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Today, Niantic Spatial introduces Scaniverse — our flagship product and the gateway to our spatial intelligence platform and Large Geospatial Model. Here’s what’s new: 🔹Scaniverse (iOS mobile + web): Capture once, generate multiple outputs—VPS maps, meshes, and Gaussian splats (Android coming soon) 🔹Collaborative mapping: Multi-user scans fused into a...

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Niantic Spatial 🌎

957,890 subscribers

122,124 Aufrufe • vor 5 Monaten •via X (Twitter)

Wissenschaft & Technologie
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13 Kommentare

Profilbild von Ant A. 🇺🇸
Ant A. 🇺🇸vor 5 Monaten

How do we include the Insta360 footage to the Scaniverse app?

Profilbild von Fagliacci🏳️‍⚧️
Fagliacci🏳️‍⚧️vor 3 Monaten

Fuck you, genocidal bags of bitch

Profilbild von Robbie Campos
Robbie Camposvor 5 Monaten

Looking forward to the android version. I wonder how different it is from what I currently use. Looks interesting, kudos.

Profilbild von Daniel Ortega
Daniel Ortegavor 5 Monaten

This solves an actual problem. GPS drift in urban canyons and industrial sites has plagued robotics forever. VPS 2.0 addressing this at global scale? Exactly the practical engineering we need more of.

Profilbild von Dilmer
Dilmervor 5 Monaten

@tipatat Very cool thanks for sharing it!

Profilbild von Randy Wallace
Randy Wallacevor 5 Monaten

How much does this cost to use? I'm sure there are subscriptions.

Profilbild von Roberthopkins
Roberthopkinsvor 2 Monaten

What's new in Pokémon go????

Profilbild von 岡田泰彦｜光邦｜AI×印刷エヴァンジェリスト
岡田泰彦｜光邦｜AI×印刷エヴァンジェリストvor 5 Monaten

This is seriously cool. これは普通にすごいですね。

Profilbild von Jimmy6DOF
Jimmy6DOFvor 5 Monaten

when android !

Profilbild von Reuben Huffman
Reuben Huffmanvor 5 Monaten

how does this compare to PolyCam? I'm thinking of using this to scan a landscape garden and house in preparation for landscape design.

Profilbild von Chukwudalu Dumebi-Kachikwu
Chukwudalu Dumebi-Kachikwuvor 5 Monaten

Very cool

Profilbild von G-Squiddy
G-Squiddyvor 1 Monat

Mister Hanke the christmas poo the first member of the DK crew scrapes your data for G men so he can suck chinese semen

Profilbild von Alex Tolson
Alex Tolsonvor 5 Monaten

Having this for of tech in your pocket is very exciting, great stuuf

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Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.

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AI Companions

118,537 Aufrufe • vor 1 Jahr

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:
1:07

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

177,073 Aufrufe • vor 9 Monaten

Scaling campaigns overseas sounds like a creative problem. Honestly, it’s not. The real bottleneck is localization. As a product lead, I’ve lost too many weeks waiting for native voice actors, rebuilding region-specific edits, and manually fixing lip-sync issues that still looked slightly off in the final export. The worst part is that every new market turns into another production branch to maintain. That simply does not scale. So over the last few weeks, I started testing a few different AI localization workflows with our own ecommerce video ads to see which ones could actually survive real production conditions. Wizstar_official ended up being the one we kept coming back to. Not because it generated the flashiest demo. Because it was the first one that consistently held together once we pushed it into actual multi-market production. The Video Translation workflow supports 12 major languages, which already covers most global consumer markets we care about. But what stood out during testing was how natural the localization sounded. Not “translated”. Actually localized. The tone, pacing, and delivery felt native enough that most people on our team genuinely stopped noticing it was AI-generated after a few runs. More importantly, the video itself stays intact. Audio and visual timing remain aligned after translation, lip-sync holds even during side angles and faster speech, and multi-character scenes stay surprisingly stable instead of collapsing into mismatched cuts. That matters a lot more in production than benchmark style demos. We also tested it against a few other tools internally, and Wizstar consistently handled complex scenes better, especially when multiple speakers, product close-ups, and fast pacing were involved. The output needed significantly less cleanup before going live. Video Reference was another reason we kept using it. Being able to reuse existing high-performing ecommerce structures instead of rebuilding creative logic market by market saves an unreasonable amount of time. Seedance 2.0 also supports face input and multi-model orchestration, which noticeably improves character consistency and scene stability across longer sequences. After a few projects, Wizstar quietly became part of our workflow. We can now produce localized ecommerce creatives in minutes instead of rebuilding entire pipelines around every market. If you want to test it yourself: New users get free credits on signup. First subscription is $19 and includes a complimentary 30-second Ecommerce Agent workflow to test features like Product to Video. Let the tools handle the production overhead. The side-by-side comparison below shows one of our English masters translated into Spanish while keeping almost the exact same pacing and vibe intact. #Wizstar #AIVideo #GrowthHacking
0:43

Scaling campaigns overseas sounds like a creative problem. Honestly, it’s not. The real bottleneck is localization. As a product lead, I’ve lost too many weeks waiting for native voice actors, rebuilding region-specific edits, and manually fixing lip-sync issues that still looked slightly off in the final export. The worst part is that every new market turns into another production branch to maintain. That simply does not scale. So over the last few weeks, I started testing a few different AI localization workflows with our own ecommerce video ads to see which ones could actually survive real production conditions. Wizstar_official ended up being the one we kept coming back to. Not because it generated the flashiest demo. Because it was the first one that consistently held together once we pushed it into actual multi-market production. The Video Translation workflow supports 12 major languages, which already covers most global consumer markets we care about. But what stood out during testing was how natural the localization sounded. Not “translated”. Actually localized. The tone, pacing, and delivery felt native enough that most people on our team genuinely stopped noticing it was AI-generated after a few runs. More importantly, the video itself stays intact. Audio and visual timing remain aligned after translation, lip-sync holds even during side angles and faster speech, and multi-character scenes stay surprisingly stable instead of collapsing into mismatched cuts. That matters a lot more in production than benchmark style demos. We also tested it against a few other tools internally, and Wizstar consistently handled complex scenes better, especially when multiple speakers, product close-ups, and fast pacing were involved. The output needed significantly less cleanup before going live. Video Reference was another reason we kept using it. Being able to reuse existing high-performing ecommerce structures instead of rebuilding creative logic market by market saves an unreasonable amount of time. Seedance 2.0 also supports face input and multi-model orchestration, which noticeably improves character consistency and scene stability across longer sequences. After a few projects, Wizstar quietly became part of our workflow. We can now produce localized ecommerce creatives in minutes instead of rebuilding entire pipelines around every market. If you want to test it yourself: New users get free credits on signup. First subscription is $19 and includes a complimentary 30-second Ecommerce Agent workflow to test features like Product to Video. Let the tools handle the production overhead. The side-by-side comparison below shows one of our English masters translated into Spanish while keeping almost the exact same pacing and vibe intact. #Wizstar #AIVideo #GrowthHacking

