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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...

35,440 Aufrufe • vor 10 Tagen •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- run the expensive nodes last, always the catch is that a graph is an extremely confident machine: point it at an outdated rate book and it prices an entire hospital off it without a single node noticing, because no node is asked to doubt the input, only to process it so the nodes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 ↓

Argona

38,189 Aufrufe • vor 1 Monat

[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 Aufrufe • vor 2 Jahren

Destra Network| True decentralized Storage solution How Destra Decentralized File Storage solves the centralization issues in Filecoin & IPFS Filecoin and IPFS have become synonymous with decentralized file storage, highly hyped alternatives to traditional centralized data storage systems. However, a deep dive into their architecture reveals a crucially centralized component: both networks rely on a set of centralized bootstrap nodes. Whenever a new node wants to join the Filecoin or IPFS network, it must first communicate with these bootstrap nodes to obtain information about other peers, placing complete trust in the provided information. The Filecoin team operates these bootstrap nodes on centralized cloud servers. These nodes serve as the initial points of contact for new nodes entering the network, acting as directories to facilitate the network's mesh topology, and thus are critical components of the architecture. Major Centralization Concerns: 1. Surveillance: Centralized nodes offer a concentrated point for surveillance. Authorities or malicious actors could target these nodes to gather data on user activities, node interactions, and network dynamics, posing significant privacy risks. 2. Central Control and Censorship: The operators of bootstrap nodes potentially wield significant control over the network. They can influence which parts of the network are more discoverable or enforce certain network rules, leading to a form of gatekeeping. This centralized control contrasts with the decentralization ethos of blockchain and peer-to-peer technologies. 3. Single Points of Failure: Bootstrap nodes act as central hubs through which traffic and connections are often routed, especially during the initial connection phase. This setup can create single points of failure. If these nodes are compromised, go offline, or experience technical issues, new nodes may struggle to integrate into the network, potentially taking down the whole network. At Destra, we are building the Destra Decentralized File Storage to ensure that such centralization or pseudo-decentralization has no place in our architecture. As always, we are committed to our vision of 100% decentralization, delivering an unprecedented level of decentralization to our users and the broader ecosystem. As we continue developing the Destra Decentralized File Storage, we will keep on publishing regular updates to our community.

Destra Network

10,985 Aufrufe • vor 2 Jahren

OptimAI Lite Node v1.1: Built for Scale, Designed for You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:

OptimAI Network

76,486 Aufrufe • vor 1 Jahr

Stateless History Node is almost like a regular Ethereum node, but it doesn't store state and it doesn't have EVM execution. It's used only for syncing events and thus - is faster and gives you FREE INDEXING. You don't have to pay 6 figures for RPC anymore! Just spin up a Stateless History Node, plug rindexer or Ponder there, and enjoy free (AND FAST!!) indexing! This node is syncing >1000 blocks per second at my local pc (less than 6hrs for the whole Ethereum), and it should use less than 200GB - which means you can host it on a MacMini, Hetzner or whatever. You can futhermore filter that by using block ranges or bloom filters, etc - I haven't developed this yet. What you see is a proof of concept. It works via native devp2p 'eth' protocol, but with EIP4444 and The Prune we would have to also support era1 archives and Portal Network. But so far it works - there are plenty of peers serving historical receipts, and they serve them FAST! If you run Stateless History Node you can also serve the blocks and receipts - so that could help to preserve archival data too. For now there is no data validation yet (and even no data storage - that's a very early PoC), but we can verify validity of chain by simultaneously running a lightweight CL node (or not lightweight if you're extremely paranoid). And then support verifying the hashes of receipts and blocks with their parents, maintaining full integrity and zero trust. It's also written in rust, btw. So, I guess, at least for Ethereum Mainnet the era of RPC's pumping moneybags is over - there's finally a local, trustless and free indexing alternative available. Too sad this won't work for Optimism / Base , cause despite introducing P2P after Bedrock - they haven't enabled receipts transfer in the protocol (or at least I couldn't find one). Arbitrum is even sadder - I don't believe there is a P2P layer at all - you just have to run your own node, hold state and execute blocks to get events. There is hope - Paradigm recently released Ress - stateless execution, but it requires nodes to support Witness preparation & exchange - but this could work for L2s - cause the main blocker for local RPCs rn is huge state (VPS with TB storage cost a lot), and the second blocker is EVM forks makes it hard to hold a node - it needs to be maintained, upgraded, etc. Ress at least solves the state part. But anyways, I will try to continue working on this and release some MVP version with RPC endpoint and data storage soon - follow the updates!

