Welcome to Nofinity

• The 10-year yield is approaching 5.2%, the 30-year near 5.12%. The market signals that the Fed’s rates are not high enough.
• Inflationary forces (tariffs, Middle East conflict, AI buildout) persist. Expectations drive prices higher.

• Historically, rising long yields eventually trigger a growth scare (e.g., 2023 banking crisis), which brings yields back down.
• Currently we are in the up phase: markets fear inflation more than recession.

• High yields pressure long-duration assets — including cryptocurrencies.
• Bitcoin has not outperformed the S&P 500 for years; its valuation is at 2021 levels.
• Altcoins suffer more: they are a zero-interest-rate phenomenon. Liquidity is tight (global net liquidity ~$25 trillion vs. $30 trillion in 2021).

• The analyst admits being perplexed: Bitcoin at ~$84,000. Technically a golden cross appears, but a drop below the May high would be bearish.
• Opportunity cost: If Bitcoin yields similar returns to the S&P over five years, investors question the extra risk.

• The Fed must hike aggressively to calm long yields — but it hasn’t.
• Current policy is too accommodative (Fed funds rate 4% vs. 2-year yield 4.9%).
• Without a real growth scare or crisis, bond vigilantes will keep revolting.

• Watch yields: They dictate direction for risk assets.
• A crisis (like the pandemic) is needed to boost liquidity and crypto sustainably. Until then, crypto remains an underperformer.

• AI experts consistently underestimate the speed and capabilities of AI models. Adoption is not yet optimal, but value creation is enormous.
• AI agents are revolutionizing business models by attacking inertia-based companies (e.g., bank fees, subscriptions). They are fearless, not lazy, and not confused.
• Meta's Muse is a personal assistant for social networks, already reaching 300,000 downloads. Developed under Alexander Wang, who increased Meta's market cap by $200 billion.

• Oracle's Force Majeure for Project Jupiter (New Mexico) due to missing permits for gas pipelines. This affects the entire Stargate plan ($500 billion).
• High credit default risks for Oracle, Blue Owl, Bloom Energy, etc. – bonds are being rated junk level.
• Data center construction is severely hindered: 300 US communities have imposed moratoriums, but only 1.5 GW out of 38 GW are affected.

• Memory is sold out through 2027, prices rising. Large customers like Elon Musk receive shipments, others don't.
• Nvidia B200 continues to generate high revenue (~$7.88 per GPU hour) and pays off quickly.

• Google's Project Sun Catcher launches a prototype satellite with TPUs in 6 days – first actual data centers in space.
• SpaceX will connect these with Starship and Starlink – sole provider for space compute.
• xAI's Grokbot saves SpaceX 200 customer service staff – cost per inquiry drops from $28-65 to 20 cents.

• Bernie Sanders wants to ban AI, 20 years prison for AI development. This drives companies out of California.
• China is advancing – either you lead or fall behind. Singapore is training 80,000 finance workers in AI.
• AI saves lives: A patient with a rare blood cancer was saved by an AI-repurposed drug combination.

• Solana is recruiting institutional experts to become the native currency in the AI agent era.
• Next show with Aaron Bernett on SpaceX valuation – TAM is huge, moat unbeatable.

This video shows how to build an extremely fast AI trading bot using Jev and Claude. The author (Miles) demonstrates a live high-frequency trading bot running on Hyperliquid.

• Jev is not a traditional LLM, but a System One model.
• It doesn't generate text; instead, it evaluates states and returns structured decisions with probability scores.
• This enables fast classification at a fraction of the cost of Claude.

• Claude (the LLM) handles strategy development, backtesting, and open-ended analysis.
• Jev takes care of fast decision-making during execution – ideal for high-frequency trading with low latency (~300–500 ms).

• The full setup prompt is available for free in the school community.
• Jev is deployed via Vercel, with Claude Code acting as the control center.
• The bot can run on paper trading or live accounts – the architecture is identical.

• There is no magic strategy: 99% of strategies lose money.
• Use backtesting (e.g., custom engines, Quantedia, NFX) and test strategies on paper first.
• Only switch to a live account after successful forward tests.

• Jev can read live headlines (e.g., Bloomberg, Reuters, Yahoo Finance) and classify them as bullish, bearish, or neutral within milliseconds.
• This makes it possible to build automated news trading systems.

• Alpaca: Simple, supports stocks and crypto, paper and live trading via API.
• Bybit: Better for crypto with lower fees; connect via VPS, MCP, or Trigger Trade.
• Important: set risk rules (max position, daily loss, kill switch) and use alerts or manual approvals for large trades.