Leo Reed

121,883 Aufrufe • vor 4 Monaten

Update on Parallel studios // Parallel TCG The brand-new, overhauled user experience for Parallel TCG is now complete, shaped by community feedback and refined through extensive optimization and game design. A significant amount of work has gone into elevating the game to the standard of world-class traditional TCGs. We are excited to bring it to mainstream mobile distribution platforms in the coming month through a global rollout across iOS and Android alongside the release of the next expansion set, Haven, and Parallel TCG World Championships Season 2. Parallel Colony We presented Colony at GDC in San Francisco at Google Cloud invitation, where it received a very strong response from the market. We are now in the final stages of completing the build and content ahead of a public iOS and Android release, currently targeted for April. Questions have come up around Colony’s visual resemblance to the lane-runner format often seen in mobile ads. That is a deliberate strategic choice, based on the fact that these formats remain among the few that continue to scale efficiently in mobile user acquisition. At its core, Colony is an AI-driven 4X title built with a deeper level of polish that we believe sets it apart from much of what is currently available in the market. Sanctuary We have slowed development on Sanctuary in order to prioritize the releases of TCG and Colony. Sanctuary remains a long-term project for us, but beyond the playtests we have already run, we are not setting hard timelines or release commitments at this stage. Wayfinder / AI AI is emerging as one of the most important technology shifts globally, driven by the rapid advancement of LLMs and autonomous agents. We believe this transition will create significant new infrastructure needs, and we have a number of internal AI initiatives underway to position ourselves accordingly. More will be shared on these initiatives in due course. Wayfinder Foundation 🧭 has shipped several functional upgrades, but we see a much larger opportunity for it to evolve into critical infrastructure for agents. We are also continuing to expand the utility of Prompt and explore adjacent use cases that we believe can unlock meaningful value for the broader market. As capital, attention, and product innovation increasingly converge around AI, our focus is on building tools that can serve as enabling infrastructure for that shift. SDK: Autolab: Cloud agent launcher: Skill: Kaparthys Loop x Defi: Try the Wayfinder skill on your OpenClaw instance today — just paste this to your bot: install the wayfinder openclaw skill by git cloning to our skills directory, read the skill.md and take me through the setup process. Outlook Over the near term, our focus is on execution across both gaming and AI. In gaming, the releases of Parallel TCG and Colony mark major milestones for the company, and we are eager to see how these titles perform from a growth perspective once they are in market. While sentiment across gaming remains soft, we believe differentiated, high-quality products can still find traction. On the AI side, our focus is on finding product-market fit for both Wayfinder’s role in enabling on-chain AI to interact with blockchains and for a new tool we have been developing around broader agent utility. The AI market continues to evolve at an extraordinary pace, and we are actively testing where these products can deliver the strongest utility and market relevance. We have been developing something new that leverages Wayfinder but has broader applicability, and we are actively testing it before bringing it to market. More will be shared as things progress.
0:40

Update on Parallel studios // Parallel TCG The brand-new, overhauled user experience for Parallel TCG is now complete, shaped by community feedback and refined through extensive optimization and game design. A significant amount of work has gone into elevating the game to the standard of world-class traditional TCGs. We are excited to bring it to mainstream mobile distribution platforms in the coming month through a global rollout across iOS and Android alongside the release of the next expansion set, Haven, and Parallel TCG World Championships Season 2. Parallel Colony We presented Colony at GDC in San Francisco at Google Cloud invitation, where it received a very strong response from the market. We are now in the final stages of completing the build and content ahead of a public iOS and Android release, currently targeted for April. Questions have come up around Colony’s visual resemblance to the lane-runner format often seen in mobile ads. That is a deliberate strategic choice, based on the fact that these formats remain among the few that continue to scale efficiently in mobile user acquisition. At its core, Colony is an AI-driven 4X title built with a deeper level of polish that we believe sets it apart from much of what is currently available in the market. Sanctuary We have slowed development on Sanctuary in order to prioritize the releases of TCG and Colony. Sanctuary remains a long-term project for us, but beyond the playtests we have already run, we are not setting hard timelines or release commitments at this stage. Wayfinder / AI AI is emerging as one of the most important technology shifts globally, driven by the rapid advancement of LLMs and autonomous agents. We believe this transition will create significant new infrastructure needs, and we have a number of internal AI initiatives underway to position ourselves accordingly. More will be shared on these initiatives in due course. Wayfinder Foundation 🧭 has shipped several functional upgrades, but we see a much larger opportunity for it to evolve into critical infrastructure for agents. We are also continuing to expand the utility of Prompt and explore adjacent use cases that we believe can unlock meaningful value for the broader market. As capital, attention, and product innovation increasingly converge around AI, our focus is on building tools that can serve as enabling infrastructure for that shift. SDK: Autolab: Cloud agent launcher: Skill: Kaparthys Loop x Defi: Try the Wayfinder skill on your OpenClaw instance today — just paste this to your bot: install the wayfinder openclaw skill by git cloning to our skills directory, read the skill.md and take me through the setup process. Outlook Over the near term, our focus is on execution across both gaming and AI. In gaming, the releases of Parallel TCG and Colony mark major milestones for the company, and we are eager to see how these titles perform from a growth perspective once they are in market. While sentiment across gaming remains soft, we believe differentiated, high-quality products can still find traction. On the AI side, our focus is on finding product-market fit for both Wayfinder’s role in enabling on-chain AI to interact with blockchains and for a new tool we have been developing around broader agent utility. The AI market continues to evolve at an extraordinary pace, and we are actively testing where these products can deliver the strongest utility and market relevance. We have been developing something new that leverages Wayfinder but has broader applicability, and we are actively testing it before bringing it to market. More will be shared as things progress.

