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Forex trading is broken not because the market doesn’t work, but because expectations are wrong. In this conversation with a forex creator TRIPN , we broke down: - why most beginners fail - what selling education in Ghana actually looks like - where forex education is heading - and...

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Michael Asiedu

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14,009 Aufrufe • vor 4 Monaten •via X (Twitter)

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[The next retail mania won’t be forex, it’ll be crypto social trading] “Social trading is gonna get a lot more popular.” “You can hit 100x’s and 1000x’s in crypto. You cannot do that in forex.” “The real money comes from being early.” Ansem 🐂🀄️ compared today’s crypto market to the forex boom during COVID. Back then, people paid for trading groups hoping to find alpha. Forex never offered the kind of upside they were chasing. Crypto is different. The biggest winners catch new narratives before everyone else. That’s why he sees social trading becoming a core part of the next cycle, where finding the right people matters as much as finding the right trade.
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[The next retail mania won’t be forex, it’ll be crypto social trading] “Social trading is gonna get a lot more popular.” “You can hit 100x’s and 1000x’s in crypto. You cannot do that in forex.” “The real money comes from being early.” Ansem 🐂🀄️ compared today’s crypto market to the forex boom during COVID. Back then, people paid for trading groups hoping to find alpha. Forex never offered the kind of upside they were chasing. Crypto is different. The biggest winners catch new narratives before everyone else. That’s why he sees social trading becoming a core part of the next cycle, where finding the right people matters as much as finding the right trade.

Kaiz 🐂🀄️

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Losses in forex is inevitable Basically what happens is that when a trader loses money in the Forex market, the money does not go to any particular person or entity. Instead, the money is considered a loss and disappears from the trader's account. When a trader loses money, the losses are simply deducted from their trading account balance. When a stock tumbles and an investor loses money, the money doesn't get redistributed to someone else. Drops in account value reflect dwindling investor interest and a change in investor perception of the stock. The best way to deal with a big trading loss is to take a small break. Consider your strategy and your position size before jumping back in. When you do decide you are ready, start small. If you are trading as an institutional trader, you will be taxed just as any other employee. In case you are a retail Forex trader, you will need to report your profits and losses. Retail Forex traders use a Form 1040 or Form 1040NR in the USA. Either trading too big or too often is the most common reason why Forex traders fail. Overtrading might be caused by unrealistically high profit goals, market addiction, or insufficient capitalization. The maximum loss per trade can be categorized into three levels: conservative, moderate, and aggressive. Traders who are conservative about safeguarding their capital will take a chance of no more than 1% on their total trading capital per trade. Good morning traders
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Losses in forex is inevitable Basically what happens is that when a trader loses money in the Forex market, the money does not go to any particular person or entity. Instead, the money is considered a loss and disappears from the trader's account. When a trader loses money, the losses are simply deducted from their trading account balance. When a stock tumbles and an investor loses money, the money doesn't get redistributed to someone else. Drops in account value reflect dwindling investor interest and a change in investor perception of the stock. The best way to deal with a big trading loss is to take a small break. Consider your strategy and your position size before jumping back in. When you do decide you are ready, start small. If you are trading as an institutional trader, you will be taxed just as any other employee. In case you are a retail Forex trader, you will need to report your profits and losses. Retail Forex traders use a Form 1040 or Form 1040NR in the USA. Either trading too big or too often is the most common reason why Forex traders fail. Overtrading might be caused by unrealistically high profit goals, market addiction, or insufficient capitalization. The maximum loss per trade can be categorized into three levels: conservative, moderate, and aggressive. Traders who are conservative about safeguarding their capital will take a chance of no more than 1% on their total trading capital per trade. Good morning traders

