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Selective disclosure using zero-knowledge proofs (ZKPs) vs fully homomorphic encryption (FHE). Two approaches to privacy. Very different trade-offs. I D R I S breaks down: 👉 performance 👉 trust models 👉 real-world use cases

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Midnight

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15,252 次观看 • 5 个月前 •via X (Twitter)

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Building the next gen of blockchain apps takes more than performance. It takes privacy by design. In 3 mins, I D R I S breaks down what Midnight is, how devs can get started, & where rational privacy unlocks real-world use cases. Ready to build? ➡️
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Building the next gen of blockchain apps takes more than performance. It takes privacy by design. In 3 mins, I D R I S breaks down what Midnight is, how devs can get started, & where rational privacy unlocks real-world use cases. Ready to build? ➡️

Midnight

17,694 次观看 • 9 个月前

M E S S I E R | Horizon Horizon 2.0 is live! 🎉 👉 A compliance-first #privacy solution for serious users, small businesses, and enterprises. 📲 Now with a full PWA mobile app experience and fund transfers without the need for a wallet connection, eliminating the attack surface and third party wallet exposure. ☑️ Privacy is secured using #zkSNARK cryptographic proofs, combined with compliance friendly activity reports for tax and accounting. 🛡 Powered by audited infrastructure with compliance aligned guardrails, Horizon delivers privacy designed for real world use within regulated frameworks.
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M E S S I E R | Horizon Horizon 2.0 is live! 🎉 👉 A compliance-first #privacy solution for serious users, small businesses, and enterprises. 📲 Now with a full PWA mobile app experience and fund transfers without the need for a wallet connection, eliminating the attack surface and third party wallet exposure. ☑️ Privacy is secured using #zkSNARK cryptographic proofs, combined with compliance friendly activity reports for tax and accounting. 🛡 Powered by audited infrastructure with compliance aligned guardrails, Horizon delivers privacy designed for real world use within regulated frameworks.

MESSIER | M87

77,303 次观看 • 8 个月前

I’ve been using Typeless ( Typeless) almost daily, and it’s changed how I write. Most of the time, I don’t type... I just speak! Messy thoughts in, clean text out. I use it while walking, between calls, even during quick coffee breaks It removes that “I need to sit and write this” friction completely. The new Translation Mode helps too, speak in your language, get instant translated text. I still tweak a line or two. But the time savings are real. This post? Spoken. Not typed. Try it here 👉
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I’ve been using Typeless ( Typeless) almost daily, and it’s changed how I write. Most of the time, I don’t type... I just speak! Messy thoughts in, clean text out. I use it while walking, between calls, even during quick coffee breaks It removes that “I need to sit and write this” friction completely. The new Translation Mode helps too, speak in your language, get instant translated text. I still tweak a line or two. But the time savings are real. This post? Spoken. Not typed. Try it here 👉

Mujeeb Ahmed

27,198 次观看 • 9 个月前

BITCOIN RAILS #69: ZERO-KNOWLEDGE PROOFS FOR POST-QUANTUM BITCOIN | with Benedikt Bünz 🔗 YOUTUBE: 🌿 SPOTIFY: While many Bitcoiners remain hopeful the network will embrace zero-knowledge proofs for protocol-level use cases, practical adoption has remained limited outside of BitVM and a handful of experimental proposals. Most of the world’s ZKP research has circled around Ethereum and other ecosystems, where significant resources have been dedicated to advancing these systems, particularly for scaling and privacy applications. One of the most notable contributors to this research is Benedikt Bünz ☕️ — professor of cryptography at NYU and collaborator of Dan Boneh, who together inarguably form one of the strongest blockchain-applied cryptography teams in the world. The pair recently announced they’ll be leading the new post-quantum cryptography unit localhost research — the first dedicated PQ research effort within a major Bitcoin development organization. With Benedikt leading the charge on the use of zero-knowledge proofs for post-quantum mitigation, the question emerges: will the post-quantum transition be the catalyst to finally bring zero-knowledge proofs to Bitcoin's core protocol? In more detail, Benedikt and I discuss: - Why ZKPs haven’t been widely adopted by the Bitcoin technical community — and why the threat of quantum computers may change that posture going forward - How ZKPs could be used for signature batching to address larger post-quantum signatures in Bitcoin’s post-quantum era - Why Bitcoiners will likely prioritize hash-based signatures as an initial post-quantum scheme — rather than more efficient but less proven alternatives (e.g., lattice-based) - How ZKPs have evolved over the last decade and may finally be ready for Bitcoin’s strict requirements around trust assumptions - Why Benedikt and Dan are teaming up with localhost research to create the first post-quantum cryptography unit within a major Bitcoin development organization + what they hope to accomplish This episode of Bitcoin Rails is brought to you by: LayerTwo Labs LayerTwo Labs — developing research, software, and technologies for scaling Bitcoin via the integration of Drivechains (BIP 300/301) Hashi on Sui — a primitive for executing Bitcoin DeFi transactions, without having to trust a federated bridge or other centralized entity BitBox BitBox — an open-source Bitcoin-only hardware wallet, with smooth UX and no compromises on security. Check out Bitbox [dot] swiss and use code BITCOINRAILS to get a discount TIMESTAMPS: 00:00 — Intro 00:22 — Benedikt's background 04:10 — How Benedikt got into Bitcoin and cryptography 07:42 — First ZK project: proving exchange solvency after Mt. Gox 16:01 — ZK proofs explained 27:43 — How security gets popular & ZK proofs in Bitcoin 35:49 — Other applications of ZK proofs for Bitcoin 40:15 — Why ZK proofs haven't been adopted in Bitcoin 50:46 — Why the Bitcoin post-quantum transition needs ZK proofs 01:07:00 — Cryptographic agility and why lattices win 01:18:00 — The size problem and how SNARKs solve it 01:33:57 — Proving a Bitcoin block in a laptop in 1.5 seconds 01:37:12 — Bitcoin as the primary quantum target 01:40:47 — ZK proofs for seed phrase recovery
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BITCOIN RAILS #69: ZERO-KNOWLEDGE PROOFS FOR POST-QUANTUM BITCOIN | with Benedikt Bünz 🔗 YOUTUBE: 🌿 SPOTIFY: While many Bitcoiners remain hopeful the network will embrace zero-knowledge proofs for protocol-level use cases, practical adoption has remained limited outside of BitVM and a handful of experimental proposals. Most of the world’s ZKP research has circled around Ethereum and other ecosystems, where significant resources have been dedicated to advancing these systems, particularly for scaling and privacy applications. One of the most notable contributors to this research is Benedikt Bünz ☕️ — professor of cryptography at NYU and collaborator of Dan Boneh, who together inarguably form one of the strongest blockchain-applied cryptography teams in the world. The pair recently announced they’ll be leading the new post-quantum cryptography unit localhost research — the first dedicated PQ research effort within a major Bitcoin development organization. With Benedikt leading the charge on the use of zero-knowledge proofs for post-quantum mitigation, the question emerges: will the post-quantum transition be the catalyst to finally bring zero-knowledge proofs to Bitcoin's core protocol? In more detail, Benedikt and I discuss: - Why ZKPs haven’t been widely adopted by the Bitcoin technical community — and why the threat of quantum computers may change that posture going forward - How ZKPs could be used for signature batching to address larger post-quantum signatures in Bitcoin’s post-quantum era - Why Bitcoiners will likely prioritize hash-based signatures as an initial post-quantum scheme — rather than more efficient but less proven alternatives (e.g., lattice-based) - How ZKPs have evolved over the last decade and may finally be ready for Bitcoin’s strict requirements around trust assumptions - Why Benedikt and Dan are teaming up with localhost research to create the first post-quantum cryptography unit within a major Bitcoin development organization + what they hope to accomplish This episode of Bitcoin Rails is brought to you by: LayerTwo Labs LayerTwo Labs — developing research, software, and technologies for scaling Bitcoin via the integration of Drivechains (BIP 300/301) Hashi on Sui — a primitive for executing Bitcoin DeFi transactions, without having to trust a federated bridge or other centralized entity BitBox BitBox — an open-source Bitcoin-only hardware wallet, with smooth UX and no compromises on security. Check out Bitbox [dot] swiss and use code BITCOINRAILS to get a discount TIMESTAMPS: 00:00 — Intro 00:22 — Benedikt's background 04:10 — How Benedikt got into Bitcoin and cryptography 07:42 — First ZK project: proving exchange solvency after Mt. Gox 16:01 — ZK proofs explained 27:43 — How security gets popular & ZK proofs in Bitcoin 35:49 — Other applications of ZK proofs for Bitcoin 40:15 — Why ZK proofs haven't been adopted in Bitcoin 50:46 — Why the Bitcoin post-quantum transition needs ZK proofs 01:07:00 — Cryptographic agility and why lattices win 01:18:00 — The size problem and how SNARKs solve it 01:33:57 — Proving a Bitcoin block in a laptop in 1.5 seconds 01:37:12 — Bitcoin as the primary quantum target 01:40:47 — ZK proofs for seed phrase recovery