//Kalos

14,525 Aufrufe • vor 6 Monaten

Efsane Platform Introduction I. Platform Overview • Platform Positioning: EFSANE (main domain is the world's fastest-growing blockchain news portal, serving as the core gateway to the entire ecosystem. The platform integrates multiple modules, including predictions, live streaming, games, and social networking, striving to provide users with a one-stop on-chain entertainment and interactive experience. • Core Mission: To establish a secure, reliable, low-threshold, and diverse on-chain entertainment platform, enabling users to conveniently participate in Gem (GEM) trials, USDT live games, prediction markets, live streaming interactions, and community exchanges, forming a complete closed-loop ecosystem. II. Core Values ​​and Features 1. One-Stop Ecosystem Hub • Integrated Sub-Channel Access: The main site homepage and user center clearly display channels for various modules, including prediction network, live streaming, blockchain games, and efschat (social networking), allowing users to directly access their desired scenarios without having to navigate multiple platforms. • Unified Asset and Account System: Centrally displays Gem/GEM and USDT balances, records participation in each module and historical returns, and enables one-stop asset management. • Unified Notifications and Customer Support: Integrates platform announcements, event reminders, and reward notifications, providing multiple customer service channels to significantly enhance the overall user experience. 2. Brand Trust and Security Transparency • Operational Data Announcements: The platform publicly discloses core metrics such as registered users, daily active users, withdrawal success rate, and total bonus pool, ensuring data authenticity and verifiability. • Compliance and Audit Visualization: Displays security audit summaries, risk control systems, and compliance instructions, allowing users to immediately perceive the platform's professionalism and credibility. • Risk Warnings and User Education: Key pages and workflows prominently highlight participation risks, and provide resources such as operation guides, video tutorials, and live streams. 3. Diverse Gameplay and Incentive Design • Gem/GEM Beginner Mechanism: Users can earn gems by signing in, completing tasks, or participating in events, allowing them to try out the game before converting, lowering the barrier to entry. • USDT Payment and Real Earnings Mechanism: Used in advanced games and predictive gameplay, ensuring authentic payment and cash-out mechanisms, enhancing asset authenticity and building trust. • Cross-module Incentive Mechanism: A task system enables cross-module linkage. For example, completing prediction tasks earns rewards in the live streaming/gaming modules, fostering deeper user engagement. • Multi-tiered Promotion Revenue Mechanism: Through an invitation code system and a three-tiered fission reward structure, promoters can earn high commissions, with commissions increasing to higher levels during special periods, stimulating user enthusiasm for cross-platform sharing. 4. Social and Community-Driven • Community Aggregation Portal: Enables cross-scenario discussion and sharing among users of modules like prediction, gaming, and live streaming. • User-generated Content Creator System: Encourages users to contribute high-quality content such as tutorials, guides, and reviews, providing incentives and resource support to outstanding creators and streamers. • Interactive Operational Activities: Regularly organize AMAs, online competitions, and data review livestreams to enhance user engagement and a sense of belonging to the platform. 5. Technical and User Experience Assurance • High-availability Architecture: The platform utilizes CDN acceleration, load balancing, and site-wide SSL/TLS encryption to ensure stable access and data security. • Full-Device Support and Multi-Language Optimization: Compatible with mobile and desktop devices, it supports a multi-language interface, offers a simple registration process, and quickly guides new users onboarding. • Behavioral Data-Driven Optimization: Analyze user behavior to deliver precise recommendations, improving gameplay conversion rates and user retention. III. Introduction to Key Modules (Platform Portal and Linked Examples) 1. Prediction Module ( Provides prediction scenarios for multiple sectors, including the crypto market, hot events, and sports events. Gameplay includes time-limited battles, binary options, and multiple-choice intervals. It features transparent settlement, a leaderboard mechanism, and integration with live streaming and the main platform's asset system. 2. Live Streaming Channel Showcases project roadshows, platform tutorials, live event broadcasts, and community interactive live streams to enhance user engagement and trust. It supports both gem and USDT tipping mechanisms and can be directly linked to the main platform's event page or task guide. 3. Chain Game Entertainment Channel Offers a diverse selection of games, from casual mini-games to competitive GameFi, supporting gem trials and USDT live trading. A leaderboard and tournament system is integrated with the main site's asset management and livestreaming content. 4. Social Community Users can participate in discussions, post content, and share task results in interest-based zones. A creator development system and content governance structure are established, serving as a hub for cross-module communication and feedback. 5. Other Expandable Portals The platform can subsequently expand subdomains such as dedicated event pages, tutorial pages, and creator centers as needed, all under the main domain for unified management. IV. User Flow Examples 1. First Visit: Users visit and register/log in. The homepage displays featured events and module portals, encouraging participation in gem trials or popular gameplay. 2. Onboarding: New users receive gem trial coupons and are guided through live tutorials or tutorials to quickly understand the platform's core mechanics. 3. Multi-Scenario Participation: Users can choose to participate in prediction betting, game battles, watch live streams and give rewards, join communities to express their opinions, or complete tasks and invite friends. 4. Asset Management and Withdrawal: Users can centrally view their Gem and USDT balances and earnings on the platform and withdraw them or use them to participate in other modules. Promotional earnings and commission details are displayed simultaneously. 5. Sticky Loop: The system periodically pushes cross-module tasks, community events, leaderboard incentives, and other content to promote continuous user engagement and platform retention. V. Trust and Compliance Assurance • Operational Transparency: The platform regularly publishes key data and security audit information to ensure openness and verifiability. • Risk Control Mechanism: Key processes such as withdrawals, deposits, and prediction participation are equipped with anomaly detection and anti-cheating mechanisms; large-scale transactions require KYC review. • Compliance Strategy: The platform monitors the regulatory status of crypto entertainment and prediction mechanisms in various markets and implements grayscale openness, geographic restrictions, and compliance disclosure procedures. • Privacy Compliance: The platform strictly adheres to local data protection laws to safeguard user privacy and security, and clearly states the scope of data usage in the user agreement. VI. Brand and Promotional Positioning • Suggested Platform Slogan: • " A one-stop on-chain entertainment platform with low barriers to entry, high transparency, and real returns." • "Gem Trials, USDT Play, the new standard for secure and reliable on-chain entertainment." • Core Marketing: Focus on beginner gem experiences, real USDT withdrawals, diverse gameplay options, and safety and compliance mechanisms. • Promotional Channels: Includes Telegram, Discord, and WhatsApp groups, livestream promotions with influencers (KOLs), and SEO/advertising (using keywords such as "on-chain entertainment platform" and "GameFi Real Returns"). VII. Technical and Operational Support System • Multilingual Operational Capabilities: Currently supports Chinese, English, Turkish, and Japanese, and will gradually expand to 16+ languages ​​globally, providing a localized experience for the international market. • Data-Driven Growth Analysis: Build a full-chain conversion analysis system to monitor new user conversion rates, retention rates, paying behavior, and task completion. • Customer Support and User Feedback Mechanism: Provide a multilingual customer service portal for immediate responses to user questions; promptly integrate community suggestions into product iterations and provide regular announcements. • Platform Optimization and Emergency System: Develop a security incident emergency response plan to ensure rapid platform recovery in the event of an emergency; continuously optimize the user experience through a data feedback mechanism. VIII. Future Development Outlook • Deep Ecosystem Development: Continuously optimize existing gameplay and module integrations, and explore the introduction of new economic mechanisms such as NFT incentives, DeFi mining, or staking. • Technology Evolution: Follow cutting-edge technologies such as Layer 2 expansion, off-chain settlement, and AI-powered recommendations to improve transaction efficiency and user experience accuracy. • Compliance Expansion Strategy: Promote legal operations in regions with mature regulations, and proactively prepare for compliance in high-potential markets to mitigate legal risks. • Community Brand Ecosystem: Cultivate a community of core players, influencers (KOLs), and creators, building a trusted brand image and enhancing user belonging through online livestreams and offline salons. 🔗 Register as a new user and receive $6. Join now:
0:41

Efsane Platform Introduction I. Platform Overview • Platform Positioning: EFSANE (main domain is the world's fastest-growing blockchain news portal, serving as the core gateway to the entire ecosystem. The platform integrates multiple modules, including predictions, live streaming, games, and social networking, striving to provide users with a one-stop on-chain entertainment and interactive experience. • Core Mission: To establish a secure, reliable, low-threshold, and diverse on-chain entertainment platform, enabling users to conveniently participate in Gem (GEM) trials, USDT live games, prediction markets, live streaming interactions, and community exchanges, forming a complete closed-loop ecosystem. II. Core Values ​​and Features 1. One-Stop Ecosystem Hub • Integrated Sub-Channel Access: The main site homepage and user center clearly display channels for various modules, including prediction network, live streaming, blockchain games, and efschat (social networking), allowing users to directly access their desired scenarios without having to navigate multiple platforms. • Unified Asset and Account System: Centrally displays Gem/GEM and USDT balances, records participation in each module and historical returns, and enables one-stop asset management. • Unified Notifications and Customer Support: Integrates platform announcements, event reminders, and reward notifications, providing multiple customer service channels to significantly enhance the overall user experience. 2. Brand Trust and Security Transparency • Operational Data Announcements: The platform publicly discloses core metrics such as registered users, daily active users, withdrawal success rate, and total bonus pool, ensuring data authenticity and verifiability. • Compliance and Audit Visualization: Displays security audit summaries, risk control systems, and compliance instructions, allowing users to immediately perceive the platform's professionalism and credibility. • Risk Warnings and User Education: Key pages and workflows prominently highlight participation risks, and provide resources such as operation guides, video tutorials, and live streams. 3. Diverse Gameplay and Incentive Design • Gem/GEM Beginner Mechanism: Users can earn gems by signing in, completing tasks, or participating in events, allowing them to try out the game before converting, lowering the barrier to entry. • USDT Payment and Real Earnings Mechanism: Used in advanced games and predictive gameplay, ensuring authentic payment and cash-out mechanisms, enhancing asset authenticity and building trust. • Cross-module Incentive Mechanism: A task system enables cross-module linkage. For example, completing prediction tasks earns rewards in the live streaming/gaming modules, fostering deeper user engagement. • Multi-tiered Promotion Revenue Mechanism: Through an invitation code system and a three-tiered fission reward structure, promoters can earn high commissions, with commissions increasing to higher levels during special periods, stimulating user enthusiasm for cross-platform sharing. 4. Social and Community-Driven • Community Aggregation Portal: Enables cross-scenario discussion and sharing among users of modules like prediction, gaming, and live streaming. • User-generated Content Creator System: Encourages users to contribute high-quality content such as tutorials, guides, and reviews, providing incentives and resource support to outstanding creators and streamers. • Interactive Operational Activities: Regularly organize AMAs, online competitions, and data review livestreams to enhance user engagement and a sense of belonging to the platform. 5. Technical and User Experience Assurance • High-availability Architecture: The platform utilizes CDN acceleration, load balancing, and site-wide SSL/TLS encryption to ensure stable access and data security. • Full-Device Support and Multi-Language Optimization: Compatible with mobile and desktop devices, it supports a multi-language interface, offers a simple registration process, and quickly guides new users onboarding. • Behavioral Data-Driven Optimization: Analyze user behavior to deliver precise recommendations, improving gameplay conversion rates and user retention. III. Introduction to Key Modules (Platform Portal and Linked Examples) 1. Prediction Module ( Provides prediction scenarios for multiple sectors, including the crypto market, hot events, and sports events. Gameplay includes time-limited battles, binary options, and multiple-choice intervals. It features transparent settlement, a leaderboard mechanism, and integration with live streaming and the main platform's asset system. 2. Live Streaming Channel Showcases project roadshows, platform tutorials, live event broadcasts, and community interactive live streams to enhance user engagement and trust. It supports both gem and USDT tipping mechanisms and can be directly linked to the main platform's event page or task guide. 3. Chain Game Entertainment Channel Offers a diverse selection of games, from casual mini-games to competitive GameFi, supporting gem trials and USDT live trading. A leaderboard and tournament system is integrated with the main site's asset management and livestreaming content. 4. Social Community Users can participate in discussions, post content, and share task results in interest-based zones. A creator development system and content governance structure are established, serving as a hub for cross-module communication and feedback. 5. Other Expandable Portals The platform can subsequently expand subdomains such as dedicated event pages, tutorial pages, and creator centers as needed, all under the main domain for unified management. IV. User Flow Examples 1. First Visit: Users visit and register/log in. The homepage displays featured events and module portals, encouraging participation in gem trials or popular gameplay. 2. Onboarding: New users receive gem trial coupons and are guided through live tutorials or tutorials to quickly understand the platform's core mechanics. 3. Multi-Scenario Participation: Users can choose to participate in prediction betting, game battles, watch live streams and give rewards, join communities to express their opinions, or complete tasks and invite friends. 4. Asset Management and Withdrawal: Users can centrally view their Gem and USDT balances and earnings on the platform and withdraw them or use them to participate in other modules. Promotional earnings and commission details are displayed simultaneously. 5. Sticky Loop: The system periodically pushes cross-module tasks, community events, leaderboard incentives, and other content to promote continuous user engagement and platform retention. V. Trust and Compliance Assurance • Operational Transparency: The platform regularly publishes key data and security audit information to ensure openness and verifiability. • Risk Control Mechanism: Key processes such as withdrawals, deposits, and prediction participation are equipped with anomaly detection and anti-cheating mechanisms; large-scale transactions require KYC review. • Compliance Strategy: The platform monitors the regulatory status of crypto entertainment and prediction mechanisms in various markets and implements grayscale openness, geographic restrictions, and compliance disclosure procedures. • Privacy Compliance: The platform strictly adheres to local data protection laws to safeguard user privacy and security, and clearly states the scope of data usage in the user agreement. VI. Brand and Promotional Positioning • Suggested Platform Slogan: • " A one-stop on-chain entertainment platform with low barriers to entry, high transparency, and real returns." • "Gem Trials, USDT Play, the new standard for secure and reliable on-chain entertainment." • Core Marketing: Focus on beginner gem experiences, real USDT withdrawals, diverse gameplay options, and safety and compliance mechanisms. • Promotional Channels: Includes Telegram, Discord, and WhatsApp groups, livestream promotions with influencers (KOLs), and SEO/advertising (using keywords such as "on-chain entertainment platform" and "GameFi Real Returns"). VII. Technical and Operational Support System • Multilingual Operational Capabilities: Currently supports Chinese, English, Turkish, and Japanese, and will gradually expand to 16+ languages ​​globally, providing a localized experience for the international market. • Data-Driven Growth Analysis: Build a full-chain conversion analysis system to monitor new user conversion rates, retention rates, paying behavior, and task completion. • Customer Support and User Feedback Mechanism: Provide a multilingual customer service portal for immediate responses to user questions; promptly integrate community suggestions into product iterations and provide regular announcements. • Platform Optimization and Emergency System: Develop a security incident emergency response plan to ensure rapid platform recovery in the event of an emergency; continuously optimize the user experience through a data feedback mechanism. VIII. Future Development Outlook • Deep Ecosystem Development: Continuously optimize existing gameplay and module integrations, and explore the introduction of new economic mechanisms such as NFT incentives, DeFi mining, or staking. • Technology Evolution: Follow cutting-edge technologies such as Layer 2 expansion, off-chain settlement, and AI-powered recommendations to improve transaction efficiency and user experience accuracy. • Compliance Expansion Strategy: Promote legal operations in regions with mature regulations, and proactively prepare for compliance in high-potential markets to mitigate legal risks. • Community Brand Ecosystem: Cultivate a community of core players, influencers (KOLs), and creators, building a trusted brand image and enhancing user belonging through online livestreams and offline salons. 🔗 Register as a new user and receive $6. Join now:

EFSANE

28,263 Aufrufe • vor 11 Monaten

Fast Company just published a great piece on World Labs , Fei-Fei Li , Marble, and the idea that spatial intelligence / world models may be one of the next big shifts in AI. I was happy to be quoted in the article, but I also wanted to share more context about my own experience with World Labs and Marble, and why this direction is especially interesting to me. My starting point: volumetric capture — For the past few years I’ve been exploring and using volumetric capture and reconstruction (photogrammetry, NeRFs, 3D Gaussian Splats) mostly capturing locations around Montreal. Alleys, museums, urban interiors. I love every step of it: the capture itself, the pipeline, and what can be done with the output. Turning real spaces into real-time explorable systems. I do this personally, sharing explorations here, and professionally as chief technologist, and co-founder of Dpt. Physical reality + generative manipulation — In my work I’m especially drawn to mixing physical reality with generative and digital manipulation: using physical interfaces (light, clay, ink, ... ) to drive generative AI pipelines, building mixed reality prototypes that reshape your surroundings, or starting from real captured spaces and transforming them using tools like Marble. Like many people, I saw the World Labs announcement on Twitter in September 2024, and Marble when it surfaced in early December. But by then, I already had a sense something was coming. The first conversation — As someone deep into volumetric capture and radiance fields, I obviously knew about Ben Mildenhall and his pioneering work on NeRF. To my surprise, Ben reached out to me in late June 2024. He’d been following some of my experiments and wanted to chat about my process and workflows and how I was using this “stuff” creatively. At that point he didn’t share what he was building, but we had a genuinely great conversation about radiance fields, AI, and my work. He was curious about the creative perspective, not just the technical one. When the World Labs announcement dropped a few months later, it all made sense. I understood what Ben had been working on, and why the creative angle mattered to them. Then in August 2025, he invited me to try the Marble beta, and I’ve been experimenting with it since. Experimenting with Marble — The first thing I used Marble for was materializing scene and world concepts during ideation at the studio, and seeing if and how it could fit into our production pipeline. In parallel, I dove into a series of experiments focused on world manipulation: starting from real captured spaces and transforming them using Marble. I’d already been exploring that idea using img2img diffusion with ControlNet on NeRF renders, real-time video streams, and even mixed reality using headset camera feeds. But Marble brings something different. It generates persistent, spatially cohesive 3D worlds that can be rendered in real time across a wide range of devices. That’s a real shift. Experiment 01: Parallel Realities — The first experiment, Parallel Realities, starts from a volumetric capture of a real location, reconstructed as 3D Gaussian Splats. Using Marble, I generate an alternate version of that same space, something informed by the original architecture: abandoned, nature-reclaimed, alternate era. Then, using Spark (World Labs’ 3D Gaussian Splatting renderer for THREE.js) I make both realities coexist in the same spatial coordinate system. From there, I use a portal UX mechanic to let the user step between the real reconstruction and the Marble-generated version. Experiment 02: Hidden Depth The second experiment, Hidden Depth, does not transform a space as much as expand it. A captured location has a visual boundary (a mural, a doorway, a dark corridor) and Marble generates what exists beyond it. For example: a Montreal alley has a painted mural; step through it and you’re inside a world informed by what is actually depicted there. World Labs showcased part of this work here: And in their Spark 2.0 post: The project page is here: Why this matters to me — Being able to start from a real 3D Gaussian Splat scene and manipulate it with Marble opens up a lot of ideas. The 3DGS pipeline is becoming an increasingly compelling foundation for exploration, experimentation, and storytelling. What matters most to me right now is more control. The more I can steer the generated scene or world, the more useful the tool becomes. I want more features like the already existing multiple input images and Chisel, the blockout-based approach. I would like better local control, the ability to expand a generated world more and more while preserving coherence, and the ability to directly import 3D Gaussian Splat scenes to be used as a starting point. I want more ways to shape the result, not just a “prompt and hope” approach. — It is exciting to see this field moving from research and demos toward actual creative workflows.
1:04

