I am a Musician , Writer, Photographer , 3D Artist and AI artist #ℕ𝔽𝕋 #𝕒𝕣𝕕𝕒𝕧𝕒𝕟

Metaverse
Joined February 2011
Bagel = @Polymarket , but built for your phone 📱 @bagel_win brings prediction markets to mobile in 150+ countries. Sports, esports, crypto, finance and trending events, all in one app. Copy trading and social trading let you see what other traders are doing. AI-powered insights give you more context before you make a call. Flexible order types mean you're not stuck with just one way to trade. There's also a Rewards Center and a VIP system, so staying active can actually pay off. Campaigns are the part I'm watching most closely. Rewards and details can change, so I'd rather check what's live now than find out too late. More campaigns are coming too, so keep an eye out. Whether it's a football match or a big crypto event, you can jump in from anywhere. It's simple, mobile-first, and easy to explore. I'm testing it out myself. Will share what I find. Worth a look if you're into prediction markets 👀 @KaitoAI
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Ardavan Homayounfar retweeted
From years of capturing moments on mobile to embracing a new vision with my Lumix S9! 📸✨ Every festive frame holds an emotion, a story, and a little piece of my heart. 🥹🙏 Show me your love and support as this new chapter begins! ❤️🔥 #lumix
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Ardavan Homayounfar retweeted
maps describe places. machines need to understand transitions. that difference is starting to show up in @vangrid_io's latest bounty activity. the live board has now crossed 1,000 bounties with 24 bulk orders, but look at what buyers are asking for lately: station concourses and platform edges city hall entrances bus stops car parks loading docks ferry terminals these are not just locations. they are interaction zones where people, vehicles and machines enter, exit, queue, navigate or transfer between environments. that data becomes much more useful for physical ai than another generic street scan. a robot needs to know where movement changes. where access starts. where boundaries sit. where humans and infrastructure interact. this makes vangrid look less like a project trying to map every square meter of the world. it is gradually building something more task-specific: a market where buyers can request the exact physical context their systems are missing. the dataset does not need to contain everything. it needs to contain the places where decisions happen
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Ardavan Homayounfar retweeted
the latest @vangrid_io bounties are getting surprisingly specific. not just warehouses or streets. a carved wooden chest. a cooler wall. a warehouse barrier with its floor fixings. service riser doors. utility boxes mounted on poles. the grid has now crossed 1,000 bounties with 24 bulk orders, and this shift toward smaller physical assets is worth watching. because physical ai eventually needs more than scene-level understanding. a robot can know it is inside a warehouse and still fail if it does not understand the geometry of the barrier, door, pallet or equipment directly in front of it. that creates another layer of data demand. world → building → room → object → interaction traditional mapping gets weaker as you move down that stack. distributed phone capture becomes more useful. vangrid may start by mapping places, but the more interesting dataset could eventually be the long tail of physical objects inside those places machines do not only need a map of reality they need to understand what reality is made of @vangrid_io
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Ardavan Homayounfar retweeted
An AI agent can spend hundreds of dollars getting a location filmed Or just $0.01 checking whether the data already exists That's a detail I almost missed in @vangrid_io docs Vangrid isn't only building a marketplace where people get paid to capture real places It also has an x402 API for accessing existing onchain-attested capture records The pricing is pretty interesting $0.01 for a spatial query $0.005 for a specific observation Free to verify its provenance All paid in USDC on Base or Arc No account or API key required And they've already published an MCP server that connects these tools to AI agents But there's an important catch That one-cent query doesn't buy you a 3D model It returns metadata like coarse location, timestamps, capture hashes and Base attestations Useful for checking what data exists and where it came from Not a replacement for commissioning fresh footage That's what makes the model interesting to me Two different services in the same network Need to know whether a location has already been captured? Query the existing records Need a new 3D reconstruction? Post a bounty and pay someone to capture it But cheap API pricing doesn't automatically mean demand We still need evidence that agents are making repeated paid queries Because building the infrastructure is one thing Getting autonomous software to spend real money on physical-world information is another That's the metric I'd watch next for @vangrid_io
🤝 Paid partnership
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Ardavan Homayounfar retweeted
