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Instead of using UE, I built my own Vulkan engine for full control over rendering and memory. Features: - Data-driven rendering - Preallocated arenas, no runtime heap - Cascaded shadow maps - Temporal AA Currently building a fully 3D voxel GUI. #cubeworld #vulkan

Wolfram von Funck

151,763 subscribers

448,322 次观看 • 1 年前 •via X (Twitter)

科学技术游戏 #cubeworld #vulkan

Anya Rossi• Live Now

Private livecam show

31 条评论

TBGlitch RDC2026 的头像

TBGlitch RDC20261 年前

HE LIVES

Squreek 的头像

Squreek1 年前

Cubeworld post in 2025‽

🌊Inumi Gessui🍦 | The All-Devouring Wolf【VCAN】的头像

🌊Inumi Gessui🍦 | The All-Devouring Wolf【VCAN】1 年前

Holy shit you still working on this? I mean it's cool and all that you're working on the GUI, but there are a LOT more that needs your attention. Give us back XP from mobs so we can level up, remove the region lock system for weapon/armor/specials, remove the stats debuffs when out of region (due to region lock system removal) and make it so that the further you are from spawn region the mobs become stronger (Ex: spawn region 1-10, region that share a border with spawn region 11-20 and so on) with at a certain point having the mobs scale based on player level (This will allow players to keep having challenging regions). These are the 4 things that comes to mind, but i'm sure there are more.

47 的头像

471 年前

its over bro let it go

carter 的头像

carter1 年前

So, you’ve gone from Picroma Picroma 2 UE5 and now Vulkan? Dude can you just give it up? You’ve been gaslighting me for 13 years.

HefTheNottyCoon (Commissions always open!) 的头像

HefTheNottyCoon (Commissions always open!)1 年前

Look who's back from the dead! Out of funds again, buddy?

Gent Lemann 的头像

Gent Lemann1 年前

Holy shit. You're alive?

The Open Mat Show 的头像

The Open Mat Show1 年前

Holy shit 🙏🙏😭😭

Dreadric 的头像

Dreadric1 年前

Thank you for all the work you're doing, we might complain about a lot, but we still believe in you and your work.

SAROCK🦝🧷 的头像

SAROCK🦝🧷1 年前

Cube world, my beloved ♥️ Until we couldn't explore due to the region locked gear & levels ;_;

Dshoopy 🇺🇸 的头像

Dshoopy 🇺🇸1 年前

Wollay, please bring this to completion then get into a content release cycle, that is all we ask for.

WhiteRice 的头像

WhiteRice1 年前

He lives??

©soba 的头像

©soba1 年前

its been a WHILE since i got a notification from this account, glad you’re still working on cube world, much love!!

Marty 的头像

Marty1 年前

Are you remaking cube world? Because that game was destined for greatness

Bitkoning 的头像

Bitkoning1 年前

You launched around the same time Minecraft came to life. You could have been a multi millionaire or even billionaire if you weren't a dickhead. Idiot. Accept help or stay consistent. Scammer fucker.

iRaphahell 的头像

iRaphahell1 年前

What's the expected release date? 2030-2040?

Tripalink 的头像

Tripalink1 年前

This stuff is cool. But this isn't what's needed. What's needed is game design upgrades. Can't just update the graphics and release another mess. Gotta switch out the time your spending on the graphics and research game design when your making... a game

ultimacommanderofautism 的头像

ultimacommanderofautism1 年前

how many times can he scam the same playerbase?

Sree 的头像

Sree1 年前

a lot of people won't get how insane this is. genuinely fire, and it looks so smooth too🔥

Joffrey_00 的头像

Joffrey_001 年前

Wollay mindset: make only one update in july 2013 with low content, take holidays 6 years and resold game on steam with one update, take holidays 4 years no update, take holidays 1 year no update In 2025 most steam games released and abandoned it's normal I ❤️ CW but its 💼

RathGames 的头像

RathGames1 年前

He returns once more

Elias Maks 的头像

Elias Maks1 年前

VAMO LOCOO VOLVIÓ VAAAAMOOOSS

ArkDev 的头像

ArkDev1 年前

Sounds like a nightmare

SquidyBandit 的头像

SquidyBandit1 年前

All of us here since day 1 knowing exactly how this turns out:

edward L rizz 的头像

edward L rizz1 年前

I love cube world!!!!!

