Arthur Morgan

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Arthur Morgan

Arthur Morgan

@woshopy

Building the onchain future | Web3

Katılım Nisan 2024
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Arthur Morgan
Arthur Morgan@woshopy·
𝐏𝐨𝐰𝐞𝐫 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐘𝐨𝐮𝐫 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 In today’s AI era, data is everything but not all data can be trusted. That’s where Perle Labs steps in. Perle Labs is building a sovereign intelligence layer for AI where data is not scraped blindly but expert-validated, human-verified and fully auditable on-chain. Instead of relying on anonymous crowdsourced inputs, the platform connects enterprises and governments with verified professionals to ensure every data point is accurate, traceable and trustworthy. This approach transforms human knowledge into a measurable, valuable asset bringing transparency and accountability to AI pipelines that can’t afford errors. 💡 The impact is clear: → Higher model accuracy → Reduced annotation errors → Significant cost efficiency At the center of this ecosystem is the PRL token. The PRL token acts as the coordination and incentive layer rewarding contributors for high-quality data, powering participation and aligning incentives across the network. With a fixed supply of 1 billion tokens, it ensures scarcity while supporting long-term ecosystem growth. As AI scales globally, one truth becomes clear: the future belongs to systems where human expertise isn’t ignored but verified, rewarded and owned. 📌 55,000 USD1 prize pool up for grabs in the #ToPerle community campaign don’t miss your chance to win big. I'm participating in @PerleLabs community campaign. #PerleAI #ToPerle
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Arthur Morgan retweetledi
moove.xyz
moove.xyz@moovexyz·
📣 Official Announcement 📣 Moove App is now OFFICIALLY LIVE IN BETA🔥 Fill in the form now to gain early beta access 👉 forms.office.com/r/yFVwF6LYn7 Get in early. Limited spots available. Permissionless finance. Effortless experience. Just one download away. 💛 Your money. Your move. 💛
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Arthur Morgan
Arthur Morgan@woshopy·
Just minted my @yieldbayfi Yieldo NFT 🌊 Solana is full of yield, but it's messy. Yieldbay scans your wallet and shows where you could earn more across all leading protocols. Minting = early access + no platform fees. Free mint open → app.yieldbay.fi
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SHEHAB HOSSEN (❖,❖)🌼
Most talks about Artificial Intelligence are still focused on models. The focus is, on who's creating a better model, who is making more accurate predictions. The real question is different. Where do these models. How do they grow? Most teams are still working on improving models.. If you don't decide where the model operates this progress doesn't actually grow. @OpenGradient is working on this area. Here computing power is not a resource but a shared network. GPU nodes give performance and TEE nodes make sure that execution is correct. As a result not can AI operate, but execution can also be checked. Applications, agents and blockchains can use AI without having to build their infrastructure separately. Different systems can be connected using the computing network. As AI gets more complex and agent-driven separate setups won't work anymore. Creating useful AI applications in the real world relies on access to shared computing resources. Building AI models isn't the real battleground. Where AI operates is the challenge. OpenGradient is building that challenge. @josephweb3 @SheTalksCrypto @advait_jayant @0x_Nate1
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SHEHAB HOSSEN (❖,❖)🌼@Sinthiya908

Most people think AI trading models are about guessing prices.. In short-term markets what really matters is the direction its going. OpenGradients SUI/USDT spot forecasting model does that. It tries to figure out where the market is moving in 30 minutes and 6 hours and gives a signal. When we say a bias we mean it's likely to go up. A negative bias means it's likely to go The model uses data like Open, High, Low, Close and Volume. Then it makes this data more useful through feature engineering. It also uses Lasso-based feature selection to pick the features that really help predict what's next. This helps reduce noise and prevents overfitting. For users this means getting a signal to help make decisions. For developers it's an output that can easily be used in trading logic or automated systems. This model is really meant for users or developers who want to build on it. It's not for analysis but for integrating into systems. You can use it directly in execution logic, portfolio allocation and agent-based systems. That's the goal of the @OpenGradient Model Hub. Models are not just for sharing but for reuse, as signals. When these signals are integrated into systems AI isn't a tool anymore. It becomes a layer that can make decisions. OpenGradient is working on making this shift happen. @josephweb3 @SheTalksCrypto @advait_jayant @0x_Nate1

