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Moon Dev
claude code can actually help you automate your trading in 2026
spolier: it still cant just trade for you and make you a millionaire
but any trading trading strategy can now be tested then automated with claude code
https://t.co/Q5Z2eRanlV
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claude code can actually help you automate your trading in 2026
spolier: it still cant just trade for you and make you a millionaire
but any trading trading strategy can now be tested then automated with claude code
https://t.co/Q5Z2eRanlV
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Offshore
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Moon Dev
dont regret missing tomorrow
tomorrows private zoom is going to be a movie
if you miss this, i truly dont think you want to be a successful trader
join here before it sells out: https://t.co/JbJdIbW2p9
moon dev
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dont regret missing tomorrow
tomorrows private zoom is going to be a movie
if you miss this, i truly dont think you want to be a successful trader
join here before it sells out: https://t.co/JbJdIbW2p9
moon dev
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Offshore
Video
Startup Archive
Steve Jobs: The difference between good people and great people is 50-to-1
“I’ve always considered part of my job was to keep the quality level of people in the organizations I work with very high. I mean that’s what I consider one of the few things I can contribute individually myself — versus the team that work with — is to really try to instill in the organization the goal of having only A players.”
Steve argues this is especially important in technology where there’s a huge range between the best person and the worst person:
“In a lot of fields, the difference between, say, the worst taxicab driver and the best taxicab driver to get you across town in Manhattan might be 2-to-1. The best one will get you there in 15 minutes, the worst one will get you there in half an hour… Or the best cook and the worst cook, maybe it’s 3-to-1… But in the field that I’m in. In software in particular. The difference between the best person and the worst person is about 100-to-1 or more.”
He continues:
“The difference between a good software person and a great software person is probably 50-to-1 or 25-to-1. Huge dynamic range. And therefore, I have found — and not just in software but in almost everything I’ve done — it really pays to go after the best people in the world.”
But as Steve points out, this isn’t always easy:
“It’s very painful when you have some people that are not the best people in the world, and you have to get rid of them. But I’ve found that my job has sometimes been exactly that, to get rid of some of the people that didn’t measure up. And I’ve always tried to do it in a humane way, but nonetheless it has to be done and it’s not ever fun.”
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Steve Jobs: The difference between good people and great people is 50-to-1
“I’ve always considered part of my job was to keep the quality level of people in the organizations I work with very high. I mean that’s what I consider one of the few things I can contribute individually myself — versus the team that work with — is to really try to instill in the organization the goal of having only A players.”
Steve argues this is especially important in technology where there’s a huge range between the best person and the worst person:
“In a lot of fields, the difference between, say, the worst taxicab driver and the best taxicab driver to get you across town in Manhattan might be 2-to-1. The best one will get you there in 15 minutes, the worst one will get you there in half an hour… Or the best cook and the worst cook, maybe it’s 3-to-1… But in the field that I’m in. In software in particular. The difference between the best person and the worst person is about 100-to-1 or more.”
He continues:
“The difference between a good software person and a great software person is probably 50-to-1 or 25-to-1. Huge dynamic range. And therefore, I have found — and not just in software but in almost everything I’ve done — it really pays to go after the best people in the world.”
But as Steve points out, this isn’t always easy:
“It’s very painful when you have some people that are not the best people in the world, and you have to get rid of them. But I’ve found that my job has sometimes been exactly that, to get rid of some of the people that didn’t measure up. And I’ve always tried to do it in a humane way, but nonetheless it has to be done and it’s not ever fun.”
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God of Prompt
Google just bought emotional intelligence for Gemini.
Not through an algorithm upgrade. Through humans who spent 10 years mapping how emotions actually work in speech.
What Hume AI built is different from anything else in voice AI right now. Their Empathic Voice Interface doesn’t just speak with emotion, it analyzes your vocal tones to pick up on emotional cues. 
Pitch variations. Speech rate changes. Pauses. Vocal tension. The stuff you notice instinctively when your friend is upset on the phone before they say anything.
Current AI assistants are emotionally deaf. You say “I need help NOW” through clenched teeth and Siri cheerfully chirps back. That mismatch is infuriating.
Hume’s system adapts its tone based on your emotional state, responding with calm when you’re frustrated instead of ignoring the context entirely. 
Alan Cowen (now joining DeepMind) pioneered this field. His models are built on 10+ years of research into semantic space theory. 
The tech was trained on millions of human interactions to map specific vocal patterns to emotional states. 
What Google actually acquired: the ability to make Gemini feel less like talking to a wall.
This follows their $3B Character AI licensing play last year, same pattern. They’re not building frontier capabilities in-house anymore. They’re buying the teams who cracked the hardest problems.
Voice is quickly becoming how most people interact with AI. The companies that figure out emotional nuance win. Everyone else builds expensive parrots that process words but miss everything that makes conversation human.
tweet
Google just bought emotional intelligence for Gemini.