AbelsFables 的头像

AbelsFables1 年前

Had high hopes for cubeworld but this point pretty sure ur just a scammer grabbing money from people. I had the original back in the day. Got the steam version which was hot garbage compared to the original and now another possible release? No thanks.

Tomcat 的头像

Tomcat1 年前

absolute mad lad

🐉 D. G. Jagielski 🐉 的头像

🐉 D. G. Jagielski 🐉1 年前

If you make this good, I'm ready to change my opinion about you and will spread the good news everywhere I can. For now though, I will be playing Veloren.

Anatolii 的头像

Anatolii1 年前

oh damn. I loved that game, played it 1-2 months non-stop when it came out. Was sad to see the development slow down at the time. Super excited to try it out again :)

The Tin Bull 的头像

The Tin Bull1 年前

Is it time to rehype an old dead product to make millions more just to run off again? So many years later and you're still "working" on rendering. You managed to scam quite a few people before, but I doubt it works again.

Sanuary 的头像

Sanuary1 年前

Was just thinking about Cube World not too long ago. Glad to see more progress! 👍

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Alex Barashkov

276,846 次观看 • 2 个月前

I spent 20 years architecting AAA game engines, going back to the PlayStation 1. I wrote all of that by hand. This one I did not. It is a stylized real time digital twin of the SF Bay Area, built from scratch in C++ and Vulkan. The geometry is real, the look is not. It is meant to read as a painted model of the Bay rather than a photo of it, because a photoreal copy of a place you can already see from a satellite is not worth building. The world is an 80 km square, about 6,400 km², and all of it is built from open data. USGS 3DEP 1 m lidar for the terrain. NOAA CoNED for the bay floor. USGS NHD for water. ESA WorldCover for land cover. OpenStreetMap for footprints and roads. That comes to 13 GB of streamed tiles, and 9.8 GB of it is terrain. Past the edge there is a 420 km coarse ring on Copernicus GLO-30, so the horizon does not end at a flat edge. Distant ridgelines bend down below it. There are 424,787 real building footprints in there, extruded procedurally, with heights off OSM tags and a plausibility gate that drops anything whose tagged height will not fit its own footprint. Across the whole 80 km box it rejects exactly 8 buildings, four of them towers carrying a bad height tag that used to extrude as needles over a derelict pier at Hunters Point. On top sit 41 landmarks at their real coordinates, authored by Fable as parametric Blender scripts rather than sculpted: Golden Gate, Transamerica, Coit, Sutro, Salesforce, the Ferry Building. Each one switches off the generated building underneath it. The simulation is the actual point. A 24 hour recording of real feeds plays back on a clock inside the app, at 1x up to 2000x. Caltrans PeMS for highway flow, 511 for Caltrain, OpenSky for flights, AIS for boats, METAR at SFO, OAK and SJC, and HRRR for weather. 165,356 samples across those 8 feeds. Cars spawn at the volume PeMS measured on that segment. Trains run the delays 511 reported. Planes fly the tracks OpenSky recorded. It is replay, not a live stream. Live polling is next and feeds the same clock, so it is a source swap rather than a rewrite. There is also a globe. You pick the Bay from orbit, fly down to the surface, and back out again. Reversed Z depth, HDR with a filmic tonemap, dual filter bloom, a time of day grade, and a volumetric marine layer for Karl the fog. AI agents write the issues, set the constraints, make the art direction calls, and review and validate every line of the roughly 235,000 lines of C++, GLSL, Python and PowerShell. What I built is the orchestration that holds it together, and the judgement on top of it: what to build, what to cut, and when to accept the result. The language choice is doing real work here. The hard part of AI written software is not producing the code. It is proving the code is right. C++ and Vulkan are unusually good at that, because nothing is hidden: every resource, every frame, every buffer you can read back. 28 golden image scenes compared by SSIM. An MCP server that drives the running app, so an agent can move the camera, set a sky preset, take a screenshot, and read the streaming counters back out. It validates against a live process instead of against its own diff. And a reviewer agent whose only job is to work out what the issue asked for before it reads the summary the PR wrote about itself. So I think C++ and Vulkan stay the heavy duty layer through the AI era. Not in spite of being low level. Because being performant, deterministic and checkable is exactly what an agent loop needs to push against. A managed engine hides the state that verification depends on. This is a hobby project, and it has nothing to do with my day job. Clips are below. More as I go. Map data © OpenStreetMap contributors (ODbL). Land cover: ESA WorldCover 2021 (CC BY 4.0). Terrain: USGS 3DEP and NOAA. Flight data: OpenSky Network. #Vulkan #cpp #gamedev #digitaltwin #OpenStreetMap