Isabel Foxen Duke⚡️

19,872 次观看 • 2 个月前

Not every new model needs to be the biggest model. Sometimes I'd rather have one that's fast, reliable, affordable, and actually pleasant to build with. That's the impression I got after using Tencent Hunyuan Hy3. I threw a mix of coding, reasoning, and agent tasks at it throughout the afternoon, and what stood out was how stable the experience felt. It followed instructions well, kept track of long conversations, and didn't require much prompt babysitting. The 256K context window is especially useful when working with larger projects, and Tencent says Hy3 reaches performance comparable to flagship open-source models 2–5× larger in parameter scale, which is an interesting trade-off between capability and efficiency. Being Apache 2.0 open source is another bonus, and it's currently free to try on OpenRouter for the next two weeks. Worth taking for a spin if you're evaluating models for real-world development. 👉 Thanks Tencent Hy #Hy3 #Hunyuan #OpenSource #TencentAI
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Not every new model needs to be the biggest model. Sometimes I'd rather have one that's fast, reliable, affordable, and actually pleasant to build with. That's the impression I got after using Tencent Hunyuan Hy3. I threw a mix of coding, reasoning, and agent tasks at it throughout the afternoon, and what stood out was how stable the experience felt. It followed instructions well, kept track of long conversations, and didn't require much prompt babysitting. The 256K context window is especially useful when working with larger projects, and Tencent says Hy3 reaches performance comparable to flagship open-source models 2–5× larger in parameter scale, which is an interesting trade-off between capability and efficiency. Being Apache 2.0 open source is another bonus, and it's currently free to try on OpenRouter for the next two weeks. Worth taking for a spin if you're evaluating models for real-world development. 👉 Thanks Tencent Hy #Hy3 #Hunyuan #OpenSource #TencentAI

FELIX

56,379 次观看 • 2 个月前

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Bri Teresi

23,100 次观看 • 4 个月前

Ep. 16 | Free The Money | Zcash & ZODL: Why Encryption Wins in an AI Driven World In this episode, I’m joined by Tony Margarit 🛡 , former Finance & Ops at the Electric Coin Company (ECC), the original team behind Zcash 🛡️ and the Zashi wallet. Tony breaks down what’s happening inside the Zcash ecosystem, why the former ECC team launched Zcash Open Development Lab, and what they’re building now with their newly rebranded wallet, Zodl. We talk about the bigger mission: making privacy normal and scaling private digital cash to billions. Tony explains why Zcash isn’t “corporate controlled,” and how decentralized development actually works across multiple teams and contributors. We also discuss Tachyon, a major upcoming upgrade designed to dramatically improve Zcash’s scalability and prepare the network for billions of users. We get into the product leap that matters most for real adoption: world-class UX + real utility. Tony explains Near Intents and why it’s a major unlock, enabling more seamless swaps across ecosystems so users can keep value encrypted while still interacting with other assets. Tony also explains CrossPay as a key bridge to adoption: the ability to pay friends or vendors in the currency they want while keeping your own holdings private. We cover why Zcash still supports transparent transactions, how that helps with exchange access and adoption without weakening shielded privacy, and why Ledger/Trezor shielded support could be a major catalyst for growing the shielded pool. Finally, we go deep on the AI angle — blockchain surveillance, wallet deanonymization, and why encryption (not just obfuscation) matters as AI gets more powerful. We wrap with how zero-knowledge proofs could reshape identity, governance, and everyday verification online. Sign up for ITrustCapital with this link for $100 funding bonus. See why people are opening a tax-advantaged Crypto, Gold & Silver IRA for their future: 00:00 Tony’s Background: From CPA to Crypto 4:44 Why Privacy Clicked During COVID 08:03 What Happened at ECC & Launching Zcash Open Development Lab 11:15 ZODL Wallet: UX, Utility & Why It Matters 12:18 Near Intents & CrossPay: A Major Unlock for Zcash 14:40 Shielded Pool Growth & Hardware Wallet Catalyst 18:57 Transparent vs Shielded Transactions (Why Both Exist) 21:39 AI Surveillance & Blockchain Deanonymization Risks. 25:11 Obfuscation vs Encryption 26:00 AI Agents & Micropayments 27:58 Tachyon: Scaling Zcash to Billions 31:04 Crypto, Taxes & Regulatory Friction 38:04 Zero-Knowledge Proofs Beyond Money 41:50 Privacy is normal
48:02

Ep. 16 | Free The Money | Zcash & ZODL: Why Encryption Wins in an AI Driven World In this episode, I’m joined by Tony Margarit 🛡 , former Finance & Ops at the Electric Coin Company (ECC), the original team behind Zcash 🛡️ and the Zashi wallet. Tony breaks down what’s happening inside the Zcash ecosystem, why the former ECC team launched Zcash Open Development Lab, and what they’re building now with their newly rebranded wallet, Zodl. We talk about the bigger mission: making privacy normal and scaling private digital cash to billions. Tony explains why Zcash isn’t “corporate controlled,” and how decentralized development actually works across multiple teams and contributors. We also discuss Tachyon, a major upcoming upgrade designed to dramatically improve Zcash’s scalability and prepare the network for billions of users. We get into the product leap that matters most for real adoption: world-class UX + real utility. Tony explains Near Intents and why it’s a major unlock, enabling more seamless swaps across ecosystems so users can keep value encrypted while still interacting with other assets. Tony also explains CrossPay as a key bridge to adoption: the ability to pay friends or vendors in the currency they want while keeping your own holdings private. We cover why Zcash still supports transparent transactions, how that helps with exchange access and adoption without weakening shielded privacy, and why Ledger/Trezor shielded support could be a major catalyst for growing the shielded pool. Finally, we go deep on the AI angle — blockchain surveillance, wallet deanonymization, and why encryption (not just obfuscation) matters as AI gets more powerful. We wrap with how zero-knowledge proofs could reshape identity, governance, and everyday verification online. Sign up for ITrustCapital with this link for $100 funding bonus. See why people are opening a tax-advantaged Crypto, Gold & Silver IRA for their future: 00:00 Tony’s Background: From CPA to Crypto 4:44 Why Privacy Clicked During COVID 08:03 What Happened at ECC & Launching Zcash Open Development Lab 11:15 ZODL Wallet: UX, Utility & Why It Matters 12:18 Near Intents & CrossPay: A Major Unlock for Zcash 14:40 Shielded Pool Growth & Hardware Wallet Catalyst 18:57 Transparent vs Shielded Transactions (Why Both Exist) 21:39 AI Surveillance & Blockchain Deanonymization Risks. 25:11 Obfuscation vs Encryption 26:00 AI Agents & Micropayments 27:58 Tachyon: Scaling Zcash to Billions 31:04 Crypto, Taxes & Regulatory Friction 38:04 Zero-Knowledge Proofs Beyond Money 41:50 Privacy is normal

Bri Teresi

42,633 次观看 • 7 个月前

If I want to show the world I can actually generate $1M+ from owning my own fleet of Tesla Robotaxis one day, I need the most powerful tool that models the real cash flows per vehicle, per year, with actual costs, platform fees, different Tesla models, locations, and payback periods. So today, I just built a my own Tesla Robotaxi Fleet Cash Flow Dashboard using Grok Build (fyi, the entire project came out to roughly 2,000 lines of code, which would normally take weeks of development with a team… I just did it all on my own). It lets me: • Track individual robotaxis with detailed economics (purchase cost, miles/day, revenue per mile, weekly costs, insurance, platform fees, start dates) • See daily, monthly, and annual profit per vehicle • Track real performance since each robotaxi started operating (revenue, costs, profit, and payback progress) • Run what-if scenarios with an interactive Cash Flow Calculator • Project multi-year fleet earnings with cumulative charts • View the whole fleet on a map by location • Export everything to CSV with full financials This is the tool I will use and iterate on to build a meaningful robotaxi operation one day. Grok Build made it possible to go from an idea to a fully functional, professional-grade modeling app within 1 day. Grok Build is seriously a game-changer.
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If I want to show the world I can actually generate $1M+ from owning my own fleet of Tesla Robotaxis one day, I need the most powerful tool that models the real cash flows per vehicle, per year, with actual costs, platform fees, different Tesla models, locations, and payback periods. So today, I just built a my own Tesla Robotaxi Fleet Cash Flow Dashboard using Grok Build (fyi, the entire project came out to roughly 2,000 lines of code, which would normally take weeks of development with a team… I just did it all on my own). It lets me: • Track individual robotaxis with detailed economics (purchase cost, miles/day, revenue per mile, weekly costs, insurance, platform fees, start dates) • See daily, monthly, and annual profit per vehicle • Track real performance since each robotaxi started operating (revenue, costs, profit, and payback progress) • Run what-if scenarios with an interactive Cash Flow Calculator • Project multi-year fleet earnings with cumulative charts • View the whole fleet on a map by location • Export everything to CSV with full financials This is the tool I will use and iterate on to build a meaningful robotaxi operation one day. Grok Build made it possible to go from an idea to a fully functional, professional-grade modeling app within 1 day. Grok Build is seriously a game-changer.

