AkhenOsiris
We have a not inconsequential $7.5M buy at $RDDT by a director. See if that matters today.
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The Transcript
RT @TheTranscript_: $AMAT CEO: "..we expect to grow our semiconductor equipment business more than 20% this calendar year. We see the demand profile weighted towards the second half of the calendar year, with availability of customer clean room space being a key factor pacing the rate of investment" https://t.co/PkRWv9A9Nw

Applied Materials CEO: "Our strong performance and outlook for 2026 and beyond are fueled by the acceleration of investments in AI computing."

$AMAT: +14% AH https://t.co/nxZ6xCPQid - The Transcript
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The Transcript
RT @TheTranscript_: Applied Materials CEO: "Our strong performance and outlook for 2026 and beyond are fueled by the acceleration of investments in AI computing."

$AMAT: +14% AH https://t.co/nxZ6xCPQid
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Dimitry Nakhla | Babylon Capital®
Another timeless investing lesson from Chris Hohn:

“The 𝐥𝐨𝐧𝐠𝐞𝐫 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐥𝐨𝐨𝐤 𝐨𝐮𝐭, if you’ve got a great company, 𝐭𝐡𝐞 𝐦𝐨𝐫𝐞 𝐯𝐚𝐥𝐮𝐞 𝐭𝐡𝐞𝐫𝐞 𝐢𝐬. Take a company we own — Moody’s… What do you think the average revenue growth over 100 years has been?

10%.

That’s a very unusual number over a very long time period. And so 𝐢𝐧𝐯𝐞𝐬𝐭𝐨𝐫𝐬 𝐡𝐚𝐯𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐮𝐧𝐝𝐞𝐫𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞𝐝 𝐭𝐡𝐢𝐬 𝐯𝐚𝐥𝐮𝐞, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐦𝐲𝐬𝐞𝐥𝐟… The intrinsic value compounding matters more than the stock price. If you have a great company, it will grow intrinsic value.

Here’s the thing about multiples.

𝐓𝐡𝐞𝐲 𝐦𝐚𝐭𝐭𝐞𝐫 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐠𝐫𝐨𝐰𝐭𝐡 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮 𝐥𝐨𝐨𝐤 𝐚𝐭 𝐢𝐭 𝐨𝐯𝐞𝐫 𝐚 𝐥𝐨𝐧𝐠𝐞𝐫 𝐩𝐞𝐫𝐢𝐨𝐝. 𝐁𝐮𝐭 𝐦𝐨𝐬𝐭 𝐢𝐧𝐯𝐞𝐬𝐭𝐨𝐫𝐬 𝐚𝐫𝐞 𝐮𝐧𝐰𝐢𝐥𝐥𝐢𝐧𝐠 𝐨𝐫 𝐮𝐧𝐚𝐛𝐥𝐞 𝐭𝐨 𝐢𝐧𝐯𝐞𝐬𝐭 𝐨𝐧 𝐚 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐭𝐢𝐦𝐞 𝐡𝐨𝐫𝐢𝐳𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐞𝐢𝐭𝐡𝐞𝐫 𝐭𝐡𝐞𝐲 𝐝𝐨𝐧’𝐭 𝐤𝐧𝐨𝐰 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲’𝐫𝐞 𝐝𝐨𝐢𝐧𝐠.

Which goes back to Warren Buffett’s definition of risk:

Not knowing what you’re doing.

𝐈𝐟 𝐚 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐢𝐬 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐢𝐧𝐠 𝐢𝐧𝐭𝐫𝐢𝐧𝐬𝐢𝐜 𝐯𝐚𝐥𝐮𝐞 𝐚𝐭 𝐚 𝐠𝐨𝐨𝐝 𝐫𝐚𝐭𝐞, 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐨𝐟𝐭𝐞𝐧 𝐮𝐧𝐝𝐞𝐫𝐯𝐚𝐥𝐮𝐞 𝐢𝐭 𝐰𝐡𝐞𝐧 𝐯𝐢𝐞𝐰𝐢𝐧𝐠 𝐢𝐭 𝐨𝐯𝐞𝐫 𝐚 𝐬𝐡𝐨𝐫𝐭 𝐡𝐨𝐫𝐢𝐳𝐨𝐧.

