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- You get a leetcode question and if you're lucky is an easy medium that you can solve, if you're really lucky you already solved it and can pretend you are approaching the problem the first time. Good luck if you get a hard question and you never saw it before.
- You get a home assignment, in a framework you might not know but you're expected to be fluent with it, then waste 1 hour setting up the project structure, and one more hour to find out how the framework expects you to define the CORS allow list. You are expected to deliver the project in 3 hours.
The good I see in AI is that it completely removes the need to study just for interviews, and you can also delegate all the project setup to the AI. Then you can focus on what you would test (e2e? integration? what are the boundaries? what do we mock?), how to keep the documentation, how to structure your code. You have an expensive endpoint, do I make it sync or add an async jobs framework?
Imagine you're an expert in C++ interviewing for a Django position and the interview consists of fixing a big in a repo. The bug is that a function without type hinting is modifying what is expected to be a list, but the caller is passing a tuple. Trivial after a week you work in python and you have your environment set up for type warnings, also trivial with AI and definitely not an interesting problem that shows expertise with software engineering in general.
We also did this in our last interview at work, and it was a really good indicator to see if someone just copy pasted code, or understood it after it was generated. Some candidates had a unit test fail and couldn't debug it for his life, even if he "wrote" all the code himself. Others simply did not understand the architecture they wrote, and assumed that a function defined with "async" and awaited would run in parallel from the code that called it (as if you spawned a thread)
I mean... I still do it, but it's not like before LLMs I used those as the primary source. I just googled issues and checked stackoverflow first, then moved to the documentation (which is often a mess in smaller libraries).
> What happened with AWS outage recently is only a brief foreshadow of what might eventually come to pass if this trend continues
Is there a source that the issue was caused by AI tools? I've found out it was a DNS issue but there's no mention of the root cause, at least that I've found, nor AI or LLM mentions. And it's not like we didn't have outages before chatgpt
At its best, a menu can be a nice leather "book". At its worst, it is a one page plastic menu that has been touched by hundreds of people before you and never cleaned, it is also probably ruined or broken on the corners and in the middle, and has also a lot hand-made corrections to ingredients and prices that makes reading it way more frustrating.
At the same time, a digital menu can be easy to navigate (with sections based on first courses, second courses, appetizers, etc.), it can show you the pictures of the food you're going to order and, sometimes, allows you to order without waiting for someone.
Even at their worse, I still prefer the digital version as it is more hygienic and I don't have to wait 15 minutes for a waiter if they forget to bring one to the table (and then we also have to share that between 6 people).
Location: Italy, Europe, EMEA
Remote: Yes
Willing to relocate: No
Technologies: Python, Django, DRF, Flask, AWS, Docker, PostgreSQL, Airflow
Résumé/CV: https://vittoriomattei.com/resume/resume.pdf
Email: vimattei94 [at] gmail.com
I previously worked with C++ in the automotive field. I currently work with Python and AWS stack in the AI field. I am also willing to change to another language such as Go or Rust, as I always easily adapted to new stacks and sectors.