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Quant's docs list staked QNT as a factor in which node serves your request Every call through Quant's Overledger passes a Connector, one per blockchain family, which checks node health and routes the request to a healthy node from its pool. Selection weighs each node's rolling latency and recent... show more

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36,081 次观看 • 2 个月前 •via X (Twitter)

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a contractor in Shenzhen priced a ¥12,470,900 hospital contract, about $1.7m, in one afternoon and beat firms carrying forty people he explained how he did it: the bid consultancy he used to pay took three days and ¥46,000 for the same envelope. he did this one alone, off one screen, at 11.4% margin, uploaded before the 17:00 cutoff 214 pages of tender documents read, 68 binding clauses pulled out, 9,485 building parts loaded, 14 places found where a duct and a beam sit in the same cubic metre, deepest one 38mm, all of them fixed, 3,318 lines of quantities priced and the package encrypted and uploaded before the 17:00 cutoff this is Graph Engineering: the job gets cut into small nodes, one narrow task each, wired so that one node's output is the next node's input, and any node is allowed to stop the whole run. it turns a model that answers you into a machine that finishes the job: - give every node one job and one output. a node doing two things fails at both and you cannot tell which one broke - put the cheapest rejection first. his qualification node reads clause 7.4, foreign-owned firms barred, and ends the run four seconds in, before anything expensive touches the model - what moves between nodes is a file. the model travels as a model, the quantities as a table, the price as a number - build exactly one loop: the checker finds 14 collisions, the fixer drops the duct 550mm, the checker runs again, and nothing moves on until the count is zero - cap that loop, or a graph will grind on three impossible clashes until the deadline passes - keep one node whose only job is to say no, and give it authority over everything above it - log each node's output on its own, because when the price comes out wrong you need to know which node believed the wrong thing - run the expensive nodes last, always the catch is that a graph is an extremely confident machine: point it at an outdated rate book and it prices an entire hospital off it without a single node noticing, because no node is asked to doubt the input, only to process it so the nodes that earn their keep are the ones that reject, and almost nobody builds those first bookmark this, the full build with all nine nodes and what each one hands to the next is written out in the article ↓

a contractor in Shenzhen priced a ¥12,470,900 hospital contract, about $1.7m, in one afternoon and beat firms carrying forty people he explained how he did it: the bid consultancy he used to pay took three days and ¥46,000 for the same envelope. he did this one alone, off one screen, at 11.4% margin, uploaded before the 17:00 cutoff 214 pages of tender documents read, 68 binding clauses pulled out, 9,485 building parts loaded, 14 places found where a duct and a beam sit in the same cubic metre, deepest one 38mm, all of them fixed, 3,318 lines of quantities priced and the package encrypted and uploaded before the 17:00 cutoff this is Graph Engineering: the job gets cut into small nodes, one narrow task each, wired so that one node's output is the next node's input, and any node is allowed to stop the whole run. it turns a model that answers you into a machine that finishes the job: - give every node one job and one output. a node doing two things fails at both and you cannot tell which one broke - put the cheapest rejection first. his qualification node reads clause 7.4, foreign-owned firms barred, and ends the run four seconds in, before anything expensive touches the model - what moves between nodes is a file. the model travels as a model, the quantities as a table, the price as a number - build exactly one loop: the checker finds 14 collisions, the fixer drops the duct 550mm, the checker runs again, and nothing moves on until the count is zero - cap that loop, or a graph will grind on three impossible clashes until the deadline passes - keep one node whose only job is to say no, and give it authority over everything above it - log each node's output on its own, because when the price comes out wrong you need to know which node believed the wrong thing - run the expensive nodes last, always the catch is that a graph is an extremely confident machine: point it at an outdated rate book and it prices an entire hospital off it without a single node noticing, because no node is asked to doubt the input, only to process it so the nodes that earn their keep are the ones that reject, and almost nobody builds those first bookmark this, the full build with all nine nodes and what each one hands to the next is written out in the article ↓

Argona

38,189 次观看 • 3 个月前

In a 128-rack NVL72 cluster, a node goes down about every 5 hours. That’s not a bad cluster. It’s the interruption rate Meta reports for its Prometheus cluster, spread across 2,304 nodes. Each rack fails roughly every 27 days. Here’s why that matters. A rack is 72 GPUs on one NVLink fabric, so a job that stays inside the rack runs fast, while the same job spread across racks runs up to 43% slower. The obvious move is to fill every rack with an 18-node job. Then a node goes down, and the whole job stops. With 2% of nodes down at any time (what we see in production), 30% of racks have a node down and can’t even start one. Now try something that sounds wasteful: give each job 16 nodes and leave 2 idle as spares. When a node goes down, a spare in the same rack takes its place and the job keeps going. A rack only drops out when 3 nodes are down at once, which is 0.5% of racks. Across 128 racks, that’s 2,037 usable nodes instead of 1,602. Leaving 256 nodes idle bought 27% more capacity. We wrote a blog reporting our experiences with NVL72 in production . We built the SkyPilot Topology-Aware Scheduler (TAS) to smartly and automatically place jobs optimally and recover them after failures. It keeps jobs rack-local, binpacks, swaps failed nodes for in-rack spares, and frees entire racks by moving fragmented jobs out of the way. In large training runs, SkyPilot TAS can raise training throughput by >14% ($400K+/day savings) and usable capacity by 27%.