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I sat down with who was buying $SOL under $10 and had made 7 figures trading memecoins 🐶 if you're trading this memecoin market, this is a MUST WATCH video we discussed: ➡️ how we find memecoins early ➡️ how to come up with a trading strategy ➡️ advice for beginners If you're looking for a trading group, you should join Kanto Labs Timestamps: 0:00 Intro 2:20 Why Were You Buying Solana At $8 8:52 Why Trading Is Skill Based 9:46 Best Advice For Beginners 15:32 Coming Up With YOUR Trading Strategy 20:30 Why Controlling Emotions Is Important 22:08 How Are You Finding Tokens Early 25:00 Why Being In Groups Is Essential 30:14 Closing Thoughts From Easy

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EDGE REVEALED: How an Ex-Jane Street Trader Finds Edge in Markets & Life Agustin Lebron Agustin Lebron (former Jane Street trader, author of The Laws of Trading, now working at an AI startup applying reinforcement learning to market execution) breaks down what edge really means — and how to find yours in trading, careers & life. “Edge is something that either you know or you can do that the marginal participant in that market either doesn’t or can’t.” We cover: - What edge actually means & how Jane Street builds organizational edge (their worst skill is still "pretty decent") - Why you can never truly know if you have edge — the statistical vs intuitive approaches - The consolidation of quant trading: from dozens of options firms to a handful of giants - The gamblification of everything — retail trading, sports betting & prediction markets fueling quant profits - What it was like having Sam Bankman-Fried (SBF) as a Jane Street intern: "Day one, I'm going to ask all the questions" - How to apply edge thinking to your own career: find what you're differentially good at - Raising teenagers in the AI age: why the traditional path still works, but other paths are opening up 00:00 Introduction 00:44 What is edge in financial markets 01:43 Jane Street and organizational structures for quant trading 03:46 Identifying and validating edge in trading 06:22 Navigating extreme market events and volatility 09:37 Future of quant trading and consolidation 12:15 Why quant firm profits have increased 14:42 The gamblization of everything 15:55 Who should pursue a career in quant trading 18:19 Applying the concept of edge to career and life decisions 20:56 Advice for interns to excel in quant trading 23:44 Predicting long-term success in trading interns 25:08 Sam Bankman-Fried as an intern and FTX reflections 28:15 Reasons people leave Jane Street and what they do next 31:19 Advice for young people in a changing world 36:09 Navigating job insecurity in tech-driven roles 38:55 Where to live if you want to be successful 40:28 Raising kids for a rapidly changing future 42:55 Questions young people should ask themselves 44:45 Outro and book recommendation
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EDGE REVEALED: How an Ex-Jane Street Trader Finds Edge in Markets & Life Agustin Lebron Agustin Lebron (former Jane Street trader, author of The Laws of Trading, now working at an AI startup applying reinforcement learning to market execution) breaks down what edge really means — and how to find yours in trading, careers & life. “Edge is something that either you know or you can do that the marginal participant in that market either doesn’t or can’t.” We cover: - What edge actually means & how Jane Street builds organizational edge (their worst skill is still "pretty decent") - Why you can never truly know if you have edge — the statistical vs intuitive approaches - The consolidation of quant trading: from dozens of options firms to a handful of giants - The gamblification of everything — retail trading, sports betting & prediction markets fueling quant profits - What it was like having Sam Bankman-Fried (SBF) as a Jane Street intern: "Day one, I'm going to ask all the questions" - How to apply edge thinking to your own career: find what you're differentially good at - Raising teenagers in the AI age: why the traditional path still works, but other paths are opening up 00:00 Introduction 00:44 What is edge in financial markets 01:43 Jane Street and organizational structures for quant trading 03:46 Identifying and validating edge in trading 06:22 Navigating extreme market events and volatility 09:37 Future of quant trading and consolidation 12:15 Why quant firm profits have increased 14:42 The gamblization of everything 15:55 Who should pursue a career in quant trading 18:19 Applying the concept of edge to career and life decisions 20:56 Advice for interns to excel in quant trading 23:44 Predicting long-term success in trading interns 25:08 Sam Bankman-Fried as an intern and FTX reflections 28:15 Reasons people leave Jane Street and what they do next 31:19 Advice for young people in a changing world 36:09 Navigating job insecurity in tech-driven roles 38:55 Where to live if you want to be successful 40:28 Raising kids for a rapidly changing future 42:55 Questions young people should ask themselves 44:45 Outro and book recommendation