Ran a raw performance profile on @vangrid_io mobile ingestion pipeline today, and it solves one of the hardest engineering problems in spatial DePIN: How do you transmit dense 3D point clouds from mobile edge nodes without melting device bandwidth or losing millimeter level spatial fidelity? A raw 10bsecond multi view spatial scan generates gigabytes of uncompressed point clouds, visual features, and IMU telemetry vectors. Uploading raw volumetric data from millions of smartphones is completely unsustainable. Testing their edge processing stack revealed how Vangrid overcomes the bandwidth bottleneck: ▫️ On Device Feature Quantization: Mobile edge nodes run localized spatial quantization, filtering out redundant background voxels and converting heavy 3D mesh vectors into lightweight geometric descriptors directly in RAM. ▫️ Deterministic Mesh Delta Hashing: Instead of uploading repetitive spatial geometry for previously scanned corridors, the node only transmits frame delta hashes relative to existing Base ledger state. ▫️ Zero Loss Provenance Receipts: Even with heavy edge compression, the hardware Secure Enclave binds a signed provenance_hash to the payload, ensuring the compressed 3D GLB model remains cryptographically verifiable via Ethereum Attestation Service (EAS). October 2026 Onchain Scale: • 1,250,000+ spatial captures anchored on @Base • 450,000+ active smartphone edge scouts • 4,700+ Merkle batch attestations verified via EAS • $600,000+ USDC settled directly to physical scouts If Physical AI is going to scale globally, we don't just need more data—we need hyper efficient, edge-quantized spatial data. Inspect live attestations: explorer.vangrid.io Read edge pipeline specs: docs.vangrid.io Commission spatial bounties: data.vangrid.io
🤝 Paid partnership
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Ardavan Homayounfar retweeted
i just spent the last hour walking around filming random spots with my phone and would be getting paid for it through @vangrid_io 😅 you record short clips of places, they turn those into clean 3D models that help robots and AI actually understand the real world the way we do. the two things you need to do this is just your phone and a bit of time. it is wild how something this simple is feeding the next wave of physical AI and making the whole thing accessible. this is the kind of future I want to be part of every day.
🤝 Paid partnership
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Ardavan Homayounfar retweeted
A cracked sidewalk or a blocked road can be easy to miss until someone reports it. @vangrid_io is exploring how smartphone captures could help make real-world changes easier to document. The same approach could also support Physical AI, where robots need accurate information about the places they operate. ➟ People capture real locations using their phones. ➟ The footage can be turned into 3D spatial data. ➟ Onchain records help verify where the data came from. I like the potential here in @vangrid_io . One network could help make real-world data more accessible for both AI systems and infrastructure monitoring. The key is making sure the data is accurate and useful.
🤝 Paid partnership
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Ardavan Homayounfar retweeted
better camera quality does not automatically mean better spatial data. A high resolution video can show every detail on a wall and still fail to capture the angles needed to reconstruct the space accurately. Camera movement,overlapping views and the structure of the environment all matter. That is an important distinction for @vangrid_io . Turning phone footage into useful 3D data requires more than recording clear images. The capture needs to provide enough visual information for the space to be reconstructed. For physical AI, seeing more pixels is useful. Understanding how those pixels fit together in a real environment is what makes the data valuable. @vangrid_io
🤝 Paid partnership
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Ardavan Homayounfar retweeted
One thing I find interesting about @vangrid_io is how it connects ordinary smartphones with the future of AI. We see streets, buildings and busy places every day, but for AI to understand these environments, that real world data needs to be captured and verified. Vangrid is making that possible through community powered 3D data collection, with provenance recorded onchain. Sometimes, the next big step in AI isn't about making models smarter. It's about helping them understand the world around us @vangrid_io
🤝 Paid partnership
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Ardavan Homayounfar retweeted
What if the people who capture the real world could also help shape the future of spatial data? That’s the idea that makes @vangrid_io worth watching. Think about it. Cities change every day, buildings go up, roads evolve and entire neighborhoods transform. Yet, much of the spatial information we depend on comes from centralized sources that may not reflect what’s happening on the ground right now. Vangrid is exploring a different approach - Real places become data sources users can capture physical locations based on actual demand. - People contribute directly instead of relying entirely on existing datasets, the network can bring fresh information from contributors who are physically present. - Proof matters onchain records help establish that a capture was submitted, creating a publicly verifiable layer of accountability. - From raw footage to usable models the goal is to turn real-world captures into structured 3D representations that developers and businesses can actually use. What really makes this interesting is the potential shift in how spatial data is created and accessed. Instead of depending solely on a handful of data providers, a distributed network could give more people a role in building the digital representation of our physical surroundings. And the scale reported in Vangrid’s October 12 grid update makes the project even more interesting: over 1.25 million captures, 450,000+ users and more than $600,000 in USDC settled. Those figures suggest meaningful activity, although long-term data quality, verification and freshness will be just as important as growth. The bigger vision goes beyond mapping streets. It’s about building a system where real-world information can be contributed, verified and turned into something useful for the next generation of spatial applications. The physical world is constantly changing. Shouldn’t the data representing it evolve just as quickly? That’s the question I’m watching @vangrid_io explore.
