Offshore
Video
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
tweet
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
tweet
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[...]
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[...]
Offshore
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…
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
tweet
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
tweet
X (formerly Twitter)
Moon Dev (@MoonDevOnYT) on X
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…
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…
Offshore
Photo
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
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)
tweet
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)
tweet
Offshore
Photo
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
tweet
$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
tweet
Offshore
Photo
App Economy Insights
$SPOT Spotify just hit 751M users. 🎧
Let's review the story.
• New co-CEOs
• Margin expansion
• Ad revenue vs. Big Tech
• Why YouTube is the wild card
• The "agentic media platform" pitch 👇
https://t.co/Skx9siRwMx
tweet
$SPOT Spotify just hit 751M users. 🎧
Let's review the story.
• New co-CEOs
• Margin expansion
• Ad revenue vs. Big Tech
• Why YouTube is the wild card
• The "agentic media platform" pitch 👇
https://t.co/Skx9siRwMx
tweet
Offshore
Photo
God of Prompt
RT @godofprompt: How to use LLMs for competitive intelligence (scraping, analysis, reporting): https://t.co/xlGOSpRQPy
tweet
RT @godofprompt: How to use LLMs for competitive intelligence (scraping, analysis, reporting): https://t.co/xlGOSpRQPy
tweet
Offshore
Photo
Brady Long
The era of “prompt and wait for a response” seems to be over.
As soon as I saw this I went immediately to Hugging Face to try it out. Nuts.
https://t.co/JlERquiIcQ
tweet
The era of “prompt and wait for a response” seems to be over.
As soon as I saw this I went immediately to Hugging Face to try it out. Nuts.
https://t.co/JlERquiIcQ
MiniCPM-o 4.5: Seeing, Listening, and Speaking — All at Once. 👁️👂🗣️
✨Beyond traditional turn-taking, we’ve built a Native Full-Duplex engine that allows a 9B model to see, listen, and speak in one concurrent, non-blocking stream.
Watch how it masters real-world complexity in real-time:
🔔 Proactive Auditory Interaction: Interrupts itself to alert you when it hears a "Ding!" while reading cards.
🎨 Temporal Flow Tracking: Follows your pen in real-time, narrating and "mind-reading" your drawing as you sketch.
🍎 Omni-Perception: Scans groceries & identifies prices on the fly.
✨Why it’s a category-leader:
📌Performance: Surpasses GPT-4o and Gemini 2.0 Pro on OpenCompass (Avg. 77.6).
📌Architecture: End-to-end fusion of SigLip2, Whisper, and CosyVoice2 on a Qwen3-8B base.
📌Efficiency: Full-duplex live streaming now runs locally on PCs via llama.cpp-omni.
The era of "Wait-and-Response" AI is over. Proactive, real-time intelligence is now open-source.
🚀Experience it on Hugging Face: 🔗https://t.co/KzzgiGYhVr
#MiniCPM #Omnimodal #FullDuplex #EdgeAI #OpenSource #ComputerVision tweet
Offshore
Video
Moon Dev
I just gave openclaw super powers
it can now use 8 different claude codes to code with Opus all day
So i just turned 1 autonomous ai engineer into 8 https://t.co/IucNIpzVb0
tweet
I just gave openclaw super powers
it can now use 8 different claude codes to code with Opus all day
So i just turned 1 autonomous ai engineer into 8 https://t.co/IucNIpzVb0
tweet
Offshore
Photo
The Few Bets That Matter
$CROX earnings are pretty interesting.
We’re talking about a company guiding to ~5% EPS growth without growth, only on efficiency, and excluding buybacks after repurchasing ~10% of shares outstanding FY25.
We don’t know how much they’ll buy back in FY26, but you could reasonably model ~10% EPS growth on flat revenue while the stock already trades below peers with weaker financials and fundamentals.
Add to that:
🔹DTC growth - higher margins
🔹International acceleration
🔹HEYDUDE expected to perform better H2-26
🔹Strong brand presence
And you get yourself a real repricing candidate.
If HEYDUDE reaccelerates - and guidance suggests that’s possible with a -8% guidance after a ~-17% Q1, there’s little reason for $CROX to trade at such low multiples versus peers like Nike, which is expected to grow ~4% at best with lower margins.
Plus, risk from here is lower as you'd need worst than terrible to go much lower.
At 10x earnings, you’re looking at roughly a ~$130 stock.
If HEYDUDE improves and multiples expand toward sector norms (~12x), that’s closer to ~$155.
Definitely one to keep an eye on.
tweet
$CROX earnings are pretty interesting.
We’re talking about a company guiding to ~5% EPS growth without growth, only on efficiency, and excluding buybacks after repurchasing ~10% of shares outstanding FY25.
We don’t know how much they’ll buy back in FY26, but you could reasonably model ~10% EPS growth on flat revenue while the stock already trades below peers with weaker financials and fundamentals.
Add to that:
🔹DTC growth - higher margins
🔹International acceleration
🔹HEYDUDE expected to perform better H2-26
🔹Strong brand presence
And you get yourself a real repricing candidate.
If HEYDUDE reaccelerates - and guidance suggests that’s possible with a -8% guidance after a ~-17% Q1, there’s little reason for $CROX to trade at such low multiples versus peers like Nike, which is expected to grow ~4% at best with lower margins.
Plus, risk from here is lower as you'd need worst than terrible to go much lower.
At 10x earnings, you’re looking at roughly a ~$130 stock.
If HEYDUDE improves and multiples expand toward sector norms (~12x), that’s closer to ~$155.
Definitely one to keep an eye on.
tweet