Michael Howell just told you exactly where we are in the cycle. His framework breaks liquidity into four phases: 1. Calm 2. Speculation 3. Turbulence 4. Rebound. The data places us in late speculation right now. That phase has a specific historical record where commodities and gold are the best-performing asset classes in it. Equities are not. The yield curve is flattening. Inflation pressures are building. The PBOC is tightening. These are the exact conditions that have historically pushed capital out of equities and into hard assets. Howell's allocation for this environment: 80% defensive, 20% speculative.

This is WILD! Something very large is happening in global bond markets, and most people are completely missing it (Save this). The hyperscalers, Amazon, Google, Microsoft, and Meta have collectively committed $725 billion in capital expenditure for 2026 alone, up 77% from the $410 billion record set in 2025, and Goldman Sachs projects total combined capex from 2026 through 2031 reaching $7.6 trillion. Those numbers are so large they have broken the American bond market's ability to absorb them. In 2024, not a single dollar of hyperscaler bond issuance was in a non-USD currency. In 2025, 100% of new non-USD issuance was new meaning the category barely existed the year before. By 2026, non-USD currencies already account for 48% of hyperscaler bond funding, with the euro at 52% of that slice, JPY at 15%, CAD at 14%, GBP at 12%, and CHF at 7%. Bank of America confirmed the shift, hyperscalers have doubled the non-dollar share of their bond funding to 30% of total issuance in 2026. The individual deal sizes tell the story of how fast this is moving. In May, Alphabet issued ¥576.5 billion approximately $3.6 billion in yen denominated bonds, the largest yen bond ever sold by any non-Japanese company in history, surpassing the previous record set by Berkshire Hathaway in 2019. Then Amazon came in June and issued C$14 billion in Canadian dollar bonds, the largest corporate bond ever sold in the Canadian market, attracting over C$28 billion in investor orders, nearly double the amount ultimately sold. Amazon's single Canadian deal surpassed Alphabet's previous record Canadian issuance of C$8.5 billion set just weeks earlier in May. Alphabet has now set borrowing records in yen, Canadian dollars, Swiss francs, and sterling in a single calendar year. Morgan Stanley projects euro borrowing by hyperscalers will hit €50 billion in 2026 potentially making the United States the single largest source of corporate debt issuance in the entire eurozone, ahead of France. Global AI-related debt issuance is projected to reach $570 billion for the full year 2026, according to Morgan Stanley more than double the pace of the same period last year and nearly four times the 2022 level. The AI infrastructure buildout is so capital-intensive that even the most cash-rich companies in human history, Apple, Microsoft, Alphabet, Amazon, and Meta collectively hold over a trillion dollars in cash and near-cash assets have concluded they cannot self-fund it. And they are barely started. Come join Milk Road Pro for our full breakdown, what $7.6 trillion in hyperscaler capex means for global credit markets over the next five years and our entire Ai thesis. Link below!

Tech inflation and electricity inflation.

AI is driving US inflation higher: Consumer prices for computer software and accessories surged 14.5% YoY in May, the biggest annual increase on record in data going back to 2000. Producer prices for electronic components soared 27% YoY, also the biggest increase on record. To put this into perspective, before 2026, prices for software and electronic components fell in almost every year since 2000. Memory prices alone have more than doubled, with DDR5 and DDR4 RAM prices up 290% YoY, as AI data centers absorb the vast majority of global chip supply. RAM price shocks are will likely keep inflation elevated well into 2027, adding to existing pressures from the Iran War. The AI boom is fueling technology inflation.

quants aren't better at predicting the market they're better at reading yours - before you've finished placing the order when you click buy on robinhood, your broker sells that order to a market maker in milliseconds that market maker already knows your direction before it hits the exchange they reprice accordingly. you never get the true spread this is called payment for order flow - legal since 1984, and market makers pay brokers specifically to see your orders early retail traders pay roughly $1.4 billion a year in hidden execution costs. average trader never sees a line item for it renaissance built their edge on exactly this logic. not prediction - precision on systematic pricing errors that everyone else keeps making medallion fund: 66% gross annual return, 30 consecutive years not a single negative year in three decades trading the same instruments and public markets as everyone else - completely different information layer alpha wasn't hidden in some proprietary model. it lived in FINRA dark pool reports, SEC 13F filings, ATS transaction data all public, free, sitting completely unread because retail never thought to pull it game was never about being smarter it was about seeing the full board when everyone else only sees their own hand