𝐀𝐧𝐝 𝐢𝐟 𝐲𝐨𝐮’𝐫𝐞 𝐰𝐢𝐥𝐥𝐢𝐧𝐠 𝐭𝐨 𝐡𝐨𝐥𝐝 𝐢𝐭 𝐟𝐨𝐫 𝐚 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐚𝐧𝐝 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐭𝐡𝐚𝐭 𝐢𝐧𝐭𝐫𝐢𝐧𝐬𝐢𝐜 𝐯𝐚𝐥𝐮𝐞 𝐠𝐫𝐨𝐰𝐭𝐡 𝐢𝐭 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐰𝐨𝐫𝐭𝐡 𝐦𝐨𝐫𝐞 𝐭𝐨 𝐲𝐨𝐮 𝐭𝐡𝐚𝐧 𝐭𝐨 𝐨𝐭𝐡𝐞𝐫 𝐩𝐞𝐨𝐩𝐥𝐞.”
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𝐓𝐡𝐞 𝐥𝐞𝐬𝐬𝐨𝐧: 𝘛𝘪𝘮𝘦 𝘪𝘴 𝘵𝘩𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 𝘮𝘰𝘴𝘵 𝘪𝘯𝘷𝘦𝘴𝘵𝘰𝘳𝘴 𝘮𝘪𝘴𝘱𝘳𝘪𝘤𝘦. 𝘕𝘰𝘵 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘪𝘵’𝘴 𝘩𝘪𝘥𝘥𝘦𝘯. 𝘉𝘶𝘵 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘪𝘵’𝘴 𝘱𝘴𝘺𝘤𝘩𝘰𝘭𝘰𝘨𝘪𝘤𝘢𝘭𝘭𝘺 𝘥𝘪𝘧𝘧𝘪𝘤𝘶𝘭𝘵 𝘵𝘰 𝘦𝘹𝘵𝘦𝘯𝘥. 𝘔𝘢𝘳𝘬𝘦𝘵𝘴 𝘤𝘰𝘯𝘥𝘪𝘵𝘪𝘰𝘯 𝘪𝘯𝘷𝘦𝘴𝘵𝘰𝘳𝘴 𝘵𝘰 𝘵𝘩𝘪𝘯𝘬 𝘪𝘯 𝘲𝘶𝘢𝘳𝘵𝘦𝘳𝘴, 𝘩𝘦𝘢𝘥𝘭𝘪𝘯𝘦𝘴, 𝘢𝘯𝘥 𝘱𝘳𝘪𝘤𝘦 𝘮𝘰𝘷𝘦𝘮𝘦𝘯𝘵𝘴. 𝘉𝘶𝘵 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴𝘦𝘴 𝘤𝘰𝘮𝘱𝘰𝘶𝘯𝘥 𝘰𝘷𝘦𝘳 𝘺𝘦𝘢𝘳𝘴 𝘢𝘯𝘥 𝘥𝘦𝘤𝘢𝘥𝘦𝘴. 𝘛𝘩𝘪𝘴 𝘮𝘪𝘴𝘮𝘢𝘵𝘤𝘩 𝘤𝘳𝘦𝘢𝘵𝘦𝘴 𝘰𝘯𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘱𝘦𝘳𝘴𝘪𝘴𝘵𝘦𝘯𝘵 𝘪𝘯𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘪𝘦𝘴 𝘪𝘯 𝘪𝘯𝘷𝘦𝘴𝘵𝘪𝘯𝘨: 𝘚𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘢𝘱𝘱𝘭𝘪𝘦𝘥 𝘵𝘰 𝘭𝘰𝘯𝘨-𝘥𝘶𝘳𝘢𝘵𝘪𝘰𝘯 𝘢𝘴𝘴𝘦𝘵𝘴.
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𝐎𝐯𝐞𝐫 𝐬𝐡𝐨𝐫𝐭𝐞𝐫 𝐩𝐞𝐫𝐢𝐨𝐝𝐬:

Multiples dominate outcomes
Sentiment dominates perception
Volatility dominates emotion

𝐎𝐯𝐞𝐫 𝐥𝐨𝐧𝐠𝐞𝐫 𝐩𝐞𝐫𝐢𝐨𝐝𝐬:

Earnings dominate returns
Cash flows dominate valuation
Intrinsic value dominates everything
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Multiples matter, but it’s secondary.

Growth + durability + time matter more.

And perhaps the most overlooked psychological truth:

𝘞𝘩𝘢𝘵 𝘧𝘦𝘦𝘭𝘴 𝘳𝘪𝘴𝘬𝘺 𝘵𝘰 𝘵𝘩𝘦 𝘴𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘪𝘯𝘷𝘦𝘴𝘵𝘰𝘳 𝘰𝘧𝘵𝘦𝘯 𝘧𝘦𝘦𝘭𝘴 𝘴𝘢𝘧𝘦 𝘵𝘰 𝘵𝘩𝘦 𝘭𝘰𝘯𝘨-𝘵𝘦𝘳𝘮 𝘪𝘯𝘷𝘦𝘴𝘵𝘰𝘳 𝘸𝘩𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘴 𝘵𝘩𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴.

Because volatility ≠ risk.

Uncertainty ≠ risk.

Not understanding what you own = risk.