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Full interview with the founder and CEO of Propr Lou sits down with me to discuss Hyperliquid and the future of prop trading Timestamps: 00:00:00 Intro 00:00:16 Meet Louis: from TradFi to crypto 00:01:52 How the crypto market has changed 00:03:49 Why build on Hyperliquid 00:06:52 How propr got started 00:08:40 Scaling a lean team (and the grind) 00:12:24 What a prop firm actually is 00:15:33 Agents, bots, and the future of trading 00:18:32 What Louis is most proud of 00:21:02 Thinking about the token and TGE 00:24:14 Where propr goes in 5 years 00:25:57 Best and worst trades 00:27:50 Life outside crypto + shoutouts

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The system is broken. Why capitalism, healthcare, and education are failing (and what comes next). Just sat down with Dan Schmitt — and this one goes deep. From “innovations sitting in closets” to why financialization is killing real capitalism… this is one of the most honest breakdowns of what’s actually broken in the system right now. If you care about Bitcoin, decentralization, and the future of building — don’t miss this.

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This conversation with my friend Sebastian Mallaby changed my mind on AI: The Truth About AI They Don't Want You To Know Watch/listen on X, YouTube, Spotify, Apple, or wherever you get your podcasts. Timestamps 0:00 Sebastian Mallaby Introduction 2:04 What drew Sebastian to Demis Hassabis? 3:17 Is the progress of AI impressive? 5:01 The fascinating character that is Demis 7:12 Why is this not a Silicon Valley story? 8:31 Sebastian "He isn't motivated by money." 12:17 What is DeepMind and why does it matter? 13:40 Is AI an existential risk? 15:25 Anthony "People need to understand what is going on." 18:31 The relationship between capital and transformative tech 21:06 What is AGI and who gets there first? 23:00 Is China ahead of the US in AI race? Where is Europe in this? 26:02 Has this changed Sebastian's view of the future? 27:21 Five Words

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Crypto Is About To Go Parabolic - Here’s Why With avery.apt 🇺🇸, Aptos Timestamps: 0:00 Intro 2:30 Crypto’s Time to Shine 5:15 Aptos Growth in 2024 8:00 Blockchain Scaling for Adoption 10:45 Real-World Blockchain Use Cases 13:30 Stablecoins and Global Payments 16:15 Future of Decentralized Trading 19:00 Why Aptos is Different 21:45 AI and Blockchain Merge 24:30 Government and Crypto Innovation 27:15 The Next Big Crypto Wave 30:00 How Institutions Are Adopting 32:45 Trading and Market Evolution 35:30 The Future of Web3 38:15 RWA and Tokenized Assets 41:00 Private vs Public Blockchains 43:45 AI-Powered Trading and Agents 46:30 AI in Legacy Finance 49:15 Crypto’s Biggest Adoption Hurdles 52:00 The Role of Aptos Token 54:45 What’s Coming in 2025 57:30 Final Thoughts

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Professional trader and former prop firm performance coach, Imre, breaks one of the biggest myths in trading: “Most people think edge is discipline… Or mindset… Or some secret setup.” But what if that’s completely wrong? Because if your “edge” disappears the moment conditions change… Was it ever an edge to begin with? The truth is uncomfortable: Edge isn’t what you see on the charts. It’s what’s forcing other participants to act. Rebalancing flows, Gamma exposure, Stops getting cleared; That’s where moves actually come from. And if you’re not trading the cause… You’re just reacting to the effect. So here’s the real question: Are you trading patterns… Or do you understand what’s forcing the market to move? Full Episode 👇