Teslaconomics

16,167 次观看 • 4 个月前

🤯 A localmaxxer hit ~381 tok/s on a SINGLE RTX 3090 with Qwen3.8-27B. This developer has turned a 24GB RTX 3090 into a monster Qwen inference box - w/some creativity. Four days ago 👉 ⚡ ~82 tok/s single-user Then 👉 ⚡ ~114 tok/s with optimized MTP ⚡ ~138 tok/s with DFlash2 + lookup drafting Now 👉 🔥 ~381 tok/s on ONE request How? The recipe combines ... 🧠 Qwen3.8-27B 🎮 1× RTX 3090 24GB @ 250W ⚙️ heavily optimized vLLM ⚡ DFlash2 speculative decoding 🔎 lookup-augmented drafting 📚 prefix caching 🧮 16-token verification blocks 💾 quantized KV / heads / activations DFlash2 normally proposes 7 tokens. The developer realized the verification block doesn't have to stop there. If Qwen is answering from a document already sitting in the prompt, the system can fill the remaining draft positions using tokens found directly in that context. 🎯 So the target model can verify 16 tokens at once. On a ~25K-token document reproduction task: Previous DFlash2 👉 ~260 tok/s Longer verification + context lookup 👉 🔥 ~382 tok/s Acceptance: 🤯 15 of 16 tokens per verification step That is where the crazy number comes from. ⚠️ On ordinary real-world chat prompts, the same setup is around ~133 tok/s Still extremely fast for a dense 27B model on an RTX 3090. The 381 tok/s mode shines when the answer largely comes from material already in context so these are best use cases 📚 RAG / document Q&A 💻 Coding assistants applying edits 📝 Quoting or rewriting documents 🔎 Extracting information from long prompts And another optimization 👉 With prefix caching, a second question against the same 25K-token document reportedly goes from: 🐌 22.4 sec TTFT → ⚡ 0.56 sec TTFT Because the model doesn't need to process the whole document again. 🎯 It's specifically a mode for RAG front ends and coding agents. Follow iamMess on Reddit or syv-ai on GitHub 🔗 Reddit: r/LocalLLaMA/comments/1vtup5s/ 🔗 GitHub: /syv-ai/qwen38-27b-rtx3090
1:08

🤯 A localmaxxer hit ~381 tok/s on a SINGLE RTX 3090 with Qwen3.8-27B. This developer has turned a 24GB RTX 3090 into a monster Qwen inference box - w/some creativity. Four days ago 👉 ⚡ ~82 tok/s single-user Then 👉 ⚡ ~114 tok/s with optimized MTP ⚡ ~138 tok/s with DFlash2 + lookup drafting Now 👉 🔥 ~381 tok/s on ONE request How? The recipe combines ... 🧠 Qwen3.8-27B 🎮 1× RTX 3090 24GB @ 250W ⚙️ heavily optimized vLLM ⚡ DFlash2 speculative decoding 🔎 lookup-augmented drafting 📚 prefix caching 🧮 16-token verification blocks 💾 quantized KV / heads / activations DFlash2 normally proposes 7 tokens. The developer realized the verification block doesn't have to stop there. If Qwen is answering from a document already sitting in the prompt, the system can fill the remaining draft positions using tokens found directly in that context. 🎯 So the target model can verify 16 tokens at once. On a ~25K-token document reproduction task: Previous DFlash2 👉 ~260 tok/s Longer verification + context lookup 👉 🔥 ~382 tok/s Acceptance: 🤯 15 of 16 tokens per verification step That is where the crazy number comes from. ⚠️ On ordinary real-world chat prompts, the same setup is around ~133 tok/s Still extremely fast for a dense 27B model on an RTX 3090. The 381 tok/s mode shines when the answer largely comes from material already in context so these are best use cases 📚 RAG / document Q&A 💻 Coding assistants applying edits 📝 Quoting or rewriting documents 🔎 Extracting information from long prompts And another optimization 👉 With prefix caching, a second question against the same 25K-token document reportedly goes from: 🐌 22.4 sec TTFT → ⚡ 0.56 sec TTFT Because the model doesn't need to process the whole document again. 🎯 It's specifically a mode for RAG front ends and coding agents. Follow iamMess on Reddit or syv-ai on GitHub 🔗 Reddit: r/LocalLLaMA/comments/1vtup5s/ 🔗 GitHub: /syv-ai/qwen38-27b-rtx3090

David Hendrickson

132,796 次观看 • 1 个月前

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉
0:38

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉

Md Riyazuddin

20,990 次观看 • 5 个月前

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
49:12

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,311 次观看 • 1 年前

🌆 Digital Evidence, Real Estate, and the Next Wave of Real-World Adoption Dave Berg, CPO at Constellation, breaks down how they are building real onchain infrastructure that solves real problems. Not hypothetical use cases. Not hype cycles. Actual products people can use right now. 1. Digital Evidence. Authenticity for the internet. Constellation is anchoring digital fingerprints of files, images, documents, and data streams directly onto the network. Why does this matter? Because in a world filled with AI content, fake screenshots, edited PDFs, and manipulated media, proving the origin of information is becoming one of the most valuable capabilities we have. Developers and non developers can use simple APIs, or even vibe code with AI tools like Claude, to anchor and verify data instantly. Everyday users can anchor real-world data right now onto Constellation network with zero blockchain knowledge, using devices they already use every single day. 2. Proof of Management for real assets. This leads into what might be one of the most practical DLT products released in years. Real Estate Ledger. A digital guidebook for any property: • Permits • Warranties • Proof of maintenance • Vendor history • Manuals • Insurance • Improvements • Receipts • Photos Everything tied to the property, all cryptographically timestamped. If you have ever tried to sell a house, maintain one, or prove something to an insurer, you instantly understand how useful this is. Imagine handing a buyer a clean, verified report of every repair, every vendor, every upgrade, and every warranty. Imagine builders uploading materials and documentation during construction so the next owner knows exactly what is behind the walls. Imagine insurance claims based on truth instead of paperwork chaos. This is not a pitch deck about tokenizing real estate one day. This is infrastructure that exists right now. 3. Constellation is solving real adoption problems for Web3. No need to rebuild your business to onboard. No need to run your own nodes unless you want to. No need to become a blockchain expert. Just clean APIs, onchain trust, and applications anyone can understand. Authenticity and truth are scarce assets, Constellation (DAG) is building rails that protect them. Podcast powered by Constellation²
41:01

🌆 Digital Evidence, Real Estate, and the Next Wave of Real-World Adoption Dave Berg, CPO at Constellation, breaks down how they are building real onchain infrastructure that solves real problems. Not hypothetical use cases. Not hype cycles. Actual products people can use right now. 1. Digital Evidence. Authenticity for the internet. Constellation is anchoring digital fingerprints of files, images, documents, and data streams directly onto the network. Why does this matter? Because in a world filled with AI content, fake screenshots, edited PDFs, and manipulated media, proving the origin of information is becoming one of the most valuable capabilities we have. Developers and non developers can use simple APIs, or even vibe code with AI tools like Claude, to anchor and verify data instantly. Everyday users can anchor real-world data right now onto Constellation network with zero blockchain knowledge, using devices they already use every single day. 2. Proof of Management for real assets. This leads into what might be one of the most practical DLT products released in years. Real Estate Ledger. A digital guidebook for any property: • Permits • Warranties • Proof of maintenance • Vendor history • Manuals • Insurance • Improvements • Receipts • Photos Everything tied to the property, all cryptographically timestamped. If you have ever tried to sell a house, maintain one, or prove something to an insurer, you instantly understand how useful this is. Imagine handing a buyer a clean, verified report of every repair, every vendor, every upgrade, and every warranty. Imagine builders uploading materials and documentation during construction so the next owner knows exactly what is behind the walls. Imagine insurance claims based on truth instead of paperwork chaos. This is not a pitch deck about tokenizing real estate one day. This is infrastructure that exists right now. 3. Constellation is solving real adoption problems for Web3. No need to rebuild your business to onboard. No need to run your own nodes unless you want to. No need to become a blockchain expert. Just clean APIs, onchain trust, and applications anyone can understand. Authenticity and truth are scarce assets, Constellation (DAG) is building rails that protect them. Podcast powered by Constellation²