Convergence Boy

29,823 Aufrufe • vor 7 Monaten

A good technical LLM interview question: Your LLM chatbot takes 12s before it generates the first token, and the users are complaining. So you move the model onto a GPU with 3x the computing power. The time to first token barely improves. Why did this happen? (answer below) Latency in an LLM app is a placement problem disguised as a model problem. If you profile the 12 seconds, the model's prefill itself may only account for around 1.5 seconds of it. So halving the prefill step saves just 750ms out of 12000, which is under 7%. The rest is spread across stages that never touch the GPU. The request first travels to whatever region the app runs in, and a cross-continent round trip could cost over a second before any code executes. Then the request handler starts. On a container-based serverless platform under load, this adds several seconds of cold start, paid before auth, rate limiting, or prompt assembly even begins. Retrieval adds its own hop, and the response streams back across the same distance. Optimizing a stage that was already fast cannot alter the latency that's majorly affected by other stages. Those other stages are slow for a structural reason. An LLM app runs two workloads that want opposite machines. - The request path is short, spiky, and needs to sit close to users - Inference is long-running, GPU-bound, and billed hourly, whether requests arrive or not. So the actual decision is not which model to run, but where each of these two workloads runs. There are three options, each with its own tradeoffs: > A dedicated GPU box removes inference cold starts, but it bills around the clock and lives in one location, so distant users wait out the round trip on every request > Container-based serverless scales to zero, but the request path pays a cold start, and most of these platforms have no GPU behind them. > Edge runtimes start in under a millisecond, because a WebAssembly module carries no OS or container image to boot. They handle the request path well and cannot hold a model. So the answer is not to pick one, but to split the app across two of them. The request path runs close to users, and inference runs on a dedicated GPU it calls into. That also explains the failed upgrade. More compute made a stage that was already fast faster, and left the 10.5 seconds around it untouched. To actually learn how it's done in practice, Akamai's GitHub has a reference implementation for each half. - vllm-on-lke serves Qwen2.5-7B-Instruct behind an OpenAI-compatible endpoint on one RTX 4000 Ada GPU in Linode Kubernetes Engine, with Terraform creating the cluster, both firewalls, and the GPU operator in one apply. - akamai-functions-llm-chatbot covers the front, where a WebAssembly API checks a KV cache and only calls the GPU-backed instance on a miss. Both are available on Akamai’s new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post treats generation as a single 1.5s block, but that block has its own structure, and knowing it well tells you whether a model is slow to start or slow to stream. I wrote a first-principles walkthrough of it, covering the prefill and decode split, KV caching, and where the time actually goes inside each one. Read it below. Thanks to Akamai Cloud for partnering today!

Avi Chawla

21,423 Aufrufe • vor 17 Tagen

Dagknight technical progress As would be mentioned in a still unshared post by Michael Sutton, the dagknight effort is split into v0 devnet, v1 testnet and v2 mainnet candidate. I’ve been testing the current v0-based implementation in a small devnet with the help of some testers who run nodes and miners with me. The DK work can be thought of as split into two parts: (1) implementing the actual protocol and (2) wiring it up and using it. The testing and development over the last month has been focused on (2). Obviously, DK is a consensus change for selecting parents. What’s not so obvious is that such a change affects DAA, coinbase, IBD, pruning and a lot more. Each of these areas is very sensitive and requires proper understanding to wire correctly. An important consideration and difference from GD is that DK does not focus on maximizing a property like blue work. So to maintain topological properties of blue work, an independent (free) GD implementation is kept running specifically for maintaining blue work. This allows us to keep using the property for topology. Coloring and blue score use the megachain induced by DK. The wiring around DK as of this posting is in a working state, but still needs to be reviewed. Next efforts will be focused on protocol specific components, particularly Tie-Breaking and incremental UMC. Attached are some captures from the internal devnet. The dense DAG image is what happens when things related to DAA or other similar consensus parameter causes a node to insist on their POV. The video is a recent snippet of the KGI running on the devnet showing (perhaps not obviously) DK at work. The current “dagknight” branch is now posted on the main repo. A topic in the Public R&D has been opened for Dagknight development.

coderofstuff

52,857 Aufrufe • vor 6 Monaten

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

kocer

29,945 Aufrufe • vor 6 Tagen

ALIENX 👽⛓️ Crypto: A New Frontier in Blockchain Technology ALIENX is a decentralized blockchain platform that aims to revolutionize the way we interact with digital assets. Powered by a network of AI nodes, ALIENX offers a secure, scalable, and efficient environment for various blockchain applications, including NFTs and gaming. Key Features of ALIENX Crypto: AI-Powered Nodes: ALIENX utilizes a network of AI nodes to enhance blockchain performance, security, and intelligence. These nodes continuously learn and adapt to optimize network operations. Staking: Users can stake their ALIENX tokens to earn rewards and contribute to the network's security. Staking also grants users voting rights in the ALIENX governance system. NFT Ecosystem: ALIENX is designed to support a thriving NFT ecosystem. Creators can easily mint and sell their NFTs on the platform, while collectors can discover and acquire unique digital assets. Gaming Integration: ALIENX is actively exploring partnerships with game developers to integrate blockchain technology into gaming experiences. This could enable players to own in-game assets, trade them, and participate in play-to-earn mechanics. ALIENX Token: The native token of the ALIENX ecosystem is AIX. AIX is used for various purposes, including: Governance: AIX holders can participate in governance decisions through voting on proposals. Staking: Staking AIX rewards users with additional AIX tokens. Fees: AIX is used to pay transaction fees on the ALIENX network. Why Choose ALIENX Crypto? ALIENX offers a number of advantages over other blockchain platforms, including: Enhanced Security: The AI-powered nodes provide a more secure environment for storing and transacting digital assets. Scalability: ALIENX is designed to handle a large number of transactions, making it suitable for high-demand applications. Efficiency: The AI nodes optimize network performance, resulting in faster transaction times and lower costs. Community-Driven: ALIENX is governed by its community, ensuring that the platform evolves to meet the needs of its users. Join the ALIENX Revolution: If you're looking for a blockchain platform with a bright future, ALIENX is worth considering. By leveraging AI and blockchain technology, ALIENX has the potential to become a leading player in the digital asset space. Follow us on Twitter for the latest updates and news: [ALIENX 👽⛓️] Here are some additional resources: ALIENX Website: ALIENX Funding #ALIENX #AIBlockchain #NFTRevolution #Crypto #Web3Innovation

ボス-NFT ALL CHAIN GIVEAWAY🇯🇵

279,887 Aufrufe • vor 1 Jahr