A government just proved it can delete the smartest AI on earth. One letter. One signature. By the next morning the most powerful public model in the world was switched off in every country at once. Watch what the money did next. It did not flee crypto. It did not run to gold. It poured into a token most people had never heard of, and sent it up 23 percent in a day, 36 percent on the week, while the thing that triggered all of it had nothing to do with crypto at all. The model that got killed was Anthropic’s. A near-trillion-dollar lab, its two strongest systems, dark worldwide three days after launch. A US export order barred every foreign national from them, and because no company can passport-check millions of live sessions, the only way to comply was to pull the plug on everyone, everywhere. The token that soared was Bittensor. Anthropic does not own it, did not build it, holds no stake in it. Different company. Opposite technology. And it added roughly $300 million in market value over a single weekend on a ban it had no part in. The reason is the most important thing the market learned all year. The government killed that model because there was a door to knock on. An address. A CEO. A list of employees. Every one of those is a place a letter can land. Bittensor has none of them. Its intelligence runs across more than 128 independent networks owned by no one and hosted everywhere. There is no headquarters to serve, no founder to compel, no switch to flip. You cannot subpoena math running in ten thousand places at once. So the ban did not threaten Bittensor. The ban advertised it. The US government just ran the single most expensive commercial for decentralized AI ever made, aimed it at every allocator on earth, and the token was the billboard. The Opentensor Foundation only had to state the obvious: access to intelligence should not depend on a handful of companies or governments. Friday turned that line from a pitch into a receipt. Here is the blade under the whole thing. Being the best and being unstoppable are not the same property. The frontier labs still build the most capable models alive, and it is not close. But capability sits in a company, and a company can be switched off on a Friday afternoon by a clause it never saw coming. The market just priced that gap in a single session, and it priced it fast. Be honest about the other side. Rallies like this fade more often than they hold, and the only hard proof the shutdown caused the bid is the timing. This is the market telling itself a story, and the market is not a careful witness. But the lesson under the candle outlives the candle. A state just demonstrated, in the open, that the off switch on centralized AI is real and that its hand is on it. Every serious holder of AI exposure now has one new question that will not go away. What happens to mine when the next letter goes out. The smartest AI on earth can be deleted with one signature. That was the news. The trade was everyone who decided, over a single weekend, that they would rather own the kind that cannot.

🎯 89 Claude Commands That Make You 10x More Productive Most people use Claude like a chatbot. Power users use commands. These commands help you write faster, think deeper, analyze better, and automate repetitive work. ♦️ START & SETUP • /new – Start a fresh conversation • /project – Create a dedicated workspace • /upload – Add files for analysis • /import – Bring in existing content • /scan – Read documents instantly • /voice – Talk instead of typing ♦️ CONTEXT & DIRECTION • /focus – Define the main goal • /context – Add background information • /examples – Show examples to follow • /clarify – Ask follow-up questions • /priorities – Set what's most important • /constraints – Define limitations ♦️ THINKING TOOLS • /analyze – Break down complex problems • /compare – Evaluate multiple options • /pros-cons – Weigh tradeoffs quickly • /brainstorm – Generate fresh ideas • /recommend – Get actionable suggestions • /challenge – Test assumptions ♦️ WRITING TOOLS • /write – Create content from scratch • /rewrite – Improve existing content • /shorten – Make it concise • /expand – Add more depth • /summarize – Extract key information • /proofread – Fix grammar and clarity ♦️ ORGANIZATION TOOLS • /outline – Build content structures • /table – Present information clearly • /mindmap – Visualize ideas • /flowchart – Create workflows • /key-points – Extract essentials ♦️ CODING TOOLS • /code – Generate code • /debug – Find and fix issues • /explain – Understand code faster • /optimize – Improve performance • /refactor – Clean up codebases ♦️ DATA & INSIGHTS • /analyze-data – Process datasets • /visualize – Create charts • /insights – Discover patterns • /forecast – Predict trends • /report – Generate summaries ♦️ AUTOMATION • /workflow – Design processes • /automate – Reduce manual work • /integrate – Connect tools • /tasklist – Create action plans • /checklist – Stay organized ♦️ RESEARCH • /search – Find information fast • /research – Explore topics deeply • /fact-check – Verify claims • /sources – Find references • /learn – Master new subjects 💡 Pro Tip: The best results come from combining commands. Example: → /research /summarize → /analyze /recommend → /write /improve → /workflow /automate The difference between average AI users and power users isn't the model. It's knowing how to guide it. ❤️ Like 🔁 Repost 🔖 Save Follow @Origin_AI_01 for more AI productivity tips.

Most people are still paying monthly subscriptions for software that has a free alternative sitting on GitHub. Ten worth knowing right now TradingAgents - AI multi-agent quant trading framework > github.com/TauricResearch/Tr… LibreChat - one interface for ChatGPT & Claude etc > github.com/danny-avila/Libre… HyperFrames - Open-source video generation engine > github.com/heygen-com/HyperF… Fincept Terminal - open-source Bloomberg alternative > github.com/Fincept-Corporati… MoneyPrinterTurbo - AI short-form video generation in one click > github.com/harry0703/MoneyPr… Agentic Inbox - Cloudflare's open-source AI email assistant > github.com/cloudflare/agenti… VoxCPM - AI voice cloning toolkit > github.com/OpenBMB/VoxCPM Flowsint - open-source intelligence platform > github.com/reconurge/flowsin… Agent-skills - Claude Code skills library > github.com/addyosmani/agent-… Nango - open-source API integration platform > github.com/NangoHQ/nango The real gems are inside GitHub repositories. That's where the builders are.