Generation Infinity

170,375 次观看 • 10 个月前

🚨 Most people haven't realized it, but without proper legal oversight, AI glasses might already be supporting privacy harm and CRIMINAL activity: Last year, Alexander Klöpping, a Dutch journalist, went to Amsterdam's financial district using AI glasses. After seconds-long interactions with random people on the street, the facial recognition technology powering his glasses could identify the person using publicly available data from the internet. His goal was to demonstrate how invasive facial recognition technology has become, especially since anyone can wear a device that enables real-time identification. Many people forget, but besides the more 'hyped' use cases, real-time identification can also be used to support criminal activity. For example, someone may use these types of AI glasses to identify the victim after a few seconds of small talk, then pretend to be an old acquaintance to steal or commit a violent crime. Most smart glasses have a small green light at the upper corner to signal that the camera is on, but my guess is that the majority of people will not notice it (that's what we see in the video, too). Also, more recently, I have seen smart glasses WITHOUT the green light (which would likely be illegal in some countries and U.S. states). The video is from November 2024, but has been circulating again one year later. I would say that from legal and regulatory perspectives, one year later, the problem remains the same, and little to nothing has been done to protect people from AI-led privacy invasion on the streets (which might also facilitate other types of harm and violent crime). I would also say that, in general, we are WORSE in December 2025 than we were in November 2024. Why? There is a global deregulatory trend in AI (pushed by the U.S.), and, through the Digital Omnibus, the EU is proposing changes to the GDPR and the EU AI Act that are detrimental to the protection of fundamental rights, including, of course, privacy. Despite all the odds... I'm cautiously optimistic that the pendulum will swing in the opposite direction in 2026, as deregulated AI clearly benefits no one (except tech billionaires). - 👉 Most people have no idea why privacy matters, ESPECIALLY when AI is everywhere. Make sure to share this video. 👉 To learn more about AI's legal and ethical challenges, join my newsletter's 87,500+ subscribers (link below).
1:57

🚨 Most people haven't realized it, but without proper legal oversight, AI glasses might already be supporting privacy harm and CRIMINAL activity: Last year, Alexander Klöpping, a Dutch journalist, went to Amsterdam's financial district using AI glasses. After seconds-long interactions with random people on the street, the facial recognition technology powering his glasses could identify the person using publicly available data from the internet. His goal was to demonstrate how invasive facial recognition technology has become, especially since anyone can wear a device that enables real-time identification. Many people forget, but besides the more 'hyped' use cases, real-time identification can also be used to support criminal activity. For example, someone may use these types of AI glasses to identify the victim after a few seconds of small talk, then pretend to be an old acquaintance to steal or commit a violent crime. Most smart glasses have a small green light at the upper corner to signal that the camera is on, but my guess is that the majority of people will not notice it (that's what we see in the video, too). Also, more recently, I have seen smart glasses WITHOUT the green light (which would likely be illegal in some countries and U.S. states). The video is from November 2024, but has been circulating again one year later. I would say that from legal and regulatory perspectives, one year later, the problem remains the same, and little to nothing has been done to protect people from AI-led privacy invasion on the streets (which might also facilitate other types of harm and violent crime). I would also say that, in general, we are WORSE in December 2025 than we were in November 2024. Why? There is a global deregulatory trend in AI (pushed by the U.S.), and, through the Digital Omnibus, the EU is proposing changes to the GDPR and the EU AI Act that are detrimental to the protection of fundamental rights, including, of course, privacy. Despite all the odds... I'm cautiously optimistic that the pendulum will swing in the opposite direction in 2026, as deregulated AI clearly benefits no one (except tech billionaires). - 👉 Most people have no idea why privacy matters, ESPECIALLY when AI is everywhere. Make sure to share this video. 👉 To learn more about AI's legal and ethical challenges, join my newsletter's 87,500+ subscribers (link below).

Luiza Jarovsky, PhD

370,317 次观看 • 9 个月前

🚀SEC’s Game-Changing Policy Fuels DeFi & RWA Revolution! The SEC’s Q2 2025 policy shift is setting Decentralized Finance (DeFi) on fire, with Real-World Assets (RWAs) leading the charge. Is a new DeFi Summer brewing? Join HTX to stay ahead! 🔥 Three SEC Moves Powering the Future: 1️⃣ Innovation Exemption: Highly decentralized protocols get temporary relief from registration requirements in pilot programs, cutting legal risks. 2️⃣ Functional Categorization Framework: Regulations now target on-chain operations and business logic, moving away from blanket “securities” labels for tokens. 3️⃣ Regulatory Sandbox for DAOs & RWAs: A safe space for testing DAOs and RWAs, blending cutting-edge innovation with compliance. 💡 Why RWAs Are Stealing the Show: 🔹 Easier Compliance: Tokenizing assets like real estate, bonds, or supply chain finance just got smoother, with fewer securities classification hurdles. 🔹Institutional Money Rush: The sandbox and transparency are pulling in big players. Ondo Finance’s OUSG issuance skyrocketed 40% post-policy, while Maple Finance and Centrifuge attract pension funds and asset managers. 💰 🔹Tech Breakthroughs: Zero-knowledge proofs (ZKPs), homomorphic encryption, and cross-chain solutions supercharge security, KYC/AML compliance, and multi-chain interoperability. 🔹 Market on Fire: DeFi’s TVL surged 17% (from $46B to $54B) in just one week! Governance tokens like UNI, AAVE, and MKR soared 25-60%, outpacing BTC and ETH. 📈 🔹DeFi Meets TradFi: RWAs bridge DeFi with traditional finance, unlocking tokenized real estate, invoice financing, and more for a seamless “on-chain + off-chain” ecosystem. 🌐 The Road Ahead: DeFi is shifting from “wild growth” to “compliant powerhouse.” New revenue models like protocol profit-sharing and asset management, plus hybrid governance (on-chain voting + off-chain legal frameworks), are reshaping the game. RWAs are poised to lead, with DeFi blue-chip tokens ready for a valuation reboot. The SEC’s pivot is a launchpad for institutional adoption and market maturity—#HTX is your front-row seat! 💭 My Take: The SEC’s policy is a massive win for DeFi, turbocharging RWAs and setting the stage for a potential bull run 🌸 Is the place to track this revolution! What’s your bet—will RWAs spark the next DeFi wave? 👉 Follow HTX for the hottest DeFi, RWA, and crypto updates! H.E. Justin Sun 👨‍🚀 🌞 HTX #TRX #TRONEcoStar
0:33

🚀SEC’s Game-Changing Policy Fuels DeFi & RWA Revolution! The SEC’s Q2 2025 policy shift is setting Decentralized Finance (DeFi) on fire, with Real-World Assets (RWAs) leading the charge. Is a new DeFi Summer brewing? Join HTX to stay ahead! 🔥 Three SEC Moves Powering the Future: 1️⃣ Innovation Exemption: Highly decentralized protocols get temporary relief from registration requirements in pilot programs, cutting legal risks. 2️⃣ Functional Categorization Framework: Regulations now target on-chain operations and business logic, moving away from blanket “securities” labels for tokens. 3️⃣ Regulatory Sandbox for DAOs & RWAs: A safe space for testing DAOs and RWAs, blending cutting-edge innovation with compliance. 💡 Why RWAs Are Stealing the Show: 🔹 Easier Compliance: Tokenizing assets like real estate, bonds, or supply chain finance just got smoother, with fewer securities classification hurdles. 🔹Institutional Money Rush: The sandbox and transparency are pulling in big players. Ondo Finance’s OUSG issuance skyrocketed 40% post-policy, while Maple Finance and Centrifuge attract pension funds and asset managers. 💰 🔹Tech Breakthroughs: Zero-knowledge proofs (ZKPs), homomorphic encryption, and cross-chain solutions supercharge security, KYC/AML compliance, and multi-chain interoperability. 🔹 Market on Fire: DeFi’s TVL surged 17% (from $46B to $54B) in just one week! Governance tokens like UNI, AAVE, and MKR soared 25-60%, outpacing BTC and ETH. 📈 🔹DeFi Meets TradFi: RWAs bridge DeFi with traditional finance, unlocking tokenized real estate, invoice financing, and more for a seamless “on-chain + off-chain” ecosystem. 🌐 The Road Ahead: DeFi is shifting from “wild growth” to “compliant powerhouse.” New revenue models like protocol profit-sharing and asset management, plus hybrid governance (on-chain voting + off-chain legal frameworks), are reshaping the game. RWAs are poised to lead, with DeFi blue-chip tokens ready for a valuation reboot. The SEC’s pivot is a launchpad for institutional adoption and market maturity—#HTX is your front-row seat! 💭 My Take: The SEC’s policy is a massive win for DeFi, turbocharging RWAs and setting the stage for a potential bull run 🌸 Is the place to track this revolution! What’s your bet—will RWAs spark the next DeFi wave? 👉 Follow HTX for the hottest DeFi, RWA, and crypto updates! H.E. Justin Sun 👨‍🚀 🌞 HTX #TRX #TRONEcoStar