I spent 20 years architecting AAA game engines, going back to the PlayStation 1. I wrote all of that by hand. This one I did not. It is a stylized real time digital twin of the SF Bay Area, built from scratch in C++ and Vulkan. The geometry is real, the look is not. It is meant to read as a painted model of the Bay rather than a photo of it, because a photoreal copy of a place you can already see from a satellite is not worth building. The world is an 80 km square, about 6,400 km², and all of it is built from open data. USGS 3DEP 1 m lidar for the terrain. NOAA CoNED for the bay floor. USGS NHD for water. ESA WorldCover for land cover. OpenStreetMap for footprints and roads. That comes to 13 GB of streamed tiles, and 9.8 GB of it is terrain. Past the edge there is a 420 km coarse ring on Copernicus GLO-30, so the horizon does not end at a flat edge. Distant ridgelines bend down below it. There are 424,787 real building footprints in there, extruded procedurally, with heights off OSM tags and a plausibility gate that drops anything whose tagged height will not fit its own footprint. Across the whole 80 km box it rejects exactly 8 buildings, four of them towers carrying a bad height tag that used to extrude as needles over a derelict pier at Hunters Point. On top sit 41 landmarks at their real coordinates, authored by Fable as parametric Blender scripts rather than sculpted: Golden Gate, Transamerica, Coit, Sutro, Salesforce, the Ferry Building. Each one switches off the generated building underneath it. The simulation is the actual point. A 24 hour recording of real feeds plays back on a clock inside the app, at 1x up to 2000x. Caltrans PeMS for highway flow, 511 for Caltrain, OpenSky for flights, AIS for boats, METAR at SFO, OAK and SJC, and HRRR for weather. 165,356 samples across those 8 feeds. Cars spawn at the volume PeMS measured on that segment. Trains run the delays 511 reported. Planes fly the tracks OpenSky recorded. It is replay, not a live stream. Live polling is next and feeds the same clock, so it is a source swap rather than a rewrite. There is also a globe. You pick the Bay from orbit, fly down to the surface, and back out again. Reversed Z depth, HDR with a filmic tonemap, dual filter bloom, a time of day grade, and a volumetric marine layer for Karl the fog. AI agents write the issues, set the constraints, make the art direction calls, and review and validate every line of the roughly 235,000 lines of C++, GLSL, Python and PowerShell. What I built is the orchestration that holds it together, and the judgement on top of it: what to build, what to cut, and when to accept the result. The language choice is doing real work here. The hard part of AI written software is not producing the code. It is proving the code is right. C++ and Vulkan are unusually good at that, because nothing is hidden: every resource, every frame, every buffer you can read back. 28 golden image scenes compared by SSIM. An MCP server that drives the running app, so an agent can move the camera, set a sky preset, take a screenshot, and read the streaming counters back out. It validates against a live process instead of against its own diff. And a reviewer agent whose only job is to work out what the issue asked for before it reads the summary the PR wrote about itself. So I think C++ and Vulkan stay the heavy duty layer through the AI era. Not in spite of being low level. Because being performant, deterministic and checkable is exactly what an agent loop needs to push against. A managed engine hides the state that verification depends on. This is a hobby project, and it has nothing to do with my day job. Clips are below. More as I go. Map data © OpenStreetMap contributors (ODbL). Land cover: ESA WorldCover 2021 (CC BY 4.0). Terrain: USGS 3DEP and NOAA. Flight data: OpenSky Network. #Vulkan #cpp #gamedev #digitaltwin #OpenStreetMap

Xiang Wei

79,279 次观看 • 1 个月前

Immediate Mode GUI works just fine with full CPU rendering - no GPU needed. Not even any clever region invalidation. Just caching for glyphs and such. CPU usage looks high, but the interaction is as smooth as it can be, and the computer does not feel heavy at all. I can have many such apps open and they all work just as smoothly as if there was only one open. Here in this demo you see several programs: A process monitor that reports CPU usage of running programs A "find in files" GUI that can find find text (and regex) in a project directory very quickly A font viewer that shows a sample string in all system fonts so you can find a suitable font A profile data visualizer that can read .pprof files generated by the Go runtime A disk usage analyzer that recursively calculates directory sizes to help you find out where your disk space has gone All of them are immediate mode GUI programs rendered on the CPU. All of them running at the same time. All of them run very smoothly. All their binary sizes are around 10MB. The GUI framework itself ends up being about 1MB. Proofs in the reply