Riz Iqbal

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⚫️ UNCANNY VALLEY: THE AI CLASSROOM REVOLUTION: ARE TEACHERS READY? What if AI isn’t just disrupting education… but detonating it? Ethan Mollick, Professor at The Wharton School, joins Dr Danish for one of the most explosive Uncanny Valley episodes, lifting the lid on how classrooms are collapsing, colleges are scrambling, and apprenticeships are vanishing in real time. From AI tutors replacing professors to the rise of one-person unicorns, this isn’t just a change in learning…it’s a reset of work, meaning, and what it even takes to succeed. This episode doesn’t ask whether AI will change education. It shows you how it already has. Fridays at 4:20PM ET. Only on 𝕏. 00:18 – Is AI destroying school, or forcing us to teach better? 01:13 – “100% they’re cheating.” The honesty about academic dishonesty. 02:41 – Why good pedagogy still matters—even with AI tutors. 03:51 – Elon Musk says college is obsolete. Is he right — or just early? 05:01 – “AI gives you the answer—but you don’t learn.” The Turkey study. 06:31 – From calculators to GPT: How cheating evolves—and what to do. 08:24 – What the flipped AI-powered classroom of the future looks like. 09:23 – Inside Ethan’s Wharton classes: Simulations, games, and AI everywhere. 10:09 – “AI is an always-on tutor.” What humans still do better. 11:08 – Can AI actually launch a company? Where Ethan draws the line. 12:44 – “AI cofounder” is real, but jagged edges still slow it down. 14:07 – Why bad ideas fail faster when filtered through AI. 15:45 – Confidence vs. capability: the psychology of starting up. 16:51 – The average founder is 42. What that really means for AI. 18:04 – Will a flood of new entrepreneurs fix—or break—the market? 19:50 – AI as advisor: How a chatbot could help your catering business. 21:37 – Why most Americans are founders-in-waiting—and AI unlocks them. 22:30 – Prototyping is cracked. Scaling? Not yet. 24:33 – Youth unemployment and the collapse of on-the-job learning. 25:51 – “The apprenticeship model is broken.” And how to fix it. 27:06 – Losing the talent pipeline — and why companies must step up. 28:22 – Why the youth don’t want factory jobs—and shouldn’t. 29:26 – Is AGI inevitable—or just imagined? 31:07 – What should we teach our kids? The answer might scare you. 32:27 – Bundled jobs, fragmented futures: how humans stay relevant. 33:59 – The real singularity? When we can’t predict what happens next. 35:25 – The AI assumption no one wants to question. 36:55 – What are agents, really? Why no one agrees on the definition. 37:55 – Co-intelligence vs. substitution: what agents skip over. 38:50 – Plain-English goals, rogue pricing, and collusion-by-default. 40:02 – Nested agents are here, and Wharton’s building them. 40:58 – Management > Coding: What great prompters actually do. 41:12 – Product managers might be more vital than ever.
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⚫️ UNCANNY VALLEY: THE AI CLASSROOM REVOLUTION: ARE TEACHERS READY? What if AI isn’t just disrupting education… but detonating it? Ethan Mollick, Professor at The Wharton School, joins Dr Danish for one of the most explosive Uncanny Valley episodes, lifting the lid on how classrooms are collapsing, colleges are scrambling, and apprenticeships are vanishing in real time. From AI tutors replacing professors to the rise of one-person unicorns, this isn’t just a change in learning…it’s a reset of work, meaning, and what it even takes to succeed. This episode doesn’t ask whether AI will change education. It shows you how it already has. Fridays at 4:20PM ET. Only on 𝕏. 00:18 – Is AI destroying school, or forcing us to teach better? 01:13 – “100% they’re cheating.” The honesty about academic dishonesty. 02:41 – Why good pedagogy still matters—even with AI tutors. 03:51 – Elon Musk says college is obsolete. Is he right — or just early? 05:01 – “AI gives you the answer—but you don’t learn.” The Turkey study. 06:31 – From calculators to GPT: How cheating evolves—and what to do. 08:24 – What the flipped AI-powered classroom of the future looks like. 09:23 – Inside Ethan’s Wharton classes: Simulations, games, and AI everywhere. 10:09 – “AI is an always-on tutor.” What humans still do better. 11:08 – Can AI actually launch a company? Where Ethan draws the line. 12:44 – “AI cofounder” is real, but jagged edges still slow it down. 14:07 – Why bad ideas fail faster when filtered through AI. 15:45 – Confidence vs. capability: the psychology of starting up. 16:51 – The average founder is 42. What that really means for AI. 18:04 – Will a flood of new entrepreneurs fix—or break—the market? 19:50 – AI as advisor: How a chatbot could help your catering business. 21:37 – Why most Americans are founders-in-waiting—and AI unlocks them. 22:30 – Prototyping is cracked. Scaling? Not yet. 24:33 – Youth unemployment and the collapse of on-the-job learning. 25:51 – “The apprenticeship model is broken.” And how to fix it. 27:06 – Losing the talent pipeline — and why companies must step up. 28:22 – Why the youth don’t want factory jobs—and shouldn’t. 29:26 – Is AGI inevitable—or just imagined? 31:07 – What should we teach our kids? The answer might scare you. 32:27 – Bundled jobs, fragmented futures: how humans stay relevant. 33:59 – The real singularity? When we can’t predict what happens next. 35:25 – The AI assumption no one wants to question. 36:55 – What are agents, really? Why no one agrees on the definition. 37:55 – Co-intelligence vs. substitution: what agents skip over. 38:50 – Plain-English goals, rogue pricing, and collusion-by-default. 40:02 – Nested agents are here, and Wharton’s building them. 40:58 – Management > Coding: What great prompters actually do. 41:12 – Product managers might be more vital than ever.

