zodzedzi

I have a new lightweight laptop with 16 GB of RAM, decent SSD space, and an i7 CPU.

And I would like to setup a few systems on it, to have "everything" with me as I move around while disconnected.

I'm looking for recommendations and ideas into how to set this up.

Examples of these systems would be:

a) A headless server with a dev stack: runs a build workflow, a web server, etc.

b) A stable "production" version of server (a)

c) A "desktop" dev environment: IDE, language/tools-rich

d) A "basics" desktop environment for non-tech work: office tools, maybe some media editing (even blender 3d if feeling lucky)

They won't all be running simultaneously.

I haven't worked on the OS systems and platforms level since a past life. I don't know much about virtualization/containers/etc, but I'm good with networking fundamentals, and I'm willing to learn any old/new tech.

Ideally:

1. The host OS is minimal (i.e. can only keep the core packages I need), and driver-friendly so the core hardware (compute/graphics) is efficiently accessible to each guest OS.

2. I can network the different "machines" together; so the dev desktop can use the dev server's API, and the basics desktop accesses the apps on the production server in its browser.

3. I can pull data from each system out into the host OS for data backups.

4. I can restrict network/internet access from/to any of those systems, from the host OS.

5. All systems are open-source, can work completely disconnected and do not phone home in order to function.

Thankful for any experience you can share.

My background: I'm familiar with pre-GPT machine learning and DNNs.

I read the relevant papers, went through many explanations of how transformers work.

Often those explanations spend thousands of words to explain attention at the word level, and then just say a few words about "oh and with multiple attention heads, it focuses on different aspects, and then multiple layers, and then, magic!".

What's happening in those other aspects, what are they? Are there papers that peruse what kind of concepts the model is actually building/learning in those heads and layers?

There are large teams who spend months tuning those models. Do those teams have access to those internal concepts that the model built up and organized? Is any of this work public?

In computer vision and CNNs, I recall seeing a paper once that showed that each layer of the network was learning a higher level feature than the layer before it (as an inaccurate example: first layer learns edges, second layer learns shapes, third layer texture, forth layer objects, etc, and they show you the eigenvectors of each as representatives).

E.g. I asked ChatGPT to tell me a joke about a table in a sundress in the voice of a famous stoic person. And by its response, it adequately "understands" what that person's style sounds like, basic humor, the concept of clothing and mapping that to an inanimate object (punchline: "I figured if a chair can wear a seat cushion, why can't I wear a sundress?"),...

(Obviously this is a tame example, but serves its purpose for the discussion).

Wide contribution, not driven by decisions seeking profit, etc.

Been in software engineering for more than a decade, lately in senior positions in big companies, w/ experience in large scale projects, data/machine learning, people/project/product management. The past few years became less fulfilling, and as a project I was on got cancelled I finally called it and stepped out instead of moving to another project.

It's been a few months, living off my savings, no family. I've been hacking away at some old ideas and pet projects, and eventually one will stick. For now, my end goal is a small-scale operation that pays the bills and requires minimal maintenance, and can be built by just me to start, perhaps within a year.

At big companies, there's a lot that's taken care of for you behind the scenes, and outside that environment I don't know what I don't know. Hence the question. Thanks.