ハセン حسن

10,063 次观看 • 2 个月前

This game is entirely "vibe" coded. I spent 6 months using AI to replace Unreal Engine from under our game Em-8ER. Now we're developing 93x faster than we ever did before with Unreal. Because our engine is developed to be 100% AI first instead of tacked on. It is not a toy one shotted overnight, it is a production ready engine. I made a big bet that this was the future and now I'm very confident it is. You don't need to license an expensive game engine anymore. Today's update features: - Advanced character animation system - World collision meshes - AI assisted Blender anim exporter and workflow - Character outfits and outfit swapping - Physic driven character effects - Fully networked client/server player capsules - Pilotable vehicles - Waifus The engine is called Kun-Zhi, the Tibetan word for "Ground of all" that the world springs from. This engine will be released to the public, starting with backers first. Today we're opening up to public backers again after a quiet building period where they were closed. If you would like to support our engine work and get the game, you can subscribe as a patron, or purchase a game backer pack on sale at 50% here: Corporate sponsorships are available. Send a DM. Current partners include Razer and AMD. Follow and subscribe for our first "How To" articles on AI coding and gaming dropping tomorrow.

This game is entirely "vibe" coded. I spent 6 months using AI to replace Unreal Engine from under our game Em-8ER. Now we're developing 93x faster than we ever did before with Unreal. Because our engine is developed to be 100% AI first instead of tacked on. It is not a toy one shotted overnight, it is a production ready engine. I made a big bet that this was the future and now I'm very confident it is. You don't need to license an expensive game engine anymore. Today's update features: - Advanced character animation system - World collision meshes - AI assisted Blender anim exporter and workflow - Character outfits and outfit swapping - Physic driven character effects - Fully networked client/server player capsules - Pilotable vehicles - Waifus The engine is called Kun-Zhi, the Tibetan word for "Ground of all" that the world springs from. This engine will be released to the public, starting with backers first. Today we're opening up to public backers again after a quiet building period where they were closed. If you would like to support our engine work and get the game, you can subscribe as a patron, or purchase a game backer pack on sale at 50% here: Corporate sponsorships are available. Send a DM. Current partners include Razer and AMD. Follow and subscribe for our first "How To" articles on AI coding and gaming dropping tomorrow.

Grummz

3,093,858 次观看 • 9 天前

we just rebuilt Dodo Payments from scratch. migrated entirely off framer. rewrote the whole thing in astro. the old site looked fine but under the hood it was slow. heavy javascript bundles. unnecessary client-side rendering for what is fundamentally a static marketing site. page speed scores were mid. every time we wanted to change something it felt like fighting the tool instead of building. so we nuked it. astro ships zero javascript by default. every page is static HTML unless you explicitly need interactivity. the result is a site that loads almost instantly. lighthouse scores through the roof. SEO actually works because crawlers aren't waiting for javascript to render your content. the migration took effort but the tradeoff was obvious. full control over every pixel. no vendor lock-in. no mysterious build steps. no "why is this component re-rendering on every page." if you're running a SaaS marketing site on a no-code builder and wondering why your page speed is garbage, this is your sign.

we just rebuilt Dodo Payments from scratch. migrated entirely off framer. rewrote the whole thing in astro. the old site looked fine but under the hood it was slow. heavy javascript bundles. unnecessary client-side rendering for what is fundamentally a static marketing site. page speed scores were mid. every time we wanted to change something it felt like fighting the tool instead of building. so we nuked it. astro ships zero javascript by default. every page is static HTML unless you explicitly need interactivity. the result is a site that loads almost instantly. lighthouse scores through the roof. SEO actually works because crawlers aren't waiting for javascript to render your content. the migration took effort but the tradeoff was obvious. full control over every pixel. no vendor lock-in. no mysterious build steps. no "why is this component re-rendering on every page." if you're running a SaaS marketing site on a no-code builder and wondering why your page speed is garbage, this is your sign.