Mario Nawfal

1,613,286 Aufrufe • vor 1 Jahr

From a Thai prison cell to a fintech empire processing $1.6B in international payments across 40+ banks and 250M+ users. Jonathan Low (Jonathan Low). Forbes 40 Under 40. Author of "Cell to CEO." We covered prison, banking for AI agents, RWA projects, the future of crypto in banking, vibe coding for trading, and the businesses that win the next 5 years. ⏱ Timestamps: 00:00:00 — Teaser 00:00:46 — Who is Jonathan Low 00:01:35 — What Jonathan's life was like before prison 00:02:13 — How and why Jonathan ended up in prison 00:02:57 — Prison conditions: expectations vs reality 00:07:33 — Prison became the greatest blessing 00:09:12 — How the entrepreneurial journey began after prison 00:10:34 — Why social capital matters 00:11:06 — Launched own club and took it to the top in 3 months 00:12:09 — Built an Axie Infinity gaming guild during COVID 00:13:49 — The beginning of the BipTap Group journey 00:17:23 — How Jonathan built his own banking system 00:20:07 — How to get a crypto card 00:22:03 — How much it costs to launch a white-label solution with BipTap 00:22:58 — Banking for AI agents 00:25:57 — How to build an RWA project 00:27:45 — Future of cryptocurrencies in banking 00:32:08 — The business verticals within Empire Group 00:32:48 — What Jonathan invests his money in 00:33:54 — How relationships with regulators are built 00:34:43 — Implementing AI in business 00:36:28 — Vibe coding in trading 00:40:06 — Advice for first-time founders 00:43:36 — From construction to trading: Ruslan Khairullin's journey 00:44:57 — Inner peace: why calmness is essential for founders 00:50:39 — Work-life balance for entrepreneurs 00:53:47 — $1.5 million in 24 hours on TST coin 00:55:11 — The best way to capture a market 00:55:57 — Banking for nations Watch the full conversation and let me know which part you liked the most 👇
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From a Thai prison cell to a fintech empire processing $1.6B in international payments across 40+ banks and 250M+ users. Jonathan Low (Jonathan Low). Forbes 40 Under 40. Author of "Cell to CEO." We covered prison, banking for AI agents, RWA projects, the future of crypto in banking, vibe coding for trading, and the businesses that win the next 5 years. ⏱ Timestamps: 00:00:00 — Teaser 00:00:46 — Who is Jonathan Low 00:01:35 — What Jonathan's life was like before prison 00:02:13 — How and why Jonathan ended up in prison 00:02:57 — Prison conditions: expectations vs reality 00:07:33 — Prison became the greatest blessing 00:09:12 — How the entrepreneurial journey began after prison 00:10:34 — Why social capital matters 00:11:06 — Launched own club and took it to the top in 3 months 00:12:09 — Built an Axie Infinity gaming guild during COVID 00:13:49 — The beginning of the BipTap Group journey 00:17:23 — How Jonathan built his own banking system 00:20:07 — How to get a crypto card 00:22:03 — How much it costs to launch a white-label solution with BipTap 00:22:58 — Banking for AI agents 00:25:57 — How to build an RWA project 00:27:45 — Future of cryptocurrencies in banking 00:32:08 — The business verticals within Empire Group 00:32:48 — What Jonathan invests his money in 00:33:54 — How relationships with regulators are built 00:34:43 — Implementing AI in business 00:36:28 — Vibe coding in trading 00:40:06 — Advice for first-time founders 00:43:36 — From construction to trading: Ruslan Khairullin's journey 00:44:57 — Inner peace: why calmness is essential for founders 00:50:39 — Work-life balance for entrepreneurs 00:53:47 — $1.5 million in 24 hours on TST coin 00:55:11 — The best way to capture a market 00:55:57 — Banking for nations Watch the full conversation and let me know which part you liked the most 👇