𝙏𝙝𝙚 𝙝𝙖𝙧𝙙𝙚𝙨𝙩 𝙥𝙖𝙧𝙩 𝙤𝙛 𝙘𝙤𝙢𝙥𝙤𝙪𝙣𝙙𝙞𝙣𝙜 𝙞𝙨𝙣’𝙩 𝙛𝙞𝙣𝙙𝙞𝙣𝙜 𝙜𝙧𝙚𝙖𝙩 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨𝙚𝙨. 𝙄𝙩’𝙨 𝙙𝙚𝙫𝙚𝙡𝙤𝙥𝙞𝙣𝙜 𝙩𝙝𝙚 𝙩𝙚𝙢𝙥𝙚𝙧𝙖𝙢𝙚𝙣𝙩 𝙖𝙣𝙙 𝙩𝙞𝙢𝙚 𝙝𝙤𝙧𝙞𝙯𝙤𝙣 𝙧𝙚𝙦𝙪𝙞𝙧𝙚𝙙 𝙩𝙤 𝙡𝙚𝙩 𝙩𝙝𝙚𝙢 𝙬𝙤𝙧𝙠.
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$MCO $SPGI

Video: In Good Company | Norges Bank Investment Management (02/13/2026)
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Bourbon Capital
$MA and $V still dominate most card payments in Europe https://t.co/y35U7n4ns4
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Brady Long
RT @bigaiguy: I just broke my own productivity system.

Been optimizing my workflow for 3 years.

Task managers, time blocking, the whole thing.

Then I let an AI agent run unsupervised for 48 hours.

It finished a month of research work while I was offline.

This is different 🧵 https://t.co/FaROAJkfoO
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Moon Dev
The Death Of Paid Courses: Use The RBI System To Automate Your Trades Like A Billionaire

the era of lighting your money on fire for expensive trading courses is officially dead because the actual blueprint used by billionaires like jim simons is now available to anyone with a laptop and a wifi connection. most people think you need a phd in mathematics or a seat at a wall street firm to build these systems but the truth is far more dangerous to the institutions trying to keep you out. if you can understand basic english and have the patience to click a few buttons you can build a system that trades more efficiently than a human ever could

my name is moon dev and i believe that code is the great equalizer because it removes the one thing that will always destroy your portfolio which is your own human emotion. for years i was the guy staring at the charts at three in the morning watching my pnl swing up and down only to end the day right back where i started. i spent hundreds of thousands of dollars on developers thinking i wasn't smart enough to code myself while i was simultaneously losing even more to liquidations and over trading because i couldn't follow my own rules

the real secret to winning this game isn't a magical indicator or a high ticket mentorship but a simple three step process i call the rbi system. rbi stands for research backtest and implement and it is the exact framework that allowed jim simons to run up a net worth of thirty one billion dollars. most traders skip the first two steps and jump straight to implementation which is essentially just gambling with a fancy name. if you don't know if your strategy worked in the past you are just hoping it works in the future and hope is not a strategy

you start with deep research by looking at papers or listening to podcasts where the actual pros share their logic. once you have an idea you must backtest it against years of historical data to see if it actually has an edge over a long enough timeline. this is where the math either saves you or warns you that your idea is trash before you ever put a single dollar at risk. once you have a winner in the past then you move to the implementation phase where the bot executes the logic without fear or greed

there is a hidden trap in the data that can make a strategy look like a gold mine when it is actually a ticking time bomb waiting to wipe you out. i recently built a liquidation bot that showed an eight hundred percent return but when i looked closer the drawdown was ninety nine percent. a ninety nine percent drawdown means one bad trade almost resets your entire account to zero which is a certified recipe for disaster. most people would see the big return number and start running the bot immediately but a real system builder knows that extreme numbers usually point to a bug in the code

i found out that the data i was using was messy and including coins i didn't even want to trade which was skewing the entire result. by using ai tools like cursor you can identify these anomalies and refactor your code to be more robust and specific. we updated the bot to only look for long liquidations as an entry point because when everyone else is getting forced out of their positions that is usually the best time for us to step in. once we cleaned the data and refined the logic the strategy became a sustainable system instead of a high stakes gamble

the unseen tax on your trading profits isn't the exchange fees but the massive api costs that eat away at your bottom line while you sleep. many traders run up huge bills using premium data providers for things as simple as checking their wallet balance or getting a token price. i found myself hitting credit limits and burning through cash just to keep the bots alive until i realized i could switch my calls to free alternatives like morales. moving your price checks and balance calls to a free tier is instant profit because a dollar saved in expenses is exactly the same as a dollar made in the market[...]

