HYPERWEAVE://
HYPERWEAVE:///APPLICATIONS_CATALOG

WHAT GETS BUILT
ON HYPERWEAVE

Hyperweave is the substrate — a peer-owned, location-aware fabric where every node knows where it is, how capable it is, and where its neighbors are. The applications below are where that combination is structurally better than anything else, where it composes with one integration layer, and where it does not belong.

01
NATIVE_FIT

AI · Agents · Robotics · Edge.

These applications need three properties at once: peer-owned infrastructure, geographic locality, and capability-tier awareness. AI inference routing, agentic communication, autonomous fleets, and digital twins all become native primitives here — no other distributed substrate combines them.

01

AI Agent Mesh

AI agents that find and talk to each other based on where they are

THE_PROBLEM

Today's AI agents route every conversation, tool call, and hand-off through central servers in distant data centers. When agents need to share live context or collaborate, that round-trip wastes time and bandwidth. Nothing lets one agent find the nearest peer with the right skill.

HYPERWEAVE_FIT

Hyperweave gives every agent a location. An agent can ask "who's near me with this skill or this data?" and get an answer in one hop. Coordination happens locally; the central API becomes optional.

Same cost per agent at 10 million as at 10 thousandEXPLORE
02

Edge AI / LLM Inference

Send each query to the nearest GPU that already has your model loaded

THE_PROBLEM

Centralized AI providers route every query to a handful of data centers. Latency suffers, data leaves your region, and capacity bottlenecks during peak hours. Meanwhile community GPUs — gaming PCs, workstations, smaller clouds — sit unused because nothing can find them.

HYPERWEAVE_FIT

Hyperweave finds the closest capable peer with your model loaded across providers, regions, and community capacity in one query. Every GPU on the network becomes discoverable; every query lands at the fastest available.

4.65× faster routing than other distributed networksEXPLORE
03

Federated AI Coordination

Train one model across thousands of hospitals or banks — the data never leaves their site

THE_PROBLEM

Federated learning lets institutions train shared models without sharing raw data. The problem: existing frameworks fall apart past a few hundred participants. Sites can't find each other, aggregators get overwhelmed, and model updates get lost.

HYPERWEAVE_FIT

Hyperweave handles the coordination — finding sites, electing local aggregators, tracking who contributed what, distributing the updated model. You bring the privacy crypto; Hyperweave handles the choreography across 10,000+ participants.

Scales to 10,000+ sites with no central coordinatorEXPLORE
04

Robotics, Drones & Autonomous Fleets

Shared HD maps, hazard alerts, and route data — next to the safety controller, not inside it

THE_PROBLEM

Every robotics company — Waymo, Cruise, Zipline, drone makers, warehouse robotics — builds its own cloud for maps, hazard alerts, sensor logs, and route replays. Fleets can't share data across vendors. Maps go stale. Hazards stay local.

HYPERWEAVE_FIT

Hyperweave is the shared layer for everything that's NOT in the real-time safety loop. HD maps live where the roads or airspace are. Hazards propagate to vehicles or drones in the area within a second. Three fleets sharing data means the fourth fleet inherits it for free.

Cross-fleet data sharing without picking a vendorEXPLORE
05

Spatial Digital Twins

Mirror what's happening inside a factory or grid in real time — on-premises

THE_PROBLEM

Industrial twin platforms (Siemens, GE Predix, Bentley, Esri) host everything in the vendor's cloud. Sensor data has to leave the facility, latency hurts, and switching vendors means rebuilding from scratch.

HYPERWEAVE_FIT

Hyperweave maps every sensor, valve, and pump to its physical location. State updates flow to operators in the region within a second; raw telemetry never has to leave the building. The digital twin lives where the equipment lives.