Hồng Ngọc | Ruby💎| EarnHTX

25,942 次观看 • 1 年前

Ep. 10 Free The Money | As Cash Disappears: Why Monero Succeeds Where Bitcoin Fails In this episode I’m joined by Douglas Tuman, founder of MoneroTopia and host of Monero Talk Live, to explore why Monero is not only the ultimate privacy coin, but a foundational tool for freedom in the digital age. Doug explains how Monero was built to fix Bitcoin’s core flaws. Bitcoin’s public ledger allows for the tracking of transactions and balances, while Monero encrypts the sender, receiver, and amount by default. Monero’s privacy makes Monero fully fungible, meaning every coin is identical, untraceable, and cannot be tainted or blacklisted, something Bitcoin can never achieve. Doug breaks down why Monero is more decentralized at the mining level, using ASIC-resistant proof-of-work that allows everyday CPUs to secure the network, preventing control by large industrial mining operations and state pressure unlike Bitcoin. This makes Monero harder to censor and more resilient long-term. This episode tackles the controversial topic head-on: Monero’s use on dark markets. Rather than being a weakness, it’s evidence that Monero is the most private and effective digital cash available, just as cash and encryption have always been neutral tools used by both good and bad actors. At a deeper level, this conversation is about freedom. Cash is being eliminated. Privacy is necessary to prevent governments from wielding excessive power over individuals. Sign up for ITrustCapital with this link for $100 funding bonus. See why people are opening a tax-advantaged Crypto, Gold & Silver IRA for their future: 0:54 From Bitcoin Maxi to Monero Advocate: Doug’s personal journey into crypto (and the Dogecoin hack that changed everything) 8:18 Why Monero Is Truly Private: How Monero hides the sender, receiver, and amount by default 9:45 Beyond Privacy: Monero’s dynamic block size & why it actually scales on-chain (unlike Bitcoin) 13:30 Why Monero Is More Decentralized: ASIC-resistant mining, RandomX, and “one CPU, one vote” 17:30 Bitcoin’s Fatal Flaw: Why Bitcoin is no longer digital cash—or even digital gold 19:55 Why Privacy Coins Are Rising: Surveillance, debanking, and people waking up 24:05 How to Secure Your Crypto: Wallet safety, seed phrases, and avoiding catastrophic mistakes 28:00 MoneroTopia: Building real-world adoption and living on Monero 32:05 Running for Congress: Why Doug stepped into politics to defend Monero and financial privacy 38:00 The Realization: Why building a parallel crypto economy beats fighting the system from within 44:00 The Clarity Act & Aaron Day: How new legislation threatens self-custody—and who’s exposing it 45:55 Monero vs Zcash: The real differences in privacy, governance, and philosophy 51:40 The Next Monero Upgrade: Full-chain membership proofs explained 53:25 Private by Default = Fungible 59:05 Dark Markets & the Truth About Privacy: Why Monero’s use proves it’s the best digital cash 1:03:10 XMR Bazaar: Peer-to-peer markets, no middlemen, and a parallel economy built on Monero
1:12:37

Ep. 10 Free The Money | As Cash Disappears: Why Monero Succeeds Where Bitcoin Fails In this episode I’m joined by Douglas Tuman, founder of MoneroTopia and host of Monero Talk Live, to explore why Monero is not only the ultimate privacy coin, but a foundational tool for freedom in the digital age. Doug explains how Monero was built to fix Bitcoin’s core flaws. Bitcoin’s public ledger allows for the tracking of transactions and balances, while Monero encrypts the sender, receiver, and amount by default. Monero’s privacy makes Monero fully fungible, meaning every coin is identical, untraceable, and cannot be tainted or blacklisted, something Bitcoin can never achieve. Doug breaks down why Monero is more decentralized at the mining level, using ASIC-resistant proof-of-work that allows everyday CPUs to secure the network, preventing control by large industrial mining operations and state pressure unlike Bitcoin. This makes Monero harder to censor and more resilient long-term. This episode tackles the controversial topic head-on: Monero’s use on dark markets. Rather than being a weakness, it’s evidence that Monero is the most private and effective digital cash available, just as cash and encryption have always been neutral tools used by both good and bad actors. At a deeper level, this conversation is about freedom. Cash is being eliminated. Privacy is necessary to prevent governments from wielding excessive power over individuals. Sign up for ITrustCapital with this link for $100 funding bonus. See why people are opening a tax-advantaged Crypto, Gold & Silver IRA for their future: 0:54 From Bitcoin Maxi to Monero Advocate: Doug’s personal journey into crypto (and the Dogecoin hack that changed everything) 8:18 Why Monero Is Truly Private: How Monero hides the sender, receiver, and amount by default 9:45 Beyond Privacy: Monero’s dynamic block size & why it actually scales on-chain (unlike Bitcoin) 13:30 Why Monero Is More Decentralized: ASIC-resistant mining, RandomX, and “one CPU, one vote” 17:30 Bitcoin’s Fatal Flaw: Why Bitcoin is no longer digital cash—or even digital gold 19:55 Why Privacy Coins Are Rising: Surveillance, debanking, and people waking up 24:05 How to Secure Your Crypto: Wallet safety, seed phrases, and avoiding catastrophic mistakes 28:00 MoneroTopia: Building real-world adoption and living on Monero 32:05 Running for Congress: Why Doug stepped into politics to defend Monero and financial privacy 38:00 The Realization: Why building a parallel crypto economy beats fighting the system from within 44:00 The Clarity Act & Aaron Day: How new legislation threatens self-custody—and who’s exposing it 45:55 Monero vs Zcash: The real differences in privacy, governance, and philosophy 51:40 The Next Monero Upgrade: Full-chain membership proofs explained 53:25 Private by Default = Fungible 59:05 Dark Markets & the Truth About Privacy: Why Monero’s use proves it’s the best digital cash 1:03:10 XMR Bazaar: Peer-to-peer markets, no middlemen, and a parallel economy built on Monero

Bri Teresi

37,288 次观看 • 8 个月前

Two XRP Paths: Rejection vs. Adoption 1) Total Failure Case — XRP → $0.00 (Global Rejection) For XRP to go to $0, ALL of the following must occur - not one, but collectively: A. Regulatory Extinction (Binary Kill Switch) Coordinated global classification as: Unregistered security with no path to compliance Or outright restriction in major jurisdictions (U.S., EU, Japan) Exchanges delist → liquidity evaporates Custodians refuse to hold → institutions cannot touch it 👉 Without lawful on/off ramps, price discovery dies. B. Institutional Rejection of XRPL Utility Banks choose alternatives: Private permissioned ledgers CBDC rails with no bridge asset No real transaction demand = no need for XRP as liquidity 👉 Utility collapses → speculation alone cannot sustain value long term. C. Liquidity Death Spiral Market makers exit Spreads widen → volatility spikes Capital rotates to “approved” rails 👉 A monetary asset without liquidity becomes non-money. D. Network Irrelevance Developers leave No meaningful tokenization, payments, or settlement flows XRPL becomes a ghost chain E. Loss of Trust (Final Blow) Credible exploit, governance failure, or fatal flaw Or simply: better, compliant alternative wins 🧠 XRP Truth Check To reach $0.00, XRP must fail at: Law (permission to exist) Utility (reason to be used) Liquidity (ability to transact) Trust (confidence in system integrity) That is a full-spectrum collapse, not a partial miss. 🚀 2) Adoption Case — XRP → $100 in 5 yrs (Major Integration) 🔵 Let’s flip the lens. 🔵 If XRP moves from $1.40 → $100 in 5 years, it’s a ~71× move - or 135% Compounded Annual Growth Rate (CAGR) over 5 years. A. Regulatory Clarity (Foundation Layer) Let’s tie this to: 1) Digital Asset Market Clarity Act 2) GENIUS Act What must then be true: 1) XRP is clearly not a security in secondary markets Legal frameworks enable XRP: 2) Custody 3) Settlement 4) Bank usage 5) Balance sheet treatment B. Institutional Adoption (Demand Engine) Banks, payment providers, and asset managers: • Use XRP as bridge liquidity • Integrate into cross-border settlement • Leverage XRPL for tokenization rails Think: • Treasury flows • FX settlement • Tokenized securities movement 👉 This is where real demand begins - not speculation. C. Liquidity Scaling (Critical Inflection) • Global payments: ~$100T+ annually • Capital trapped in nostro/vostro accounts • Settlement inefficiencies If XRP: 1) Reduces friction 2) Frees capital 3) Enables atomic settlement 👉 Then liquidity demand becomes structural, not optional. D. Network Effects (Compounding Reality) • More institutions → deeper liquidity • Deeper liquidity → tighter spreads • Tighter spreads → more usage 👉 This is how a neutral bridge asset gains gravitational pull. E. Monetary Role Expansion For $100 to be rationally defensible: XRP must evolve from: “crypto asset” into: neutral settlement layer for value transfer That implies: • High velocity usage • Deep global liquidity pools • Continuous transactional demand 📈 What $100 Actually Implies Let’s speak plainly: $100 XRP ≈ $5–6 trillion value Comparable to: - Gold (partial) - Major sovereign liquidity layers - Core financial infrastructure 👉 This is not a “price move” 👉 This is a monetary role transition Final Discernment with No Hype Buyers are not weighing: “Will price go up or down?” They’re weighing: “Will the XRPL/XRP system be used… or not?” Because price is downstream of one thing: Sustained, lawful, global demand for its function ⚡ The Real XRP Question If a system delivers: • Faster settlement • Lower cost • Verifiable truth • Reduced counterparty risk Then ask: Who, acting rationally, chooses a slower, more expensive, opaque alternative… if given a lawful choice? That answer - not sentiment - determines whether XRP trends toward $0… or $100. Ripple Cointelegraph CNBC SMQKE JMC Broadcasting
1:40

