainfuck_aws $(ARTIFACTS_DIR)/bootstrap strip $(ARTIFACTS_DIR)/bootstrapDeploymentAWS CLI configurationAlmost everything is done. You need to download and install AWS CLI and SAM CLI. Then you need to configure the AWS client before deployment:You should set up your access key id and secret access key. You should do it once. Then it will persist locally.SAM deploymentTo initiate deployment for the first time, you should run this command:It will prompt you about the function name, region, etc. After that, it will create a local file, samconfig.toml. The next time you can use sam deploy command.After initial configuration, it will create all the resources and deploy our application. You should get similar output to this:CloudFormation events from stack operations-----------------------------------------------------------------------------------------------------------------------------------------------------------------ResourceStatus ResourceType LogicalResourceId ResourceStatusReason -----------------------------------------------------------------------------------------------------------------------------------------------------------------CREATE_IN_PROGRESS AWS::IAM::Role BrainfuckFunctionRole - CREATE_IN_PROGRESS AWS::IAM::Role BrainfuckFunctionRole Resource creation Initiated CREATE_COMPLETE AWS::IAM::Role BrainfuckFunctionRole - CREATE_IN_PROGRESS AWS::Lambda::Function BrainfuckFunction - CREATE_IN_PROGRESS AWS::Lambda::Function BrainfuckFunction Resource creation Initiated CREATE_COMPLETE AWS::Lambda::Function BrainfuckFunction - CREATE_IN_PROGRESS AWS::ApiGateway::RestApi ServerlessRestApi - CREATE_IN_PROGRESS AWS::ApiGateway::RestApi ServerlessRestApi Resource creation Initiated CREATE_COMPLETE AWS::ApiGateway::RestApi ServerlessRestApi - CREATE_IN_PROGRESS AWS::ApiGateway::Deployment ServerlessRestApiDeployment683b01a6bf - CREATE_IN_PROGRESS AWS::Lambda::Permission BrainfuckFunctionBrainfuckPermissionPr - od CREATE_IN_PROGRESS AWS::Lambda::Permission BrainfuckFunctionBrainfuckPermissionPr Resource creation Initiated od CREATE_COMPLETE AWS::ApiGateway::Deployment ServerlessRestApiDeployment683b01a6bf - CREATE_IN_PROGRESS AWS::ApiGateway::Deployment ServerlessRestApiDeployment683b01a6bf Resource creation Initiated CREATE_IN_PROGRESS AWS::ApiGateway::Stage ServerlessRestApiProdStage - CREATE_IN_PROGRESS AWS::ApiGateway::Stage ServerlessRestApiProdStage Resource creation Initiated CREATE_COMPLETE AWS::Lambda::Permission BrainfuckFunctionBrainfuckPermissionPr - od CREATE_COMPLETE AWS::ApiGateway::Stage ServerlessRestApiProdStage - CREATE_COMPLETE AWS::CloudFormation::Stack brainfuck - -----------------------------------------------------------------------------------------------------------------------------------------------------------------CloudFormation outputs from deployed stack--------------------------------------------------------------------------------------------------------------------------------------------------------------------Outputs --------------------------------------------------------------------------------------------------------------------------------------------------------------------Key BrainfuckFunctionIamRole Description Implicit IAM Role created for Brainfuck World function Value arn:aws:iam::085583328641:role/brainfuck-BrainfuckFunctionRole-10GJIGA9HAMOU Key RestApi Description API Gateway endpoint URL for Prod stage for Brainfuck function Value Key BrainfuckFunction Description Brainfuck World Lambda Function ARN Value arn:aws:lambda:us-east-1:085583328641:function:brainfuck-BrainfuckFunction-Hh5Y3dLqjbey --------------------------------------------------------------------------------------------------------------------------------------------------------------------That means our application is deployed successfully!Application testingTo test the application, we will use curl:curl -X POST -H "Content-Type: application/json" -d '{"source":",[.,]","input":"hello"}' | jqYou should see something like this:The application will correctly

