Readit NewsAt the core of Web3 projects is the idea that one can own their identity via cryptographic proof.
Over time, this concept will find its way into many projects. Especialy into open source projects which try to make the web a better place.
For example, as soon as browsers support a DNS based on cryptographic proofs like ENS, other technologies which have URL based identities (like ActivityPub) will automatically support cryptographic identities. Which would bring it to projects like Mastodon and Bluesky automatically.
FreeHugs commented on Microsoft is integrating Copilot into Windows 11 engadget.com/microsoft-wi... · Posted by u/agomez314
The big question is if this will be a net positive or just adds more bloat.
They tried the same with Bing and failed. Bing did not see any uptick even with the new AI features:
https://gs.statcounter.com/search-engine-market-share/deskto...
My takeaway is that users are not as stupid and lazy as Microsoft thinks they are. They want a clean UI. And if they have to sign up for ChatGPT to get it, they will. ChatGPT already gained as many users as Bing has. With a clean UI, Bing might have already doubled it's userbase.
Why doesn't Microsft just remove all the clutter from Bing and enjoy the benefits of having their search engine being the default on all the Windows tablets and laptops out there? Why waste it all and make every user change their search engine to Google?
FreeHugs commented on This blog is hosted on my Android phone androidblog.a.pinggy.io/... · Posted by u/thunderbong
Why is Termux not on the Google Play Store?
FreeHugs commented on A visual book recommender nathanrooy.github.io/post... · Posted by u/squidhunter
Reminds me of https://www.literature-map.com
Which is a map of all authors in the world sorted by overlap in readership. I found some of my favorite writers by browsing it.
I wonder which approach is better suited to find something that is spot on to my interests.
When I think of my favorite books, they usually are the most popular books of their authors.
Are there any counterexamples, where an author wrote a book that is more profound than their biggest hit but got overlooked for some reason?
FreeHugs commented on Making Python faster with Rust ohadravid.github.io/posts... · Posted by u/teddykoker
Python's for loop implementation is slow, also. You can use built in utils like map() which are "native" and can be a lot faster than a for loop with a push:
https://levelup.gitconnected.com/python-performance-showdown...
I don't think it's the loop implementation. The stuff in the loop should take multiple orders of magnitude more time than the loop itself:
for poly in polygon_subset:
if np.linalg.norm(poly.center - point) < max_dist:
close_polygons.append(poly)FreeHugs commented on Making Python faster with Rust ohadravid.github.io/posts... · Posted by u/teddykoker
FreeHugs commented on Making Python faster with Rust ohadravid.github.io/posts... · Posted by u/teddykoker
One carries the entire feature set of the python runtime, the other is compiled.
The time is spent in this 3-line loop:
for poly in polygon_subset:
if np.linalg.norm(poly.center - point) < max_dist:
close_polygons.append(poly)
FreeHugs commented on Making Python faster with Rust ohadravid.github.io/posts... · Posted by u/teddykoker
The most important part of the article seems to be that this Python code is taking "an avg of 293.41ms per iteration":
def find_close_polygons(
polygon_subset: List[Polygon], point: np.array, max_dist: float
) -> List[Polygon]:
close_polygons = []
for poly in polygon_subset:
if np.linalg.norm(poly.center - point) < max_dist:
close_polygons.append(poly)
return close_polygons
use pyo3::prelude::*;
use ndarray_linalg::Norm;
use numpy::PyReadonlyArray1;
#[pyfunction]
fn find_close_polygons(
py: Python<'_>,
polygons: Vec<PyObject>,
point: PyReadonlyArray1<f64>,
max_dist: f64,
) -> PyResult<Vec<PyObject>> {
let mut close_polygons = vec![];
let point = point.as_array();
for poly in polygons {
let center = poly
.getattr(py, "center")?
.extract::<PyReadonlyArray1<f64>>(py)?
.as_array()
.to_owned();
if (center - point).norm() < max_dist {
close_polygons.push(poly)
}
}
Ok(close_polygons)
}
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