GPT-4 creates new programming language 'TenetLang,' chat AI already seems to understand programming quite well
Coder
GPT-4 Designed a Programming Language
https://lukebechtel.com/blog/gpt4-generating-code
Large-scale language models (LLMs) like GPT-4 have read billions of times every programming language in the world. While LLMs are known to be programmable , Luke wrote, 'To my knowledge, there has never been a case where a unique programming language was created with GPT-4.' So, Luke decided to use GPT-4 to create a new programming language.
Luke first asked GPT-4, 'If you were to propose a broad unified theory of software development, what would it be?' The answer he received was, 'It's difficult to propose a unified theory of software development, but we can propose 10 key principles that can serve as a comprehensive guide for developing high-quality software.' He proposed the following 10 principles.
1: Modularity
Breaking software down into small, self-contained, reusable components to simplify development, maintenance, and understanding.
2: Abstraction
By providing a well-defined interface and separation of concerns, we hide the complexity of the system and allow developers to focus on specific areas without being overwhelmed by details.
3: Scalability
Designing software to handle a wide range of workloads and demands with the ability to scale efficiently as needed.
4: Maintainability
Writing clean, easy to understand, and well-documented code for ease of maintenance, updates, and collaboration.
5: Testability
Develop a comprehensive testing strategy that includes unit, integration, and system testing to ensure the robustness and reliability of the software.
6. Adaptability
Designing software that is flexible enough to accommodate future changes, such as updated requirements, technology, and user needs.
7. Performance
Optimizing software to run efficiently without compromising functionality or maintainability, minimizing resource usage and maximizing response times.
8. Security
Implement strong security measures to protect your software and user data from unauthorized access, misuse, and vulnerabilities.
9. Collaboration
Facilitate effective communication and teamwork among developers, testers, designers and other stakeholders to ensure a shared understanding of project goals, requirements and progress.
10: Continuous improvement
Embrace a culture of continuous learning, evaluation and iterative development, improving our software and development processes over time and adapting to new methods, technologies and best practices.
Furthermore, when Luke asked, 'Give me some examples of when the above 10 principles work well and when they have difficulty,' GPT-4 output the following:
1: Modularity
Cases where it works well:
The UNIX philosophy that one task (grep, sed, awk, etc.) can be done well and combined to create complex pipelines.
Difficult cases:
Large legacy systems with tightly coupled components that make it difficult to refactor or introduce new features without breaking existing functionality.
2: Abstraction
Cases where it works well:
Object-oriented programming languages (Java, C++, etc.) promote encapsulation, hiding implementation details behind well-defined interfaces.
Difficult cases:
Too much abstraction can lead to over-engineering and make your code more complex than it needs to be.
3: Scalability
Cases where it works well:
Distributed systems such as Google MapReduce and Apache Kafka that can scale horizontally to handle large amounts of data and users.
Difficult cases:
If not designed with scalability in mind, the resulting monolithic architecture will have difficulty handling increased workloads without significant refactoring.
4: Maintainability
Cases where it works well:
Open source projects like Linux and Python have established coding standards and best practices to make collaboration and long-term maintenance easier.
Difficult cases:
A proprietary code base with poor documentation or inconsistent coding style makes the code difficult to understand and maintain.
5: Testability
Cases where it works well:
Test-Driven Development (TDD) methodology that emphasizes creating tests before implementing code, ensuring complete test coverage and more reliable software.
Difficult cases:
Complex systems with many dependencies and side effects make it difficult to isolate components for testing.
6. Adaptability
Cases where it works well:
A microservices architecture that allows individual services to be updated, scaled, and replaced independently without affecting the entire system.
Difficult cases:
Rigid and inflexible code that doesn't follow open or closed principles, making it difficult to introduce new features and adapt to changing requirements.
7. Performance
Cases where it works well:
High-performance computing (HPC) systems, where performance is important and developers optimize their code to run efficiently on specialized hardware.
Difficult cases:
Premature optimizations that result in a slight performance improvement at the expense of code readability and maintainability.
8. Security
Cases where it works well:
Secure software development practices, such as the OWASP Top 10 , to help developers avoid common security vulnerabilities.