Jev is not a replacement for an LLM, but a powerful complement for fast decisions. The technology is useful not only for trading, but also for business workflows (e.g., data classification). Early adopters can gain a real edge.

Summary follows...

• For most of its history, Bitcoin faced heavy government regulation (e.g., China, India) and was often associated with money laundering and crime.
• Donald Trump's transformation is a prime example: from harsh criticism ("Bitcoin is a scam") to a pro-Bitcoin stance, driven by campaign donations, NFTs, and geopolitical considerations.
• The U.S. establishes a Strategic Bitcoin Reserve (Executive Order on March 6, 2025), built on already-seized BTC. The goal: to hold long-term rather than sell.

• Michael Saylor and his company (now "Strategy") developed an extreme treasury strategy: raising capital through equity, convertible bonds, and preferred shares to buy massive amounts of Bitcoin.
• By the end of 2025, the company holds over 670,000 BTC, setting new standards—but not without risks, as seen in forced sales during price drops.
• More and more companies are following the trend: over 198 public firms now hold Bitcoin as a reserve asset.

• Bitcoin mining has become a political tool: Trump wants Bitcoin "mined, minted, and made in the USA".
• The focus is on cheap energy and strategic advantages—a stark reversal from earlier regulatory policies.

• ETFs and regulated custodians (like BlackRock) make Bitcoin accessible to institutions, but users often don't control the private keys—a contradiction to the original ethos of decentralization.
• Satoshi's vision of peer-to-peer cash is diluted by intermediaries, yet the software remains resistant: no government can rewrite the rules running on users' computers.

• Bitcoin has survived its trials (e.g., Mt. Gox, regulatory crackdowns) and now sits at the table with nation-states, corporations, and central banks.
• The core question remains: Can Bitcoin preserve its decentralized identity once banks and states integrate it? Or will it adapt to the traditional financial world?

Conclusion: Whether as digital gold or a political symbol—the world has taken notice of Bitcoin. The fight for the future of money is still open, but Bitcoin has already proven it cannot be beaten.

Would you like to dive deeper into a specific aspect (e.g., Strategy's sales strategy or mining developments)?

The speaker opens with the story of AlphaGo's Move 37 and Lee Sedol's Move 78, highlighting why current LLMs like ChatGPT sound generic despite AI's potential to surprise.

The 5 Pitfalls of AI-Generated Text:

1. Confidently Drunk: AI hallucinates with unwavering confidence (e.g., Google suggesting non-toxic glue for pizza).
2. A Broken Record: Predictably remixes patterns from the internet.
3. Smart and Empty: Generates pseudo-profound statements that fall apart under scrutiny ("Love without structure is a liability").
4. Clarity Lost in Noise: Overuses jargon and abstractions instead of simple ideas.
5. Boring: Lacks tension, intrigue, and a reason to keep reading.

This 5-step system helps you reclaim your unique voice from the generic "accent" of AI, turning it into a tool rather than a replacement.

• V is for Verified

  • The Goal: Fight AI hallucinations.
  • The Move: Treat AI like a researcher who needs a supervising editor. Fact-check every claim.
  • Prompt: Ask AI to extract factual claims, mark them verified/unverified, and provide primary sources.
  • Tricks: Use deep research mode; cross-verify between different AI models.

• O is for Owned

  • The Goal: Make your writing unmistakably yours.
  • The Move: Provide AI with raw material from your personal life (A Cornell study showed Indian writers using AI lost their cultural nuances, defaulting to pizza and Sylvester Stallone).
  • Prompt: Ask AI to interview you for specific personal experiences before writing anything.

• I is for Insightful

  • The Goal: Overcome the "Smart and Empty" problem.
  • The Move: Find the "So what?".
  • Tips:
    1. Insert your own story.
    2. Finish the sentences: "Most people believe X. I believe Y. The difference is Z."
    3. Use AI as your opponent. Give it your point and ask it to attack it with "So what?".

• C is for Clear

  • The Goal: Ensure your audience understands you.
  • The Move: Fight jargon and abstraction.
  • Tips:
    1. Say it out loud before writing.
    2. Sleep on it to read what's actually there, not what you meant.
    3. Use a prompt asking AI to explain your text to a 9-year-old and mark unclear sentences.

• E is for Engaging

  • The Goal: Make them want to read the next line.
  • The Move: Create stakes (like Steve Jobs' "One more thing"). AI loves fillers; you must inject tension and judgment.
  • The Verdict: This is entirely human. No prompt can replace good taste and judgment.

Content is infinite. Words are cheap. The goal is not just to produce text, but to find out what you think. Writing is the best mirror for your own mind.