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Start all of your commands with a comma

https://rhodesmill.org/brandon/2009/commands-with-comma/
99•theblazehen•2d ago•22 comments

OpenCiv3: Open-source, cross-platform reimagining of Civilization III

https://openciv3.org/
654•klaussilveira•13h ago•189 comments

The Waymo World Model

https://waymo.com/blog/2026/02/the-waymo-world-model-a-new-frontier-for-autonomous-driving-simula...
944•xnx•19h ago•549 comments

How we made geo joins 400× faster with H3 indexes

https://floedb.ai/blog/how-we-made-geo-joins-400-faster-with-h3-indexes
119•matheusalmeida•2d ago•29 comments

What Is Ruliology?

https://writings.stephenwolfram.com/2026/01/what-is-ruliology/
38•helloplanets•4d ago•38 comments

Unseen Footage of Atari Battlezone Arcade Cabinet Production

https://arcadeblogger.com/2026/02/02/unseen-footage-of-atari-battlezone-cabinet-production/
47•videotopia•4d ago•1 comments

Show HN: Look Ma, No Linux: Shell, App Installer, Vi, Cc on ESP32-S3 / BreezyBox

https://github.com/valdanylchuk/breezydemo
227•isitcontent•14h ago•25 comments

Jeffrey Snover: "Welcome to the Room"

https://www.jsnover.com/blog/2026/02/01/welcome-to-the-room/
13•kaonwarb•3d ago•17 comments

Monty: A minimal, secure Python interpreter written in Rust for use by AI

https://github.com/pydantic/monty
219•dmpetrov•14h ago•113 comments

Show HN: I spent 4 years building a UI design tool with only the features I use

https://vecti.com
327•vecti•16h ago•143 comments

Sheldon Brown's Bicycle Technical Info

https://www.sheldonbrown.com/
378•ostacke•19h ago•94 comments

Hackers (1995) Animated Experience

https://hackers-1995.vercel.app/
487•todsacerdoti•21h ago•240 comments

Microsoft open-sources LiteBox, a security-focused library OS

https://github.com/microsoft/litebox
359•aktau•20h ago•181 comments

Show HN: If you lose your memory, how to regain access to your computer?

https://eljojo.github.io/rememory/
286•eljojo•16h ago•167 comments

An Update on Heroku

https://www.heroku.com/blog/an-update-on-heroku/
409•lstoll•20h ago•275 comments

Vocal Guide – belt sing without killing yourself

https://jesperordrup.github.io/vocal-guide/
21•jesperordrup•4h ago•12 comments

Dark Alley Mathematics

https://blog.szczepan.org/blog/three-points/
87•quibono•4d ago•21 comments

PC Floppy Copy Protection: Vault Prolok

https://martypc.blogspot.com/2024/09/pc-floppy-copy-protection-vault-prolok.html
59•kmm•5d ago•4 comments

Where did all the starships go?

https://www.datawrapper.de/blog/science-fiction-decline
3•speckx•3d ago•2 comments

Delimited Continuations vs. Lwt for Threads

https://mirageos.org/blog/delimcc-vs-lwt
31•romes•4d ago•3 comments

How to effectively write quality code with AI

https://heidenstedt.org/posts/2026/how-to-effectively-write-quality-code-with-ai/
250•i5heu•16h ago•194 comments

Was Benoit Mandelbrot a hedgehog or a fox?

https://arxiv.org/abs/2602.01122
15•bikenaga•3d ago•3 comments

Introducing the Developer Knowledge API and MCP Server

https://developers.googleblog.com/introducing-the-developer-knowledge-api-and-mcp-server/
56•gfortaine•11h ago•23 comments

I now assume that all ads on Apple news are scams

https://kirkville.com/i-now-assume-that-all-ads-on-apple-news-are-scams/
1062•cdrnsf•23h ago•444 comments

Why I Joined OpenAI

https://www.brendangregg.com/blog/2026-02-07/why-i-joined-openai.html
144•SerCe•9h ago•133 comments

Learning from context is harder than we thought

https://hy.tencent.com/research/100025?langVersion=en
180•limoce•3d ago•97 comments

