Himu ⁰³

@siamssks61 @DonutAI Analyzing the market in real-time

Donut Browser Where AI Meets Trading Today most traders spend their time switching tabs analyzing the market finding trading signals and executing trades manually. @DonutAI Browser is changing that. While most platforms offer you trading tools Donut provides you with an AI driven trading environment directly within your browser. Using its Agentic AI Donut is capable of: ⇨ Analyzing the market in real-time ⇨ Identifying trading opportunities ⇨ Creating trading strategies ⇨ Risk management ⇨ Executing trades on-chain All within one window. Without switching between platforms. Without losing any opportunities. All of it happening within a single intelligent platform. And thanks to recently introduced upgrades such as D0 (next gen agentic trading system), Donut is moving even further aiming at creating a future when AI does not help traders but trades on their behalf. This is not only about introducing new Web3 technology. This is about redefining the entire trading process. From manual execution to automation, From complexity to simplicity, From reacting to acting. While other browsers help you browse, Donut helps you trade better.

@siamssks61 im bullish

@Himu0X DonutAI is getting seriously good lately trades feel way smoother now!

DonutAI has been moving fast lately and you can actually feel it with every update. From integrations like Polymarket to Donut Perps, it is not just adding features. It is becoming smarter in how it trades and executes. What stands out is that it no longer feels like a basic tool. It feels more like an assistant that understands context, reacts quickly, and keeps improving without friction. I tried a few trades recently and the execution felt noticeably smoother. Less manual effort and more clarity on what is happening makes a real difference. Another thing I noticed is how every update connects. Better decision flow, faster execution, and more intuitive interactions. If you are into trading, this is worth trying early. I got access through Glazy and honestly it is one of those things you understand better by using it. Check it out: getdonut.ai Let’s see where this goes.

@mamun73895 Clearity

@Himu0X Less manual effort and more clarity on what is happening makes a real difference.

@ledger_mind Hahah very Hot

@Himu0X hot glazy progress also day by day cooking hot

@Santo9237358218 gDonut bhai

@Himu0X Gdonut❤️

@ledger_mind আর কোপ কোথায়

@Himu0X kops post🔥

We’ve outgrown traditional trading tools. You shouldn't have to manually react to every single market movement anymore. Enter D0: Your relentless, 24/7 trading agent. While you live your life, D0 is working in the background scanning, analyzing, and acting intelligently. It doesn't just react; it stays one step ahead. Why D0 stands out: 🔹 Emotionless Execution: Zero panic, zero greed. It strictly follows your rules. 🔹 Consolidated Workflow: No need to juggle 10 different charts and news alerts. 🔹 Adaptive Intelligence: It actively learns your trading habits and evolves with you. You provide the strategy and risk limits. D0 handles the relentless execution. Stop treating automation like a simple "bot." Think of it as a professional trader that works for you round-the-clock without ever burning out. Less screen time. Smarter trades. Welcome to the new standard. @InternDonut | @DonutAI

I'm teleoperating from Bangladesh Through @PrismaXai , I’m able to control and guide a robot in real time from thousands of miles away. A perfect example of how @PrismaXai is connecting human intelligence with robotic power. Are you ready to TeleOperate with PrismaX ? @shayebackus | @PrismaXai

Most Web3 networks like Solana and Monad Labs rely on tree-based propagation. It looks efficient, but it’s still a chain of dependencies. Each hop inherits the previous one’s flaws. Packet loss doesn’t reset, it stacks. To handle this, systems use Reed–Solomon or Raptor codes. They either add redundancy or decode at every step. It works, but it’s costly. Extra bandwidth gets used, and latency builds as networks scale. The core issue is simple; •Loss compounds •Latency stacks •Throughput drops Tree structures don’t fix it, they just spread it. RLNC changes the model. Nodes recode packets instantly and forward them. No waiting, no full decoding. That shift means; •Packets are flexible, not fixed •Any node can help recovery •Recovery and forwarding happen together So loss stops accumulating . Nodes repair data as it moves instead of passing problems downstream. Result, faster propagation, lower latency, better throughput. While others decode in stages, RLNC keeps data flowing. For Optimum, that’s the edge. No tradeoff between speed and efficiency. And it opens the door to something bigger, moving beyond trees toward networks that fully use their capacity. @get_optimum | @blockchainjeff

@midnightfdn @MidnightNtwrk Big move by @MidnightNtwrk This is exactly how TradFi meets Web3 the right way compliant, private, and actually useful. Tokenized deposits with real yield + GBP backing? That’s the bridge we’ve been waiting for.