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Ardavan Homayounfar retweeted
Ever needed a 3D view of a place that hasn’t been mapped yet? @vangrid_io has a way to request one. Its Bounty Board connects buyers who need real-world spatial data with people who can capture it. ➟ Request: Buyers specify a location and the details they need. ➟ Capture: Local contributors film the area using their phones. ➟ Review: The footage is reconstructed into a 3D model, and buyers review a preview before accepting it. ➟ Payment: Bounties use USDC on Base or Arc, with funds held in escrow for eligible requests. The board shows $675K+ in settlements and requests covering warehouses, loading docks, stairwells and more. I like how Vangrid connects real-world data needs with people who can collect it.
🤝 Paid partnership
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Ardavan Homayounfar retweeted
Collecting real world data has a coordination problem. A team may need a specific location captured, but reaching that place, gathering the footage, and verifying its authenticity can take time and resources. @vangrid_io connects these pieces through a bounty based system. Anyone can request a capture of a real location, a nearby contributor records it with a phone, and the resulting proof is anchored onchain before the 3D model is delivered. What makes this approach worth watching is the way it connects local access with global demand. People can contribute data from places they already have access to, while developers and businesses can request specific environments without sending their own teams everywhere. If this model scales, it could make spatial data more accessible for robotics, simulation, mapping, and AI systems that need better representations of the physical world. The opportunity goes beyond capturing places. It is about building a more accessible pipeline between the physical world and the digital systems learning to understand it. Explore Vangrid at data.vangrid.io
🤝 Paid partnership
The physical world is now orderable. Post a bounty for a real place. Someone nearby films it on their phone. Proof of capture lands onchain. You get the 3D model. data.vangrid.io
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Ardavan Homayounfar retweeted
a capture network is only as good as the footage it lets in, and @vangrid_io doesn't take footage from just anyone. open uploads sound fair. anyone with a phone can add data. in practice they bring blurry clips, fake locations and noise nobody can trust. @vangrid_io is built the other way around. a buyer posts a bounty for one real place. the money sits in escrow while the job is open. an operator films an orbit and the buyer accepts a preview. proof of capture goes onchain, and the buyer gets the 3d model. the early product note was blunt about the reason: open uploads mean bad data, so operators are vetted. and the requests are real places people actually need modeled. loading docks, car parks, plant yards, hotel lobbies. 3d data is only worth paying for if you can trust where it came from. would you rather buy from a network anyone can upload to, or one where every capture has a buyer behind it? @vangrid_io
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Ardavan Homayounfar retweeted
かぼちゃ 🧡 美味しい季節ということで描いてみました✍🏻 ・ご飯と一緒に炊く ・お肉と焼く ・パンプキンケーキ ・天ぷら 色々なお料理で活躍する野菜! かぼちゃも好きです☺️ #イラスト #野菜 #かぼちゃ #秋の味覚 #painting #AutumnVibes #pumpkin
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Ardavan Homayounfar retweeted
Good Morning my friends😍💚 Check this out for 1000th time😂👇 We still remember! 👑 8/10 👑 6.93 xtz Help me get this sold out🥹
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A male Eastern Bluebird with a bit of a leaf stuck to his beak.
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Ardavan Homayounfar retweeted
Michael Kenna #photography Popocatepetl Volcano, Mexico City, Mexico. 2006
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A cute Tufted Titmouse on a frosty morning
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