Ruslan Khairullin

16,872 Aufrufe • vor 1 Monat

Episode 76 with Uri from Endless During this episode we talked about the future of finance, digital collectibles and AI Consumer crypto is the next biggest wave as the newer generations collect more online Hype cycles fail and we get in to what sustainability looks and feels like Timestamps 1:59 Uri Background and getting into crypto 12:13 Early crypto narratives, why fundamentals broke and how Endless𒀭 fixes it 25:35 Why hype cycles fail and what sustainable crypto actually looks like 37:55 Infrastructure, decentralization, and what builders still get wrong 48:45 NFTs, collectibles, and why the first wave failed mainstream adoption 59:47 Andy Dandy on culture, fandoms, and how digital communities form real value 1:14:00 AI, automation, and how economics are changing underneath crypto 1:25:30 Long-term thinking, generational shifts, and building for the future 1:42:50 Final reflections on consumer crypto, IP, and what comes next
1:46:55

Episode 76 with Uri from Endless During this episode we talked about the future of finance, digital collectibles and AI Consumer crypto is the next biggest wave as the newer generations collect more online Hype cycles fail and we get in to what sustainability looks and feels like Timestamps 1:59 Uri Background and getting into crypto 12:13 Early crypto narratives, why fundamentals broke and how Endless𒀭 fixes it 25:35 Why hype cycles fail and what sustainable crypto actually looks like 37:55 Infrastructure, decentralization, and what builders still get wrong 48:45 NFTs, collectibles, and why the first wave failed mainstream adoption 59:47 Andy Dandy on culture, fandoms, and how digital communities form real value 1:14:00 AI, automation, and how economics are changing underneath crypto 1:25:30 Long-term thinking, generational shifts, and building for the future 1:42:50 Final reflections on consumer crypto, IP, and what comes next

Andrew Forte

80,424 Aufrufe • vor 6 Monaten

Andrej Karpathy just said what most AI builders refuse to admit "The demo is near, but the product is far" One output can look impressive A real product has to work reliably at scale Most AI companies are still showing capabilities, not building complete products 00:00 - Why Transformers may outperform the human brain 05:09 - AI as your exocortex and who controls it 10:04 - Building AI to empower people, not replace them 15:16 - Why the demo is near but the real product is far In this 23-minute conversation, Karpathy explains where AI is actually heading Worth more than most $300 AI courses Bookmark and watch it later
23:58

