Offshore
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The Transcript
$ABNB CEO: Airbnb’s defense against disintermediation is focusing on what AI can’t replicate
"A chatbot can give you a list of homes, but it can't give you the unique ones you find on Airbnb..." https://t.co/5lwQ6BVXcD
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$ABNB CEO: Airbnb’s defense against disintermediation is focusing on what AI can’t replicate
"A chatbot can give you a list of homes, but it can't give you the unique ones you find on Airbnb..." https://t.co/5lwQ6BVXcD
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The Transcript
RT @TheTranscript_: Microsoft commits to building frontier in-house foundationreducing OpenAI dependence
"We have to develop our own foundation models, which are at the absolute frontier, with gigawatt-scale compute and some of the very best AI training teams in the world" - $MSFT AI chief
[FT]
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RT @TheTranscript_: Microsoft commits to building frontier in-house foundationreducing OpenAI dependence
"We have to develop our own foundation models, which are at the absolute frontier, with gigawatt-scale compute and some of the very best AI training teams in the world" - $MSFT AI chief
[FT]
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Offshore
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DAIR.AI
// Improving Efficiency of Evolutionary AI Agents //
Evolutionary AI agents are powerful but can be wasteful.
Systems, inspired by AlphaEvolve and OpenEvolve, iteratively generate, mutate, and refine candidate solutions using LLMs. However, every refinement step invokes the same large model regardless of task difficulty.
Most mutations don't need a 32B model.
This new research introduces AdaptEvolve, a framework that dynamically selects which model handles each evolutionary step based on intrinsic generation confidence.
Instead of routing everything through the largest available model, a lightweight decision tree router estimates whether the small model's output is sufficient or needs escalation.
The confidence signal comes from four entropy-based metrics computed on the small model's token probabilities: Mean Confidence for global assurance, Lowest Group Confidence for localized reasoning collapses, Tail Confidence for solution stability, and Bottom-K% Confidence for distinguishing noise from systematic hallucination.
A shallow decision tree, bootstrapped from just 50 warm-up examples, uses these signals to make real-time routing decisions.
What makes this practical?
The router adapts online. An Adaptive Hoeffding Tree continuously updates its decision boundaries as the evolutionary population drifts toward harder edge cases.
On LiveCodeBench, AdaptEvolve retains 97.9% of the 32B upper-bound accuracy (73.6% vs 75.2%) while cutting compute cost by 34.4%. On MBPP, the router identifies that 85% of queries are solvable by the 4B model alone, reducing cost by 41.5% while maintaining 97.1% of peak accuracy. Across benchmarks, the method reduces total inference compute by 37.9% while retaining 97.5% of the upper-bound performance.
Evolutionary agents don't need maximum capability at every step. Confidence-driven routing turns the cost-capability trade-off from a fixed choice into a dynamic, per-step decision.
Paper: https://t.co/YSNCKZuTeN
Learn to build effective AI Agents in our academy: https://t.co/LRnpZN7L4c
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// Improving Efficiency of Evolutionary AI Agents //
Evolutionary AI agents are powerful but can be wasteful.
Systems, inspired by AlphaEvolve and OpenEvolve, iteratively generate, mutate, and refine candidate solutions using LLMs. However, every refinement step invokes the same large model regardless of task difficulty.
Most mutations don't need a 32B model.
This new research introduces AdaptEvolve, a framework that dynamically selects which model handles each evolutionary step based on intrinsic generation confidence.
Instead of routing everything through the largest available model, a lightweight decision tree router estimates whether the small model's output is sufficient or needs escalation.
The confidence signal comes from four entropy-based metrics computed on the small model's token probabilities: Mean Confidence for global assurance, Lowest Group Confidence for localized reasoning collapses, Tail Confidence for solution stability, and Bottom-K% Confidence for distinguishing noise from systematic hallucination.
A shallow decision tree, bootstrapped from just 50 warm-up examples, uses these signals to make real-time routing decisions.
What makes this practical?
The router adapts online. An Adaptive Hoeffding Tree continuously updates its decision boundaries as the evolutionary population drifts toward harder edge cases.
On LiveCodeBench, AdaptEvolve retains 97.9% of the 32B upper-bound accuracy (73.6% vs 75.2%) while cutting compute cost by 34.4%. On MBPP, the router identifies that 85% of queries are solvable by the 4B model alone, reducing cost by 41.5% while maintaining 97.1% of peak accuracy. Across benchmarks, the method reduces total inference compute by 37.9% while retaining 97.5% of the upper-bound performance.
Evolutionary agents don't need maximum capability at every step. Confidence-driven routing turns the cost-capability trade-off from a fixed choice into a dynamic, per-step decision.
Paper: https://t.co/YSNCKZuTeN
Learn to build effective AI Agents in our academy: https://t.co/LRnpZN7L4c
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Offshore
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God of Prompt
RT @godofprompt: CLAUDE IS SO COOKED THIS TIME
China just dropped Kimi K2.5, the best open model for OpenClaw(ClawdBot)
It's on par with Claude Opus4.5,
but 8x CHEAPER!!!
