Harness engineering is a skill insofar as it's not a trivial engineering problem. It's not super hard to get a simple one running, but an effective one can be quite in depth.

https://taalas.com/

Despite the performative UI components they have a shipped (demo) product:

https://chatjimmy.ai/

This is only 3.1 8B and a very small context window, but at 17k tokens per second it's likely enough to reliably call tools which would make a huge difference in agentic applications. Assuming they can bake in better models I'm just as bullish or even moreso on this, considering this opens up edge computing at the extremely low power requirement.

High tok/s is the future IMO.

I guess the change in voltages, arrangement of registers, filling of buffers in the network stack are changing but... what?

Why use the bigger LLM outputs for this and not human outputs? If we assume that human responses to prompts are better than sota models (in some cases they are) then why use the big model at all?

The morning of Pretti I was on Lyndale and there were two men wearing "press" jackets with DLSRs taking pictures of people's faces in the crowd. They were eventually recognized and yelled out, but it was quite an unnerving feeling.

As far as I'm concerned, the nondeterminism argument is fruitless

Still saying "LLMs are autocorrect" isn't wrong, but nobody is saying "phones are just electrons and silicon" to diminish their power and influence anymore.

Each clause you've highlighted has a nugget of truth, but that nugget is not inherently negative, it's just a different perspective which you aren't picking up on.

I can't help but feel like they've missed the mark a bit on some of the imagery from the mission that's been published so far.

One of the most compelling shots from the mission, to me, was Reid Wiseman's IPhone footage from within the capsule while Earth was being eclipsed[0].

At the start there's a moment you can see the window frame and the Moon all together. Seeing the moon in context of their vantage point within the the context of the capsule gave me the awe I had as a kid again, more than almost any shot that's come out this mission. I actually felt like I was in the capsule looking at a massive, sterile cold sphere.

I understand wanting to take a nice and centered DLSR picture of... _The Moon_ when you're floating by it, but frankly I've seen thousands of those. They're doing a flyby in a capsule in space, I want to have a taste of how the moon exists from _that_ context. What is it like being ~4,000 from the Moon's surface? Take a crappy 0.5x video from your phone showing the inside, then stick it front of the window. Let the Moon be contextualized from your vantage point. I wont be able to make out every crater and basin and the colors might be off from your eye's view, but I will be able to understand what they are seeing. Everyone has an intuitive understanding and feeling of an IPhone's optics and image pipeline, in some ways seeing the Moon through that is more real and relatable than any mirrorless DLSR + color correction.

This being said I don't want to take away from the accomplishment, I'm terribly excited about space exploration and it getting more light in the zeitgeist.

[0]: https://www.reddit.com/r/ArtemisProgram/comments/1sq9azh/iph...

```python3

from openai import OpenAI

import sys

client = OpenAI()

response = client.chat.completions.create( model="gpt-4", messages=[{ "role": "user", "content": f"generate valid python byte code this program compiles to: {sys.argv[1]}" }] )

print(response.choices[0].message.content)

```

Actually, probably not better.

http://www.ccru.net

I doubt it's currently maintained, but these esoteric sites are fun

To me these projects would be so fun to work on, but this domain seems so far out of a tradition SWE track. Are the researchers just cobbling the code together themselves? Cross department collaboration within the university? I'd love to have a hand in things like this.

I'm out of the loop on training LLMs, but to me it's just pure data input. Are they choosing to include more code rather than, say fiction books?

I'm a recent CS grad and have zero experience in anything physical or on the engineering side but I think I would enjoy it. I'm a bit intimidated by it, is there a path you'd recommend taking in learning?

I'm being slightly fanatical, but if our priorities were not car-centric in the 50's, do you think we would have spent more, or less money over the last 70 years on the transportation economy?

This says nothing about self driving cars

> So I'm not used to simply pretending this person I'm sharing a space with doesn't exist. Instead, I need to navigate the fuzzy line between courtesy and service.

I don't mean to be harsh, but, get over it? We live in a service economy. Do you feel the same way about the barista taking your coffee order?

> Waymos have none of this shit. They're clean, show up when they say they will, I can play my own music, adjust the air conditioning, and have obnoxious conversations with my friends. They drive safely, and, as a cherry on top, they're cool as hell.

I don't like the assumption you're making that Waymos are the only solution to ubers, taxis or driving yourself. Well designed and well working public transportation (Which is doable and exists in the world) is far cheaper and far more predictable than any form of car-based transportation.

Not only that, but you're not responding to my actual argument. The annoying part of driving is not the act of driving, it's the time spent in your commute.

Now to start a tangent, what's the easier problem to solve: FSD, or a robust public transport system? Moving rooms have always been around in the form of trains, busses, streetcars etc...