Difficult cases:
Insufficient attention to security leads to reliance on software with easily exploitable vulnerabilities and outdated technology with known security flaws.
9. Collaboration
Cases where it works well:
Difficult cases:
Disjointed teams working in silos without effective communication leads to misunderstandings, duplication of effort, and delays.
10: Continuous improvement
Cases where it works well:
DevOps practices, which promote integration between development and operations teams and foster a culture of continuous learning and improvement.
Difficult cases:
Organizations that resist change or lack a culture of learning and experimentation will find it difficult to adopt new techniques, technologies, and best practices.
Next, Luke asked GPT-4, 'If you were to design a new programming language that incorporates these ideas, what would it look like?' GPT-4 responded, 'Designing a new programming language that encompasses all the principles of good software development is a complex task. I can't cover all the details in one answer. However, I can outline some features and design choices that take into account the ideas above.'
The following is an outline of the programming language output by GPT-4, which encompasses as many of the above ideas as possible.
1: Emphasize simplicity and readability
Syntax should be clear, concise, and easy to understand. Python is a good example of a programming language that uses
2: Supports multiple programming paradigms
It encourages the use of the paradigm that best suits the task, such as object-oriented, functional programming , or procedural programming , giving developers the flexibility to choose the approach that best suits their needs.
3: Strong typing and type inference
A strong type system helps detect errors early and improve code safety, while type inference reduces redundancy and improves readability. Programming languages such as TypeScript and Haskell are good examples of a balance between strong typing and type inference.
4: First-class functions and closures
Support for first-class functions and closures enables powerful functional programming techniques, such as those found in programming languages such as JavaScript, Scala, and Kotlin.
5: Immutable Data Structures
It makes the code easier to reason about by encouraging the use of immutable data structures and minimizing the risk of unintended side effects.
6: Built-in support for concurrency and parallelism
Async/await in JavaScript, Goroutines in Go, and Actors in Erlang provide language-level constructs for managing concurrency and parallel execution.
7: Extensible modular syntax
It allows developers to create custom syntax, DSLs, and libraries that integrate seamlessly with the core language, similar to macros in Lisp or implicit functions in Scala.
8. Integrated security features
Part of any programming language's design is to include security features and best practices, such as automatic memory management and bounds checking to prevent common vulnerabilities like buffer overflows .
9: Garbage Collection and Memory Safety
Automating memory management to prevent memory leaks and improve memory safety, as seen in programming languages such as Java, C#, and Rust.
10: Interoperability with other programming languages
It promotes collaboration and reuse of existing code by providing built-in support for calling functions and using libraries from other programming languages.
Regarding the above 10 ideas, GPT-4 writes, 'It is impossible to create a perfect programming language that covers all these aspects without trade-offs, but incorporating these ideas will help create a programming language that promotes good software development.'
GPT-4, which has proposed various ideas so far, has named the programming language that adheres to these 'TenetLang' and proposed further details. TenetLang will be a programming language that combines a simple syntax inspired by Python with some features of functional and object-oriented programming languages. Furthermore, GPT-4 outlined eight points in the design of TenetLang as follows:
1: Indentation-based scoping similar to Python.
2: Strong typing with type inference, inspired by TypeScript and Kotlin.
3: JavaScript-like first-class functions and closures.
4: Immutable data structures by default, mutable as an option.
5: Concise lambda syntax for anonymous functions .
6: Pattern matching and destructuring, inspired by Haskell and Rust.
7: Built-in support for concurrency using async/await and lightweight threads.
8: Interoperability with other programming languages using FFI .
Below is a simple example program output by GPT-4 that demonstrates TenetLang's syntax and functionality.
