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Find something that other people would see as infeasible or inaccessible and work on that. You are in a position of privilege that can be used to advance well-being and knowledge.

Deciding whether to explore challenges solo versus as part of a team is a crucial differentiator.


To summarize the main two take-aways, measure ROI (return on investment) and be Agile.


Have you searched through https://hn.algolia.com/?q=ask+hn+blogs ?


That's perfect, thanks!


Sounds interesting, but the github repo doesn't seem to have enough README content to explain who should use the project or how to use the project.


The original blob post [0] referenced hypergraphs and other more complicated structures like property graphs.

[0] https://news.ycombinator.com/item?id=39592444


Which can all be represented with Incidence Matrices:

https://en.wikipedia.org/wiki/Incidence_matrix


A (set) relation with N tuples is isomorphic to a N-dimensional boolean tensor. The mapping is to interpret each tuple as a coordinate in the tensor, and set the entry in that tensor to 1.


The back-and-forth exchange between blogs, each with comment threads on HN, is a great use of the Internet.


Especially because both blogs and HN comments can be linked to.


Hypertext !


Seriously, so cool. I love watching my fellow nerds debate data types online.

On a side note, is there a Bitter Lesson for datatypes, the way there is for algorithms?


Probably some variation of "hardware usually influences what the optimal datatype is way more than any theoretical runtime differences". For example, B-trees for databases needing to be adapted to the block size of the underlying hardware device. Another example: As soon as you introduce any form pointer chasing to your datatype, you are often going to struggle against relatively simple array-based options because L1 cache is crazy fast compared to RAM.


Wow, great point! Thanks for the reply; I did not even think of hardware at the time of posting.

I will look into form pointer chasing. Any good reads that you would recommend for software devs who are interested in electrical engineering or rather understanding how to build hardware?


I agree, we should invent the World Wide Web again.


This ecosystem used to be called the Blogosphere


Thanks for the suggestion. I'm new to running LLMs so I'll take a look at your suggestion [0]. My ~10 year old MacBook Air has 4GB of RAM, so I'm primarily interested in smaller LLMs.

[0] https://github.com/ggerganov/llama.cpp


You don't necessarily need to fit the model all in memory – llama.cpp supports mmaping the model directly from disk in some cases. Naturally inference speed will be affected.


Your assessment is exactly correct -- the blog post is my note-to-self about getting the repo to work. My "added value" in the post is a Dockerfile for ease of installation.


The premise towards 1-on-1s that "regular 1-on-1 meetings often feel like a burden and overhead" indicates the value of 1-on-1s isn't clear to the author. The 1-on-1s are an opportunity for supervisors to learn about the long-term goals of their employees.

The author's content presents short-term oriented assessment more appropriate for team syncs.

I've collected questions to ask in 1-on-1s here [0].

[0] https://graphthinking.blogspot.com/2022/10/one-on-one-questi...


>the value of 1-on-1s isn't clear to the author

Maybe if you read the rest of the article, you'd realize the author does understand the value of the 1-on-1, and is presenting an introduction based on his reader's [likely] misapprehension of their value (and the [anecdotally observed] fact that most people do feel they are pointless and/or drudgery)



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