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@geometrynodes tree gen + simulation. Needs some springiness.. kinda sloppy tree #b3d Based on harry blends wiggle stuff.

Miettinen Jesse - Embrace FX

64,085 subscribers

60,071 views • 6 months ago •via X (Twitter)

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Wind is tricky to get right, I made it so it works automatically based on tree parameters. #b3d #geometrynodes #procedural

Wind is tricky to get right, I made it so it works automatically based on tree parameters. #b3d #geometrynodes #procedural

Val Barashkov

330,637 views • 3 years ago

Tree generator 🌴 Including several presets and some materials Free for a few days: #b3d #geometrynodes

Tree generator 🌴 Including several presets and some materials Free for a few days: #b3d #geometrynodes

David Cescatti

96,502 views • 3 years ago

#geometrynodes simple spring force solver. Actually good be good for softbody secondary motion. #b3d Nathan pointed out some Houdini tutorial for guidance. Go read his retweet on my tree thingie.

#geometrynodes simple spring force solver. Actually good be good for softbody secondary motion. #b3d Nathan pointed out some Houdini tutorial for guidance. Go read his retweet on my tree thingie.

Miettinen Jesse - Embrace FX

29,538 views • 6 months ago

Started implementing #simulationnodes to #geometrynodes bird generator v2. Everything would still be automated with spline but simulation would eventually make it look so much more natural! Big thanks to harry blends awesome tutorial. I´ll put the link below! #b3d

Started implementing #simulationnodes to #geometrynodes bird generator v2. Everything would still be automated with spline but simulation would eventually make it look so much more natural! Big thanks to harry blends awesome tutorial. I´ll put the link below! #b3d

Miettinen Jesse - Embrace FX

51,205 views • 1 year ago

#geometrynodes water smudge mask generator (it´s a good name) is here for FREE! For B 4.0. I also dropped 3 other shader based masks like edgebevel and AO based stuff. (AO thing based on Thomas Kole (see 🦋 and 🐘) s setup) Happy masking! #b3d

#geometrynodes water smudge mask generator (it´s a good name) is here for FREE! For B 4.0. I also dropped 3 other shader based masks like edgebevel and AO based stuff. (AO thing based on Thomas Kole (see 🦋 and 🐘) s setup) Happy masking! #b3d

Miettinen Jesse - Embrace FX

136,941 views • 2 years ago

Oliver tree played George from Seinfeld!?!?! Yall 🤦‍♂️ what are we doing here. Your being swindled. This kinda stuff is why I stepped back from making short videos. Too many grifters.. but trust me..ive been working and soon you will see what I've been working on.. and its not videos. 😉🫶 these people are getting paid for desecrating the dead.

Oliver tree played George from Seinfeld!?!?! Yall 🤦‍♂️ what are we doing here. Your being swindled. This kinda stuff is why I stepped back from making short videos. Too many grifters.. but trust me..ive been working and soon you will see what I've been working on.. and its not videos. 😉🫶 these people are getting paid for desecrating the dead.

realmoonhenry

208,746 views • 2 months ago

i removed the tail on my character a while ago cause it was causing some issues.... and was kinda annoying to animate but i always wanted it to come back so now its physics based !! still needs a bunch of tweakin but yeeeee

i removed the tail on my character a while ago cause it was causing some issues.... and was kinda annoying to animate but i always wanted it to come back so now its physics based !! still needs a bunch of tweakin but yeeeee

naniel

45,820 views • 8 months ago

I just inscribed the WORLDTREE module to Termina! You can now build, save, and import entire worldtree's, cached locally, with or without full content scraping! TRY > worldtree bitmap > worldtree bitmap full This may take some time, or you can BRING YOUR OWN DATA from here: All the commands in worldtree are essentially created to contribute towards the FULL scan, but you can run them separately. The "worldtree bitmap" command strictly builds the tree with inscription ID's. The info command takes the existing worldtree cache and adds info for each inscription ID. The metadata command does the same for metadata. The content command does the same, but scrapes all text-based inscriptions, including plain-text, HTML (titles, headers, body text, comments), JS (comments), CSS (comments). The enrich command combines the info, metadata, and content commands and runs them one by one on every inscription ID on the existing tree. The full command runs "worldtree bitmap" and then enriches each inscription ID tree by tree. This all gets cache to your local computer via indexDB. Export the cache with "worldtree save". Import existing worldtrees with "worldtree file". Currently the cache is just shy of 29mb. I suggest you download the existing worldtree from the github link above, and import it into Termina. This way you don't have to spend all day having this run. The best use-case for this is if you know there have been updates to the tree and want to add to the existing repository (best to use github desktop for files over 25mb). This will be saved ready for the next modules to make use of all this juicy data! Can't wait for what's next...

