toaste_

Respects maintainers time: DSFG uses LLM to automate reviews. A human reviewer reads the LLM report, the submission, and replies with the review.

Does not respect the maintainer's time: DSFG uses an LLM to automate reviews. It auto-replies to submissions. There is an appeal process to theoretically handle errors.

The crux of polite LLM use is on whom the burden of the flood of generated output falls.

If you are the consumer of your automated output, all good. Say you generate a report and read it, the burden of inaccuracy or verbose output generated in seconds falls on you. Not only does this avoid wasting other people's time, you have the power to go fix the tool to stop wasting yours.

If the output goes directly to others, the burden of correcting inaccuracy falls on them. The burden of reading it falls on them. And they usually then have to scale some wall of automation to reach a human and convince them to fix it. Users subjected to this will be rightly pissed off.

>it’s possible I have just described an honors program, or perhaps identified a new distinction between an undergraduate degree and a graduate degree.

The author is a Professor Emeritus at Princeton.

He has described an undergraduate degree at a University, and thinks it is some hypothetical artisan education program or graduate degree.

One wonders what they teach undergraduates at Princeton these days.

Get fucked? Really? As if the quality of machine-generated prose were somehow sacred?

Is Gruber only now waking up to the idea that LLM companies do not give a shit about the quality of the writing they generate? That they're disdainful of the entire concept of writing as a profession? What rock has he been under?

If you care one iota for the quality and craft of your writing, you would already recognize that any degree of AI "processing" obliterates stylistic choices. You're left with readable text, but it's fluffy, meandering, and has a cadence to the writing that screams to the reader "every second you spent on this was wasted."

So, you have DDR4?

Pick any B550 with a VRM heatsink for $90, a 5600 for $190, and you are back in business.

Get a 5070 or 9070 before GDDR supply is completely gone if you want an actual upgrade.

As for GPU price, that Vega 64 was $500 in 2018. Even a midrange GPU hasn't been $300 since the Radeon RX 480 launched a decade ago.

Reading and understanding code is more difficult than writing code.

It is significantly easier to modify code that you personally wrote, or code that you have read and understood to fix an issue in previously. This is why the maintainers of a project change slowly over time and it takes a long time for new ones to get up to speed.

All of Bun has been rewritten by a tool. In a different language that maintainers may not be fully proficient in.

Even though the rewrite was done well, and even if we assume it's functionally equivalent to the old Zig code, there will still be future issues. And ALl of the maintainers are essentially now new hires who have never seen that code in their lives.

It's not "politics" to have an ounce of sense to foresee problems in such a project as a dependency.

When Louis Rossmann thinks your communication has a problem with going on rants, it must be pretty out there.

>Research and fact-checking assistance from Claude (Anthropic).

What paper? This is slop.

No, BitNet not requiring multiplication will not put a foundational model in your pocket. It would be nice for power if tinary models had scaled, but since it requires roughly 3x the parameters of a similarly capable model, the memory bandwidth does not scale down nearly as well.

The real trick is that a classic LLM is not useful in the scenarios the author proposes. The hypothetical livestock vet is far better served by her books and a phone call to a university ag extension to confer with colleagues than an LLM disconnected from the internet that will hallucinate nonsense.