BernhardEisvogel/Extended-Brainfuck-interpreter - GitHub

Lambda, accept HTTP request events and return HTTP responses. To work correctly, we will configure API Gateway. It will redirect all the requests to AWS Lambda and return responses to the user.Follow the same steps from CLI to create the brainfuck_aws application inside the workspace:Dependency definitionTo work with API Gateway and lambda, we should use the official lambda_http crate. It has all the required structures to work with them.We also need to serialize and deserialize all the inputs/outputs. We use serde and serde_json for that. Tokio is used to provide asynchronous I/O runtime for our application. Lastly, we use env_logger and log to provide logging functionality. The runtime will send them to Cloudwatch.[dependencies]brainfuck_interpreter = { path = '../brainfuck_interpreter' }lambda_http = "0.5"serde_json = "1.0"serde = "1.0"tokio = "1.0"env_logger = "0.9"log = "0.4"Application codeRequest and response typesOur request should contain the source code and the input:#[derive(Deserialize)]struct InterpreterRequest { source: String, input: OptionString>,}The response should return either the result of successful execution or an error message:#[derive(Serialize, Debug)]#[serde(rename_all = "lowercase")]enum InterpreterResponse { Success(String), Error(String),}Main codeDuring the startup, we should enable logging and start lambda runtime:#[tokio::main]async fn main() -> Result(), Error> { env_logger::init(); info!("Starting lambda function!"); let func = service_fn(func); lambda_http::run(func).await?; Ok(())}Lambda runtime will execute our func function every time on event fire. This function should process the request. Depending on the interpreter execution result, it will return either 200 OK with the stdout, or 400 BAD REQUEST with the error description:async fn func(event: Request) -> Resultimpl IntoResponse, Error> { debug!("Received request: {:?}", event); info!("Processing request!"); match process_request(event).await { Ok(result) => Ok(Response::builder() .status(200) .body(serde_json::to_string(&result)?)?), Err(error) => Ok(Response::builder().status(400).body(serde_json::to_string( &InterpreterResponse::Error(error.to_string()), )?)?), }}Before the execution, we should parse the payload. If it’s not a valid payload, we can return an error. Otherwise, we can start code parsing and execution. In case of any error’s inside the brainfuck library, we will return them; otherwise, we will return stdout:async fn process_request(request: Request) -> ResultInterpreterResponse, Error> { if let Some(request) = request.payload::InterpreterRequest>()? { debug!("Body is valid. Processing request"); let source = request.source; let input = request.input.unwrap_or(String::new()); let stdin = Box::new(input.as_bytes()); let result = match interpret(&source, stdin) { Ok(output) => InterpreterResponse::Success(output), Err(error) => InterpreterResponse::Error(error.to_string()), }; info!("Interpreter result: {:?}", result); Ok(result) } else { warn!("Can't process request. Invalid body"); Err("Invalid body")? }}Template definitionWe will use SAM (Serverless application mode) to simplify our deployment to AWS. SAM allows specifying only the function you need and required endpoints. Under the hood, it will create API Gateway, AWS Lambda, IAM Roles, Databases, etc.In this example, we need AWS Lambda that API Gateway will call. It will be called BrainfuckFunction, use x86_64 architecture and Amazon Linux 2 runtime. API Gateway will provide a POST /brainfuck endpoint that triggers AWS Lambda.AWSTemplateFormatVersion: '2010-09-09'Transform: AWS::Serverless-2016-10-31Resources: BrainfuckFunction: Type: AWS::Serverless::Function Properties: MemorySize:

extended Brainfuck interpreter in C GitHub

This article is the next part of Brainfuck interpreter implementation. We built the library that parses and executes Brainfuck language in the previous part. In this part, we will use this library in the CLI program and deploy the code to AWS Lambda. Let’s begin with the CLI.CLI program implementaitonFirstly, we need to create a new application in our workspace. In the terminal, go to the root workspace directory and run this command:Then, add a new application to the root Cargo.toml[workspace]members = [ "brainfuck_interpreter", "brainfuck_cli"]Dependency definitionI highly recommend using the clap library for building CLI applications. We can also use anyhow to interact with any error type easier. You should use thiserror for libraries and anyhow for applications as a rule of thumb.The last line in the dependencies uses our brainfuck_interpreter library in the same workspace.[dependencies]clap = { version = "3.1.5", features = ["derive"] }anyhow = "1.0"brainfuck_interpreter = { path = "../brainfuck_interpreter" }Application codeCLI arguments definitionclap crate allows us to define needed arguments by structure. Here how it looks likes:use clap::Parser;#[derive(Parser, Debug)]#[clap(author, version, about, long_about = None)]struct Args { source_path: String,}Our program needs the path to the source. We will read the file’s content and execute it on the fly.To read arguments in the main method, we should do this:use anyhow::Result;fn main() -> Result()> { let args = Args::parse(); // Other code}Note, we use anyhow::Result as the returned value from main. It will handle any error without the need to write different handlers for each type of error.Main application codeTo read the file’s content, we can use the standard library method fs::read_to_string(args.source_path). We need to use io::stdin as input: io::Read argument to interpret method. After that, we can write all the output to stdout.fn main() -> Result()> { let args = Args::parse(); let source = fs::read_to_string(args.source_path)?; let stdin = io::stdin(); let result = interpret(&source, Box::new(stdin))?; io::stdout().write_all(result.as_bytes())?; Ok(())}CLI TestingLet’s execute the program:You will receive this output:error: The following required arguments were not provided: USAGE: brainfuck_cli For more information try --helpLet’s see the help page:Output:brainfuck_cli 0.1.0USAGE: brainfuck_cli ARGS: OPTIONS: -h, --help Print help information -V, --version Print version informationNote how descriptive and easy to use our application is. clap library provided best practices of CLI software development for free.To run a sample Hello world application located at brainfuck_examples/hello.bf, you should execute this command:cargo run -- ../brainfuck_examples/hello.bfHello World!A more sophisticated example, cat.bf, will output everything from the input. You should start the program, type anything and then press CTRL + D in Linux/Mac or CTRL + Z in Windows to finish stdin:cargo run -- ../brainfuck_examples/cat.bfI love rust[CTRL+D]I love rustYou can also redirect stdin from other files:echo "blah blah" | cargo run -- ../brainfuck_examples/cat.bfblah blahAWS Lambda program implementaitonThis application will run inside the AWS. Download eXtended BrainFuck interpreter 1.0 - A tiny BrainFuck interpreter. eXtended BrainFuck interpreter is a command line utility that offers users an interpreter for the Brainfuck programming