I just inscribed the WORLDTREE module to Termina! You can now build, save, and import entire worldtree's, cached locally, with or without full content scraping! TRY > worldtree bitmap > worldtree bitmap full This may take some time, or you can BRING YOUR OWN DATA from here: All the commands in worldtree are essentially created to contribute towards the FULL scan, but you can run them separately. The "worldtree bitmap" command strictly builds the tree with inscription ID's. The info command takes the existing worldtree cache and adds info for each inscription ID. The metadata command does the same for metadata. The content command does the same, but scrapes all text-based inscriptions, including plain-text, HTML (titles, headers, body text, comments), JS (comments), CSS (comments). The enrich command combines the info, metadata, and content commands and runs them one by one on every inscription ID on the existing tree. The full command runs "worldtree bitmap" and then enriches each inscription ID tree by tree. This all gets cache to your local computer via indexDB. Export the cache with "worldtree save". Import existing worldtrees with "worldtree file". Currently the cache is just shy of 29mb. I suggest you download the existing worldtree from the github link above, and import it into Termina. This way you don't have to spend all day having this run. The best use-case for this is if you know there have been updates to the tree and want to add to the existing repository (best to use github desktop for files over 25mb). This will be saved ready for the next modules to make use of all this juicy data! Can't wait for what's next...

bitoshi blockamoto 🧱 BITMAP 🟧

12,684 views • 11 months ago

Mark Cuban says Elon f#cked up Twitter "First of all, Twitter X is such a cesspool now. It's a shithole. Instagram and Tik Tok are still chill where you can mess with people and have fun, but you just can't do it on Twitter" "The way it works is called the nearest tree algorithm. If you are a big user on X and you have a lot of followers and those followers engage with you, then not only does your stuff get elevated but all the people you engage with get elevated" "So when Elon interacts with some of these crazy people, their stuff gets elevated and the people that they talk to gets elevated"

Mark Cuban says Elon f#cked up Twitter "First of all, Twitter X is such a cesspool now. It's a shithole. Instagram and Tik Tok are still chill where you can mess with people and have fun, but you just can't do it on Twitter" "The way it works is called the nearest tree algorithm. If you are a big user on X and you have a lot of followers and those followers engage with you, then not only does your stuff get elevated but all the people you engage with get elevated" "So when Elon interacts with some of these crazy people, their stuff gets elevated and the people that they talk to gets elevated"

dank

1,938,672 views • 4 months ago

BANDIT KNIGHT is a 2.5D single-player stealth game that focuses on thievery: -Small indie game team based in Japan -Pickpocket and swipe treasure -Outsmart puzzles -Unlock secret pathways -Handcrafted world -Vast deserts -Endless seas -Bustling towns -Mysterious vaults -Unlock powerful upgrades and new abilities -Quest system -Crafting -Skill tree -Different equipment -Over 650 collectibles (and counting!) -Steal as fast as possible to achieve a high combo score -There is an overworld map where you can choose your next location -The game features some story with dialogue -Great sense of humor -Some metroidvania elements -You can fight enemies with your knife -You have your own base -No release date yet -Playtest out now on Steam

BANDIT KNIGHT is a 2.5D single-player stealth game that focuses on thievery: -Small indie game team based in Japan -Pickpocket and swipe treasure -Outsmart puzzles -Unlock secret pathways -Handcrafted world -Vast deserts -Endless seas -Bustling towns -Mysterious vaults -Unlock powerful upgrades and new abilities -Quest system -Crafting -Skill tree -Different equipment -Over 650 collectibles (and counting!) -Steal as fast as possible to achieve a high combo score -There is an overworld map where you can choose your next location -The game features some story with dialogue -Great sense of humor -Some metroidvania elements -You can fight enemies with your knife -You have your own base -No release date yet -Playtest out now on Steam

GermanStrands

46,638 views • 8 months ago

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!

Avi Chawla

973,546 views • 7 months ago

Excited to announce that I've shipped my first game! Written from the ground up in Three.js in 3 months and published on VIVERSE Official Some tech stuff that I'm really proud of: - fully custom spherical harmonics prt lighting. Giving a baked diffuse look while still allowing for dynamic objects - Runs completely inside a WebWorker (rendering & simulation) using OffscreenCanvas - Supports all input types: desktop, mobile, gamepad (non-trivial if your app runs in a worker!) - No Postprocessing! No bloom - everything you see is faked via mesh based shaders for performance reasons Huge thanks to VIVERSE Official who have made this possible by letting me into their Creator Program. It's not easy to make a living off gamedev on the web yet, so this is great for the ecosystem. All links below: #VIVERSE #threejs #IndieGameDev

Excited to announce that I've shipped my first game! Written from the ground up in Three.js in 3 months and published on VIVERSE Official Some tech stuff that I'm really proud of: - fully custom spherical harmonics prt lighting. Giving a baked diffuse look while still allowing for dynamic objects - Runs completely inside a WebWorker (rendering & simulation) using OffscreenCanvas - Supports all input types: desktop, mobile, gamepad (non-trivial if your app runs in a worker!) - No Postprocessing! No bloom - everything you see is faked via mesh based shaders for performance reasons Huge thanks to VIVERSE Official who have made this possible by letting me into their Creator Program. It's not easy to make a living off gamedev on the web yet, so this is great for the ecosystem. All links below: #VIVERSE #threejs #IndieGameDev

Brian Breiholz

14,587 views • 1 year ago