physicsgraph

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 ?

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

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

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

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...

Previous post: https://news.ycombinator.com/item?id=25607809

The training data used [0] is written using "leaps of logic" which provide context for how the solution was derived. There isn't sufficient detail in the training data to formally check each step using a Computer Algebra System [1]. Therefore, whether or not any output from the OpenAI software is actually correct is left as an exercise for the user. Answering "Was the hallucination correct?" involves manually checking each step.

This advancement is good, but it's limited by the precision of the training data.

[0] https://artofproblemsolving.com/wiki/index.php/2015_AIME_II_...

[1] https://en.wikipedia.org/wiki/Computer_algebra_system

I agree that Approval voting is easier to understand and implement. I got to experience this a few years ago when I tried to implement RCV for a comparison with Approval voting.

I've been active in reforming voting systems since I think that makes a difference to how politics works.

Is there a path or actions that address class issues other than the standard "run for office" and "vote for people who will address class-related issues"?

A useful counter-balance perspective is provided by [0] Tyranny of Structurelessness.

[0] https://www.jofreeman.com/joreen/tyranny.htm

This post is well-timed.

I'm aware of many previous posts on HNews and the main documentation [0]. Does anyone have suggestions for getting started from a non-CompSci perspective? I am comfortable with Python but I'm not confident that suffices.

0: http://lamport.azurewebsites.net/tla/hyperbook.html