stoneyhrm1

"Pass the salt? You mean pass the sodium chloride?"

I'm free to be corrected because I'm no expert in the field but isn't RAG just enriching context, it doesn't have to be semantic search, it could be an API call or grabbing info from a database.

Like it or not vibe coding is here to stay, I too don't agree with the concept but have told people in my org that I've 'vibe coded' this or 'vibe coded' that. To us it just means we used AI to write most of the code.

I would never have it be put into production without any type of review though, it's more for "I vibe coded this cool app, take a look, maybe this can be something bigger..."

The LLM endpoint via ollama or huggingface is not the one executing MCP tool calls, that is on behalf of the client that is interacting with the LLM. All the LLM does is take input as a prompt and produce a text output, that's it. Anything else is just a wrapper.

I understand the concern here but isn't this the same as making any other type of server public? This is just regarding servers hosting LLMs, which I wouldn't even consider a huge security concern vs hosting a should-be-internal tool publicly.

Servers that shouldn't be made public are made public, a cyber tale as old as time.

What exactly is being moved? It's trained on human data, you can't make code more perfect than what is written out there by a human.

> revolutionary breakthroughs in essentially all field

This doesn't really make sense outside computers. Since AI would be training itself, it needs to have the right answers, but as of now it doesn't really interact with the physical world. The most it could do is write code, and check things that have no room for interpretation, like speed, latency, percentage of errors, exceptions, etc.

But, what other fields would it do this in? How can it makes strives in biology, it can't dissect animals, it can't figure more out about plants that humans feed into the training data. Regarding math, math is human-defined. Humans said "addition does this", "this symbol means that", etc.

I just don't understand how AI could ever surpass anything human known before we live by the rules defined by us.

I'd think it would be more autistic to continue to use and have interest in something that's been superseded by something far more easier and efficient.

Who would you think is weirder, the person still obsessed with horse & buggies, or the person obsessed with cars?

I understand the author's sentiment but industries don't exist solely because somebody wants them to. I mean, sure, hobbies can exist, but you won't be paid well (or even at all) to work with them.

Software engineering pays because companies want people to develop software. It pays so well because it's hard, but the coding portion is become easier. Vibe coding and AI is here to stay, the author can choose to ignore it and go preach to a dying field (specifically, writing code, not CS), or embrace it. We should be happy we no longer need to type away if and for loops 20 times and instead can focus on high level architecture.

I mean you can boil anything down to it's building blocks and make it seem like it didn't 'decide' anything. When you as a human decide something, your brain and it's neurons just made some connections with an output signal sent to other parts that resulting in your body 'doing' something.

I don't think LLMs are sentient or any bullshit like that, but I do think people are too quick to write them off before really thinking about how a nn 'knows things' similar to how a human 'knows' things, it is trained and reacts to inputs and outputs. The body is just far more complex.

I thought this too but then began to think about it from the perspective of the programmers trying to make it imitate human learning. That's what a nn is trying to do at the end of the day, and in the same way I train myself by reading problems and solutions, or learning vocab at a young age, it does so by tuning billions of parameters.

I think these models do learn similarly. What does it even mean to reason? Your brain knows certain things so it comes to certain conclusions, but it only knows those things because it was ''trained'' on those things.

I reason my car will crash if I go 120 mph on the other side of the road because previously I have 'seen' where the input is a car going 120mph has a high probability of producing a crash, and similarly have seen input where the car is going on the other side of the road, producing a crash. Combining the two would tell me it's a high probability.

Spoken like a true project manager that every engineer hates.

> "good catch, I'll fix that"

I see this a lot and even done so myself, I think a lot of people in the industry are a bit too socially-aware and think if they start a discussion they look like they're trying too hard.

It's stupid yes, but plenty of times I've started discussions only to be brushed off or not even replied to, and I believed it was because my responses were too long and nobody actually cared.