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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 просмотров • 2 месяцев назад •via X (Twitter)

Комментарии: 33

Фото профиля Pavlo Tkachenko
Pavlo Tkachenko2 месяцев назад

More details about our journey and a link to Git:

Фото профиля Pavlo Tkachenko
Pavlo Tkachenko2 месяцев назад

Shotour to the ML gurru @stspanho !

Фото профиля John Furr - Base Layer Robotics
John Furr - Base Layer Robotics2 месяцев назад

@specs This is really cool dude. Nice work.

Фото профиля Pavlo Tkachenko
Pavlo Tkachenko2 месяцев назад

@specs Thanks a lot!!!

Фото профиля Purvesh Shende
Purvesh Shende2 месяцев назад

@specs bro is living my childhood dream.

Фото профиля Dawn
Dawn1 месяц назад

@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! 🚀

Фото профиля Xianyao Wei
Xianyao Wei2 месяцев назад

@specs Cool project and Nice color

Фото профиля Max Petrusenko
Max Petrusenko2 месяцев назад

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

Фото профиля Pavlo Tkachenko
Pavlo Tkachenko2 месяцев назад

@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.

Фото профиля Tyke
Tyke2 месяцев назад

@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.

Фото профиля Lucas Martinic
Lucas Martinic2 месяцев назад

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

Фото профиля andrés
andrés2 месяцев назад

@specs this is so cool

Фото профиля HackerTwins
HackerTwins2 месяцев назад

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

Фото профиля Caio Alves
Caio Alves2 месяцев назад

@specs Amazing! 🔥

Фото профиля Makaroni
Makaroni2 месяцев назад

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

Фото профиля Glitch 81 ᯅ
Glitch 81 ᯅ2 месяцев назад

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

Фото профиля Sebastian Buzdugan
Sebastian Buzdugan2 месяцев назад

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

Фото профиля Frame
Frame2 месяцев назад

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

Фото профиля Brosko
Brosko2 месяцев назад

@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 🔥

Фото профиля 비엔피알
비엔피알23 дней назад

@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!

Фото профиля Michael Mendoza
Michael Mendoza2 месяцев назад

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

Фото профиля Pavlo Tkachenko
Pavlo Tkachenko2 месяцев назад

@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 )

Фото профиля Arturo Barbero
Arturo Barbero2 месяцев назад

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

Фото профиля Torpedo
Torpedo2 месяцев назад

@specs This is what xreal could have been smh

Фото профиля Calliope
Calliope2 месяцев назад

@specs 👀 oh baby that looks good

Фото профиля Shadow Defense
Shadow Defense2 месяцев назад

@specs specs costs $3500, too much

Фото профиля Carry
Carry2 месяцев назад

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

Фото профиля FrankIndie | AIGlasses | 🕶️ | 💰
FrankIndie | AIGlasses | 🕶️ | 💰2 месяцев назад

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.

Фото профиля Vishal
Vishal2 месяцев назад

@specs this is so cool, dude.

Фото профиля 𝕹𝖎𝖈𝖍𝖔𝖑𝖆𝖎
𝕹𝖎𝖈𝖍𝖔𝖑𝖆𝖎2 месяцев назад

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

Фото профиля Lidoor L. Joseph
Lidoor L. Joseph2 месяцев назад

@specs Amazing

Фото профиля Leslie Barry
Leslie Barry2 месяцев назад

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

Фото профиля olympus_roots ⚕️
olympus_roots ⚕️2 месяцев назад

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

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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!

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973,889 просмотров • 8 месяцев назад