Andrej Karpathy just said what most AI builders refuse to admit "The demo is near, but the product is far" One output can look impressive A real product has to work reliably at scale Most AI companies are still showing capabilities, not building complete products 00:00 - Why Transformers may outperform the human brain 05:09 - AI as your exocortex and who controls it 10:04 - Building AI to empower people, not replace them 15:16 - Why the demo is near but the real product is far In this 23-minute conversation, Karpathy explains where AI is actually heading Worth more than most $300 AI courses Bookmark and watch it later

rari

37,193 Aufrufe • vor 19 Tagen

🔥 Trade Secrets: Episode 1 Why is a macro investor with 30+ years of experience watching Dogecoin so closely? In Cointelegraph’s new show, Ciaran Lyons sits down with Wall Street veteran Jordi Visser to unpack why Dogecoin is his altcoin of choice. And what it could signal for the next market uptrend. 00:00 — Intro 00:57 — Does Bitcoin still have a chance of being 2026's best-performing asset? 01:34 — Is the AI boom helping or hurting crypto? 03:33 — Why Dogecoin is Jordi Visser's altcoin of choice 05:14 — Why crypto still carries negative sentiment 06:48 — Why the Federal Reserve won't be cutting rates anytime soon 10:03 — Stablecoin growth is mirroring the rise of meme stocks 12:15 — What could the US CLARITY Act mean for Bitcoin's price? 14:50 — Why VC money isn't essential for crypto anymore 16:45 — How retail traders can profit from AI agents 19:00 — Can Bitcoin actually fail? 21:00 — The biggest misconceptions people still have about crypto 25:10 — Why crypto investors are becoming impatient and greedy 27:30 — Is $1,000 better invested in Bitcoin or AI stocks right now? 29:50 — Don't start trading until Dogecoin breaks out 31:45 — Michael Saylor is betting money printing will continue 33:30 — What would US government Bitcoin accumulation mean for BTC? 35:30 — Should you trust crypto price predictions? 38:55 — How high can Bitcoin go in 2026?
41:24

🔥 Trade Secrets: Episode 1 Why is a macro investor with 30+ years of experience watching Dogecoin so closely? In Cointelegraph’s new show, Ciaran Lyons sits down with Wall Street veteran Jordi Visser to unpack why Dogecoin is his altcoin of choice. And what it could signal for the next market uptrend. 00:00 — Intro 00:57 — Does Bitcoin still have a chance of being 2026's best-performing asset? 01:34 — Is the AI boom helping or hurting crypto? 03:33 — Why Dogecoin is Jordi Visser's altcoin of choice 05:14 — Why crypto still carries negative sentiment 06:48 — Why the Federal Reserve won't be cutting rates anytime soon 10:03 — Stablecoin growth is mirroring the rise of meme stocks 12:15 — What could the US CLARITY Act mean for Bitcoin's price? 14:50 — Why VC money isn't essential for crypto anymore 16:45 — How retail traders can profit from AI agents 19:00 — Can Bitcoin actually fail? 21:00 — The biggest misconceptions people still have about crypto 25:10 — Why crypto investors are becoming impatient and greedy 27:30 — Is $1,000 better invested in Bitcoin or AI stocks right now? 29:50 — Don't start trading until Dogecoin breaks out 31:45 — Michael Saylor is betting money printing will continue 33:30 — What would US government Bitcoin accumulation mean for BTC? 35:30 — Should you trust crypto price predictions? 38:55 — How high can Bitcoin go in 2026?

