Comments URL: https://news.ycombinator.com/item?id=47390574

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=46391624

Points: 1

# Comments: 0

I had already established from previous chat that upon asking for server.c file, llm's answer was working correctly. Rest of the sentence is just me asking it to use and not use certain header files which it uses by default when you ask it to generate server.c file.Thats because from docs of , I thought it had all relevant bindings for the socket programming to work correctly.

I would say, the sentence logically makes sense.

One with ChatGPT about dbms questions and one with claude about socket programming.

Looking back are some questions a little stupid ? Yes. But affcourse they are! I am coming with zero knowledge trying to learn how the socket programming is happening here ? Which functions are begin pulled from which header files, etc.

In the end I just followed along with a random youtube video. When you say, you can get LLM to do anything, I agree. Now that I know how socket programming is happening, for next question in assignment about writing code for crc with socket programming, I asked it to generate code for socket programming, made the necessary changes, asked it generate seperate function for crc, integrated it manually and voila, assignment done.

But this is the execution phase, when I have the domain knowledge. During learning when the user asks stupid questions and the LLM's answer keep getting stupider, then using them is not practical.

They often can not generate relatively trivial code When they do, they can not explain that code. For example, I was trying to learn socket programing in C. Claude generated the code, but when I stared asking about stuff, it regressed hard. Also, often the code is more complex than it needs to be. When learning a topic, I want that topic, not the most common relevant code with all the spagheti used on github.

For other subjects, like dbms, computer network, when asking about concepts, you better double check, because they still make stuff up. I asked ChatGPT to solve prev year question for dbms, and it gave a long, answer which looked good on surface. But when I actually read through because I need to understand what it is doing, there were glaring flaws. When I point them out, it makes other mistakes.

So, LLMs struggle to generate concise to the point code. They can not explain that code. They regularly make stuff up. This is after trying Claude, ChatGPT and Gemini with their paid versions in various capacities.

My bottom line is, I should NEVER use a LLM to learn. There is no fine line here. I have tried again and again because tech bros keep preaching about sparks of AGI, making startup with 0 coding skills. They are either fools or genius.

LLMs are useful strictly if you already know what you are doing. That's when your productivity gains are achieved.

Comments URL: https://news.ycombinator.com/item?id=41704818

Points: 71

# Comments: 54

Also, the world still mostly speaks C99 when iterative improvements are constantly being made up to C23.

However a lot of new infrastructure is being developed in rust. Infact it can be argued that the very reason it should be in rust is because it is critical. I think there would be great value if a person can efficiently thread between both rust and c rather than competing.

But then what would you advice to do ? There are nightmare patterns in rust and c++. Looks to me as pick your poison just that one is new and other has been around for fair bit.

Instead of quick and dirty, it allows to embed the logic in the type system such that the compiler can help you. Getting off the ground is much harder but staying there is easier.

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As college student I have invested time into rust because I see value in the language but there are absolutely zero entry level jobs or internships. To get my foot in, my only real options are C/C++.

Google has now proposed a syntax inspired by these approaches. However, I am afraid how well it would be adopted. As someone new to SQL, nearly every DB seem to provide its own SQL dialect which becomes cumbersome very quickly.

Whereas PRQL feels something like Apache Arrow which can map to other dialects.

With the new model, I am seeing alot of how open source they are and can be build upon. Is it now completely open source or similar to their last models ?

Now this one time ticket needs to generated before entering the metro station and the qr code is scanned at * both entry and exit*.

I think the entire system works on daily rotating ticket id validated using unique hashes where a ticket validity period is tracked. I think this should be enough to ensure non-reuse of same ticket.

The caveat is, I have always only bough one time ticket which is the only mode allowed in qr. For daily traveller's, they need to buy token/card which is NFC based.