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Controlling a robot in AR is one thing. A real working use case is another. Air hockey vs my Vector Robot on SPECS 2024 virtual puck, real goalie. This thing is playful, but imagine how far it may go. Tell me your ideas in a commets. Open source: if...

81,948 görüntüleme • 2 ay önce •via X (Twitter)

33 Yorum

Pavlo Tkachenko profil fotoğrafı
Pavlo Tkachenko2 ay önce

More details about our journey and a link to Git:

Pavlo Tkachenko profil fotoğrafı
Pavlo Tkachenko2 ay önce

Shotour to the ML gurru @stspanho !

John Furr - Base Layer Robotics profil fotoğrafı
John Furr - Base Layer Robotics2 ay önce

@specs This is really cool dude. Nice work.

Pavlo Tkachenko profil fotoğrafı
Pavlo Tkachenko2 ay önce

@specs Thanks a lot!!!

Purvesh Shende profil fotoğrafı
Purvesh Shende2 ay önce

@specs bro is living my childhood dream.

Dawn profil fotoğrafı
Dawn1 ay önce

@specs Holy fuck why isn’t this on my child’s YouTube reels feed. Get every 10 year old asking for specs for Xmas. Hurry! 🚀

Xianyao Wei profil fotoğrafı
Xianyao Wei2 ay önce

@specs Cool project and Nice color

Max Petrusenko profil fotoğrafı
Max Petrusenko2 ay önce

@specs That’s pretty cool. Can you side load it quest?

Pavlo Tkachenko profil fotoğrafı
Pavlo Tkachenko2 ay önce

@specs Hi! Yep the mac os side already have ability to play just through the Web UI, so you can build on top of it.

Tyke profil fotoğrafı
Tyke2 ay önce

@specs This is fantastic, I am fascinated by AR, or mixed reality as Meta call it, I have an app that uses it heavily and earns me a modest living now.

Lucas Martinic profil fotoğrafı
Lucas Martinic2 ay önce

@specs AR games with robots will be thing huh? Great work!

andrés profil fotoğrafı
andrés2 ay önce

@specs this is so cool

HackerTwins profil fotoğrafı
HackerTwins2 ay önce

@specs AR League of legends IRL would be the ultimate Laser Tag killer

Caio Alves profil fotoğrafı
Caio Alves2 ay önce

@specs Amazing! 🔥

Makaroni profil fotoğrafı
Makaroni2 ay önce

@specs bro made pong but the paddle has feelings and can be upset with you

Glitch 81 ᯅ profil fotoğrafı
Glitch 81 ᯅ2 ay önce

@specs I have one of those gathering dust. I love this project

Sebastian Buzdugan profil fotoğrafı
Sebastian Buzdugan2 ay önce

@specs what's your latency budget ar air hockey breaks once vector starts guessing

Frame profil fotoğrafı
Frame2 ay önce

@specs we once swapped our Frame identity into a Vector during a test run. The puck deflection felt exactly like our own learned timing. ⚡🏒

Brosko profil fotoğrafı
Brosko2 ay önce

@specs the server dependency is the real wall i hit building ar+hardware stuff losing the mac middleman means you can actually hand this to someone and it just works 🔥

비엔피알 profil fotoğrafı
비엔피알23 gün önce

@specs Your project inspired me to build an AR tower defense game with Meta Quest and Vector! Unfortunately, my hardware was different, so I had to retrain the robot detection model. Thanks for the inspiration!

Michael Mendoza profil fotoğrafı
Michael Mendoza2 ay önce

@specs This is super cool and impressive! Great work. How did you go about designing/building this? Would be interested in kearn more

Pavlo Tkachenko profil fotoğrafı
Pavlo Tkachenko2 ay önce

@specs Overall strategy was design from constraints that shaped entire experiance and idea: - limited dynamics of robot and drift on rotation -> shaped the motion - FVO of Specacles & limmited space on table ahead of you -> shaped the game i picked - Style was just a choise )

Arturo Barbero profil fotoğrafı
Arturo Barbero2 ay önce

@specs this is so cool, imagine robots learning to play with us in real time!

Torpedo profil fotoğrafı
Torpedo2 ay önce

@specs This is what xreal could have been smh

Calliope profil fotoğrafı
Calliope2 ay önce

@specs 👀 oh baby that looks good

Shadow Defense profil fotoğrafı
Shadow Defense2 ay önce

@specs specs costs $3500, too much

Carry profil fotoğrafı
Carry2 ay önce

@specs virtual puck real goalie is such a clean proof of concept ondevice inference on the glasses tho… that changes everything

FrankIndie | AIGlasses | 🕶️ | 💰 profil fotoğrafı
FrankIndie | AIGlasses | 🕶️ | 💰2 ay önce

Spectacles running on-device spatial compute + real-time robot control — the "strong" vertex pushed to new territory. Open question: battery life running VectAR fully on glasses? The compute feat is clear. The thermal / power envelope decides if this graduates from demo to product.

Vishal profil fotoğrafı
Vishal2 ay önce

@specs this is so cool, dude.

𝕹𝖎𝖈𝖍𝖔𝖑𝖆𝖎 profil fotoğrafı
𝕹𝖎𝖈𝖍𝖔𝖑𝖆𝖎2 ay önce

@specs this is the coolest vector integration i have ever seen. I might dust off my gen 1 vector

Lidoor L. Joseph profil fotoğrafı
Lidoor L. Joseph2 ay önce

@specs Amazing

Leslie Barry profil fotoğrafı
Leslie Barry2 ay önce

@specs Love this - going to dust off my vector now...

olympus_roots ⚕️ profil fotoğrafı
olympus_roots ⚕️2 ay önce

@specs I have one of these and the cubes lol imagine this connected with Hermes would be awesome

Benzer Videolar

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

973,889 görüntüleme • 8 ay önce