Two XRP Paths: Rejection vs. Adoption 1) Total Failure Case — XRP → $0.00 (Global Rejection) For XRP to go to $0, ALL of the following must occur - not one, but collectively: A. Regulatory Extinction (Binary Kill Switch) Coordinated global classification as: Unregistered security with no path to compliance Or outright restriction in major jurisdictions (U.S., EU, Japan) Exchanges delist → liquidity evaporates Custodians refuse to hold → institutions cannot touch it 👉 Without lawful on/off ramps, price discovery dies. B. Institutional Rejection of XRPL Utility Banks choose alternatives: Private permissioned ledgers CBDC rails with no bridge asset No real transaction demand = no need for XRP as liquidity 👉 Utility collapses → speculation alone cannot sustain value long term. C. Liquidity Death Spiral Market makers exit Spreads widen → volatility spikes Capital rotates to “approved” rails 👉 A monetary asset without liquidity becomes non-money. D. Network Irrelevance Developers leave No meaningful tokenization, payments, or settlement flows XRPL becomes a ghost chain E. Loss of Trust (Final Blow) Credible exploit, governance failure, or fatal flaw Or simply: better, compliant alternative wins 🧠 XRP Truth Check To reach $0.00, XRP must fail at: Law (permission to exist) Utility (reason to be used) Liquidity (ability to transact) Trust (confidence in system integrity) That is a full-spectrum collapse, not a partial miss. 🚀 2) Adoption Case — XRP → $100 in 5 yrs (Major Integration) 🔵 Let’s flip the lens. 🔵 If XRP moves from $1.40 → $100 in 5 years, it’s a ~71× move - or 135% Compounded Annual Growth Rate (CAGR) over 5 years. A. Regulatory Clarity (Foundation Layer) Let’s tie this to: 1) Digital Asset Market Clarity Act 2) GENIUS Act What must then be true: 1) XRP is clearly not a security in secondary markets Legal frameworks enable XRP: 2) Custody 3) Settlement 4) Bank usage 5) Balance sheet treatment B. Institutional Adoption (Demand Engine) Banks, payment providers, and asset managers: • Use XRP as bridge liquidity • Integrate into cross-border settlement • Leverage XRPL for tokenization rails Think: • Treasury flows • FX settlement • Tokenized securities movement 👉 This is where real demand begins - not speculation. C. Liquidity Scaling (Critical Inflection) • Global payments: ~$100T+ annually • Capital trapped in nostro/vostro accounts • Settlement inefficiencies If XRP: 1) Reduces friction 2) Frees capital 3) Enables atomic settlement 👉 Then liquidity demand becomes structural, not optional. D. Network Effects (Compounding Reality) • More institutions → deeper liquidity • Deeper liquidity → tighter spreads • Tighter spreads → more usage 👉 This is how a neutral bridge asset gains gravitational pull. E. Monetary Role Expansion For $100 to be rationally defensible: XRP must evolve from: “crypto asset” into: neutral settlement layer for value transfer That implies: • High velocity usage • Deep global liquidity pools • Continuous transactional demand 📈 What $100 Actually Implies Let’s speak plainly: $100 XRP ≈ $5–6 trillion value Comparable to: - Gold (partial) - Major sovereign liquidity layers - Core financial infrastructure 👉 This is not a “price move” 👉 This is a monetary role transition Final Discernment with No Hype Buyers are not weighing: “Will price go up or down?” They’re weighing: “Will the XRPL/XRP system be used… or not?” Because price is downstream of one thing: Sustained, lawful, global demand for its function ⚡ The Real XRP Question If a system delivers: • Faster settlement • Lower cost • Verifiable truth • Reduced counterparty risk Then ask: Who, acting rationally, chooses a slower, more expensive, opaque alternative… if given a lawful choice? That answer - not sentiment - determines whether XRP trends toward $0… or $100. Ripple Cointelegraph CNBC SMQKE JMC Broadcasting

Rob Cunningham

44,184 次观看 • 4 个月前

This Day in United States History 🇺🇸🇺🇸🇺🇸 On July 13, 1993, major U.S. newspaper headlines included: THE MIDWEST FLOODING: On the Des Moines; Flood Damage Immobilizes Des Moines - with the New York Times reporting: Des Moines, July 12 -- Under brilliant skies that mocked the misery below, Iowa's capital city was immobilized for a second day today as most businesses shut down and residents struggled without running water after floods engulfed the city's water-filtering system over the weekend. Most city agencies were shuttered, the Polk County Courthouse was closed and hospitals were refusing all but emergency patients. The fire department operated with an emergency supply of tank water that city officials acknowledged would not be enough to fight a serious fire. At the urging of city officials and business owners, most office workers stayed home because sprinkler and security systems were not working in downtown office buildings and the city did not have enough water to protect them in case of fire. What were the aftermaths of the 1993 floods? 👉 50 lives were lost in the 1993 Great Midwest Flood 👉 Over 1,000 levees failed or were overtopped. 👉 Barge traffic on the Mississippi and Missouri Rivers halted for nearly two months. 👉 Bridges, roads, and railroads were washed out or inaccessible across a wide area. 👉 Water supplies were disrupted (e.g., Des Moines lost its water treatment plant). The 1993 flood was a wake-up call. It killed dozens, caused tens of billions in damage, and displaced tens of thousands — but its most enduring effects were policy shifts toward smarter floodplain management, property buyouts, wetland restoration, and a recognition that simply building more levees isn’t always the best long-term solution. (Living in St. Louis, during these historic floods, I remember them very well. I actually volunteered, on sandbag duty, across the Mississippi River, in Alton, IL. It was the most backbreaking work I have ever done - but very rewarding.) On This Date In The Music World 🎶🎶🎶 While the Midwest Floods were taking place, Hawaiian ukulele player, Israel Kamakawiwo'ole found himself a breakout success, with the release of the cover, Somewhere Over the Rainbow. Made popular by the movie, Wizard of Oz, and popularized by Judy Garland and Ray Charles, this young man's cover is perhaps one of the more memorable of the song's long history. Enjoy a portion of last night's ride with me. After two days of torrential rains, the sun came out and the roads started to dry. But the real magical part of last evening were the gorgeous cloud formations. Volume UP!!! ⬆️🌈😎✌️ Somewhere Over the Rainbow 🎶🎶🎶 Ooh, ooh, ooh Ooh, ooh Somewhere over the rainbow Way up high And the dreams that you dream of Once in a lullaby Somewhere over the rainbow Bluebirds fly And the dreams that you dream of Dreams really do come true Someday, I wish upon a star Wake up where the clouds are far behind me Where trouble melts like lemon drops High above the chimney top That's where you'll find me Somewhere over the rainbow Bluebirds fly And the dreams that you dare to Oh why, oh why can't I? Well, I see trees of green and red roses too I'll watch them bloom for me and you And I think to myself What a wonderful world Well, I see skies of blue and I see clouds of white And the brightness of day I like the dark And I think to myself what a wonderful world The colors of the rainbow so pretty in the sky And also on the faces of people passing by I see friends shaking hands saying How do you do? They're really saying I, I love you I hear babies cry and I watch them grow They'll learn much more then we'll know And I think to myself what a wonderful world World Someday I wish upon a star Wake up where the clouds are far behind me Where trouble melts like lemon drops High above the chimney top That's where you'll find me Oh, somewhere over the rainbow Way up high And the dreams that you dare to Why oh, why can't I? Ooh, ooh Ooh, ooh 🎶🎶🎶
3:20