Ayush Agarwal

21,285 次观看 • 6 个月前

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

472,273 次观看 • 1 年前

I built the graph engineering pipeline instead of just reading about it. had to share what i found THE ENTIRE GRAPH ENGINEERING PIPELINE—ROOTED IN REAL, OPEN-SOURCE BUILDING, NOT SLIDES. Representation. Ontologies. Entity extraction. Relationships. Events. QA gate. Fusion. Embeddings. Actual memory your agent can navigate instead of hand-waving. And the best part: the end-to-end path already exists in public repos. Strwythura lays out the full ontology workflow, including entity resolution plus embedding-boosted GraphRAG. llm2kg tackles entity + relation extraction aligned to an ontology, persists it in Neo4j with vector embeddings, and ships a ReAct agent that queries the graph directly. No “coming soon.” Just working code you can point at your own data today.

I built the graph engineering pipeline instead of just reading about it. had to share what i found THE ENTIRE GRAPH ENGINEERING PIPELINE—ROOTED IN REAL, OPEN-SOURCE BUILDING, NOT SLIDES. Representation. Ontologies. Entity extraction. Relationships. Events. QA gate. Fusion. Embeddings. Actual memory your agent can navigate instead of hand-waving. And the best part: the end-to-end path already exists in public repos. Strwythura lays out the full ontology workflow, including entity resolution plus embedding-boosted GraphRAG. llm2kg tackles entity + relation extraction aligned to an ontology, persists it in Neo4j with vector embeddings, and ships a ReAct agent that queries the graph directly. No “coming soon.” Just working code you can point at your own data today.

CyrilXBT

13,516 次观看 • 8 天前

Live2D 5.3 is officially here with groundbreaking features! While VTube Studio (VTS) support might take some time, you can actually use 5.3 features RIGHT NOW using #NizimaLIVE (nizima LIVE - Live2D公式VTuberアプリ )! Here is my model made in 5.3 running smoothly!!!👇 ✅Pros: -Official App: Developed by Live2D themselves. -5.3 Beta Support: Includes new Blend Modes and Off-screen Rendering! -Perfect Sync: Full iOS support. -Precision: Extremely fine-tuned parameter adjustments. -Effects: Built-in preset effects. ⚠️Notes: -Commercial use requires a paid subscription. -No direct Vbridger support (though parameters like JawOpen, Funnel, Pucker, and Shrug are compatible! Plenty of room for expressions). -Lacks YouTube/Twitch integration plugins. Give the free version a try! If you have any questions, feel free to ask me👍

Live2D 5.3 is officially here with groundbreaking features! While VTube Studio (VTS) support might take some time, you can actually use 5.3 features RIGHT NOW using #NizimaLIVE (nizima LIVE - Live2D公式VTuberアプリ )! Here is my model made in 5.3 running smoothly!!!👇 ✅Pros: -Official App: Developed by Live2D themselves. -5.3 Beta Support: Includes new Blend Modes and Off-screen Rendering! -Perfect Sync: Full iOS support. -Precision: Extremely fine-tuned parameter adjustments. -Effects: Built-in preset effects. ⚠️Notes: -Commercial use requires a paid subscription. -No direct Vbridger support (though parameters like JawOpen, Funnel, Pucker, and Shrug are compatible! Plenty of room for expressions). -Lacks YouTube/Twitch integration plugins. Give the free version a try! If you have any questions, feel free to ask me👍

Noy Ururuka🎀🐺illust/rigger

29,257 次观看 • 8 个月前

I gave my local AI agents persistent memory without sending a single byte to a cloud API. It’s called Hindsight. Instead of your agent forgetting everything the moment a session ends, it extracts facts, builds a knowledge graph, and recalls exactly what matters, next time you ask. → Three core operations: retain (store a fact), recall (search memories), reflect (reason across everything it knows), not just a vector database bolted onto a chatbot → Runs fully local when paired with Ollama, no API keys, no cloud costs, and no user data transiting through a third-party provider → Ships as an MCP server out of the box, so Claude, Cursor, or any MCP-compatible client gets persistent memory in one Docker command → Builds “mental models,” living summaries that auto-update as memories accumulate, instead of you re-reading a growing pile of raw logs → Per-user memory isolation lets you run one instance for multiple people or agents without their memories bleeding into each other → Reports top results on the LongMemEval long-term memory benchmark, per the project’s own published numbers Every AI agent today is stateless by default, it knows nothing about yesterday’s conversation unless you re-paste it. Hindsight’s answer: real structured memory, running on your own hardware if you want it there. MIT License. Built by

