735 karma · joined July 9, 2015
Perhaps the greatest benefit I've found in using logseq is in organizing the scientific literature I'm reading. At any point while writing, linking a paper I've read (the PDF itself, as well as my notes on it) is a few keystrokes away. This linking allows me to externalize the context with which I know a paper. I just need to browse the backlinks to see all the ways in which I've made reference to it over the entire history of my notes.
This is really cool. I wonder how difficult it would be to allow users to wire this up to arbitrary user-specified VPNs e.g. through WireGuard.
Is there a standard way of describing bugs? What resources/standards have people found useful to refer to when reporting one?
Logseq does not currently have templates integrated the way Notion does, but it wouldn't be difficult to programmatically create them since they're just plain markdown files. There's a plugin ecosystem for logseq now, so I think it's just a matter of time before someone writes one to generate templates.
[0]: https://logseq.com/
If you're in a similar boat, I would recommend checking the framework out. Lua's a pleasure to program in and you can focus on the game development itself instead of getting bogged down in the details of rust / cpp. In fact I've been thinking lately about how easy it would be to use it for things other than games -- quick prototyping of graphical simulations, psychophysics experiments, etc.
[0]: https://love2d.org/
While I think this is technically true, academics care a great deal about pedigree, in the “he/she went to $FAMOUSLAB, they must be a great scientist” kind of way. The most productive, well-funded, and well-known labs tend to be at the top institutions. I wish it wasn’t this way, but that’s how it is. If you want to maximize your chances of being successful in academia post-PhD, then you have to go to one of these places. For industry, I don’t think it matters as much.
[0]: https://www.reddit.com/r/GrapheneOS/comments/bddq5u/os_secur...
[0]: https://www.biorxiv.org/content/10.1101/214262v1.abstract
[0]: https://stevenson.lab.uconn.edu/scaling/ [1]: https://www.nature.com/articles/nn.3776 [2]: https://doi.org/10.1016/j.conb.2015.04.003 [3]: https://doi.org/10.1016/j.conb.2019.02.002 [4]: https://arxiv.org/abs/2104.00145 [5]: https://doi.org/10.1016/j.neuron.2017.05.025