In a 128-rack NVL72 cluster, a node goes down about every 5 hours. That’s not a bad cluster. It’s the interruption rate Meta reports for its Prometheus cluster, spread across 2,304 nodes. Each rack fails roughly every 27 days. Here’s why that matters. A rack is 72 GPUs on one NVLink fabric, so a job that stays inside the rack runs fast, while the same job spread across racks runs up to 43% slower. The obvious move is to fill every rack with an 18-node job. Then a node goes down, and the whole job stops. With 2% of nodes down at any time (what we see in production), 30% of racks have a node down and can’t even start one. Now try something that sounds wasteful: give each job 16 nodes and leave 2 idle as spares. When a node goes down, a spare in the same rack takes its place and the job keeps going. A rack only drops out when 3 nodes are down at once, which is 0.5% of racks. Across 128 racks, that’s 2,037 usable nodes instead of 1,602. Leaving 256 nodes idle bought 27% more capacity. We wrote a blog reporting our experiences with NVL72 in production . We built the SkyPilot Topology-Aware Scheduler (TAS) to smartly and automatically place jobs optimally and recover them after failures. It keeps jobs rack-local, binpacks, swaps failed nodes for in-rack spares, and frees entire racks by moving fragmented jobs out of the way. In large training runs, SkyPilot TAS can raise training throughput by >14% ($400K+/day savings) and usable capacity by 27%.

SkyPilot

23,087 次观看 • 3 天前

In Blenders Shader Nodes you now have access to the Switch node, Index Switch node and various string nodes. All of these got ported over from #GeometryNodes and are useful when building higher level shader assets. #b3d #devfund

In Blenders Shader Nodes you now have access to the Switch node, Index Switch node and various string nodes. All of these got ported over from #GeometryNodes and are useful when building higher level shader assets. #b3d #devfund

Jacques Lucke

16,013 次观看 • 2 个月前

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)

Tom Yeh

46,779 次观看 • 2 年前

The design of Geoff is pure genius. For anyone operating a node: idle network nodes use our “Moonlight” co-processor to provide GPUs to external networks. Your node will be productive 24/7.💡 🔑

The design of Geoff is pure genius. For anyone operating a node: idle network nodes use our “Moonlight” co-processor to provide GPUs to external networks. Your node will be productive 24/7.💡 🔑

PAULY

21,946 次观看 • 7 个月前

The best new feature in Blender 4 is NODE TOOLS 🔥 Before this, you could only use geometry nodes as modifiers. Now you can use them as quick one-time operations! This is beyond powerful. Best thing to ever happen to Blender Here's a simple node tree to make pipes from edges:

The best new feature in Blender 4 is NODE TOOLS 🔥 Before this, you could only use geometry nodes as modifiers. Now you can use them as quick one-time operations! This is beyond powerful. Best thing to ever happen to Blender Here's a simple node tree to make pipes from edges:

passivestar

180,849 次观看 • 3 年前

Turns out the full node can travel light: same full verification and 97% less storage. We previously said substantially lower storage would require a separate light node with different security assumptions. Well, we got one thing wrong. Our upcoming upgrade delivers that reduction to the full nodes, with full transaction verification stays intact. Igra node storage: 1TB -> 30GB. Storage growth: 150GB -> 4GB per month at current activity. Running a node and verifying Igra yourself is now practical for far more people. Node operators and attesters: details and documentation coming shortly. Decentralize the world!

Turns out the full node can travel light: same full verification and 97% less storage. We previously said substantially lower storage would require a separate light node with different security assumptions. Well, we got one thing wrong. Our upcoming upgrade delivers that reduction to the full nodes, with full transaction verification stays intact. Igra node storage: 1TB -> 30GB. Storage growth: 150GB -> 4GB per month at current activity. Running a node and verifying Igra yourself is now practical for far more people. Node operators and attesters: details and documentation coming shortly. Decentralize the world!

Igra Labs

12,805 次观看 • 1 个月前

🔥 The final #CESS Testnet #Venus will live on July 31, 2024! 🔥 Countdown: 3 days! 🌍 Venus will operate as a parallel network to the future #CESS mainnet, offering miners, developers, and community users a prime chance for early participation and testing. 💎 With major upgrades for consensus nodes and storage nodes, storage node rewards will total up to 6 million CESS tokens!