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Tanvir Nabil
Tanvir Nabil@tanvirnabil25·
𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗨𝗽𝗴𝗿𝗮𝗱𝗲𝘀 𝘄𝗶𝘁𝗵 𝗚𝗮𝘂𝘀𝘀 The process of upgrading a blockchain or distributed system no longer has to be disruptive. @RialoHQ is using Gauss, the next generation of reconfiguration technology, which makes upgrades seamless, safe and efficient. In traditional SMR systems, the consensus and execution layers are tightly integrated, which makes upgrades difficult and risky. 𝐆𝐚𝐮𝐬𝐬 𝐬𝐨𝐥𝐯𝐞𝐬 𝐭𝐡𝐢𝐬 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐰𝐚𝐲𝐬: ▪ Decouples consensus and execution: Execution is only aware of the transactions it needs, with consensus operating in the background. ▪ Minimizes downtime: Validators in the new configuration are ready to go in advance, with the old configuration continuing to process transactions. ▪ Guarantees safety: Intersection of quorums ensures that configurations cannot be finalized in conflict. ▪ Allows modular upgrades: Consensus parameters, validators and consensus protocols can all be upgraded independently without requiring execution to stop. 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐥𝐲, 𝐆𝐚𝐮𝐬𝐬 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐭𝐡𝐞 𝐜𝐨𝐧𝐜𝐞𝐩𝐭 𝐨𝐟 𝐢𝐧𝐧𝐞𝐫 𝐚𝐧𝐝 𝐨𝐮𝐭𝐞𝐫 𝐥𝐨𝐠𝐬: ▫️Inner Log: This includes everything that is ordered by consensus. This includes transactions and coordination entries. ▫️Outer Log: This is sanitized and only includes transactions for execution. This approach enables the system to lie to the execution layer about the coordination messages while still providing total safety. Gauss treats reconfigurations as a first class concern. This changes a previously high risk and disruptive process into a smooth and predictable one. This means that for Rialo, this provides safer, faster and more flexible upgrades without impacting real world usage. Gauss provides a blockchain system for Rialo that is upgradable without headaches, it is now just another transaction in the system.
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Rialo@RialoHQ

x.com/i/article/2036…

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J. Sʏzo
J. Sʏzo@MyBlood263849·
AI doesn’t just need better models. It needs better execution. @OpenGradient introduces a decentralized compute layer inference is scalable and verifiable. From centralized bottlenecks to distributed,proof-backed execution. Models define capability. Infrastructure makes it real.
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MD Rubel Islam
MD Rubel Islam@Rubelislam2023·
Rialo Recent System Updates @RialoHQ continues to evolve with a focus on stability clarity and control here is what is new Improved Signal Precision Signals are now more focused and actionable reduced noisy alerts clearer root cause visibility and faster understanding of system state operators spend less time guessing and more time acting Stronger Boundary Enforcement System boundaries are tighter with stricter validation better failure isolation and stronger dependency control issues stay contained and do not spread easily Smarter Load Management Load handling is more adaptive with improved throttling better queue prioritization and smoother traffic distribution the system reacts more intelligently under pressure Enhanced Recovery Flow Recovery is now more structured with faster detection cleaner rollback paths and staged restoration stability returns with less disruption Better Observability Visibility has improved across the system with clearer tracing more meaningful metrics and stronger anomaly detection understanding behavior is now easier Reduced Coordination Overhead System design now reduces unnecessary dependency clearer ownership fewer cross system interactions and safer independent changes teams can move faster with less friction The Result Rialo is now more stable under load easier to operate faster to recover and clearer to understand The Rialo Direction Continuous improvement is not about adding more it is about making systems clearer safer and more predictable Rialo evolves by strengthening its foundation not by increasing complexity @itachee_x @JanCamenisch @noblesnft
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Zayn
Zayn@zaynnn_eth·
Just went through this new model developed by @OpenGradient and I must say, it’s actually quite interesting. The new model is based on short-term forecasting for SUI/USDT. What’s interesting is that instead of focusing on how big the price movement is going to be, it’s focusing on where the price is going to be. This is because, in short term trading, where the price is going is actually more important than how much the price is going to move. It’s based on traditional feature engineering techniques combined with Lasso techniques to filter out noise and only focus on what’s relevant. The idea is to keep things simple and practical. What’s also interesting is that the output is so simple that: ◑If positive: go long ◑If negative: go short That’s it! Not only is it simple but also practical. Of course, the real value is not in predicting but in how this model is designed to be used. For instance, it can be used in trading strategy execution, portfolio/vault allocation, and agent based decision making. So, all in all, it’s a great example of how AI is being developed and how it’s not just about making better and better tools but better and better systems.
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OpenGradient (∇, ∇)@OpenGradient