Fast Company just published a great piece on World Labs , Fei-Fei Li , Marble, and the idea that spatial intelligence / world models may be one of the next big shifts in AI. I was happy to be quoted in the article, but I also wanted to share more context about my own experience with World Labs and Marble, and why this direction is especially interesting to me. My starting point: volumetric capture — For the past few years I’ve been exploring and using volumetric capture and reconstruction (photogrammetry, NeRFs, 3D Gaussian Splats) mostly capturing locations around Montreal. Alleys, museums, urban interiors. I love every step of it: the capture itself, the pipeline, and what can be done with the output. Turning real spaces into real-time explorable systems. I do this personally, sharing explorations here, and professionally as chief technologist, and co-founder of Dpt. Physical reality + generative manipulation — In my work I’m especially drawn to mixing physical reality with generative and digital manipulation: using physical interfaces (light, clay, ink, ... ) to drive generative AI pipelines, building mixed reality prototypes that reshape your surroundings, or starting from real captured spaces and transforming them using tools like Marble. Like many people, I saw the World Labs announcement on Twitter in September 2024, and Marble when it surfaced in early December. But by then, I already had a sense something was coming. The first conversation — As someone deep into volumetric capture and radiance fields, I obviously knew about Ben Mildenhall and his pioneering work on NeRF. To my surprise, Ben reached out to me in late June 2024. He’d been following some of my experiments and wanted to chat about my process and workflows and how I was using this “stuff” creatively. At that point he didn’t share what he was building, but we had a genuinely great conversation about radiance fields, AI, and my work. He was curious about the creative perspective, not just the technical one. When the World Labs announcement dropped a few months later, it all made sense. I understood what Ben had been working on, and why the creative angle mattered to them. Then in August 2025, he invited me to try the Marble beta, and I’ve been experimenting with it since. Experimenting with Marble — The first thing I used Marble for was materializing scene and world concepts during ideation at the studio, and seeing if and how it could fit into our production pipeline. In parallel, I dove into a series of experiments focused on world manipulation: starting from real captured spaces and transforming them using Marble. I’d already been exploring that idea using img2img diffusion with ControlNet on NeRF renders, real-time video streams, and even mixed reality using headset camera feeds. But Marble brings something different. It generates persistent, spatially cohesive 3D worlds that can be rendered in real time across a wide range of devices. That’s a real shift. Experiment 01: Parallel Realities — The first experiment, Parallel Realities, starts from a volumetric capture of a real location, reconstructed as 3D Gaussian Splats. Using Marble, I generate an alternate version of that same space, something informed by the original architecture: abandoned, nature-reclaimed, alternate era. Then, using Spark (World Labs’ 3D Gaussian Splatting renderer for THREE.js) I make both realities coexist in the same spatial coordinate system. From there, I use a portal UX mechanic to let the user step between the real reconstruction and the Marble-generated version. Experiment 02: Hidden Depth The second experiment, Hidden Depth, does not transform a space as much as expand it. A captured location has a visual boundary (a mural, a doorway, a dark corridor) and Marble generates what exists beyond it. For example: a Montreal alley has a painted mural; step through it and you’re inside a world informed by what is actually depicted there. World Labs showcased part of this work here: And in their Spark 2.0 post: The project page is here: Why this matters to me — Being able to start from a real 3D Gaussian Splat scene and manipulate it with Marble opens up a lot of ideas. The 3DGS pipeline is becoming an increasingly compelling foundation for exploration, experimentation, and storytelling. What matters most to me right now is more control. The more I can steer the generated scene or world, the more useful the tool becomes. I want more features like the already existing multiple input images and Chisel, the blockout-based approach. I would like better local control, the ability to expand a generated world more and more while preserving coherence, and the ability to directly import 3D Gaussian Splat scenes to be used as a starting point. I want more ways to shape the result, not just a “prompt and hope” approach. — It is exciting to see this field moving from research and demos toward actual creative workflows.

Hugues Bruyère

69,960 Aufrufe • vor 3 Monaten

Understanding the BitTorrent Swarm — A Broader Look With Real Data Dynamics BitTorrent isn’t just a file-sharing protocol; it’s one of the most efficient large-scale distribution systems ever designed. At its core lies a simple but powerful principle: when users contribute bandwidth, the entire network accelerates. This is the swarm and its efficiency can be explained through clear data patterns and network behavior. 🔹 The Swarm Model: How Participation Becomes Performance In a traditional client-server setup, bandwidth is fixed. If 10,000 users try to download a 1 GB file from one server with 1 Gbps bandwidth: ➠ Maximum theoretical throughput per user: 0.1 Mbps ➠ Average download time: 2–3 hours ➠ Server overload: very likely BitTorrent rewrites this logic. When 10,000 users join a swarm and each contributes only 50–200 Kbps of upload bandwidth, the network’s total available throughput multiplies thousands of times. This is why, in real swarm studies: ➠ Larger swarms consistently show 30–400% faster download speeds ➠ Popular torrents reach equilibrium within minutes, not hours ➠ Throughput per user remains stable even under heavy demand BitTorrent’s efficiency grows with usage — something centralized systems struggle with. 🔹 Why More Peers = More Speed (Backed by Data Behavior) BitTorrent breaks files into hundreds or thousands of small pieces. Each piece circulates among peers using a strategy called rarest-first ensuring no piece becomes a bottleneck. Here’s what the data shows: 1. Bandwidth multiplication effect If each peer contributes: ➠ 100 peers × 100 Kbps upload = 10 Mbps swarm capacity ➠ 5,000 peers × 150 Kbps upload = 750 Mbps swarm capacity ➠ 20,000 peers × 200 Kbps upload = 4 Gbps swarm capacity This turning point when collective bandwidth surpasses any server is why torrents of large files often download faster than centralized sources. 2. Availability resilience Even if 90% of peers leave, as long as one full copy exists across the swarm’s collective pieces, the file is recoverable without interruption. 3. Load balancing automatically occurs BitTorrent’s choking/unchoking algorithm ensures: ➠ High-bandwidth peers exchange more data ➠ Low-bandwidth peers still participate ➠ No single peer becomes a bottleneck The data flow adapts in real time based on peer performance. 🔹 The Swarm’s Global Impact: Why It Still Matters BitTorrent traffic routinely accounts for: ➠ 10–20% of global internet upload traffic (varies by region) ➠ Multiple petabytes of data exchanged daily ➠ Millions of active swarms at any given time The model works because it scales with demand: ➠ More users → more bandwidth. ➠ More bandwidth → faster delivery. ➠ Faster delivery → stronger swarm health. This “self-reinforcing cycle” is a core reason decentralized systems from Web3 storage to blockchain data sync borrow heavily from BitTorrent’s architecture. 🔹 The Big Picture The BitTorrent swarm illustrates an important truth about decentralized networks: Efficiency doesn’t come from the center it comes from participation. When thousands of people contribute small amounts of bandwidth, the result is a global system capable of speeds that outperform traditional content delivery models. This is not just technology; it’s cooperative acceleration at internet scale. In One Line Files move faster when everyone contributes and BitTorrent proves it with real data. H.E. Justin Sun 👨‍🚀 🌞 BitTorrent #TRONEcoStar #BitTorrent #SwarmNetwork #DataAnalysis #DecentralizedSystems #P2P
1:05

Understanding the BitTorrent Swarm — A Broader Look With Real Data Dynamics BitTorrent isn’t just a file-sharing protocol; it’s one of the most efficient large-scale distribution systems ever designed. At its core lies a simple but powerful principle: when users contribute bandwidth, the entire network accelerates. This is the swarm and its efficiency can be explained through clear data patterns and network behavior. 🔹 The Swarm Model: How Participation Becomes Performance In a traditional client-server setup, bandwidth is fixed. If 10,000 users try to download a 1 GB file from one server with 1 Gbps bandwidth: ➠ Maximum theoretical throughput per user: 0.1 Mbps ➠ Average download time: 2–3 hours ➠ Server overload: very likely BitTorrent rewrites this logic. When 10,000 users join a swarm and each contributes only 50–200 Kbps of upload bandwidth, the network’s total available throughput multiplies thousands of times. This is why, in real swarm studies: ➠ Larger swarms consistently show 30–400% faster download speeds ➠ Popular torrents reach equilibrium within minutes, not hours ➠ Throughput per user remains stable even under heavy demand BitTorrent’s efficiency grows with usage — something centralized systems struggle with. 🔹 Why More Peers = More Speed (Backed by Data Behavior) BitTorrent breaks files into hundreds or thousands of small pieces. Each piece circulates among peers using a strategy called rarest-first ensuring no piece becomes a bottleneck. Here’s what the data shows: 1. Bandwidth multiplication effect If each peer contributes: ➠ 100 peers × 100 Kbps upload = 10 Mbps swarm capacity ➠ 5,000 peers × 150 Kbps upload = 750 Mbps swarm capacity ➠ 20,000 peers × 200 Kbps upload = 4 Gbps swarm capacity This turning point when collective bandwidth surpasses any server is why torrents of large files often download faster than centralized sources. 2. Availability resilience Even if 90% of peers leave, as long as one full copy exists across the swarm’s collective pieces, the file is recoverable without interruption. 3. Load balancing automatically occurs BitTorrent’s choking/unchoking algorithm ensures: ➠ High-bandwidth peers exchange more data ➠ Low-bandwidth peers still participate ➠ No single peer becomes a bottleneck The data flow adapts in real time based on peer performance. 🔹 The Swarm’s Global Impact: Why It Still Matters BitTorrent traffic routinely accounts for: ➠ 10–20% of global internet upload traffic (varies by region) ➠ Multiple petabytes of data exchanged daily ➠ Millions of active swarms at any given time The model works because it scales with demand: ➠ More users → more bandwidth. ➠ More bandwidth → faster delivery. ➠ Faster delivery → stronger swarm health. This “self-reinforcing cycle” is a core reason decentralized systems from Web3 storage to blockchain data sync borrow heavily from BitTorrent’s architecture. 🔹 The Big Picture The BitTorrent swarm illustrates an important truth about decentralized networks: Efficiency doesn’t come from the center it comes from participation. When thousands of people contribute small amounts of bandwidth, the result is a global system capable of speeds that outperform traditional content delivery models. This is not just technology; it’s cooperative acceleration at internet scale. In One Line Files move faster when everyone contributes and BitTorrent proves it with real data. H.E. Justin Sun 👨‍🚀 🌞 BitTorrent #TRONEcoStar #BitTorrent #SwarmNetwork #DataAnalysis #DecentralizedSystems #P2P

