Surely for most the desire is just an LLM provider that doesnt store or sell their queries (including by national actors). As long as that is allowed to happen surely its the answer for the vast majority.
Surely its the same hierarchy as before. For most complex things you start with high level to get something running quickly then move towards low level when you bed down the spec and need more safety, error reporting, speed etc.
Yeah its huge demand upswing from the growth of openclaw and similar pushing resources. Very clear from recent changes and announcement around this [0]
Fwiw there are worse delays from second tier providers like moonshot's kimik2.5 that are also popular for agentic use.
Its a well researched area. My understanding is for most use cases and data like this R trees outperform as bounding box comparisons are fast to run and the bounding boxes tend to be well organised to chunk data efficiently. H3 is a looser area and you may find lots of your points are clustered in a few grids so you end up doing more expensive detailed intersection calculations. Of course it all depends a little on your data, use case and to some extent the parameters chosen for the spatial index. But I think safe to say now based on industry experience that r trees do a very good job 99.9% of the time.
You can of course also use h3 in postgis directly as well as r trees. Its helps significantly for heatmap creation and sometimes for neighbourhood searches.
For use cases like this - long term geospatial people still use postgis as foundational - mainly for its speed at scale and spatial indexing.
For the wider tech world - I would say postgres suffers from being "old tech" and somewhat "monolithic". There have been a lot of trends against it (e.g. nosql, fleeing the monolith, data lakes). But also more practically for a lot of businesses geospatial is not their primary focus - they bring other tech stacks so something like postgis can seem like duplication if they already use another database, data storage format or data processing pipeline. Also some of the proliferation of other software and file formats have made some uses cases easier without postgis.
Really Id say the most common path ive seen for people who dont have an explicit geospatial background who are starting to implement it is to avoid postgis until it becomes absolutely clear that they need it.
Agree with this. They are re-solving a problem that has been solved better by others before (with R-trees).
They may well be using some data storage where spatial indexing is not possible or standard. Geoparquet is a common one now - a great format in many ways but spatial indexing isnt there.
Postgres may be out of fashion but still an old fashioned postgis server is the simplest solution sometimes.
Or just do what the Japanese do - remove unlimited (and overnight) on-street parking in urban areas and require anyone owning a car to prove they have a private parking spot to house it
Looks great. I wish there was similar advancement for full 3d tiles. The only real option at the moment is cesiums 3d tiles format which is nowhere near as fast as it could/should be
The other side is lack of colourfast pigments back then. Underlayers would be cheap and colourfast. Top layers would usually be more expensive and deteriorate much more quickly.
If you read the rhetoric it is not about removing commercial exploitation of children. It is about removing child bullying, grooming and algorithms that lead to things like misogynist content and eating disorders.
I generally agree with parent commenter - some of this will be helped by the ban but theres a serious risk a small number will go through fringe social media even less policed or normalised than the big American ones and have much higher risk on some of these issues than before.
I dont think youd get less rich-people-friendly decisions from ccongress. It may well be the opposite. Certainly it removes some of the separation of powers.
One view is that the western idea of "good taste" was informed by people looking at greek and roman statues and buildings and incorrectly assuming they were always intended to be plain.
I can see it for programmers. Here you can use industry standard python libraries (shapely, geopandas etc.). Nobody really wants to learn PyQGIS (the python interface for qgis). So while qgis is much more full featured for "desktop" gis (designed to compete with esri arcgis) i can see the use case here for people who want to build their own extensions and port code from this to other python projects more easily.
A huge part of poorly designed roads is wider lanes (and parking spaces) that allow/encourage huge cars. Its been proven that narrower lanes correlate strongly with lower crash and fatality rates (e.g. [0] below) yet lane widths are under pressure to increase with larger vehicles, and every time this happens the vehicles get larger again.