Using LLMs at Oxide

I'll give it a shot.

Text, images, art, and music are all methods of expressing our internal ideas to other human beings. Our thoughts are the source, and these methods are how they are expressed. Our true goal in any form of communication is to understand the internal ideas of others.

An LLM expresses itself in all the same ways, but the source doesn't come from an individual - it comes from a giant dataset. This could be considered an expression of the aggregate thoughts of humanity, which is fine in some contexts (like retrieval of ideas and information highly represented in the data/world), but not when presented in a context of expressing the thoughts of an individual.

LLMs express the statistical summation of everyone's thoughts. It presents the mean, when what we're really interested in are the data points a couple standard deviations away from the mean. That's where all the interesting, unique, and thought provoking ideas are. Diversity is a core of the human experience.

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An interesting paradox is the use of LLMs for translation into a non-native language. LLMs are actively being used to better express an individual's ideas using words better than they can with their limited language proficiency, but for those of us on the receiving end, we interpret the expression to mirror the source and have immediate suspicions on the legitimacy of the individual's thoughts. Which is a little unfortunate for those who just want to express themselves better.

> That junior engineer possibly hasn't programmed without the tantalizing, even desperately tempting option to be assisted by an LLM.

This gives me somewhat of a knee jerk reaction.

When I started programming professionally in the 90s, the internet came of age and I remember being told "in my days, we had books and we remembered things" which of course is hilarious because today you can't possibly retain ALL the knowledge needed to be software engineer due to the sheer size of knowledge required today to produce a meaningful product. It's too big and it moves too fast.

There was this long argument that you should know things and not have to look it up all the time. Altavista was a joke, and Google was cheating.

Then syntax highlighting came around and there'd always be a guy going "yeah nah, you shouldn't need syntax highlighting to program, you screen looks like a Christmas tree".

Then we got stuff like auto-complete, and it was amazing, the amount of keystrokes we saved. That too, was seen as heresy by the purists (followed later by LSP - which many today call heresy).

That reminds me also, back in the day, people would have entire Encyclopaedia on DVDs collections. Did they use it? No. But they criticised Wikipedia for being inferior. Look at today, though.

Same thing with LLMs. Whether you use them as a powerful context based auto-complete, as a research tool faster than wikipedia and google, as rubber-duck debugger, or as a text generator -- who cares: this is today, stop talking like a fossil.

It's 2025 and junior developers can't work without LSP and LLM? It's fine. They're not in front of a 386 DX33 with 1 book of K&R C and a blue EDIT screen. They have massive challenged ahead of them, the IT world is complete shambles, and it's impossible to decipher how anything is made, even open source.

Today is today. Use all the tools at hand. Don't shame kids for using the best tools.

We should be talking about sustainability of such tools rather than what it means to use them (cf. enshittification, open source models etc.)

Writing is an expression of an individual, while code is a tool used to solve a problem or achieve a purpose.

The more examples of different types of problems being solved in similar ways present in an LLM's dataset, the better it gets at solving problems. Generally speaking, if it's a solution that works well, it gets used a lot, so "good solutions" become well represented in the dataset.

Human expression, however, is diverse by definition. The expression of the human experience is the expression of a data point on a statistical field with standard deviations the size of chasms. An expression of the mean (which is what an LLM does) goes against why we care about human expression in the first place. "Interesting" is a value closely paired with "different".

We value diversity of thought in expression, but we value efficiency of problem solving for code.

There is definitely an argument to be made that LLM usage fundamentally restrains an individual from solving unsolved problems. It also doesn't consider the question of "where do we get more data from".

>the code you actually want to ship is so far from what LLMs write

I think this is a fairly common consensus, and my understanding is the reason for this issue is limited context window.

Medication is littered with warning labels but humans still use it to combat illness. Social media can harm mental health yet people still use it. Pick whatever other example you'd like.

There are things in life that have high risks of harm if misused yet people still use them because there are great benefits when carefully used. Being aware of the risks is the key to using something that can be harmful, safely.

There was a comment recently by HN's most enthusiastic LLM cheerleader, Simon Willison, that I stopped reading almost immediately (before seeing who posted it), because it exuded the slop stench of an LLM: https://news.ycombinator.com/item?id=46011877

However, I was surprised to see that when someone (not me) accused him of using an LLM to write his comment, he flatly denied it: https://news.ycombinator.com/item?id=46011964

Which I guess means (assuming he isn't lying) if you spend too much time interacting with LLMs, you eventually resemble one.