catalina ossa

50,874 Aufrufe • vor 10 Monaten

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇
2:16

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇

Markus J. Buehler

55,189 Aufrufe • vor 6 Monaten

Today, we’re announcing Kosmos, our newest AI Scientist, available to use now. Users estimate Kosmos does 6 months of work in a single day. One run can read 1,500 papers and write 42,000 lines of code. At least 79% of its findings are reproducible. Kosmos has made 7 discoveries so far, which we are releasing today, in areas ranging from neuroscience to material science and clinical genetics, in collaboration with our academic beta testers. Three of these discoveries reproduced unpublished findings; four are net new, validated contributions to the scientific literature. AI-accelerated science is here. Our core innovation in Kosmos is the use of a structured, continuously-updated world model. As described in our technical report, Kosmos’ world model allows it to process orders of magnitude more information than could fit into the context of even the longest-context language models, allowing it to synthesize more information and pursue coherent goals over longer time horizons than Robin or any of our other prior agents. In this respect, we believe Kosmos is the most compute-intensive language agent released so far in any field, and by far the most capable AI Scientist available today. The use of a persistent world model also enables single Kosmos trajectories to produce highly complex outputs that require multiple significant logical leaps. As with all of our systems, Kosmos is designed with transparency and verifiability in mind: every conclusion in a Kosmos report can be traced through our platform to the specific lines of code or the specific passages in the scientific literature that inspired it, ensuring that Kosmos’ findings are fully auditable at all times. We are also using this opportunity to announce the launch of Edison Scientific, a new commercial spinout of FutureHouse, which will be focused on commercializing our agents and applying them to automate scientific research in drug discovery and beyond. Edison will be taking over management of the FutureHouse platform, where you can access Kosmos alongside our Literature, Molecules, and Precedent agents (previously Crow, Phoenix, and Owl). Edison will continue to offer free tier usage for casual users and academics, while also offering higher rate limits and additional features for users who need them. You can read more about this spinout on our blog, below. A few important notes if you’re going to try Kosmos. Firstly, Kosmos is different from many other AI tools you might have played with, including our other agents. It is more similar to a Deep Research tool than it is to a chatbot: it takes some time to figure out how to prompt it effectively, and we have tried to include guidelines on this to help (see below). It costs $200/run right now (200 credits per run, and $1/credit), with some free tier usage for academics. This is heavily discounted; people who sign up for Founding Subscriptions now can lock in the $1/credit price indefinitely, but the price ultimately will probably be higher. Again, this is less chatbot and more research tool, something you run on high-value targets as needed. Some caveats are also warranted. Firstly, we find that 80% of Kosmos findings are reproducible, which also means 20% are not -- some things it says will be wrong. Also, Kosmos certainly does produce outputs that are the equivalent to several months of human labor, but it also often goes down rabbit holes or chases statistically significant yet scientifically irrelevant findings. We often run Kosmos multiple times on the same objective in order to sample the various research avenues it can take. There are still a bunch of rough edges on the UI and such, which we are working on. Finally, we are aware that the 6 month figure is much greater than estimates by other AI labs, like METR, about the length of tasks that AI Agents can currently perform. You can read discussion about this in our blog post. Huge congratulations to our team that put this together, led by Ludovico Mitchener and Michaela Hinks: Angela Yiu, Benjamin Chang, Sid Narayanan, Edwin Melville-Green, Albert Bou, Arvis Sulovari, Oz Wassie, Jon Laurent. A particular shout out to Michael Skarlinski and his team that rebuilt the platform for this launch, especially Andy Cai Andy Cai, Richard Magness, Remo Storni, Tyler Nadolski Tyler Nadolski, Mayk Caldas Mayk Caldas, Sam Cox Sam Cox and more. This work would not have been possible without significant contributions from academic collaborators Mathieu Bourdenx, Eric Landsness, Dániel Barabási, Nicky Evans, Tonio Buonassisi, Bruna Gomes, Shriya Reddy, Martha Foiani, and Randall Bateman. We also want to thank our numerous supporters, especially Eric Schmidt, who has been a tremendous ally. We will have more to say about our supporters soon!
0:44

Today, we’re announcing Kosmos, our newest AI Scientist, available to use now. Users estimate Kosmos does 6 months of work in a single day. One run can read 1,500 papers and write 42,000 lines of code. At least 79% of its findings are reproducible. Kosmos has made 7 discoveries so far, which we are releasing today, in areas ranging from neuroscience to material science and clinical genetics, in collaboration with our academic beta testers. Three of these discoveries reproduced unpublished findings; four are net new, validated contributions to the scientific literature. AI-accelerated science is here. Our core innovation in Kosmos is the use of a structured, continuously-updated world model. As described in our technical report, Kosmos’ world model allows it to process orders of magnitude more information than could fit into the context of even the longest-context language models, allowing it to synthesize more information and pursue coherent goals over longer time horizons than Robin or any of our other prior agents. In this respect, we believe Kosmos is the most compute-intensive language agent released so far in any field, and by far the most capable AI Scientist available today. The use of a persistent world model also enables single Kosmos trajectories to produce highly complex outputs that require multiple significant logical leaps. As with all of our systems, Kosmos is designed with transparency and verifiability in mind: every conclusion in a Kosmos report can be traced through our platform to the specific lines of code or the specific passages in the scientific literature that inspired it, ensuring that Kosmos’ findings are fully auditable at all times. We are also using this opportunity to announce the launch of Edison Scientific, a new commercial spinout of FutureHouse, which will be focused on commercializing our agents and applying them to automate scientific research in drug discovery and beyond. Edison will be taking over management of the FutureHouse platform, where you can access Kosmos alongside our Literature, Molecules, and Precedent agents (previously Crow, Phoenix, and Owl). Edison will continue to offer free tier usage for casual users and academics, while also offering higher rate limits and additional features for users who need them. You can read more about this spinout on our blog, below. A few important notes if you’re going to try Kosmos. Firstly, Kosmos is different from many other AI tools you might have played with, including our other agents. It is more similar to a Deep Research tool than it is to a chatbot: it takes some time to figure out how to prompt it effectively, and we have tried to include guidelines on this to help (see below). It costs $200/run right now (200 credits per run, and $1/credit), with some free tier usage for academics. This is heavily discounted; people who sign up for Founding Subscriptions now can lock in the $1/credit price indefinitely, but the price ultimately will probably be higher. Again, this is less chatbot and more research tool, something you run on high-value targets as needed. Some caveats are also warranted. Firstly, we find that 80% of Kosmos findings are reproducible, which also means 20% are not -- some things it says will be wrong. Also, Kosmos certainly does produce outputs that are the equivalent to several months of human labor, but it also often goes down rabbit holes or chases statistically significant yet scientifically irrelevant findings. We often run Kosmos multiple times on the same objective in order to sample the various research avenues it can take. There are still a bunch of rough edges on the UI and such, which we are working on. Finally, we are aware that the 6 month figure is much greater than estimates by other AI labs, like METR, about the length of tasks that AI Agents can currently perform. You can read discussion about this in our blog post. Huge congratulations to our team that put this together, led by Ludovico Mitchener and Michaela Hinks: Angela Yiu, Benjamin Chang, Sid Narayanan, Edwin Melville-Green, Albert Bou, Arvis Sulovari, Oz Wassie, Jon Laurent. A particular shout out to Michael Skarlinski and his team that rebuilt the platform for this launch, especially Andy Cai Andy Cai, Richard Magness, Remo Storni, Tyler Nadolski Tyler Nadolski, Mayk Caldas Mayk Caldas, Sam Cox Sam Cox and more. This work would not have been possible without significant contributions from academic collaborators Mathieu Bourdenx, Eric Landsness, Dániel Barabási, Nicky Evans, Tonio Buonassisi, Bruna Gomes, Shriya Reddy, Martha Foiani, and Randall Bateman. We also want to thank our numerous supporters, especially Eric Schmidt, who has been a tremendous ally. We will have more to say about our supporters soon!