This Day in United States History 🇺🇸🇺🇸🇺🇸 On July 13, 1993, major U.S. newspaper headlines included: THE MIDWEST FLOODING: On the Des Moines; Flood Damage Immobilizes Des Moines - with the New York Times reporting: Des Moines, July 12 -- Under brilliant skies that mocked the misery below, Iowa's capital city was immobilized for a second day today as most businesses shut down and residents struggled without running water after floods engulfed the city's water-filtering system over the weekend. Most city agencies were shuttered, the Polk County Courthouse was closed and hospitals were refusing all but emergency patients. The fire department operated with an emergency supply of tank water that city officials acknowledged would not be enough to fight a serious fire. At the urging of city officials and business owners, most office workers stayed home because sprinkler and security systems were not working in downtown office buildings and the city did not have enough water to protect them in case of fire. What were the aftermaths of the 1993 floods? 👉 50 lives were lost in the 1993 Great Midwest Flood 👉 Over 1,000 levees failed or were overtopped. 👉 Barge traffic on the Mississippi and Missouri Rivers halted for nearly two months. 👉 Bridges, roads, and railroads were washed out or inaccessible across a wide area. 👉 Water supplies were disrupted (e.g., Des Moines lost its water treatment plant). The 1993 flood was a wake-up call. It killed dozens, caused tens of billions in damage, and displaced tens of thousands — but its most enduring effects were policy shifts toward smarter floodplain management, property buyouts, wetland restoration, and a recognition that simply building more levees isn’t always the best long-term solution. (Living in St. Louis, during these historic floods, I remember them very well. I actually volunteered, on sandbag duty, across the Mississippi River, in Alton, IL. It was the most backbreaking work I have ever done - but very rewarding.) On This Date In The Music World 🎶🎶🎶 While the Midwest Floods were taking place, Hawaiian ukulele player, Israel Kamakawiwo'ole found himself a breakout success, with the release of the cover, Somewhere Over the Rainbow. Made popular by the movie, Wizard of Oz, and popularized by Judy Garland and Ray Charles, this young man's cover is perhaps one of the more memorable of the song's long history. Enjoy a portion of last night's ride with me. After two days of torrential rains, the sun came out and the roads started to dry. But the real magical part of last evening were the gorgeous cloud formations. Volume UP!!! ⬆️🌈😎✌️ Somewhere Over the Rainbow 🎶🎶🎶 Ooh, ooh, ooh Ooh, ooh Somewhere over the rainbow Way up high And the dreams that you dream of Once in a lullaby Somewhere over the rainbow Bluebirds fly And the dreams that you dream of Dreams really do come true Someday, I wish upon a star Wake up where the clouds are far behind me Where trouble melts like lemon drops High above the chimney top That's where you'll find me Somewhere over the rainbow Bluebirds fly And the dreams that you dare to Oh why, oh why can't I? Well, I see trees of green and red roses too I'll watch them bloom for me and you And I think to myself What a wonderful world Well, I see skies of blue and I see clouds of white And the brightness of day I like the dark And I think to myself what a wonderful world The colors of the rainbow so pretty in the sky And also on the faces of people passing by I see friends shaking hands saying How do you do? They're really saying I, I love you I hear babies cry and I watch them grow They'll learn much more then we'll know And I think to myself what a wonderful world World Someday I wish upon a star Wake up where the clouds are far behind me Where trouble melts like lemon drops High above the chimney top That's where you'll find me Oh, somewhere over the rainbow Way up high And the dreams that you dare to Why oh, why can't I? Ooh, ooh Ooh, ooh 🎶🎶🎶

The Original Grey Beard Biker™ (Michael)

13,176 次观看 • 2 个月前

Here is how I am using AI right now at work, at home, and for my finances... Every founder, executive or investor I talk with these days wants to know how others are using AI in their daily lives. I figured it would be helpful to pull back the curtain on what I am using and how I have implemented the various products. There are three areas where I have adopted AI in a material way: professionally, financially, and personally. Professional use of AI On the professional side, I am currently using Grok Grok Bot extensively. I started with a Chief of Staff bot that I put in charge of the entire operation, followed by a number of more specialized bots for various bodies of work (talent recruiter, product designer, podcast researcher, book launch manager, email organizer, etc). Once I had the initial team of bots set up, I spent about an hour “onboarding” the Chief of Staff to my professional life. I treated this exactly how I would onboard a human Chief of Staff. I explained each business I am involved with, including their products, business model, personnel, metrics, and goals. I explicitly called out what the business is doing well and where we need to improve. I also gave the Chief of Staff access to relevant systems (email, calendar, Slack, analytics dashboards, etc). Once I had given as much context as I thought necessary, I asked the Chief of Staff to create an overview document to send me so I could double-check the accuracy and thoroughness of the bots understanding. I also asked the CoS bot to interview me for any other information that would be relevant to ensuring the bot could help me. This entire process was fairly quick and painless, but I believe it was the single most important thing I did to get value from Grok Bot. The more context that the AI system has, the more helpful it can be. That context can come from static, institutional knowledge or it can come from dynamic daily updates like email and Slack messages. After getting the bots set up and giving them context, I have done two other things that I think are worth sharing. The first is that my team of bots holds a daily standup meeting where they all come together and share what they did yesterday, what they are going to do today, and what they need my help or approval on (aka what they are blocked on). These “exec meeting” or daily standup allows for the bots to collaborate in a more seamless way, while also creating a very simple process for the Chief of Staff bot to put together a daily brief for me on what happened yesterday, what is going to happen today, and where I am needed to unblock productivity. The second thing I have done is treat the AI system as the brain of the company. Most people try to use AI as an augmentation to themselves, which can be helpful to a degree. I have flipped the relationship though. I look at my job as persistently giving the AI bots as much context as possible, so I can leverage their superhuman intelligence to make decisions and achieve our goals. For example, the recruiter bot recently surfaced a number of very high-quality candidates for an open role we have. After meeting with each candidate, I wrote a quick message to the recruiter bot to tell it what I liked about the person, what I thought were potential issues, improvements for future searches, and what the next steps were with each individual. All of that information and context is getting stored in the bot’s memory, which will compound over time and help us improve as an organization. Quick pro tip: If you are worried about putting all of the context into a single system’s memory, but unsure if that is the system you will use forever, you can have Grok Bot or another system dump their memory and context into a Notion document as well. This way you have a duplicate copy of the memory so it can be referenced by any AI system you use in the future. My takeaway from using Grok Bot to manage our companies is that we are having to hire less people, we are seeing a direct impact on revenue growth, and it appears to drive higher quality in our decision-making process. That is a win-win-win. I highly recommend going through these steps to setup your system correctly and it will pay off big time later on. Financial use of AI On the financial side, it was nearly impossible to find a good AI product to use for personal finance. Everything seemed to be a Chat-GPT wrapper that technically worked from an engineering standpoint, but didn’t solve any of the user problems I was facing. A big issue is that most of the fintech products are focused on budgeting and saving, rather than investing and growing your portfolio. This is why I eventually spent the time and money to build CFO Silvia. I went through a similar process of getting Silvia set up with the necessary context. I attached my bank accounts, brokerage accounts, crypto accounts, and credit cards, along with uploading real estate, cars, collectibles, and private investments. Silvia allows me to dynamically track the value of these assets (and my overall net worth) in real-time. But the real unlock for me has been talking to Silvia about two specific topics: tax and estate planning. As most of you know, I am not a frequent trader, so although you could use Silvia for stock analysis or trading activities, that is not my approach to investing. Instead, I have had great success in using Silvia to find creative and valuable tax mitigation strategies that are personalized to my situation, including ideas that had not previously been surfaced by my accountants, lawyers, or tax experts. Additionally, I have used Silvia for estate planning purposes. I am married and have four children, so there is a decent amount of complexity and opportunities to pursue. Having a dedicated resource with superhuman intelligence and the full context of my personal financial situation has been incredibly powerful. One funny thing I have noticed is that I am willing to tell Silvia certain things that I would hesitate to tell other humans (financial goals, areas of concern, etc) and I ask numerous “dumb” questions that I would probably shy away from asking a human. Regardless of why I feel more comfortable talking to the AI product, it has unlocked a few different ideas and strategies that I was previously unaware of, so that has been an added bonus to using the product. If you aren’t using AI to help manage your finances, I think it is a no brainer to start using the technology. I am biased towards Silvia since we built it, but you can give it a try for free here: Personal use of AI On the personal side, I use almost all of the traditional AI products (Chat-GPT, Claude, Grok, Gemini, Perplexity, etc). Those are well understood at this point, but one product that I started using recently that I am impressed with is Instinct AI. They have built a personal assistant AI bot that you communicate with through iMessage or SMS. The experience has been delightful, but I am most excited about the bot’s ability to anticipate the second or third-step in a process before I have to tell it anything. For example, Instinct got access to my calendar and immediately started identifying scheduling conflicts and asked me if I would like the bot to reach out to one of the parties to reschedule. I never told it to look for conflicts, nor did I tell it I wanted help rescheduling things. It’s “instincts” knew what the basic task would be and began executing. Another example is that Instinct was told my wife is Polina, so whenever it deems something important to the household or family, Instinct will add Polina to the calendar invite, communicate the information to her, or ask me if Polina should be aware of the information. This is very helpful for someone like me who has too many things floating around in my brain and should always do a better job of keeping Polina informed about various things. Lastly, Instinct is very helpful in scanning my personal email and understanding what is most important. It ignores things that are trivial, but somehow can parse out the high priority items, summarize them for me in a text message, draft a response to the email, and then ask me for permission to respond. As I said, it is the most impressive personal assistant AI product I have used so far. So those are the three big areas that I use AI today and the specific products I have incorporated into my life. Before I let you go, I figured I could share some best practices I have learned as well. I also make sure to tell AI bots they are not allowed to respond to any message or email without my explicit approval. This reduces the risk of having a bot go rogue with a message or commitment that I am not onboard with. I also ensure that each bot only has read access to our business systems like an analytics dashboard, etc. While I am a big proponent of using these products and believe they will fundamentally transform how we operate professionally, I am still not ready to let them loose without human oversight. I am sure that will change in the coming weeks and months, but I need more time to get comfortable with that level of delegation and trust. I hope this overview was helpful for each of you. It would be great if you could respond to this post with any products you are using or tips/tricks that you have learned to get more productivity and value in your life. I love writing these letters each day because I learn just as much from me as I learn from you all. Onwards!
12:57

