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Take a look at these templates I made a while back https://github.com/subidit/rover-resume

I tried to avoid custom commands and environments to keep it simple. Your content in org text should fit nicely with this.

It also has a template where the preamble is stored in different file such that you can try a different look by just un/commenting a different preamble file.


i've used rover resume before. thank you!

I remember watching it on YouTube. Really liked the book design. How expensive/difficult is it to have each page in different colour? Tell me more about the page quality. Also it looked like "product sans" at first glance, if yes then how is it licensed? Why didn't you choose flat binding?

Anyway congratulations on the book release. It's really a beautiful creation and worthy of being an art piece.


Thank you so much! Love your questions.

> How expensive/difficult is it to have each page in different colour? The production was very specific to our design needs. For this edition, each book cost me more than $65 to produce.

> it looked like "product sans" at first glance We have licensed all our fonts which are: ABC White, Affairs and IBM Plex Mono.

> Tell me more about the page quality The paper is Fedrigoni Arena Rough Extra White

> Why didn't you choose flat binding? This question is great! We wanted to have that characteristic curve of opened books. It was very much a thoughtful decision.


I made a list of most popular monospaced fonts on hacker news[1].

[1] https://github.com/subidit/typophilia?tab=readme-ov-file#fon...


Since the repo description says "Awesome list" (and it is, in fact, awesome!) might I recommend tagging it with https://github.com/topics/awesome so folks not lucky enough to be in this thread can also enjoy your content?


Glad that you liked it. Will do that in future. I'm still trying to organise the info in a meaningful manner without being overwhelming.

My intent is to make a collection of just the impactful players (influential typefaces, designers and foundries) in a concise manner. I have many other lists in the pipeline (other files in the repo). Finding it really difficult to be objective/professional about (some silly) typeface when writing about it. Although I don't want to put my opinions on a list about fonts but still feel so strongly about it that most descriptions are still left blank. Hopefully I'll be able to compile a (not so long) list about fonts very soon.


Wow, thank you for the comprehensive work.


Simple tweaks like adjusting colors or fonts can make a big difference in the look and feel of the resume. I also chose not to use `fontspec` to avoid xelatex etc. There are a few [more templates showing other possibilities](https://github.com/subidit/rover-resume).

I crafted the options for formatting with LaTeX enthusiasts in mind, anticipating their desire to personalize the layout effortlessly. But not many people are actually modifying anything.

I thought to include working code snippets showing how to achive a particular formatting, like different ways to format SKILLS section or the NAME and contract information header. But feel that would make it look more messy. Ultimately ended up dumping those snippets in a `user-guide.md` file. I feel it could be better organised. Please advise. Thanks.


This makes sense but I feel there might be an opportunity for people who knows how to train compact versions of these LLMs which run on local machines and solve a very specific use case for that business.

What could be a bootcamp like curriculum for someone who wants to learn only how to train a LLM (upto a sellable standard)?


I want to say “go back in time and start a PhD program 7 years ago” in that in a PhD program, no matter what the subject, you learn to solve problems on your own.

Because it is such a new thing I don’t think there is any curriculum available, what you really can do is “learning through doing” and also reading papers both to get some idea of what people are doing (I like the papers where run of the mill researchers solve run of the mill problems because that gives me an idea of what to expect with my run of the mill problem) and also to pick out results to try to replicate.

My best advice right now is that it is important to scale down to something that lets you do a lot of experiments. For instance I have a classifier that takes about 30 seconds to train and I can rapidly iterate on it because I can run it 1000s of times a day. I have another that takes 30 minutes and I am loathe to put effort into it because it so easily becomes a tarpit. Thus start with something that gets meaningful results in a minimal time, get really comfortable with it, then scale it up deliberately until it is large enough. Feel free to send me an email if you want to talk more.


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