我是「谁是藏镜人」,专注于挖掘 AI 在工程和物理世界演进的底层逻辑。
这里的解读不只有新闻,还有背后的工程深度。点个关注 @VedaAI00,带你看清 AI 的下一步。
原推传送门:teapawt.net/synabreu/status/209655…
Hi Friends, for my first project created with GPT-6 Astra, I built the entire city of Seoul in 3D—a small revolution in digital geography! 😄 This interactive 3D miniature covers all of Seoul and its surrounding areas, and you can explore it yourself. It includes:
• All 25 districts of Seoul and approximately 267,000 simplified building models
• Rotate, zoom, and pan controls, plus an automated fly-through of 14 landmarks
• Day, sunset, and night modes
• Mobile touch support
The buildings were generated from real-world map data, with their shapes simplified for performance. Terrain and building heights are exaggerated by 4× to make the cityscape easier to see.
The entire process—from creating the project to completing deployment—took 43 minutes and 38 seconds minutes.
Please note that the buildings are simplified models based on locations and footprints recorded in OpenStreetMap. Where height data is unavailable, estimated heights derived from the source data are used. The landmark models are interpretive recreations designed to emphasize their distinctive features, rather than survey-grade or photorealistic reconstructions of every building.
The project is based on real road, waterway, elevation, and building data.
• Maps and buildings: OpenFreeMap / OpenMapTiles / © OpenStreetMap contributors · ODbL
Data snapshot: August 30, 2026
• Terrain: AWS Terrain Tiles / Mapzen · SRTM and other sources
• District boundaries: southkorea/seoul-maps · Statistics Korea’s 2013 boundaries
These may differ from Seoul’s current administrative boundaries.
• 3D rendering: Three.js
• Additional components: terrain, road, and building data, subject to their respective licenses
seoul-3d-atlas.synabreu.chat…
我是「谁是藏镜人」,专注于挖掘 AI 在工程和物理世界演进的底层逻辑。
这里的解读不只有新闻,还有背后的工程深度。点个关注 @VedaAI00,带你看清 AI 的下一步。
原推传送门:teapawt.net/ashebytes/status/20962…
我是「谁是藏镜人」,专注于挖掘 AI 在工程和物理世界演进的底层逻辑。
这里的解读不只有新闻,还有背后的工程深度。点个关注 @VedaAI00,带你看清 AI 的下一步。
原推传送门:teapawt.net/thelacoast/status/2096…
I've updated and cleaned up my Starbase LA permit map to better visualize the regulatory filing activity since the Spacex annoucement. 3 total permits filed so far.
The 20 square points on the map represent proposed soil borings to depths of 100'-230'. This permit is approved and work is ongoing. (Originally there were 25 borings permitted, but the permit was later amended to just 20. There was also a second permit filed for additional borings, but it was cancelled the next day).
The 19 small white dots on the map represent proposed test pits included in the 3rd permit application. This one was filed last week and is pending approval. It's different from the first 2 permits as it involves bucket excavation and sampling to a depth of just 10 feet.
No actual construction permits have been applied for yet. This is all for planning and design.
In the map, you can clearly see a potential construction pattern and I've color coded those accordingly. Although this map is currently updated, I've seen these plot locations change before and they may change again. It does at least give you another sense of scale for this infrastructure. It also shows how they are having to work around the private land.
Final note - I've seen plenty of X posts talking about the dredge barges and support equipment currently in Freshwater Bayou. I've mentioned this before, but it has nothing to do with this project. The bayou south of the locks is constantly silting in and the U.S. Army Corps of Engineers conducts periodic dredging to maintain the 16 ft required depths. They do this every few years and now they are doing it again. Dredged material is pumped into the gulf on the west side of the channel (always west side due to the counter clockwise gulf loop current so it doesn't fall back in). It's not going on Starbase. I'll probably do another post explaining this since it is getting so much attention.
我是「谁是藏镜人」,专注于挖掘 AI 在工程和物理世界演进的底层逻辑。
这里的解读不只有新闻,还有背后的工程深度。点个关注 @VedaAI00,带你看清 AI 的下一步。
原推传送门:teapawt.net/matthewwmullin/status/…
Thanks to LLMS, we are living through the Golden Age of custom geospacial applications.
Currently working on a project to combine the raw 1 m airborne LIDAR datasets from the @USGS with the 25 m @NASA GEDI waveform data which will provide forest height & density. Data fusion!
Working with Codex/Astra... and resuming when weekly credits reset.
LIDAR datasets are huge, the seamless USGS 1 m coverage can range between 200 GB to 1 TB + depending on the state.
Check out amazing work from the researchers, scientists, engineers and pilots over at the United States Geological Service:
usgs.gov/3d-elevation-progra…
我是「谁是藏镜人」,专注于挖掘 AI 在工程和物理世界演进的底层逻辑。
这里的解读不只有新闻,还有背后的工程深度。点个关注 @VedaAI00,带你看清 AI 的下一步。
原推传送门:teapawt.net/matthewwmullin/status/…
Thanks to LLMS, we are living through the Golden Age of custom geospacial applications.
Currently working on a project to combine the raw 1 m airborne LIDAR datasets from the @USGS with the 25 m @NASA GEDI waveform data which will provide forest height & density. Data fusion!
Working with Codex/Astra... and resuming when weekly credits reset.
LIDAR datasets are huge, the seamless USGS 1 m coverage can range between 200 GB to 1 TB + depending on the state.
Check out amazing work from the researchers, scientists, engineers and pilots over at the United States Geological Service:
usgs.gov/3d-elevation-progra…
我是「谁是藏镜人」,专注于挖掘 AI 在工程和物理世界演进的底层逻辑。
这里的解读不只有新闻,还有背后的工程深度。点个关注 @VedaAI00,带你看清 AI 的下一步。
原帖:teapawt.net/TheHumanoidHub/status/…
Humanoid robots can now play tennis in the style of Nadal, Federer, and Djokovic, learned from broadcast footage.
AdaPT (Noitom Robotics, Shanghai AI Lab, Dobot, SJTU) extracts professional player motions from TV broadcasts, retargets them to a humanoid, and learns both rallying and serving while preserving each player's distinctive style.
The core problem: decoupled planning-and-tracking works in sim but degrades badly on real hardware. Their fix is speed adaptation, training the tracker on randomized execution speeds while the planner learns to adjust motion speed to the incoming ball.
Deployed on Unitree G1 and the full-size Dobot Atom.
Worth noting on the sensing: the robot has no onboard vision. Ball and robot position both come from external infrastructure, a 35-camera motion-capture arena in the lab.
Paper: humanoidtennis.github.io/Ada…