🔥 The final #CESS Testnet #Venus will live on July 31, 2024! 🔥 Countdown: 3 days! 🌍 Venus will operate as a parallel network to the future #CESS mainnet, offering miners, developers, and community users a prime chance for early participation and testing. 💎 With major upgrades for consensus nodes and storage nodes, storage node rewards will total up to 6 million CESS tokens!

CESS Network

17,396 次观看 • 2 年前

🛠 Node System Upgrade 🛠 To transition the Master/Common Nodes to our new zkChain, Oceanum, the Node Token claims will be temporarily unavailable from July 15 to September 30. During this maintenance period: ✅ Your Node rewards will continue to accumulate in your account. ✅ No rewards will be lost. Once the integration is complete, you'll be able to claim again and even run your own AI nodes without delegation!

🛠 Node System Upgrade 🛠 To transition the Master/Common Nodes to our new zkChain, Oceanum, the Node Token claims will be temporarily unavailable from July 15 to September 30. During this maintenance period: ✅ Your Node rewards will continue to accumulate in your account. ✅ No rewards will be lost. Once the integration is complete, you'll be able to claim again and even run your own AI nodes without delegation!

Bluwhale

24,763 次观看 • 3 个月前

Loving the new mesh node in Blender 5.2, makes doing these little panel cuts in Geometry Nodes super easy. Just made a tutorial on this. What have you done with the bevel node?

Loving the new mesh node in Blender 5.2, makes doing these little panel cuts in Geometry Nodes super easy. Just made a tutorial on this. What have you done with the bevel node?

SouthernShotty

20,038 次观看 • 2 个月前

📢 Impossible Cloud Network's Node Sale is coming in December! 📢 The countdown begins! This is your chance to become a node operator with ICN, helping to build a secure, decentralized cloud infrastructure while earning rewards for your contribution. More info on the Node Sale will be released soon, so stay tuned! 🚀

📢 Impossible Cloud Network's Node Sale is coming in December! 📢 The countdown begins! This is your chance to become a node operator with ICN, helping to build a secure, decentralized cloud infrastructure while earning rewards for your contribution. More info on the Node Sale will be released soon, so stay tuned! 🚀

Impossible Cloud Network

91,360 次观看 • 1 年前

$AIPUMP Trading is Live - Staking for Nodes that Earn Protocol Fees Starts on Friday Introducing a novel approach to nodes that rewards early adopters. Stake $AIPUMP to farm nodes at 0 cost. Nodes are rewarded from protocol fees. Node farming goes live on Friday. Unstake any time. Farm AI nodes with your tokens. aiPump is the first platform to offer the token community fee-earning nodes for free. ⇢ Kucoin ⇢ Gate ⇢ MEXC ⇢ Aerodrome + Uniswap on Base AIPUMP/USDC 0xfbecd19292b1eFfEAA7B2e61F5101DdB6744A1Fb ⇢ Raydium on Solana AIPUMP/USDC 2w3A2P5juwg234spHKfps7WReWoVmujtErqjaZm9VaiP

$AIPUMP Trading is Live - Staking for Nodes that Earn Protocol Fees Starts on Friday Introducing a novel approach to nodes that rewards early adopters. Stake $AIPUMP to farm nodes at 0 cost. Nodes are rewarded from protocol fees. Node farming goes live on Friday. Unstake any time. Farm AI nodes with your tokens. aiPump is the first platform to offer the token community fee-earning nodes for free. ⇢ Kucoin ⇢ Gate ⇢ MEXC ⇢ Aerodrome + Uniswap on Base AIPUMP/USDC 0xfbecd19292b1eFfEAA7B2e61F5101DdB6744A1Fb ⇢ Raydium on Solana AIPUMP/USDC 2w3A2P5juwg234spHKfps7WReWoVmujtErqjaZm9VaiP

aiPump

249,811 次观看 • 1 年前

Agent Skills are now live for GOAT BitVM2 nodes. Run a challenger, bridge in, peg out, upgrade your node - all through natural language. No CLI. No manual setup. Tell an agent what you want and it executes end-to-end. Recently shipped Testnet fixes: → Resolved peg-in processing delays → Fixed node stalling edge case → Improved edge case handling across the board → More accurate bridge-out stats Skills: Full Testnet guide:

Agent Skills are now live for GOAT BitVM2 nodes. Run a challenger, bridge in, peg out, upgrade your node - all through natural language. No CLI. No manual setup. Tell an agent what you want and it executes end-to-end. Recently shipped Testnet fixes: → Resolved peg-in processing delays → Fixed node stalling edge case → Improved edge case handling across the board → More accurate bridge-out stats Skills: Full Testnet guide:

GOAT Network 🟡

72,067 次观看 • 8 个月前