From 10,000 to 10 million entities on the same protocolEXPLORE
06

DePIN Networks

Wireless coverage, mapping cars, GPU clouds, telemetry meshes — without inventing a chain

THE_PROBLEM

DePIN networks like Helium, Hivemapper, Render, DIMO, and Geodnet all had to invent custom routing or run on chains that weren't built for it. They lose the one thing they should leverage most: geography. Coverage proofs become consensus rounds; spatial queries become broadcasts.

HYPERWEAVE_FIT

Hyperweave makes geography and capability the routing primitives. "Which hotspots cover this neighborhood?" or "Find a GPU within 50 km" is one query. Coverage proofs become signatures from your physical neighbors.

From 50,000 devices to 50 million, flat per-device costDESIGN_BRIEF
07

AR/VR Spatial Computing

Anchors, scenes, and avatars that live where the real-world place lives

THE_PROBLEM

Apple Vision Pro, Niantic, Microsoft Mesh all own your spatial data. If their cloud is down — or you're offline — your AR doesn't work. Portability across providers doesn't exist.

HYPERWEAVE_FIT

Spatial anchors and 3D scenes are stored at their physical coordinates on the network. Your headset fetches the anchors near you in one hop. No central provider holds the keys to your AR world.

Local-network-speed anchor loads at any network sizeDESIGN_BRIEF
08

Smart Cities & IoT

City-wide sensor networks without a centralized broker bottleneck

THE_PROBLEM

Smart cities funnel everything — traffic, air quality, parking, transit — through AWS IoT Core, Azure IoT Hub, or Google Cloud IoT. The broker is a single point of failure, costs scale with sensor count, and queries throw away the geographic structure.

HYPERWEAVE_FIT

Sensors connect directly to nearby neighbors. Gateways aggregate locally. Regional servers handle cross-region queries. "Air quality within this district" becomes a structured query, not a database scan.

100,000 sensors costs the same per sensor as 1,000DESIGN_BRIEF
09

IoT at Industrial Scale

Hundreds of millions of factory, farm, and logistics sensors — no per-message bill

THE_PROBLEM

Industrial IoT, agriculture, logistics, and energy generate huge volumes of geographic data. Centralized brokers (AWS IoT Core, Azure IoT Hub) charge per message, become single points of failure, and force every reading to leave the local network.

HYPERWEAVE_FIT

Tiny edge devices ($5 ESP32, battery sensors) carry just a handful of neighbor connections. Gateways aggregate locally; only summaries and anomalies travel up. The protocol cost stays flat as you add sensors.

Per-sensor cost doesn't grow with the networkDESIGN_BRIEF
10

Decentralized CDN

A content network anyone can run nodes for — built for the long tail Cloudflare can't reach

THE_PROBLEM

Cloudflare and Akamai handle big customers well, but charge $0.08/GB egress — pricing out indie creators, scientific datasets, and emerging-market hosting. IPFS exists but doesn't bias by location: a user in Mumbai often pulls from Atlanta.

HYPERWEAVE_FIT

Every chunk lands deterministically near its consumers. Run an edge node in your region; nearby users pull from you in one hop. Operators get paid for serving local traffic.

4.65× faster than today's distributed file networksEXPLORE
11

Sovereign Computing & Data Residency

Jurisdiction rules enforced by the network itself, not by an app-layer policy

THE_PROBLEM

GDPR in Europe, data localization laws in India, China, and Brazil — every region wants its data to stay home. Building this on AWS regions is policy at the application layer; policies get misconfigured. Compliance becomes a promise instead of a guarantee.

HYPERWEAVE_FIT

Tag a region constraint into the data itself. Replicas literally cannot land outside an approved region. Compliance becomes provable from any node's state.

Compliance you can prove, not just promiseDESIGN_BRIEF
12

Crisis Mesh Networks

Coordination that keeps working when the internet doesn't

THE_PROBLEM

Hurricane wipes out towers. Earthquake breaks ISPs. Wartime networks go down. Twitter, WhatsApp, even ham radio fall over. Existing mesh apps (Briar, Bridgefy) don't scale past a few hundred nodes.

