>Oh c'mon, is this what really impresses you?

You'll be impressed once the successor of chatGPT takes your job. You realize that chatGPT wasn't trained to be a programmer right? They did virtually nothing to make it a good programmer. It learned programming as a side effect. Wait till they make the thing targetted towards programming.

>The code examples are also super-bad, incorrect and don't even actually fulfil your initial requirements: magic constants, use of undefined variables, "customer_address in country_1", "print", supporting merely 2 hardcoded currencies and languages, and routing them with "if", while also providing "else" fallback that you never implied in your requirements.

All of what you said is true yet you are blind if you can't see why it's revolutionary. In fact it can do better. You can specify all the requirements you want. No use of undefined variables, no hardcoded currencies. More flexibility more features no routing with if statements. It will do all of what you asked it to. And this is a tool that only learned how to program as a side effect. If you're not impressed, you're in denial.

>Honestly there's a good number of people who don't understand objective limits and properties of chatGTP, despite it actually totally being revolutionary.

No. I'm sorry but you're biased. Artists are ALREADY starting lawsuits because the art is on par with what they create. I don't know if you realize this but drawing something photorealistically is actually significantly HARDER then programming. There's literally no Bootcamp to produce a fine artist in a year because it's brutally hard to do. If AI can crack art, there's not much more time before it cracks programming. Don't kid yourself.

>but in any professional environment it can only be used in a super-limited scope, supervised by an actual professional. Basically just an enhanced "monkey with a typewriter".

Of course. EVEN with supervision, chatGPT isn't even useful enough to be used in a professional environment. I AGREE. The successor to chatGPT, however will be. In fact one of the successors to chatGPT can replace a persons job. Nobody is afraid of chatGPT taking their job. They are afraid of chatGPT because it is a herald about the AI in the future that WILL.

>Real "squishy stuff" would be something around "handling personal data", "ensuring verifiability and correctness", "productive quality assurance", "robust and scalable systems architecture", "managing complexity in a way that doesn't require rebuilding the whole thing as soon as something inevitably changes", "observability", "productive documentation and self-documenting approaches to work" - you know, the REAL squishy stuff that REAL professionals have to deal with, not some totally vague abstract BS.

chatGPT says:

Handling Personal Data: A software engineer at a healthcare company is tasked with implementing a system to store and process patient data. They use the programming language Python and the Django web framework to build the system. To ensure the privacy and security of personal data, the engineer implements industry-standard encryption techniques for data at rest and in transit, as well as implementing user authentication and authorization mechanisms. The engineer also conducts regular security audits and penetration testing to identify and address potential vulnerabilities.

Ensuring Verifiability and Correctness: An engineer at a financial services company is responsible for building and maintaining a system that processes financial transactions. They use the programming language Java and the Spring framework to build the system. To ensure the verifiability and correctness of the system, the engineer implements automated unit tests, integration tests, and end-to-end tests to verify that the system behaves as expected. They also use code review tools like SonarQube and Crucible to ensure that the code is maintainable, readable and adheres to company coding standards.

Productive Quality Assurance: An engineer at a e-commerce company is tasked with building and maintaining a system that processes online orders. They use the programming language Ruby and the Rails framework to build the system. To ensure the quality of the system, the engineer works closely with the QA team to establish a test-driven development process and use tools like Selenium and Cucumber to automate functional and acceptance testing. They also use performance testing tools like Apache JMeter to ensure the system can handle high traffic loads.

Robust and Scalable Systems Architecture: An engineer at a social media company is responsible for building and maintaining a system that can handle millions of users and handle high traffic loads. They use the programming language Go and the Microservices architecture to build the system. To ensure the system is robust and scalable, the engineer designs the system to be highly available and fault-tolerant. They use load balancers like HAProxy and Nginx to distribute traffic across multiple servers and use service discovery tools like Consul and Etcd to automatically route requests to the appropriate service.

Managing Complexity: An engineer at a logistics company is tasked with building and maintaining a system that tracks packages and shipments as they move through the supply chain. They use the programming language Python and the Flask web framework to build the system. To manage complexity, the engineer uses a microservices architecture to break the system into smaller, more manageable pieces. They also use containerization technology like Docker to package and deploy the system, which makes it easier to manage and scale. They also use monitoring tools like Prometheus and Grafana to track the system performance and identify potential issues.

Observability: An engineer at a SaaS company is responsible for building and maintaining a system that provides analytics and reporting features to customers. They use the programming language Node.js and the Express.js framework to build the system. To ensure observability, the engineer uses logging and monitoring tools like ELK stack, Grafana and Prometheus to track system performance and identify potential issues. They also use distributed tracing tools like Zipkin and Jaeger to track request and response times across multiple services.

Productive documentation and self-documenting approaches to work: An engineer at a cloud service provider is tasked with building and maintaining a system that automates the provisioning and management of cloud resources. They use the programming language Python and the Flask web framework to build the system. To ensure productive documentation, the engineer uses a documentation-as-code approach and stores all documentation in the same repository as the code. They use tools like Sphinx and Doxygen to automatically generate API documentation from code comments. They also automatically generate documentation from code comments and maintain the documentation up-to-date. They also use self-documenting approaches like using clear and descriptive variable names, function names and comments in the code, making it easy for other developers to understand the codebase. They also use a wiki system like Confluence to store additional information, such as design decisions, meeting notes, and user guides.

Literally. You want to drill down on more squishy stuff? The squishy stuff is what's easy for chatGPT because chatGPT is SPECIFICALLY trained on that stuff. It's the coding and math that it has more trouble with because it's NOT trained specifically on code and math.