OpenGradient Model Highlight: SUI/USDT Short-Term Spot Forecasting Model A SUI/USDT model designed to predict short-horizon spot price movements using structured signals from market data. 🧵👇🏻

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sᴛғᴜ
sᴛғᴜ@stfu0911·
sᴛғᴜ@stfu0911

The Hidden Crisis in AI Development (And How Perle Labs is Solving It) Everyone is talking about the race for bigger, faster, and more advanced AI models. But very few are talking about the fragile foundation these models are currently built on: the data. The reality is that AI systems are only as reliable as the datasets they ingest. Today, the industry relies heavily on black-box data pipelines and traditional, anonymous crowdsourcing for data labeling. The result? Unseen biases, glaring inconsistencies, and costly hallucinations. For enterprises, governments, and organizations deploying mission-critical AI, "good enough" data simply isn't an option anymore. Enter Perle Labs. Positioned as the "sovereign intelligence layer for AI," Perle Labs is completely rethinking how training data is collected, validated, and utilized. Instead of relying on unaccountable, distributed crowds, Perle has built a purpose-built infrastructure designed to transform true human expertise into verifiable, high-quality AI training data. Here is how Perle Labs is changing the landscape of machine learning: 🔹 On-Chain, Auditable Data What truly sets Perle apart is its commitment to absolute transparency. By integrating Web3 and blockchain technology, every single data point, review, and evaluation is recorded on-chain. This creates an immutable, auditable track record. AI developers no longer have to guess about the quality of their data they can explicitly verify its origin and accuracy, reducing uncertainty and building absolute trust. 🔹 Empowering Human Expertise AI needs human nuance, and Perle recognizes that. The platform features a transparent, performance-based reputation system for data contributors. High-performing domain experts are recognized, fairly incentivized, and granted access to more complex, higher-value tasks. It’s an ecosystem where genuine expertise is finally measured, respected, and rewarded. 🔹 Enterprise-Grade Scalability Whether it’s indexing massive datasets, aligning AI models with human feedback, or curating specialized knowledge, Perle simplifies complex data pipelines. It provides the tools necessary for rigorous quality assessment without sacrificing the speed required to train competitive models. Built by the Best This isn’t just a theoretical concept. Perle Labs is backed by $17.5 million in funding and was built by a powerhouse team of veterans from Meta, Amazon, and MIT. They are successfully bridging the gap between human intelligence and machine learning at an unprecedented scale. The future of AI doesn't just belong to the developers with the most computing power; it belongs to the models trained on the most trustworthy data. Perle Labs isn't just feeding AI they are raising the standard of truth, transparency, and accountability in machine learning. As AI continues to shape our world, the infrastructure behind it must be flawless. Perle Labs is building exactly that. Participating in @PerleLabs community campaign #PerleAI #ToPerle

