gimili

I love the word "meat proxy" :)

For me what matters most is whether the person understood the core of the thing they are forwarding. Usually a wall of text is contraindicative of that.

I want my team to be able to write the core idea on a whiteboard without any help before going ahead and sharing or implementing it.

Wrote it up here of when (instead of if) to use AI in any given context: https://assistedeverything.substack.com/p/ai-bowtie

Thinking about a similar question, I've tried to come up with this framework: https://assistedeverything.substack.com/p/ai-bowtie Maybe it helps in your interviews.

I think you will find that both AI-pilled as well as AI-sceptics will actually agree on that approach, because it rejects the question of "how much" and rather reflects it towards the "when to use AI". Depending on who it is, you can then talk more about the beginning, end or center piece of it.

I don't think (a) is a pure UI thing.

Think of the difference of using a single-user application to e.g. make mockups for websites or a collaborative environment like figma, in which you happen to also be able to have AI collaborate with you. Very different usecases and solving collaboration workflows, etc. is non trivial.

I guess for (c) both things will exist. Local will be done for 2 reasons: - data sovereignty (e.g. companies wanting to have applications that are purely trained/fine tuned on their own data; but that improvement is not shared) - privacy (anything from an AI having access to all your email and calendar up to having intimate "friendships" with AI)

I think it is more complex than the author thinks.

There are clearly defensible aspects for ai startups. Specifically I think these are: a) in-context and collaborative features (since working alone with ai through a chat box is unlikely the only way we will interact) b) gated knowledge/data (since commonly available technology can be leveraged with unique data) c) edge computing and offline usecases won't be the center piece for many classical companies and therefore can be very well exploited.

I wrote up a framework to assess LLM powered Startups/Ideas here: https://assistedeverything.substack.com/p/the-three-hills-mo...

I like the calculation; makes things more clear.

However, to your last point: You are saying that nuclear power plants then are insured to risk. Can you point me in your sources to the place where it is shown how nuclear power plant operators today then are insuring "to risk" the risk of nuclear disaster and all costs and risks of the disposal?

Not a hardware startup per-se, but we are ex-satellite engineers who out if frustration with available tooling have developed the engineering software every hardware engineer deserves: https://www.valispace.com

Also just released the first hardware engineering ai assistant: https://youtu.be/uLEOPpqiUok

We're taking a shot at hardware engineering: www.valispace.com/ai/

And I see similar things happening for major fields such as education, law, art, software development, management, etc. Here I looked into a few examples of what is happening in these fields already: https://assistedeverything.substack.com/p/the-age-of-assiste...

For 6 years I had a long distance relationship between Peru and Germany.

When you are in different timezones it can actually be nice to fall asleep with the other person "close" to you; so we kept Skype running while one of us went to bed and the other person was working on the PC.

Unfortunately the internet connection would regularly drop, ending the Skype call. Now you did not want to wake the other person by calling them.

So I wrote a small script that would allow you to send a secret word in the chat and invoke the other persons' Skype to call you instead automatically.

Kept our relationship healthy. Now we've been married for nearly 10 years and are happily living together :)

We're integrating GPT3/4 functionalities into our hardware engineering SAAS: https://www.valispace.com/ai/

It mainly helps with 2 things:

- allowing engineers to develop their products much faster (especially doing good requirements engineering for now)

- allowing us to demo to/onboard users with data from their specific usecase (prepopulate their trial account)

Hardware engineering at first does not seem like an obvious choice for LLMs, but I think that it will be those vertical solutions that will still surprise us all the most.

Here are some more details, how hardware design gets concretely aided by LLMs: https://assistedeverything.substack.com/p/todays-ai-sucks-at...

I found it interesting that there start to be best-practices for professors and teachers on how to deal with ChatGPT in a class room [1].

One I really liked a lot: >And if you don't trust the AI to output correct content, teach your students critical thinking by using Assisted Teaching to write history essays and let your students find mistakes in it.

Another way (if you want to go the more traditional way), is the Flipped Classroom [2] method, in which students self-teach at home and do the "homework" in the classroom.

I agree with others here that there is no way to ban these technologies; there are just better and worse ways to deal with it.

[1]: https://assistedeverything.substack.com/p/the-age-of-assiste... [2]: https://en.wikipedia.org/wiki/Flipped_classroom

In my opinion what we are seeing is the age of "Assisted Everything" [1].

I am very certain that in 2 years' time no white-collar job will be performed anymore alone in front of a PC. You will always collaborate with an assistant.

Personally this makes me super excited. As a Satellite Engineer, I always wanted to have a Jarvis-like experience and was super disappointed, when I found that the hardware engineering world was only paper-pushing excel files. Finally the future is here and engineers can focus on solving problems instead of on tasks that can be automated.

[1] https://assistedeverything.substack.com/p/the-age-of-assiste...

Lots of usecases actually need creative "hallucinations" where they are valuable.

Even e.g. to develop hardware such as planes and cars: https://assistedeverything.substack.com/p/todays-ai-sucks-at...

We're exploring Hardware Design with GPT: https://assistedeverything.substack.com/p/todays-ai-sucks-at...

Founder of Valispace here (we made WTFormula). We'd be happy to hear from you if you find our digital cheat sheet useful and how we could improve it!

Great work; this looks awesome. I am wondering what products would look like, if hardware engineers applied this to the modeling of future products. At my startup valispace.com for now we only allow for a simple propagation of worst-case values (gaussian distribution or worst-case stacking), but I think that specially for early design phases it would be of huge help and foresee problems in complex projects early on. Do you know of anyone using guesstimate for hardware engineering purposes?

Offtopic question: anybody knows what kind of software they might have used to build their slides? Some of the animations are really neat.