[code]class Person {
let name: String
let age: Int
constructor(name: String, age: Int) {
this.name = name
this.age = age
}
toString() -> String {
return f'{this.name} ({this.age})'
}
}
async function fetchPersons() -> List[Person] {
// Simulate an asynchronous fetch operation using lightweight threads
await sleep(1000)
return [
Person('Alice', 32),
Person('Bob', 28),
Person('Carol', 35),
Person('Dave', 23)
]
}
function filterAdults(persons: List[Person]) -> List[Person] {
return persons.filter(p => p.age >= 18)
}
function sortByName(persons: List[Person]) -> List[Person] {
return persons.sortBy(p => p.name)
}
async function main() {
let persons = await fetchPersons()
let adults = filterAdults(persons)
let sortedAdults = sortByName(adults)
for person in sortedAdults {
print(person)
}
}
main()[/code]
The above example program meets several criteria for a new programming language proposed by GPT-4, including 'simplicity and readability,' 'multiple programming paradigms,' 'strong typing and type inference,' 'immutable data structures,' 'concurrency,' and 'interoperability.' Luke wrote, 'Basically, TenetLang is like a baby of Typescript and Python.'
Luke noticed that distributed computing was not covered, so he asked GPT-4, 'Can you improve this programming language to make it useful for distributed computing?'
GPT-4 then replied, 'The TenetLang examples so far have focused primarily on concurrency within a single process and therefore have not explicitly addressed distributed computing. To improve TenetLang to better support distributed computing, we need to introduce features that facilitate communication and coordination between distributed components.'
Here are five ideas for doing so:
1: Message passing and lightweight actors
It incorporates a built-in message passing mechanism and lightweight actors (inspired by Erlang or Akka) that allow asynchronous, non -blocking communication between distributed components.
2: Remote Procedure Call (RPC)
Integrating support for RPC allows communication between services running on different machines or in different programming languages by providing built-in libraries or frameworks for popular RPC protocols such as gRPC or Apache Thrift.
3: Serialization and Deserialization
Make it easy to send complex data structures between distributed components, including support for efficient serialization and deserialization of data. Consider building in support for popular formats like JSON, BSON, and Protocol Buffers.
4: Distributed Data Structures
Provides built-in support for distributed data structures that can be shared and manipulated across multiple nodes, such as distributed hash tables (DHTs) and distributed databases.
5: Fault tolerance
It makes it easier to build resilient distributed systems by integrating features that promote fault tolerance, such as support for supervision trees, automatic retries, and timeouts.
Below is a 'simple example of a program showing syntax and functionality' using an improved version of TenetLang that incorporates the above ideas.
[code]import actors
import rpc
class Person {
// Same as before...
}
// RPC service definition for a remote data source
service PersonService {
rpc fetchPersons() -> List[Person]
}
actor PersonFetcher {
let service: PersonService
constructor(service: PersonService) {
this.service = service
}
receive(msg) {
case FetchPersons => {
let persons = this.service.fetchPersons()
reply(persons)
}
}
}
function filterAdults(persons: List[Person]) -> List[Person] {
// Same as before...
}
function sortByName(persons: List[Person]) -> List[Person] {
// Same as before...
}
async function main() {
// Connect to a remote PersonService
let personService = rpc.connect(PersonService, 'http://example.com/person-service')
// Create a PersonFetcher actor
let fetcher = PersonFetcher(personService)
// Send a FetchPersons message to the fetcher and await the
let persons = await fetcher.send(FetchPersons)
let adults = filterAdults(persons)
let sortedAdults = sortByName(adults)
for person in sortedAdults {
print(person)
}
}
main()[/code]
Luke also proposed adding new features to TenetLang that would make it easier to incorporate LLMs like GPT-3 and ChatGPT. GPT-4 proposed the introduction of a built-in library to provide a simple and convenient interface for interacting with chat AI models. This library would handle complex tasks like API requests, authentication, and data serialization/deserialization.
However, since this library focuses on providing a simple interface for interacting with LLMs, Luke asks what can be done to achieve deeper integration. GPT-4 then suggests that developers should be able to use the AI model's capabilities while coding to help with programming. Therefore, GPT-4 proposes 'implementing a function that makes 'smart suggestions' by incorporating LLMs into the programming language itself.' This would enable developers to complete their code and make refactoring suggestions.
TenetLang, the programming language Luke created while interacting with GPT-4, is available on GitHub.
GitHub - tenetlang/tenetlang: A GPT-Designed Language Built for Humans
https://github.com/tenetlang/tenetlang
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