HYPERWEAVE_FIT

Phones, laptops, and field gear connect directly over Wi-Fi, Bluetooth, or LoRa. One device with any working uplink — satellite, restored cell, working ISP — bridges the local mesh back to the global network.

City-scale mesh on commodity devicesDESIGN_BRIEF
02
STRONG_FIT_WITH_INTEGRATION

With one extra layer, you can build...

Hyperweave handles the network plumbing — finding peers, moving data, healing under failure. Add one specialized layer on top (an editing library, an encryption protocol, a scheduler) and you can build things that aren't possible on centralized infrastructure today.

Model & Dataset Distribution

Move huge AI models (200 GB – 2 TB) around the network without paying anyone's bandwidth bill. A copy lives near the uploader so same-region downloads finish in one hop; popular models replicate further automatically.

REQUIRES: A name registry so people can refer to models by name

Personal Data Mesh

Your data lives across your own phone, laptop, home server, and a few trusted peers. No iCloud, no Google Drive, no per-app sync server, no provider seeing what's in your files.

REQUIRES: A library for editing shared docs (Automerge, Yjs)

App Sync Without Sync Servers

Linear, Notion, Obsidian — every modern note-taking app builds its own sync infrastructure. Hyperweave is that sync layer, peer-owned. The editing library handles merges; Hyperweave moves the bytes.

REQUIRES: An editing library (Automerge, Yjs, Loro)

IPFS, Done Right

Same idea as IPFS — files identified by their content hash, distributed across volunteers — with two upgrades: downloads pull from peers near you, and finding content is instant instead of a multi-second DHT walk.

REQUIRES: An incentive layer to pay storage operators

Peer-Owned Messaging

Signal-style messaging without Signal's central server. Encrypted messages land at the recipient's cell on the network. Sender and recipient in the same region means the message stays in that region.

REQUIRES: An end-to-end encryption layer (Signal Protocol, MLS)

Decentralized Social Protocol

The federation layer that Bluesky, Mastodon, and Nostr have all needed. Posts live near the followers, discovery is built into the network, and the design scales to a billion users.

REQUIRES: An identity standard (DIDs, Verifiable Credentials)

Mission-Critical That Keeps Working

When AWS, GitHub, or Cloudflare go down, your network keeps working. Health systems, payment settlement, defense logistics, emergency communications — anywhere a centralized outage isn't acceptable.

REQUIRES: A consistency overlay for transactional workloads

Scientific & Industrial Edge Compute

Telescopes, particle accelerators, gene sequencers, and climate sensors all stream data at the edge. Hyperweave puts that data near the instrument and routes processing jobs to the nearest capable computer. Cloud HPC becomes the spillover, not the default.

REQUIRES: A job scheduler and pipeline orchestrator
PROTOCOL_METRICS

PERFORMANCE BENCHMARKS

Measured on a multi-region cloud harness, N=10–100, against top-tier production DHTs. Numbers correspond to Table I and §11.C of the Hyperweave arXiv paper.

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FASTER MEDIAN LATENCY
vs top-tier DHTs (retrieve p50)
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FASTER TAIL LATENCY
vs top-tier DHTs (retrieve p99)
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FASTER RECOVERY
post-churn rejoin vs top-tier DHTs
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HIGHER CHURN SUCCESS
@ 20 % sudden kill vs top-tier DHTs
HYPERWEAVE_MESH_ACTIVE
START_BUILDING_TODAY

READY TO DECENTRALIZE?

Access the developer preview and start building with Hyperweave's geo-intelligent mesh network. Enterprise licensing available for production deployments.

READ_WHITEPAPER
QUICK_START
$ npm install @hyperweave/core
$ hyperweave register
$ hyperweave start
▋ Node started. Connected to 1,247 peers.
PATENT_PENDING
ENTERPRISE_READY
GEO_INTELLIGENT
SELF_HEALING