Sam Rodriques

732,887 Aufrufe • vor 10 Monaten

People are undoubtedly a little alarmed at having unwittingly helped build a 3D map of the world for Niantic by contributing 30 billion crowdsourced images. I interviewed Niantic's CTO Brian McClendon about exactly this in a TED interview last year -- he's also the guy who co-created Google Earth. But let's put it in perspective. Pokestop data isn't what you think it is. It's not a surveillance panopticon of your neighborhood. These are static captures of parks, statues, murals, landmarks -- the places people congregate. Brian described it as "building the map from the bottom up, from the locations where people spend time." Think of these 20 million waypoints as basically the inverse of what Google mapped with Street View. Google mapped the drivable streets. Niantic mapped where people actually hang out. Cool data, genuinely useful for visual positioning -- but very different from what the headlines imply. And lest we forget that Niantic is just one of many companies quietly building their own map of the world right now -- and they're all capturing different facets of reality: >🚶 person-level: Axon body cams on hundreds of thousands of officers. Meta Ray-Ban glasses capturing first-person POV at scale -- overseas operators reviewing images every time someone says "Hey Meta." > 🚗 vehicle-level: Tesla dashcams on every car in the fleet, massive onboard compute extracting and distilling data to the cloud. Waymo with cm-accurate 3D maps of every city they operate in. Fleet telematics cameras on delivery vehicles globally. > 🏠 street & home-level: Flock Safety deploying CCTV across neighborhoods and cities. Amazon with Ring cameras on every doorstep and mailroom (recently got dragged over that Super Bowl commercial about fusing all these cams together to find your dog) plus dashcams on every Prime delivery van. Roomba mapping your floor plan every time it vacuums -- Amazon wanted that data badly enough to try acquiring iRobot for $1.7B before regulators shut it down. > 🥽 headset-level: Apple Vision Pro and Meta Quest build a 3D model of whatever room you're in every time you put them on. Between Ring, Roomba, and your headset, your entire home is being spatially understood by at least three different companies. >📍platform-level: Google with Street View cars, aerial planes, satellite imagery, and live location from every Android phone in your pocket. Apple doing the same with mapping cars AND every LiDAR iPhone is quietly a 3D scanner. And yeah, despite the "Apple is too privacy-conscious" narrative, they're collecting location data too. >🏃 trajectory-level: Strava mapped every running and cycling trail on Earth -- and accidentally exposed secret military bases in Afghanistan and Syria because soldiers logged their jogs. When you aggregate enough individual trajectories, patterns emerge that were never supposed to be visible. > 🛰️ space-level: Planet Labs imaging the entire Earth's landmass every single day from orbit. Vantor capturing it in higher detail. Iceye doing it in 3D using SAR. If something changes anywhere on the planet -- a building goes up, a forest burns down, a military convoy moves -- before-and-after imagery within 24 hours. Fused together -- we have everything from body cam to dashcam to doorbell to phone to satellite -- every layer of physical reality is being mapped by somebody right now. Different sensors, different angles, different purposes. Same pattern. The interesting part is how they incentivize it. Google spends billions. Mapillary tried altruism. Hivemapper grinds with crypto. Pokémon GO cracked something none of them could: a game mechanic that subsidizes the scanning behavior. You're not building a map. You're catching pokemon. The map is just a side effect. 3D scanning is still a niche hobby for reality capture nerds like me. The moment somebody gamifies dense 3D capture at scale -- not posed photos but actual geometry -- that's when this blows wide open. Niantic sold the games for $3.5B but kept the spatial platform, with a data-sharing agreement in place. One team makes the game great, the other builds the spatial infrastructure underneath. Incentives finally aligned. Gaming is becoming a way for humans to contribute real-world trajectories that help physical AI learn about the real world. Google does it with live traffic. Tesla does it with autopilot. The mechanic is different but the pattern is identical -- and most people are already part of at least one -- if not a majority -- of these datasets whether they realize it or not.
0:37

People are undoubtedly a little alarmed at having unwittingly helped build a 3D map of the world for Niantic by contributing 30 billion crowdsourced images. I interviewed Niantic's CTO Brian McClendon about exactly this in a TED interview last year -- he's also the guy who co-created Google Earth. But let's put it in perspective. Pokestop data isn't what you think it is. It's not a surveillance panopticon of your neighborhood. These are static captures of parks, statues, murals, landmarks -- the places people congregate. Brian described it as "building the map from the bottom up, from the locations where people spend time." Think of these 20 million waypoints as basically the inverse of what Google mapped with Street View. Google mapped the drivable streets. Niantic mapped where people actually hang out. Cool data, genuinely useful for visual positioning -- but very different from what the headlines imply. And lest we forget that Niantic is just one of many companies quietly building their own map of the world right now -- and they're all capturing different facets of reality: >🚶 person-level: Axon body cams on hundreds of thousands of officers. Meta Ray-Ban glasses capturing first-person POV at scale -- overseas operators reviewing images every time someone says "Hey Meta." > 🚗 vehicle-level: Tesla dashcams on every car in the fleet, massive onboard compute extracting and distilling data to the cloud. Waymo with cm-accurate 3D maps of every city they operate in. Fleet telematics cameras on delivery vehicles globally. > 🏠 street & home-level: Flock Safety deploying CCTV across neighborhoods and cities. Amazon with Ring cameras on every doorstep and mailroom (recently got dragged over that Super Bowl commercial about fusing all these cams together to find your dog) plus dashcams on every Prime delivery van. Roomba mapping your floor plan every time it vacuums -- Amazon wanted that data badly enough to try acquiring iRobot for $1.7B before regulators shut it down. > 🥽 headset-level: Apple Vision Pro and Meta Quest build a 3D model of whatever room you're in every time you put them on. Between Ring, Roomba, and your headset, your entire home is being spatially understood by at least three different companies. >📍platform-level: Google with Street View cars, aerial planes, satellite imagery, and live location from every Android phone in your pocket. Apple doing the same with mapping cars AND every LiDAR iPhone is quietly a 3D scanner. And yeah, despite the "Apple is too privacy-conscious" narrative, they're collecting location data too. >🏃 trajectory-level: Strava mapped every running and cycling trail on Earth -- and accidentally exposed secret military bases in Afghanistan and Syria because soldiers logged their jogs. When you aggregate enough individual trajectories, patterns emerge that were never supposed to be visible. > 🛰️ space-level: Planet Labs imaging the entire Earth's landmass every single day from orbit. Vantor capturing it in higher detail. Iceye doing it in 3D using SAR. If something changes anywhere on the planet -- a building goes up, a forest burns down, a military convoy moves -- before-and-after imagery within 24 hours. Fused together -- we have everything from body cam to dashcam to doorbell to phone to satellite -- every layer of physical reality is being mapped by somebody right now. Different sensors, different angles, different purposes. Same pattern. The interesting part is how they incentivize it. Google spends billions. Mapillary tried altruism. Hivemapper grinds with crypto. Pokémon GO cracked something none of them could: a game mechanic that subsidizes the scanning behavior. You're not building a map. You're catching pokemon. The map is just a side effect. 3D scanning is still a niche hobby for reality capture nerds like me. The moment somebody gamifies dense 3D capture at scale -- not posed photos but actual geometry -- that's when this blows wide open. Niantic sold the games for $3.5B but kept the spatial platform, with a data-sharing agreement in place. One team makes the game great, the other builds the spatial infrastructure underneath. Incentives finally aligned. Gaming is becoming a way for humans to contribute real-world trajectories that help physical AI learn about the real world. Google does it with live traffic. Tesla does it with autopilot. The mechanic is different but the pattern is identical -- and most people are already part of at least one -- if not a majority -- of these datasets whether they realize it or not.