I gave my local AI agents persistent memory without sending a single byte to a cloud API. It’s called Hindsight. Instead of your agent forgetting everything the moment a session ends, it extracts facts, builds a knowledge graph, and recalls exactly what matters, next time you ask. → Three core operations: retain (store a fact), recall (search memories), reflect (reason across everything it knows), not just a vector database bolted onto a chatbot → Runs fully local when paired with Ollama, no API keys, no cloud costs, and no user data transiting through a third-party provider → Ships as an MCP server out of the box, so Claude, Cursor, or any MCP-compatible client gets persistent memory in one Docker command → Builds “mental models,” living summaries that auto-update as memories accumulate, instead of you re-reading a growing pile of raw logs → Per-user memory isolation lets you run one instance for multiple people or agents without their memories bleeding into each other → Reports top results on the LongMemEval long-term memory benchmark, per the project’s own published numbers Every AI agent today is stateless by default, it knows nothing about yesterday’s conversation unless you re-paste it. Hindsight’s answer: real structured memory, running on your own hardware if you want it there. MIT License. Built by

Harman

11,032 次观看 • 2 个月前

BREAKING - EARLY EXCLUSIVE: XPENG Partners with Google Maps to Fuel Global Expansion The global autonomous driving landscape is becoming even more competitive! XPENG has announced a milestone global partnership with Google Maps, bringing a complete refresh of its in-car navigation and advanced driver assistance systems (ADAS). This major upgrade will come standard in the newly debuted XPENG L03. At the official launch event for the L03 in Munich, Germany, XPENG Chairman and CEO He Xiaopeng announced that the Next-Gen AI SUV Coupe will launch simultaneously in 64 countries and regions. XPENG is building its native in-car navigation system using Google Maps’ Auto SDK for vehicles outside China, which I’m sure MANY of you will be excited about. First in APAC to Adopt Google Maps Auto SDK XPENG is the first automaker from the APAC region to ship a vehicle with Google Maps Auto SDK integration. Instead of using your smartphone for basic screen mirroring, drivers get a native, custom in-car map application designed entirely by XPENG and powered directly by Google Maps. This integration brings: Real-time precision: Live traffic-aware guidance and highly accurate place search. Smart EV routing: Direct in-car EV energy estimation and trip planning. Native control: Full compatibility with voice control and multi-screen display transfer. Laying the Foundation for Global Autonomous Driving This partnership goes beyond finding directions. XPENG is officially utilizing Google Maps' in-vehicle Map Data Services to support its flagship full-scenario ADAS, Next Generation Pilot (NGP), as well as its foundational assist system, XPILOT ASSIST. To scale XPENG's advanced driving systems internationally, future applications of NGP (VLA 2.0) must rely on highly accurate map data and navigation maneuvers. Google Map Data Services will serve as the cornerstone for this phased global rollout, excluding markets in the Commonwealth of Independent States (CIS). Think of it as a fusion of XPENG’s technology with Google Maps Technology to get a more robust, intelligent, powerful, and safe autonomous driving experience. Overseas users of the XPENG L03 will be the very first to experience this upgraded, Google-powered native navigation system. My Thoughts I personally love and use Google Maps, so I’m glad to see this partnership, and I find it really neat how XPENG and Google Maps technology will work together. What are your thoughts and questions on XPENG integrating Google Maps natively to power its global self-driving tech? Let me know in the replies, and I will try to find the answers to your questions.