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sᴛғᴜ
sᴛғᴜ@stfu0911·
Tired of AI models that hallucinate or underperform? The problem isn't the code it’s the data. Traditional labeling is messy and unverified. @PerleLabs is changing the game with a Web3-based infrastructure for high-quality, human-verified training data. ✅ On-chain traceability ✅ Verifiable contributor reputation ✅ Enterprise-grade accuracy Built by veterans from Meta, Amazon, and MIT. Better data leads to better AI. Get in early #PerleAI #ToPerle
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Muhtasim Muiz (❖,❖)
Muhtasim Muiz (❖,❖)@muhtasimmuiz·
Financial privacy is often misunderstood in today’s digital world. It’s frequently framed as something suspicious or unnecessary but historically, privacy has always been a natural part of how money works. Physical cash transactions never required public verification or permanent exposure. They were simple, direct and personal. As financial systems move on-chain, this default assumption has flipped. Transparency has become the norm, sometimes at the cost of individual autonomy. While transparency can improve trust and accountability at a system level, it shouldn’t come at the expense of personal security and freedom. This is where zero-knowledge (ZK) technology is changing the conversation. Projects like Miden ( @0xMiden ) are exploring how to preserve privacy without sacrificing verifiability. Instead of exposing every detail, ZK allows transactions to be validated while keeping sensitive data hidden. This creates a balance where systems remain trustworthy, but individuals retain control. Financial privacy is not about hiding wrongdoing. It’s about protecting identity, preventing exploitation and maintaining dignity in an increasingly digital economy. Just as we expect privacy in communication, we should expect the same in how we transact. The future of finance doesn’t have to choose between transparency and privacy. With the right design, it can and should have both.
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Arthur Morgan
Arthur Morgan@woshopy·
@Tojizeninhc Absolutely right staying positive and consistent turns Web3 ups and downs into steady growth.
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TOJI
TOJI@Tojizeninhc·
Stay Positive in Web3 The Web3 journey has good days and bad days. Some days you grow fast. Other days things feel slow. That is normal. The important thing is to stay positive and keep going. Everyone in Web3 is learning and growing step by step.
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Hasinur
Hasinur@hasinur1995·
Content No. 13 with @get_optimum Now a days people talk about decentralization like it's a free upgrade. Add more validators, get more security. Simple. But nobody really talks about what happens to the network underneath when you do that. @get_optimum is working on something that actually matters here. When validator counts grow, every new node adds more communication paths. More paths means more messages flying around, more chances for delays, and more bandwidth getting eaten up just to keep everyone in sync. At a certain scale, a bloated validator set can quietly make a network slower and less reliable which kind of defeats the whole point. What Optimum is building targets that specific problem. Smarter peer-to-peer data distribution, cutting out redundant transmissions, and tightening up synchronization so nodes stay aligned without drowning in traffic. The goal is to make growth feel smooth instead of painful. This is the kind of infrastructure work that never trends but always matters. Security and performance are usually treated like a tradeoff. More of one means less of the other. Optimum is trying to break that assumption at the network layer which is exactly where it needs to be solved. Global networks need global coordination. That problem only gets harder
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Islam Turaf
Islam Turaf@isturaf·
gMum Guyz @Get_Optimum is redefining decentralized networking with a powerful balance of simplicity and control. Whether using the Optimum Proxy for seamless subscription and publication management or switching to direct P2P mode for ultra-low latency developers get the flexibility they need. With fine-tuned control over mesh parameters like fanout and advanced messaging protocols ( GRAFT, PRUNE, IHAVE, IWANT ), Optimum enables precise optimization of bandwidth latency, and network reliability. Automatic node discovery ensures effortless peer connections while the gRPC API makes integration into dApps real-time systems and experimental frameworks smooth and efficient. From beginners to advanced builders Optimum provides the tools to create scalable resilient and high performance communication layers powering the next wave of decentralized innovation. @blockchainjeff @shariaronchain @f1nk1r
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𝐃𝐎𝐍
𝐃𝐎𝐍@md_don_Morph·
𝐀𝐈 𝐢𝐬 𝐚𝐦𝐚𝐳𝐢𝐧𝐠… 𝐮𝐧𝐭𝐢𝐥 𝐢𝐭 𝐥𝐞𝐚𝐫𝐧𝐬 𝐟𝐫𝐨𝐦 𝐢𝐭𝐬𝐞𝐥𝐟. I saw this concept illustrated perfectly AI trained on synthetic AI data slowly loses clarity and accuracy. 𝗢𝗻𝗲 𝗰𝗼𝗽𝘆 → 𝗼𝗸𝗮𝘆, 𝗳𝗶𝘃𝗲 𝗰𝗼𝗽𝗶𝗲𝘀 → 𝗰𝗼𝗻𝗳𝘂𝘀𝗲𝗱, 𝘁𝗲𝗻 𝗰𝗼𝗽𝗶𝗲𝘀 → 𝗯𝗿𝗼𝗸𝗲𝗻. This is exactly why human-verified, expert-reviewed data matters. PerleLabs is building a system where every dataset is traceable, auditable, and contributors earn real reputation. 𝗙𝗲𝗲𝗹𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗯𝗶𝗴𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀… 𝗶𝘁’𝘀 𝗯𝗲𝘁𝘁𝗲𝗿, 𝗵𝗼𝗻𝗲𝘀𝘁 𝗱𝗮𝘁𝗮. #PerleAI #ToPerle — participating in @PerleLabs community campaign"
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