Bilawal Sidhu

204,351 Aufrufe • vor 6 Monaten

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman
2:52

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman

Varun

190,566 Aufrufe • vor 1 Jahr

After regrouping with our investors and the team, I’ve made the difficult decision to wind down Hike completely. Our US business, launched just nine months ago, is off to a strong start. But scaling globally would require a full recap, a reset that is not the best use of capital or time. The Big Question → We could raise the capital, but the real question is: is it worth it? Is this a climb worth pivoting for? For the first time in 13 years, my answer is no. Not for me, not for my team, and not for our investors. Why? 1. RMG was never the destination. It was a way to test unit economics and traction in India while working toward a bigger vision. In hindsight, starting in India locked us into the model and regulatory headwinds, turning a temporary path into a more permanent one. 2. The Gaming Nation vision is real, but we may be too early. The world will eventually move toward a Nation-type model in gaming and Web3 - Company 2.0. But crypto regulation is still developing globally, and we don’t want to repeat India, where we hoped for clarity that never came. 3. And most importantly, if doing a full reset, is this where I’d put my own capital and energy today? For the first time, the answer is no. The world has changed in the last decade - and so have I. There are more important problems to solve and bigger opportunities to deploy brilliant talent and capital. Looking Back & Lessons The last 13 years have been immense. Hike Messenger reached 40M MAUs and became the 35th most loved consumer brand in India at its peak. With Rush, we built a brand new kind of Casual PvP gaming platform and scaled it to 10M users and $500M+ in gross revenue in just 4 years. Our execution was super, but we could never quite make it stick. There are clear lessons to carry forward, especially on market selection: 1. Be careful with winner-take-all markets. To win, you need to go global. 2. Don’t build for today’s tech constraints. Build on the spring/summer of new technologies. 3. Regulatory clarity matters. Risk is fine; uncertainty is not. More importantly momentum is everything. And build what your heart and mind are deeply excited about. It’s the conviction that carries you through. This is both a disappointment and a hard outcome. But I choose to look on the bright side: the learnings are invaluable, and my conviction for what’s next is even stronger. To everyone who has been part of this journey - our users, our team, our investors, and our community - thank you. As a CEO, you’re only as strong as your team, and I want to give a special shout-out to mine - an incredible group of people who gave this everything. Hike This chapter ends, but the climb continues. Looking Forward I’ve always thrived at building at the forefront of technology. Over the last decade, in the little time I had to explore outside of Hike, I kept returning to the same three frontiers. And now, they feel like the great canvases for decades to come → 1. AI → For the first time, technology has both intelligence and memory. Imagine products that don’t just serve us functionally but truly know us - systems that adapt, grow, and partner with us. As a UX-first builder, this is the most exciting time to be building software. 2. Breakthroughs in Energy → Human progress has always been bound by energy. The world’s demand for energy is rising faster than ever. Breakthrough approaches, especially in physics are needed to power the future. The last century gave us mastery of fine matters and electricity, the next will move deeper, at the intersection of science and spirituality - into what yogis call divine magnetism and physicists call the quantum world or electromagnetism. From there will come technologies that today feel impossible to imagine. 3. Mastery of the Self → As AI takes on more of our work, a deeper question will rise: what now defines us? When productivity is no longer the measure of worth, humanity will turn inward. Man’s evolution will move from the intellect to intuitive attunement - a deeper connection with ourselves and the divine (which we’ll realise are one and the same). The tools, spaces, and guides that help us explore this inner world will be as transformative as any innovation in the outer one - unlocking the next level of humanity’s potential. If you put these together, a picture emerges: → the cost of intelligence trending to zero → the cost of energy trending to zero → and the cost of willpower falling lower and lower. Just imagine a future where willpower is infinite, energy is abundant, and intelligence is at our fingertips. This is the future I will help build — and it’s where I’ll be contributing in the decades to come. This new chapter will look very different from the last one 🚀 Video for perspective. Full substack post link below.
0:40

After regrouping with our investors and the team, I’ve made the difficult decision to wind down Hike completely. Our US business, launched just nine months ago, is off to a strong start. But scaling globally would require a full recap, a reset that is not the best use of capital or time. The Big Question → We could raise the capital, but the real question is: is it worth it? Is this a climb worth pivoting for? For the first time in 13 years, my answer is no. Not for me, not for my team, and not for our investors. Why? 1. RMG was never the destination. It was a way to test unit economics and traction in India while working toward a bigger vision. In hindsight, starting in India locked us into the model and regulatory headwinds, turning a temporary path into a more permanent one. 2. The Gaming Nation vision is real, but we may be too early. The world will eventually move toward a Nation-type model in gaming and Web3 - Company 2.0. But crypto regulation is still developing globally, and we don’t want to repeat India, where we hoped for clarity that never came. 3. And most importantly, if doing a full reset, is this where I’d put my own capital and energy today? For the first time, the answer is no. The world has changed in the last decade - and so have I. There are more important problems to solve and bigger opportunities to deploy brilliant talent and capital. Looking Back & Lessons The last 13 years have been immense. Hike Messenger reached 40M MAUs and became the 35th most loved consumer brand in India at its peak. With Rush, we built a brand new kind of Casual PvP gaming platform and scaled it to 10M users and $500M+ in gross revenue in just 4 years. Our execution was super, but we could never quite make it stick. There are clear lessons to carry forward, especially on market selection: 1. Be careful with winner-take-all markets. To win, you need to go global. 2. Don’t build for today’s tech constraints. Build on the spring/summer of new technologies. 3. Regulatory clarity matters. Risk is fine; uncertainty is not. More importantly momentum is everything. And build what your heart and mind are deeply excited about. It’s the conviction that carries you through. This is both a disappointment and a hard outcome. But I choose to look on the bright side: the learnings are invaluable, and my conviction for what’s next is even stronger. To everyone who has been part of this journey - our users, our team, our investors, and our community - thank you. As a CEO, you’re only as strong as your team, and I want to give a special shout-out to mine - an incredible group of people who gave this everything. Hike This chapter ends, but the climb continues. Looking Forward I’ve always thrived at building at the forefront of technology. Over the last decade, in the little time I had to explore outside of Hike, I kept returning to the same three frontiers. And now, they feel like the great canvases for decades to come → 1. AI → For the first time, technology has both intelligence and memory. Imagine products that don’t just serve us functionally but truly know us - systems that adapt, grow, and partner with us. As a UX-first builder, this is the most exciting time to be building software. 2. Breakthroughs in Energy → Human progress has always been bound by energy. The world’s demand for energy is rising faster than ever. Breakthrough approaches, especially in physics are needed to power the future. The last century gave us mastery of fine matters and electricity, the next will move deeper, at the intersection of science and spirituality - into what yogis call divine magnetism and physicists call the quantum world or electromagnetism. From there will come technologies that today feel impossible to imagine. 3. Mastery of the Self → As AI takes on more of our work, a deeper question will rise: what now defines us? When productivity is no longer the measure of worth, humanity will turn inward. Man’s evolution will move from the intellect to intuitive attunement - a deeper connection with ourselves and the divine (which we’ll realise are one and the same). The tools, spaces, and guides that help us explore this inner world will be as transformative as any innovation in the outer one - unlocking the next level of humanity’s potential. If you put these together, a picture emerges: → the cost of intelligence trending to zero → the cost of energy trending to zero → and the cost of willpower falling lower and lower. Just imagine a future where willpower is infinite, energy is abundant, and intelligence is at our fingertips. This is the future I will help build — and it’s where I’ll be contributing in the decades to come. This new chapter will look very different from the last one 🚀 Video for perspective. Full substack post link below.

Kavin

24,481 Aufrufe • vor 1 Jahr