Here is how I am using AI right now at work, at home, and for my finances... Every founder, executive or investor I talk with these days wants to know how others are using AI in their daily lives. I figured it would be helpful to pull back the curtain on what I am using and how I have implemented the various products. There are three areas where I have adopted AI in a material way: professionally, financially, and personally. Professional use of AI On the professional side, I am currently using Grok Grok Bot extensively. I started with a Chief of Staff bot that I put in charge of the entire operation, followed by a number of more specialized bots for various bodies of work (talent recruiter, product designer, podcast researcher, book launch manager, email organizer, etc). Once I had the initial team of bots set up, I spent about an hour “onboarding” the Chief of Staff to my professional life. I treated this exactly how I would onboard a human Chief of Staff. I explained each business I am involved with, including their products, business model, personnel, metrics, and goals. I explicitly called out what the business is doing well and where we need to improve. I also gave the Chief of Staff access to relevant systems (email, calendar, Slack, analytics dashboards, etc). Once I had given as much context as I thought necessary, I asked the Chief of Staff to create an overview document to send me so I could double-check the accuracy and thoroughness of the bots understanding. I also asked the CoS bot to interview me for any other information that would be relevant to ensuring the bot could help me. This entire process was fairly quick and painless, but I believe it was the single most important thing I did to get value from Grok Bot. The more context that the AI system has, the more helpful it can be. That context can come from static, institutional knowledge or it can come from dynamic daily updates like email and Slack messages. After getting the bots set up and giving them context, I have done two other things that I think are worth sharing. The first is that my team of bots holds a daily standup meeting where they all come together and share what they did yesterday, what they are going to do today, and what they need my help or approval on (aka what they are blocked on). These “exec meeting” or daily standup allows for the bots to collaborate in a more seamless way, while also creating a very simple process for the Chief of Staff bot to put together a daily brief for me on what happened yesterday, what is going to happen today, and where I am needed to unblock productivity. The second thing I have done is treat the AI system as the brain of the company. Most people try to use AI as an augmentation to themselves, which can be helpful to a degree. I have flipped the relationship though. I look at my job as persistently giving the AI bots as much context as possible, so I can leverage their superhuman intelligence to make decisions and achieve our goals. For example, the recruiter bot recently surfaced a number of very high-quality candidates for an open role we have. After meeting with each candidate, I wrote a quick message to the recruiter bot to tell it what I liked about the person, what I thought were potential issues, improvements for future searches, and what the next steps were with each individual. All of that information and context is getting stored in the bot’s memory, which will compound over time and help us improve as an organization. Quick pro tip: If you are worried about putting all of the context into a single system’s memory, but unsure if that is the system you will use forever, you can have Grok Bot or another system dump their memory and context into a Notion document as well. This way you have a duplicate copy of the memory so it can be referenced by any AI system you use in the future. My takeaway from using Grok Bot to manage our companies is that we are having to hire less people, we are seeing a direct impact on revenue growth, and it appears to drive higher quality in our decision-making process. That is a win-win-win. I highly recommend going through these steps to setup your system correctly and it will pay off big time later on. Financial use of AI On the financial side, it was nearly impossible to find a good AI product to use for personal finance. Everything seemed to be a Chat-GPT wrapper that technically worked from an engineering standpoint, but didn’t solve any of the user problems I was facing. A big issue is that most of the fintech products are focused on budgeting and saving, rather than investing and growing your portfolio. This is why I eventually spent the time and money to build CFO Silvia. I went through a similar process of getting Silvia set up with the necessary context. I attached my bank accounts, brokerage accounts, crypto accounts, and credit cards, along with uploading real estate, cars, collectibles, and private investments. Silvia allows me to dynamically track the value of these assets (and my overall net worth) in real-time. But the real unlock for me has been talking to Silvia about two specific topics: tax and estate planning. As most of you know, I am not a frequent trader, so although you could use Silvia for stock analysis or trading activities, that is not my approach to investing. Instead, I have had great success in using Silvia to find creative and valuable tax mitigation strategies that are personalized to my situation, including ideas that had not previously been surfaced by my accountants, lawyers, or tax experts. Additionally, I have used Silvia for estate planning purposes. I am married and have four children, so there is a decent amount of complexity and opportunities to pursue. Having a dedicated resource with superhuman intelligence and the full context of my personal financial situation has been incredibly powerful. One funny thing I have noticed is that I am willing to tell Silvia certain things that I would hesitate to tell other humans (financial goals, areas of concern, etc) and I ask numerous “dumb” questions that I would probably shy away from asking a human. Regardless of why I feel more comfortable talking to the AI product, it has unlocked a few different ideas and strategies that I was previously unaware of, so that has been an added bonus to using the product. If you aren’t using AI to help manage your finances, I think it is a no brainer to start using the technology. I am biased towards Silvia since we built it, but you can give it a try for free here: Personal use of AI On the personal side, I use almost all of the traditional AI products (Chat-GPT, Claude, Grok, Gemini, Perplexity, etc). Those are well understood at this point, but one product that I started using recently that I am impressed with is Instinct AI. They have built a personal assistant AI bot that you communicate with through iMessage or SMS. The experience has been delightful, but I am most excited about the bot’s ability to anticipate the second or third-step in a process before I have to tell it anything. For example, Instinct got access to my calendar and immediately started identifying scheduling conflicts and asked me if I would like the bot to reach out to one of the parties to reschedule. I never told it to look for conflicts, nor did I tell it I wanted help rescheduling things. It’s “instincts” knew what the basic task would be and began executing. Another example is that Instinct was told my wife is Polina, so whenever it deems something important to the household or family, Instinct will add Polina to the calendar invite, communicate the information to her, or ask me if Polina should be aware of the information. This is very helpful for someone like me who has too many things floating around in my brain and should always do a better job of keeping Polina informed about various things. Lastly, Instinct is very helpful in scanning my personal email and understanding what is most important. It ignores things that are trivial, but somehow can parse out the high priority items, summarize them for me in a text message, draft a response to the email, and then ask me for permission to respond. As I said, it is the most impressive personal assistant AI product I have used so far. So those are the three big areas that I use AI today and the specific products I have incorporated into my life. Before I let you go, I figured I could share some best practices I have learned as well. I also make sure to tell AI bots they are not allowed to respond to any message or email without my explicit approval. This reduces the risk of having a bot go rogue with a message or commitment that I am not onboard with. I also ensure that each bot only has read access to our business systems like an analytics dashboard, etc. While I am a big proponent of using these products and believe they will fundamentally transform how we operate professionally, I am still not ready to let them loose without human oversight. I am sure that will change in the coming weeks and months, but I need more time to get comfortable with that level of delegation and trust. I hope this overview was helpful for each of you. It would be great if you could respond to this post with any products you are using or tips/tricks that you have learned to get more productivity and value in your life. I love writing these letters each day because I learn just as much from me as I learn from you all. Onwards!

Anthony Pompliano 🌪

83,069 次观看 • 28 天前