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Gradient goes beyond static content, utilizing idle compute resources from edge devices, personal computers, and local servers to perform real-time processing tasks. The thesis is similar to Pipe’s: operations like AI inference or serverless functions can happen closer to the user, reducing latency. Gradient reported surpassing 20,000 connected nodes... show more

Solana

3,993,743 subscribers

326,604 views • 1 year ago •via X (Twitter)

Science & Technology

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Solana1 year ago

Delivering content quickly from servers to end users is crucial for apps and services, whether it's static files like images or dynamic data like AI responses. Slow delivery can ruin user experience—think buffering videos or lagging web pages. Solana-based DePIN networks @pipenetwork and @Gradient_HQ incentivize users to contribute excess storage and compute power, respectively. By leveraging a larger network of nodes and tapping into existing infrastructure, they can potentially offer faster delivery, lower costs, and greater reliability.

Solana1 year ago

Pipe Network is a decentralized content delivery network (dCDN). Traditional CDNs cache content (for e.g., images, videos, and webpages) across distributed servers. When users request content, it’s delivered by the closest server, reducing latency compared to an origin server. @pipenetwork takes this design further by incentivizing people to contribute their excess storage and bandwidth. The thesis is simple: more nodes = higher chance a node is closer to the user = faster delivery. The development team behind Pipe recently announced a $10 million raise led by @multicoincap, with plans to launch mainnet in 2025. Learn more from @DavidRhodus' product keynote at Breakpoint:

Solana1 year ago

What Solana DePIN projects are you excited about? If you’re curious about the broader landscape beyond content delivery DePIN, check out @yashhsm’s report: plus @Syndica_io’s recently released Aug 2024 Solana DePIN Deep Dive

⚡ rift 🐐 🛡💚🌙꧁IP꧂1 year ago

Bullish on @Gradient_HQ — The open layer for edge compute on Solana. Set up your #GradientSentry Node and be part of the future. " DePin Is The Future " #GradientNetwork #Solana #DePIN

XRP_Cro 🔥 AI / Gaming / DePIN1 year ago

💵Earn passive income with Gradient Network - The open layer for edge compute on @Solana ✅ Backed by top tier VCs: @SolanaFndn, @multicoincap, @PanteraCapital and @sequoia Sign up here: 👉 Stay online for 72 hours of uptime to earn #DePIN $GRASS

MarmZzz1 year ago

10% Bonus use this ref link

Kaorychang1 year ago

@Gradient_HQ

Joy Osaretin. (Ø,G)1 year ago

@Gradient_HQ What are u waiting for UZOM1Z

crypto mania ❤❤❤1 year ago

@Gradient_HQ Use my CODE: BQH3RJ

artemis || 🌳1 year ago

🚀Join me on @Gradient_HQ — The open layer for edge compute on Solana. Set up your #GradientSentry Node and be part of the future. #GradientNetwork

cromatoforo1 year ago

Use code 𝗡𝗦𝗤𝗧𝗜𝟴 for extra awesomeness

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OptimAI Network

44,249 views • 1 year ago

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Sami Kassab

35,706 views • 5 months ago

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Orbit AI🛰️

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286,936 views • 1 year ago

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Sen. Sally Eaves

10,484 views • 1 year ago

We're teaming up with mtndao 🗻 to host their first ever Demo Day on February 25th 🏔️ This will be the first stop on our mission to find and showcase the best new builders and products of 2026, on the road to Breakpoint

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Solana

284,187 views • 5 months ago

Elon Musk: “I want to live long enough to see the Mass Driver on the moon, because that’s going to be incredibly epic” SpaceX’s Lunar Mass Driver is a planned electromagnetic launch system on the Moon, similar to a coilgun-style catapult, designed to launch solar powered AI compute satellites into orbit or deep space at very low cost The idea is to build these satellites on the Moon using local resources, making it possible to scale AI infrastructure far beyond Earth’s limits, potentially reaching more than 100 terawatts per year compared with roughly 100 gigawatts per year from Earth via Starship The Moon makes this practical because of its low gravity, lack of atmosphere, and abundant solar power, which allow payloads to be accelerated efficiently to about 2.4 km/s escape velocity. The concept was revealed in February 2026 during an xAI all hands after the SpaceX and xAI merger, with renders showing a long launch track at “Moonbase Alpha” Elon Musk called it “incredibly epic” and important for beyond terawatt AI, and footage released in March 2026 showed a conceptual version of how it could work

Elon Musk: “I want to live long enough to see the Mass Driver on the moon, because that’s going to be incredibly epic” SpaceX’s Lunar Mass Driver is a planned electromagnetic launch system on the Moon, similar to a coilgun-style catapult, designed to launch solar powered AI compute satellites into orbit or deep space at very low cost The idea is to build these satellites on the Moon using local resources, making it possible to scale AI infrastructure far beyond Earth’s limits, potentially reaching more than 100 terawatts per year compared with roughly 100 gigawatts per year from Earth via Starship The Moon makes this practical because of its low gravity, lack of atmosphere, and abundant solar power, which allow payloads to be accelerated efficiently to about 2.4 km/s escape velocity. The concept was revealed in February 2026 during an xAI all hands after the SpaceX and xAI merger, with renders showing a long launch track at “Moonbase Alpha” Elon Musk called it “incredibly epic” and important for beyond terawatt AI, and footage released in March 2026 showed a conceptual version of how it could work

SMX 🇺🇸

25,635 views • 3 months ago

Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill the best know-how into an ever-expanding library. ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. “Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of *training* compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread:

Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill the best know-how into an ever-expanding library. ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. “Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of training compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread:

Jim Fan

199,983 views • 18 days ago