BREAKING - EARLY EXCLUSIVE: XPENG Partners with Google Maps to Fuel Global Expansion The global autonomous driving landscape is becoming even more competitive! XPENG has announced a milestone global partnership with Google Maps, bringing a complete refresh of its in-car navigation and advanced driver assistance systems (ADAS). This major upgrade will come standard in the newly debuted XPENG L03. At the official launch event for the L03 in Munich, Germany, XPENG Chairman and CEO He Xiaopeng announced that the Next-Gen AI SUV Coupe will launch simultaneously in 64 countries and regions. XPENG is building its native in-car navigation system using Google Maps’ Auto SDK for vehicles outside China, which I’m sure MANY of you will be excited about. First in APAC to Adopt Google Maps Auto SDK XPENG is the first automaker from the APAC region to ship a vehicle with Google Maps Auto SDK integration. Instead of using your smartphone for basic screen mirroring, drivers get a native, custom in-car map application designed entirely by XPENG and powered directly by Google Maps. This integration brings: Real-time precision: Live traffic-aware guidance and highly accurate place search. Smart EV routing: Direct in-car EV energy estimation and trip planning. Native control: Full compatibility with voice control and multi-screen display transfer. Laying the Foundation for Global Autonomous Driving This partnership goes beyond finding directions. XPENG is officially utilizing Google Maps' in-vehicle Map Data Services to support its flagship full-scenario ADAS, Next Generation Pilot (NGP), as well as its foundational assist system, XPILOT ASSIST. To scale XPENG's advanced driving systems internationally, future applications of NGP (VLA 2.0) must rely on highly accurate map data and navigation maneuvers. Google Map Data Services will serve as the cornerstone for this phased global rollout, excluding markets in the Commonwealth of Independent States (CIS). Think of it as a fusion of XPENG’s technology with Google Maps Technology to get a more robust, intelligent, powerful, and safe autonomous driving experience. Overseas users of the XPENG L03 will be the very first to experience this upgraded, Google-powered native navigation system. My Thoughts I personally love and use Google Maps, so I’m glad to see this partnership, and I find it really neat how XPENG and Google Maps technology will work together. What are your thoughts and questions on XPENG integrating Google Maps natively to power its global self-driving tech? Let me know in the replies, and I will try to find the answers to your questions.

M. Brandon Lee | THIS IS TECH TODAY

65,878 次观看 • 2 个月前

MVP of Multiview Video → Camera parameters + 3D keypoints. Visualized with Rerun The basic pipeline as of right now looks like this: 1. Capture 🔴 – Using 4 iPhones and an Insta360 Go. iPhone videos are captured via Final Cut Pro Multicam for easy sync and the exocentric view; the Insta360 Go is used for the egocentric view. 2. Sync 🕒 – Custom Gradio app using two Rerun viewers and callbacks for easily aligning frame timestamps so the ego and exo views are aligned. 3. Calibrate 🎯 – Use VGGT from Jianyuan and AI at Meta to get intrinsics/extrinsics for sparse cameras. 4. Estimate 3D 🕺 – Use RTMLib whole‑body keypoint estimator on each frame, then triangulate in 3D. What's missing? 1. No temporal coherence: I’m estimating keypoints one frame at a time and one camera at a time. This leads to a lot of jittering. For now, I plan on adding a One Euro Filter to help with jittering. Long term, I'd want to train a multiview keypoint estimator 2. Kinematic fitting is still missing; this is my next goal. The output will be joint angles, as explored in my previous posts. 3. Missing dense point cloud: VGGT seems to fail for me here. I’m looking to explore using MP‑SFM as a method for generating dense multiview depth maps + normals (plus it has a friendlier license compared to VGGT). 4. Eventually, creation of 4D Gaussian splatting using something akin to DN‑splatter—my long‑term goal is a data engine that provides poses/depths/splats/keypoints/etc.

MVP of Multiview Video → Camera parameters + 3D keypoints. Visualized with Rerun The basic pipeline as of right now looks like this: 1. Capture 🔴 – Using 4 iPhones and an Insta360 Go. iPhone videos are captured via Final Cut Pro Multicam for easy sync and the exocentric view; the Insta360 Go is used for the egocentric view. 2. Sync 🕒 – Custom Gradio app using two Rerun viewers and callbacks for easily aligning frame timestamps so the ego and exo views are aligned. 3. Calibrate 🎯 – Use VGGT from Jianyuan and AI at Meta to get intrinsics/extrinsics for sparse cameras. 4. Estimate 3D 🕺 – Use RTMLib whole‑body keypoint estimator on each frame, then triangulate in 3D. What's missing? 1. No temporal coherence: I’m estimating keypoints one frame at a time and one camera at a time. This leads to a lot of jittering. For now, I plan on adding a One Euro Filter to help with jittering. Long term, I'd want to train a multiview keypoint estimator 2. Kinematic fitting is still missing; this is my next goal. The output will be joint angles, as explored in my previous posts. 3. Missing dense point cloud: VGGT seems to fail for me here. I’m looking to explore using MP‑SFM as a method for generating dense multiview depth maps + normals (plus it has a friendlier license compared to VGGT). 4. Eventually, creation of 4D Gaussian splatting using something akin to DN‑splatter—my long‑term goal is a data engine that provides poses/depths/splats/keypoints/etc.

Pablo Vela

42,785 次观看 • 1 年前