Understanding Neural Network, Visually

https://visualrambling.space/neural-network/
287•surprisetalk•3d ago•41 comments

I spent 5 years in DevOps – Solutions engineering gave me what I was missing

https://infisical.com/blog/devops-to-solutions-engineering
147•vmatsiiako•18h ago•67 comments

Show HN: R3forth, a ColorForth-inspired language with a tiny VM

https://github.com/phreda4/r3
72•phreda4•13h ago•14 comments

Female Asian Elephant Calf Born at the Smithsonian National Zoo

https://www.si.edu/newsdesk/releases/female-asian-elephant-calf-born-smithsonians-national-zoo-an...
29•gmays•9h ago•12 comments
Open in hackernews

Show HN: High-resolution surface analysis with Lidar data

https://github.com/r-follador/delta-relief
66•folli•8mo ago

Comments

schobi•8mo ago
Thanks for sharing!

The description confused me, as it describes the use of a real Lidar measurements to detect "change" in the terrain. But certainly, it can't be a temporal change before and after... to detect medieval settings in the data. Is the area still changing differentlybetween scans over multi year's? I don't think so.

I think this is visualization code highlighting natural VS. human train structures, at known locations of old settlements? Showing different approaches on how to visualize the man-made heights in the terrain.

But still, I'm lost how this could help finding new ones..

folli•8mo ago
I think the examples should make it more clear. Thanks to the high resolution of the data, you can see subtle changes in the slope (aka relief aka microtopography) that could hint to underlying remains of human settlements (usually some suspicious geometric patterns that you would not expect in a natural terrain).

See also here for an in-depth discussion on the potential use of such data: https://www.mdpi.com/2072-4292/15/6/1569

How do you suggest to change the description to make it less confusing?

folli•8mo ago
Here's another article about the use of such data in South America: https://www.nationalgeographic.com/history/article/maya-lase...

Of course, nothing so exciting to be discovered in Switzerland anymore ;)

weinzierl•8mo ago
"Buildings and vegetation are removed, revealing the underlying topography."

I understand how vegetation could be removed, but buildings? How is that accomplished?

folli•8mo ago
SwissTopo has a separate dataset of buildings and structures in Switzerland, so they basically just subtract it from the LiDAR data.
Geo_ge•8mo ago
A raw point cloud is run through a series of processing steps to label each point with a class, e.g. "Ground", "Low/Medium/High Vegetation", "Building", "Transmission Tower", etc.

https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-...

There will be a different algorithm for each feature class. For example, points that are part of a building might be identified by finding groups of points that form a very flat surface. ML models can also do this based on training data.

https://pro.arcgis.com/en/pro-app/latest/tool-reference/3d-a...

The final digital elevation model (DEM) is then just taking the "Ground" class from the classified point cloud and using them to triangulate a surface. This differs from a digital surface model (DSM), which will triangulate a surface based on ground+building+vegetation points.

thebruce87m•8mo ago
Removing vegetation seems like a harder problem than buildings. Buildings generally have cuboids and other standard shapes, but how do you determine the difference between small trees, big trees, bracken etc?

It the Scotland we have heather that can coat hills but I’m not sure that you’d be able to tell the difference between that and a forest canopy to assume a height and then subtract. Maybe there’s more than the point cloud to work with.

Geo_ge•8mo ago
Aerial survey LiDAR can process multiple returns from a single laser pulse. So, some energy might be reflected back from a leaf, but some energy will pass through (or around) the leaf, hit the ground, then reflect back to the sensor. Some systems can record 5+ points from a single laser pulse.

With this information, you can filter the point cloud to only include points from the final return, which is likely to be the ground/a solid surface unless the vegetation is very dense.

0_____0•8mo ago
You don't even need multireturn, typically your point cloud will have points from the tree or whatever plus some that returned from the structure behind it.
lyu07282•8mo ago
> LiDAR has some interesting use cases in archaeology (Caspari, 2023), particularly for uncovering man-made structures that are hidden beneath vegetation or subtle terrain changes. It allows archaeologists to identify features such as ancient roads, walls, building foundations, and agricultural terraces that may be invisible to the naked eye or conventional aerial photography.

Wasn't this also how the cities in the Amazon were discovered as well? These maps are fascinating. I can see ancient structures everywhere! Then again I'm not a trained archeologist.