Cointelegraph

75,942 Aufrufe • vor 1 Monat

Jordan Peterson on why writing is the most powerful skill you can learn: 1. writing and thinking are the same thing. there is no difference between them. when you learn to write clearly you are not learning to express thoughts you already have. you are learning to have the thoughts in the first place. 2. universities never tell students why they are writing. the answer they get is you need the grade. the real answer is you need to learn to think. because thinking is what makes you act effectively in the world and win the battles you choose to fight. 3. if you can think, speak, and write you are absolutely dangerous. people give you money. they give you opportunities. you have influence. peterson says nothing can get in your way. that is what you are at university for. 4. the most articulate person in the room always wins the argument. peterson has watched staggeringly successful people his whole life. the common thread: you do not want to argue with them. not because they are aggressive. because their points are organized and yours probably aren't. 5. someone who cannot write has done almost everything wrong before they start. wrong words, wrong sentences, wrong paragraph order, wrong structure, wrong conclusion. peterson says marking a bad essay is agony because the answer to what did i do wrong is essentially everything.
4:01

Jordan Peterson on why writing is the most powerful skill you can learn: 1. writing and thinking are the same thing. there is no difference between them. when you learn to write clearly you are not learning to express thoughts you already have. you are learning to have the thoughts in the first place. 2. universities never tell students why they are writing. the answer they get is you need the grade. the real answer is you need to learn to think. because thinking is what makes you act effectively in the world and win the battles you choose to fight. 3. if you can think, speak, and write you are absolutely dangerous. people give you money. they give you opportunities. you have influence. peterson says nothing can get in your way. that is what you are at university for. 4. the most articulate person in the room always wins the argument. peterson has watched staggeringly successful people his whole life. the common thread: you do not want to argue with them. not because they are aggressive. because their points are organized and yours probably aren't. 5. someone who cannot write has done almost everything wrong before they start. wrong words, wrong sentences, wrong paragraph order, wrong structure, wrong conclusion. peterson says marking a bad essay is agony because the answer to what did i do wrong is essentially everything.

Jaynit

104,964 Aufrufe • vor 1 Monat

Why Galaxy Might Be the Most Undervalued Crypto x AI Stock w/ Duncan What do Meta, Amazon, Google, and Microsoft …have in common? They’re spending $330B on AI infra this year. And one crypto company is building the AI infrastructure to power them. Tune in to know more ⏱ TIME POINTS ⏱ 00:00 – Intro 00:58 – Where Are We in the Bull Market? 03:11 – Is the 4-Year Cycle Dead? 06:25 – How Public Markets Are Changing Crypto 12:13 – Figure Markets 12:48 – KGeN 13:18 – Why Duncan Loves $GLXY 16:10 – The AI Data Center Opportunity 27:23 – Risks of Building at AI Scale 33:33 – What’s Galaxy’s Endgame? 36:05 – AI & Robots: What’s Coming and When 43:46 – How to Invest in Humanoid Robots 47:50 – What Could Slow Down Robot Adoption? 50:18 – Where Duncan Finds Alpha 53:29 – Final Thoughts
55:26

Why Galaxy Might Be the Most Undervalued Crypto x AI Stock w/ Duncan What do Meta, Amazon, Google, and Microsoft …have in common? They’re spending $330B on AI infra this year. And one crypto company is building the AI infrastructure to power them. Tune in to know more ⏱ TIME POINTS ⏱ 00:00 – Intro 00:58 – Where Are We in the Bull Market? 03:11 – Is the 4-Year Cycle Dead? 06:25 – How Public Markets Are Changing Crypto 12:13 – Figure Markets 12:48 – KGeN 13:18 – Why Duncan Loves $GLXY 16:10 – The AI Data Center Opportunity 27:23 – Risks of Building at AI Scale 33:33 – What’s Galaxy’s Endgame? 36:05 – AI & Robots: What’s Coming and When 43:46 – How to Invest in Humanoid Robots 47:50 – What Could Slow Down Robot Adoption? 50:18 – Where Duncan Finds Alpha 53:29 – Final Thoughts

Milk Road

59,357 Aufrufe • vor 11 Monaten

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 Aufrufe • vor 8 Monaten