Not through an algorithm upgrade. Through humans who spent 10 years mapping how emotions actually work in speech.
What Hume AI built is different from anything else in voice AI right now. Their Empathic Voice Interface doesn’t just speak with emotion, it analyzes your vocal tones to pick up on emotional cues. 
Pitch variations. Speech rate changes. Pauses. Vocal tension. The stuff you notice instinctively when your friend is upset on the phone before they say anything.
Current AI assistants are emotionally deaf. You say “I need help NOW” through clenched teeth and Siri cheerfully chirps back. That mismatch is infuriating.
Hume’s system adapts its tone based on your emotional state, responding with calm when you’re frustrated instead of ignoring the context entirely. 
Alan Cowen (now joining DeepMind) pioneered this field. His models are built on 10+ years of research into semantic space theory. 
The tech was trained on millions of human interactions to map specific vocal patterns to emotional states. 
What Google actually acquired: the ability to make Gemini feel less like talking to a wall.
This follows their $3B Character AI licensing play last year, same pattern. They’re not building frontier capabilities in-house anymore. They’re buying the teams who cracked the hardest problems.
Voice is quickly becoming how most people interact with AI. The companies that figure out emotional nuance win. Everyone else builds expensive parrots that process words but miss everything that makes conversation human.
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Dimitry Nakhla | Babylon Capital®
RT @DimitryNakhla: Chris Hohn, founder of TCI, views high incremental margins as one of the strongest signals of a company’s moat & pricing power💸
Incremental operating margin tells you how much additional operating income a business generates for every additional $1 of revenue.
Here’s how to calculate it:
Pick two periods (Year 1 → Year 2)
Year 1 Revenue: $10B
Year 2 Revenue: $12B
Δ 𝐂𝐡𝐚𝐧𝐠𝐞: $𝟐𝐁
Year 1 Operating Income: $2B
Year 2 Operating Income: $3B
Δ 𝐂𝐡𝐚𝐧𝐠𝐞: $𝟏𝐁
𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐌𝐚𝐫𝐠𝐢𝐧 = Δ 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐈𝐧𝐜𝐨𝐦𝐞 / Δ 𝐑𝐞𝐯𝐞𝐧𝐮𝐞
So: $1B / $2B = 50%
𝘔𝘦𝘢𝘯𝘪𝘯𝘨: 50 cents of every new $1 of revenue fell to operating profit.
___
Why this matters: High incremental margins usually signal low incremental costs, pricing power, & structural operating leverage — the traits that allow a great business to compound faster as it scales. It’s one of the cleanest ways to see whether a company’s moat is strengthening & efficiently scaling.
I’ve included 4 high-quality stocks with their incremental operating margins since 2021 for further example 👇🏽
$FICO $MSFT $MA $NFLX
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RT @DimitryNakhla: Chris Hohn, founder of TCI, views high incremental margins as one of the strongest signals of a company’s moat & pricing power💸
Incremental operating margin tells you how much additional operating income a business generates for every additional $1 of revenue.
Here’s how to calculate it:
Pick two periods (Year 1 → Year 2)
Year 1 Revenue: $10B
Year 2 Revenue: $12B
Δ 𝐂𝐡𝐚𝐧𝐠𝐞: $𝟐𝐁
Year 1 Operating Income: $2B
Year 2 Operating Income: $3B
Δ 𝐂𝐡𝐚𝐧𝐠𝐞: $𝟏𝐁
𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐌𝐚𝐫𝐠𝐢𝐧 = Δ 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐈𝐧𝐜𝐨𝐦𝐞 / Δ 𝐑𝐞𝐯𝐞𝐧𝐮𝐞
So: $1B / $2B = 50%
𝘔𝘦𝘢𝘯𝘪𝘯𝘨: 50 cents of every new $1 of revenue fell to operating profit.
___
Why this matters: High incremental margins usually signal low incremental costs, pricing power, & structural operating leverage — the traits that allow a great business to compound faster as it scales. It’s one of the cleanest ways to see whether a company’s moat is strengthening & efficiently scaling.
I’ve included 4 high-quality stocks with their incremental operating margins since 2021 for further example 👇🏽
$FICO $MSFT $MA $NFLX
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Fiscal.ai
TSMC management expects to grow revenue at a 25% CAGR from 2024 to 2029.
Why wouldn't this work from here?
$TSMC https://t.co/vcgwlDoFIA
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TSMC management expects to grow revenue at a 25% CAGR from 2024 to 2029.
Why wouldn't this work from here?
$TSMC https://t.co/vcgwlDoFIA
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Startup Archive
RT @foundertribune: "Good Products Are Hard to Vary" by Naval Ravikant https://t.co/K32IpwivHH
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RT @foundertribune: "Good Products Are Hard to Vary" by Naval Ravikant https://t.co/K32IpwivHH
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