Searching through millions of points in an instant
The Glimmer — thefugue.space
A brief history of my attempts to bring Spatial Computing to the wider audience..
Rome2Rio
Discover how to get anywhere by searching plane, train, bus, car and ferry routes
Worldbuilding with Spatial Intelligence
The next stage in the development of AIs is to give them spatial intelligence.
The Powerful AI Tool That Cops (or Stalkers) Can Use to Geolocate Photos in Seconds
For months members of the public have been using GeoSpy, a tool trained on millions of images that can find the location a photo was taken based on soil, architecture, and more. It's GeoGuesser at scale.
Tips and Tricks for Bluesky Search - Bluesky
Let’s dive into all the tips and tricks for advanced Bluesky search!
GitHub - clarisma/geodesk: Fast and storage-efficient spatial database engine for OpenStreetMap data
Fast and storage-efficient spatial database engine for OpenStreetMap data - clarisma/geodesk
Spacedrive — A local-first data engine for everything you own
Index any data source. Search everything from one place. Keep it on your machine.
Index Network
You know that moment when you meet the right person and your next move clicks into place? That sense of magic? You shouldn't have to wait for luck. Index helps your others find you.
Index Network
You know that moment when you meet the right person and your next move clicks into place? That sense of magic? You shouldn't have to wait for luck. Index helps your others find you.
Spatial Data Science
Data science is concerned with finding answers to questions on the basis of available data, and communicating that effort. Besides showing the results, this communication involves sharing the data used, but also exposing the path that led to the answers in a comprehensive and reproducible way. It also acknowledges the fact that available data may not be sufficient to answer questions, and that any answers are conditional on the data collection or sampling protocols employed.
CompactLTJ: Space & Time Efficient Leapfrog Triejoin on Graph Databases
Leapfrog Triejoin (LTJ) is arguably the most practical and popular worst-case-optimal (wco) algorithm for solving basic graph patterns in graph databases. Its main drawback is that it needs the database triples (subject, predicate, object) represented as paths in a trie, for each of the six orders of subject, predicate, and object. The resulting blowup in space makes most systems disregard LTJ or implement it only partially, which makes their corresponding algorithms non-wco. In this paper we show that, by using compact data structures, it is possible to build an index that at the same time matches the query time performance of the fastest classic wco index, and uses a fraction of the space of non-wco indices (which are much slower). Concretely, we make use of compact tree representations to store functional tries using one bit per trie edge, instead of one pointer, and further reduce the space by storing partial tries. Our most compact variant uses 5–6 times less space than classic wco implementations and 2–3 times less than classic non-wco systems. At solving queries, it is on par with the fastest classic wco system, and 30–40 times faster than non-wco systems. We further incorporate improved query resolution strategies into CompactLTJ variants, which makes it considerably faster than classic wco systems as well, on queries that do not output too many results. Finally, we show how CompactLTJ can incorporate dynamism without altering its performance, even under very demanding update regimes. We leave a public fully-functional implementation of CompactLTJ that can be directly used by practitioners.
Fast regex search: indexing text for agent tools · Cursor
How we're building indexes for regular expression search so agents can find text in large monorepos without the 15-second ripgrep waits.