Midnight 🤝 Monument Bank Monument is set to become the first UK-regulated bank to tokenize retail customer deposits on a public blockchain — representing interest-bearing savings as digital tokens while remaining fully backed, redeemable in GBP, and protected under existing regulatory frameworks. Built on Midnight’s privacy-enhancing blockchain infrastructure, this approach ensures that transaction data remains shielded and accessible only to authorized participants — enabling the use of blockchain technology while maintaining the confidentiality and compliance required in regulated financial services. The initiative begins with a target of £250 million in tokenized deposits and represents the first phase in a broader rollout to expand access to tokenized financial products. Over time, this includes enabling exposure to asset classes such as private equity and structured products, and introducing more flexible lending models — capabilities historically reserved for institutional and private banking clients. Together, this partnership demonstrates how regulated financial institutions can bring traditional financial products on-chain — unlocking a more flexible, accessible, and programmable financial system without compromising privacy or regulatory standards.

@sizu1r @himuK0105 @get_optimum Gt

@himuK0105 @get_optimum Works on any blockchain without changing consensus

Most data layers in blockchain do one thing: store data and make it available. @get_optimum does something radically different. It doesn’t just focus on storing data It obsesses over how data moves across the entire network. 📍Traditional data layers: •Ensure availability & verifiability •Optimize for security & correctness •Assume propagation is “good enough” 📍Optimum’s approach: Treats data propagation as the core problem. It optimizes: • Smart distribution between nodes • Bandwidth efficiency • Lightning-fast reconstruction 📍Few things makes Optimum special: → Not just availability → Movement of data → Cuts redundant transmissions dramatically → Powered by advanced RLNC (Random Linear Network Coding) → Works on top of any blockchain without touching consensus or execution At massive scale, the real bottleneck isn’t just computation… It’s communication. •Slow data movement = Validators falling out of sync •Higher latency •Capped throughput Other layers ask: “How do we store data securely?” Optimum asks: “How do we move data efficiently across a decentralized network?” Transmit less. Deliver more. @blockchainjeff | @get

@himuK0105 Perle is the solutions of AI model collapsing?

𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐫𝐢𝐬𝐢𝐬 𝐢𝐧 𝐀𝐈 𝐅𝐞𝐰 𝐏𝐞𝐨𝐩𝐥𝐞 𝐀𝐫𝐞 𝐓𝐚𝐥𝐤𝐢𝐧𝐠 𝐀𝐛𝐨𝐮𝐭 AI is advancing faster than ever. Models are getting larger, more powerful, and more integrated into critical systems across healthcare, law, and infrastructure. But beneath this rapid progress lies a growing problem that few are addressing. 𝐀𝐈 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐥𝐥𝐚𝐩𝐬𝐞 As the race toward increasingly powerful AI models accelerates, a quiet yet critical risk is emerging: model collapse. Unlike traditional threats such as cyberattacks or malware, model collapse stems from within. It occurs when AI systems are repeatedly trained on their own synthetic outputs, triggering a gradual decline in quality. Over time, these models lose diversity, nuance, and reliability, becoming less capable and more fragile. A landmark 2024 study published in Nature (Shumailov et al.) demonstrated this phenomenon with compelling evidence. The research showed that when generative models are recursively trained on AI-generated data, rare events, edge cases, and subtle human patterns begin to vanish. With each generation, the model becomes more homogeneous, increasingly error-prone, and prone to producing hallucinations or meaningless outputs. Meanwhile, the AI industry continues to consume trillions of tokens, yet the supply of authentic, human-generated data is shrinking as synthetic content floods the internet. This creates a dangerous feedback loop, where AI increasingly learns from itself rather than from reality. In high-stakes domains such as medical diagnosis, legal analysis, and autonomous systems, the consequences are serious. Model collapse is not just a technical issue, it poses a real risk to accuracy, safety, and trust in AI systems. 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐀𝐫𝐞 𝐅𝐚𝐢𝐥𝐢𝐧𝐠 Most modern AI training depends on opaque and centralized data pipelines that lack transparency. Data sources are often unclear, verification processes are weak, and there is no reliable, immutable record of who contributed what or how data quality was maintained. This absence of trust directly accelerates model collapse. Synthetic data continues to circulate and compound over time without proper validation or correction, degrading overall model performance. Regulators are beginning to respond. Frameworks such as the EU AI Act and recent U.S. executive orders now emphasize the need for strong data provenance, especially for high-risk AI systems. At the same time, enterprises including hospitals, governments, and Fortune 500 companies are demanding auditable and trustworthy data before deploying AI in critical environments. Without a reliable data foundation, innovation slows and large-scale adoption becomes increasingly difficult. 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥 𝐀𝐧𝐭𝐢𝐝𝐨𝐭𝐞 : 𝐏𝐞𝐫𝐥𝐞 𝐋𝐚𝐛𝐬’ 𝐒𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐋𝐚𝐲𝐞𝐫 Perle Labs is building what it calls the sovereign intelligence layer for AI, a fundamentally different approach to data infrastructure where every piece of training data is human-verified, expert-validated, and fully auditable on-chain. The platform operates with a global network of over 15,000 vetted experts across more than 70 countries, including 2,500+ physicians and 530+ specialists, covering 27 languages. Unlike traditional systems, the entire workforce is fully in-house, with no reliance on anonymous crowdsourcing. Each data contribution undergoes a rigorous multi-layer validation process, including annotation, peer review, and expert oversight. This structured pipeline ensures consistently high-quality outputs, with average quality scores exceeding 4.8 out of 5. All records are securely stored on the Solana blockchain, making provenance, attribution, and contributor reputation immutable. This allows AI labs to audit the complete chain of custody for any dataset, ensuring transparency, trust, and accountability at every step. 𝐓𝐫𝐮𝐬𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐍𝐞𝐰 𝐂𝐨𝐦𝐩𝐮𝐭𝐞 At ETHDenver 2026, Perle Labs CEO Ahmed Rashad, formerly of Scale AI and MIT, introduced a powerful idea: “Trust is the new compute.” He emphasized that model collapse is a self-reinforcing feedback loop with no natural correction. Without continuous input from genuine human intelligence, AI systems risk degrading over time. To address this, every contribution must be securely recorded on-chain, ensuring it remains transparent and tamper-proof. Perle’s tokenomics are designed with a strong focus on the community. Out of a total supply of 10 billion $PRL tokens, 37.5% is allocated to contributors, supported by a fair and structured vesting model. The project has already raised over $17.5 million from leading investors, including Framework Ventures, CoinFund, and HashKey Capital. Perle is not simply improving data quality. It is building a decentralized data economy where human expertise is sovereign, AI systems are trustworthy, and innovation is distributed more equitably. By addressing model collapse at its foundation and making trust verifiable on-chain, Perle is laying the groundwork for the next generation of reliable, ethical, and sovereign artificial intelligence. What do you think? Is model collapse the biggest hidden bottleneck in AI today? Or can synthetic data be fixed with better filtering? Share your thoughts below! #PerleAI #ToPerle — participating in @PerleLabs community campaign

@rabby__x Lol bot

First Eid With her 💖

@himuK0105 @PerleLabs Lmaoooo 🤣🤣 Cant be funny than this

Perle Labs in nutshell 😂 . #PerleAI #ToPerle — participating in @PerleLabs community campaign

@jahid0x @shahrianazim6 yes

@Himu0X @shahrianazim6 Cooked , but feeling sad for him.

So you got kidnap by a women ? Or your Girlfrend took all your money ? its too easy to make fool everyone @shahrianazim6 ?

@luffyzorousof20 @shahrianazim6 More impression more money!

@Himu0X @shahrianazim6 he is real scammer bro I think he create this drama for giving impressions lol