It's currently the #1 most used model for OpenClaw and the #1 most used model overall on OpenRouter!
Here's everything you should know:
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RT @godofprompt: CLAUDE IS SO COOKED THIS TIME
China just dropped Kimi K2.5, the best open model for OpenClaw(ClawdBot)
It's on par with Claude Opus4.5,
but 8x CHEAPER!!!
It's currently the #1 most used model for OpenClaw and the #1 most used model overall on OpenRouter!
Here's everything you should know:
tweet
Offshore
Video
DAIR.AI
RT @omarsar0: Just incredible that this is possible today.
One of my favorite MCP tools as of late.
Just prompt to generate beautiful excalidraw diagrams. https://t.co/YgH57NOAoT
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RT @omarsar0: Just incredible that this is possible today.
One of my favorite MCP tools as of late.
Just prompt to generate beautiful excalidraw diagrams. https://t.co/YgH57NOAoT
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Offshore
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Fiscal.ai
Amazon's e-commerce business is more profitable than it has ever been.
9% Operating Margins in North America.
How much more profitable can this segment get?
$AMZN https://t.co/ZOmpWA5z55
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Amazon's e-commerce business is more profitable than it has ever been.
9% Operating Margins in North America.
How much more profitable can this segment get?
$AMZN https://t.co/ZOmpWA5z55
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Offshore
Video
Illiquid
Ben offers a great intro to Taiwan. I also agree that Substacks should be bundled or rolled up, and tried to do it myself.
Interestingly, at close to the 6 month mark, I’m only slightly behind Ben when he first started, despite having a much smaller target audience. I have two weeks to catch him!
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Ben offers a great intro to Taiwan. I also agree that Substacks should be bundled or rolled up, and tried to do it myself.
Interestingly, at close to the 6 month mark, I’m only slightly behind Ben when he first started, despite having a much smaller target audience. I have two weeks to catch him!
The tech world closely reads @benthompson's Stratechery, and for good reason. On Cheeky Pint, we discuss ads coming to AI, the Saaspocalypse, TSMC, and the media business. (Plus: how to visit Taiwan.)
00:00:20 Visiting Taiwan
00:04:59 Aggregation and AI
00:23:53 TikTok/Bytedance
00:29:58 Aggregation and AI redux
00:35:31 Agentic commerce
00:45:08 Is SaaS canceled?
00:52:21 Stratechery
01:03:36 How Ben uses AI
01:06:06 The TSMC break
01:13:53 Rapid fire
01:20:53 Feedback on Stripe tweet
Offshore
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Pristine Capital
RT @realpristinecap: A massive regime change is underway in 2026🔄
For the first time in years, the "Growth at any price" trade is taking a backseat. We are seeing a powerful rotation into Value and Small Caps as market breadth expands 👇 https://t.co/W51cleqVR1
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RT @realpristinecap: A massive regime change is underway in 2026🔄
For the first time in years, the "Growth at any price" trade is taking a backseat. We are seeing a powerful rotation into Value and Small Caps as market breadth expands 👇 https://t.co/W51cleqVR1
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Offshore
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App Economy Insights
📊 This Week in Visuals
$CSCO $SHOP $AMAT $AZN $KO $MCD $TMUS $ANET $APP $NTES $HOOD $ABNB $MAR $HLT $HERMES $RACE $F $NET $DDOG $COIN $ADYEY $FISV $KHC $EXPE $QSR $TWLO $TOST $Z $DKNG $PINS $HUBS $LYFT $KVYO $MNDY
https://t.co/ZAyrJNoouN
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📊 This Week in Visuals
$CSCO $SHOP $AMAT $AZN $KO $MCD $TMUS $ANET $APP $NTES $HOOD $ABNB $MAR $HLT $HERMES $RACE $F $NET $DDOG $COIN $ADYEY $FISV $KHC $EXPE $QSR $TWLO $TOST $Z $DKNG $PINS $HUBS $LYFT $KVYO $MNDY
https://t.co/ZAyrJNoouN
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Offshore
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App Economy Insights
🗓️ What are you watching this week?
• Tuesday: $PANW $CDNS
• Wednesday: $ADI $MCO $FIG $BKNG $GPN
• Thursday: $MELI $NU $WMT $BABA $KLAR $LYV
All visualized in our newsletter! https://t.co/aALlk4Yk2n
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🗓️ What are you watching this week?
• Tuesday: $PANW $CDNS
• Wednesday: $ADI $MCO $FIG $BKNG $GPN
• Thursday: $MELI $NU $WMT $BABA $KLAR $LYV
All visualized in our newsletter! https://t.co/aALlk4Yk2n
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Moon Dev
The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail
most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day
i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies
the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you
i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night
the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand
this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once
when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market
you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery
most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world [...]
The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail
most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day
i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies
the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you
i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night
the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand
this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once
when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market
you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery
most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world [...]