this is why you must constantly iterate on your systems because the markets are always evolving and your edge will eventually decay. jim simons always said you have to make your systems better and better because that is exactly what everyone else is trying to do. trading is a competitive sport and the moment you stop improving your code is the moment you start falling behind. it took me ten years in tech to finally realize that code is just a language and anyone can learn it if they have a big enough problem to solve

the most important contract you will ever sign is the one you make with yourself at the start of this journey. i decided to learn live on youtube and show every single line of code because it forced me to stay disciplined and honest about my progress. when you automate your trading you are essentially signing a non negotiable agreement that the bot will handle the execution while you handle the research. the bot doesn't care about the news or how you feel today it only cares about the parameters you set which is the only way to survive in this industry

you don't need a degree or a massive bankroll to start building these systems today. you just need to realize that the tools of the elite are now in your hands if you are willing to learn how to use them. with fully automated systems trading for me i finally got my time back which was the whole reason i started trading in the first place. the code is the equalizer that levels the playing field for the retail trader and once you see how it works you will never look at a chart the same way again
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God of Prompt
RT @godofprompt: 🚨 I just read Google DeepMind’s new paper called "Intelligent AI Delegation."

And it quietly exposes why 99% of AI agents will fail in the real world.

Here’s the paper:

Most “AI agents” today aren’t agents.

They’re glorified task runners.

You give them a goal.
They break it into steps.
They call tools.
They return an output.

That’s not delegation.

That’s automation with better marketing.

Google’s paper makes a brutal point:

Delegation isn’t just splitting tasks.

It’s transferring authority, responsibility, accountability, and trust across agents dynamically.

And almost no current system does this.

Here’s what they argue real delegation actually requires:

1. Dynamic assessment

Before assigning a task, an agent must evaluate:

- Capability
- Resource availability
- Risk
- Cost
- Verifiability
- Reversibility

Not just “who has the tool?”

But: “Who should be trusted with this specific task under these constraints?”

That’s a massive shift.

2. Adaptive execution

If the delegatee underperforms…

You don’t wait for failure.

You reassign mid-execution.

Switch agents.
Escalate to a human.
Restructure the task graph.

Current agents are brittle.
Real agents need recovery logic.

3. Structural transparency

Today’s AI-to-AI delegation is opaque.

If something fails, you don’t know:

- Was it incompetence?
- Misalignment?
- Bad decomposition?
- Malicious behavior?
- Tool failure?

The paper proposes enforced auditability and verifiable completion.

In other words:

Agents must prove what they did.

Not just say they did it.

4. Trust calibration

This is huge.

Humans routinely over-trust AI.
AI agents may over-trust other agents.
Both are dangerous.

Delegation must align trust with actual capability.

Too much trust = catastrophe.
Too little trust = wasted potential.

5. Systemic resilience

This is the part nobody is talking about.

If every agent delegates to the same high-performing model…

You create a monoculture.

One failure.
System-wide collapse.

Efficiency without redundancy = fragility.

Google explicitly warns about cascading failures in agentic economies.

That’s not sci-fi.
That’s distributed systems reality.

The paper also breaks down:

- Principal-agent problems in AI
- Authority gradients between agents
- “Zones of indifference” (agents complying without critical thinking)
- Transaction cost economics for AI markets
- Game-theoretic coordination
- Hybrid human-AI delegation models

This isn’t a toy-agent paper.

It’s an operating system blueprint for the “agentic web.”

The core idea:

Delegation must be a protocol.
Not a prompt.

Right now, most “multi-agent systems” are:

Agent A → Agent B → Agent C

With zero formal responsibility structure.

In a real delegation framework:

• Roles are defined
• Permissions are bounded
• Verification is required
• Monitoring is enforced
• Market coordination is decentralized
• Failures are attributable

That’s enterprise-grade infrastructure.

And we don’t have it yet.

The most important line in the paper?

Automation is not just about what AI can do.

It’s about what AI *should* do.

That distinction will decide:

- which startups survive
- which enterprises scale
- which ai deployments implode

We’re entering the phase where:

Prompt engineering → Agent engineering → Delegation engineering.

The companies that figure out intelligent delegation protocols first will build:

• Autonomous economic systems
• Scalable AI marketplaces
• Human-AI hybrid orgs
• Resilient agent swarms

Everyone else will ship brittle demos.

This paper isn’t flashy.

No benchmarks.
No model release.
No hype numbers.

Just a 42-page warning:

If we don’t build adaptive, accountable delegation frameworks…

The agentic web collapses under its own complexity.

And honestly?

They’re probably right. tweet

Javier Blas
OIL MARKET: Based on her last known position, the Gerald Ford aircraft carrier would need a ~1 week to reach Gibraltar, and another ~3-4 days for the East Mediterranean (and that assumes inmediate departure, and no re-supply stops. Add some margin, and it's ~2 weeks in total)
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Benjamin Hernandez😎
$COIN: Institutional FOMO +15% ₿

Coinbase is ripping as institutions chase the BTC beta. Derivatives revenue is the secret driver here.

We are watching the $200 psychological magnet.

The "Crypto-Proxy" watchlist is in the pinned post.
$AMD $MU $PLTR $SOFI https://t.co/NknkEocuhb
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