A Bloomberg Terminal costs $30,000 a year. Here's how to build 90% of it for free. Wall Street doesn't advertise this. But every function that matters has a free alternative in Python. Here's the DIY version (with Python):

As @PradeepBonde suggests everyone should have a trade factory. Do you have one?

🎯 10 Claude Connectors That Save Hours Every Week Claude becomes far more powerful when connected to the tools you already use. ➡️ Gmail • Draft replies and summarize long email threads • Search your inbox using natural language ➡️ Google Drive • Open, analyze, and summarize documents instantly • Find files with simple descriptions instead of exact names ➡️ Notion • Access wikis, notes, and databases directly in Claude • Create and update content without leaving the conversation ➡️ Slack • Catch up on busy channels in minutes • Draft messages before sending them to your team ➡️ GitHub • Review pull requests and troubleshoot code faster • Analyze issues and generate contextual solutions ➡️ Jira • Check sprint progress and ticket status conversationally • Surface blockers and updates without manual digging ➡️ Figma • Review design systems for inconsistencies • Generate FigJam workflows from plain English prompts ➡️ Google Calendar • Schedule meetings and manage availability effortlessly • Get a quick overview of your upcoming week ➡️ Blender • Control and automate 3D workflows with natural language • Script and modify scenes using Blender's Python API ➡️ Adobe Creative Cloud • Access tools across Photoshop, Premiere Pro, and more • Edit, resize, and repurpose creative assets faster The real productivity boost isn't better prompts. It's eliminating the constant switching between apps. ❤️ Like 🔁 Repost 🔖 Save Follow @Origin_AI_01 for more AI and productivity insights.

Everyone is talking about which AI tool is best. Very few are talking about how to get the best out of the tools they already have. The biggest productivity unlock isn't a new model. It's better execution. Simple habits can transform your results: ✓ Give clear objectives ✓ Share complete context ✓ Define what success looks like ✓ Review outputs critically ✓ Test before deploying ✓ Build repeatable workflows AI rewards precision. The more clearly you think, the better it performs. The gap between beginners and power users isn't intelligence. It's process. Those who learn to collaborate with AI instead of merely prompting it will have a significant advantage in the years ahead. Are you using AI as a search engine, an assistant, or a true collaborator? #AI #ArtificialIntelligence #FutureOfWork #Productivity #Innovation #PromptEngineering #TechTrends #DigitalTransformation #CareerGrowth.

Not to take anything away from Elon Musk’s financial achievement, but understanding the difference between sovereign wealth and subordinated wealth is useful when saying that he has become the world’s first trillionaire. People see Forbes rankings and think that’s where the real money is. The House of Saud, Abu Dhabi’s Al Nahyan family, Qatar’s Al Thani family, the Rothschilds, the Rockefellers, sovereign wealth families, and the broader financial-industrial complex have spent generations building influence over capital, banking, energy, debt markets, institutions, and the rules of settlement itself. Most billionaire wealth is marked-to-market. It is a quoted value based on public company shares, liquidity, credit conditions, lenders, institutional shareholders, asset managers, and collateral rules. Direct control of wealth is different. Much of the world’s wealth is not publicly visible. Private companies, trusts, foundations, sovereign entities, and offshore structures rarely appear on rich lists. Privacy, control, and asset protection are features of wealth preservation, not bugs. You’re a trillionaire when you can send a trillion dollars of Bitcoin in a single transaction (and nobody in the world can do that yet), or load a trillion dollars of gold onto a ship in your name and move it without permission. Everything else is subordinate to someone else’s balance sheet, rules, and approval. Visible wealth and actual financial power are not the same thing. I encourage sovereign wealth over subordinated wealth. Only sovereign wealth is true freedom. Follow the money.

50 signals each right 52% of the time outperform any single signal right 70% of the time that's not an opinion. that's z-field math a 52% signal alone is noise - coin flip territory, not worth a position on its own but stack 50 independent ones, normalize each by historical sharpe, and build a composite z-score accuracy jumps to 63-65%, sharpe ratio compounds and drawdowns compress more independent signals you add, even weak ones, the more noise cancels and edge survives citadel doesn't search for perfect signals. they build systems to aggregate weak ones framework is z-field aggregation - every signal gets weighted, scored, stacked when composite crosses a threshold: trade. when it doesn't: flat gut feel and chart patterns don't enter the equation retail spent years chasing a signal with no false positives quants built math to make 50 imperfect ones into one math has been in every stats textbook - published, free, 40 years Bookmark this nobody in your trading group is combining signals. they're still hunting for just one

🚨 JUST IN: Exodus and Ondo Finance launched Exodus Markets, bringing 200 tokenized stocks, ETFs, and RWAs to Solana.