I am currently on a CS major and I can definitely say that whether it differs compared to days before heavily depends on the lecturers.
But never the less the usage of LLMs in order to finish homework/be done with tests in a matter of minutes has widely spread. On the other hand the idea of cheating and it's drawbacks have stayed the same - (not em dash, chill) That is robbing yourself of applicable knowledge.
The current idea and motive behind CS majors is dragging us first through ANSI C so we can learn to program.
I have a suspicion that the methodology of ascertaining knowledge has become stricter on programming laboratories compared to before. We are required to create an initial program for a specific lesson and we essentially have a sizable test every week, which consists of adding onto our code. The amount of points we gain is heavily time dependent and in order to finish code quickly we need to understand it already.
Some claim they are able to use chatgpt on those lessons and in my opinion they are digging their own grave because we have very strict rules on passing and rumors day not a lot passed the subject in the last year, a third supposedly.
Some people are already predicting our replacement, but you just have to know that's utter bullshit.
That's why I stopped using AI for exercises because I realized I might fail if I do the initial exercises with usage of LLMs, because I will get slower if I continue to so.
To summarize the CS majors are starting to produce people with no real desire to learn programming and to survive we need to repeat last year's exercises in order to get accustomed to reading poorly written exercises. A lot of tests can be easily cheated off which affects negatively real world experience.
With my small team we're working on a dense integration layer between client data, bank statements, and invoices along with dedicated software in cooperation with accountants to simplify flow of data from independent payment processors and pairing appropriate payments with corresponding invoices in one database, in code.
This project makes use of existing database infrastructure and parses data from multiple banks including caveats and quirks of some banks improper handling of data.
This project aims to ease the work of accountants and administration as currently a lot of correcting mistakes and pairing to the correct invoice is done manually.
The project is made in python however the modularity we set ourselves to implement allows for quick, easy and hassle free corrections of code with use of project schematics, like builders, dependency injections etc. Discovered a great tool for running tests efficiently https://docs.astral.sh/uv/ .
Also for data retrieval from a remotely located database. DO NOT USE pyodbc, USE mssql library. pyodbc is unoptimized in terms of receiving high amounts of streamed data, it can't keep up. That alone has dropped the time of execution from 18 minutes down to about 20 seconds.
Also making use of typer and data classes for ensuring correct types of data.
Well it's not something somebody does perfectly on the first try, from my experience or rather If I put myself to the same idea I would fully know that I'd be way better at making a game after 6 months of fucking around.
Essentially the hardest step is to throw yourself into the big enough fire that easier and simpler things would seem like a child's play.
Even less time is fine but throwing yourself at the hard stuff you don't know how to do is smart, cus after that If You Were to repeat it, it'd be easier for you to do.
Niche or not, it's about being satisfied of the project.
So it's more about who you are as a person, I like to throw myself into fire and I fully understand that I might get disenchanted quickly, but simpler tasks or projects will be easy easier to make.
I've got to admit throwing myself into the deep end is always how I've learned
It's been difficult at times, but in the end I've always found it more rewarding
I think I'm just struggling with trying to do something so different to what I've spent a lot of my career doing whilst being really aware this is such a challenging field
It's a bit like when I first decided to go all in on being a founder over 15 years ago
Moreover openai (i.e. chatgpt) is facing a tricky financial situation. Openai's sources of income pertain to profitability from investors and API/subscription business model. Supposedly they are losing on every subscriber. And the reason why it held up is because there was a lot of hope generated causing investors to overinvest.
With the past investments of Nvidia into openai, the business model held up. Lately, AMD has caught up enough in AI efficiency that openai decided to built an entire stack of servers in AMD. openai's boyfriend i.e Nvidia did not like that and with Nvidia unable to guarantee return of investments via sales of it's cards to openai, Jensen Huang i.e. CEO of Nvidia is backing off from investing 100B$ simultaneously tanking a -6% of stock in a week.
But never the less the usage of LLMs in order to finish homework/be done with tests in a matter of minutes has widely spread. On the other hand the idea of cheating and it's drawbacks have stayed the same - (not em dash, chill) That is robbing yourself of applicable knowledge.
The current idea and motive behind CS majors is dragging us first through ANSI C so we can learn to program.
I have a suspicion that the methodology of ascertaining knowledge has become stricter on programming laboratories compared to before. We are required to create an initial program for a specific lesson and we essentially have a sizable test every week, which consists of adding onto our code. The amount of points we gain is heavily time dependent and in order to finish code quickly we need to understand it already.
Some claim they are able to use chatgpt on those lessons and in my opinion they are digging their own grave because we have very strict rules on passing and rumors day not a lot passed the subject in the last year, a third supposedly.
Some people are already predicting our replacement, but you just have to know that's utter bullshit.
That's why I stopped using AI for exercises because I realized I might fail if I do the initial exercises with usage of LLMs, because I will get slower if I continue to so.
To summarize the CS majors are starting to produce people with no real desire to learn programming and to survive we need to repeat last year's exercises in order to get accustomed to reading poorly written exercises. A lot of tests can be easily cheated off which affects negatively real world experience.