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Jukan
Evercore: Vera Rubin timeline pulled forward!!
Some sources believe that China export bans have enabled NVDA to leverage suppliers that previously served China-bound volumes for global product development, pulling Rubin's schedule forward by 3-6 months.
Some would even not be surprised if Rubin shipments begin by end of 2Q26.
$NVDA
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Evercore: Vera Rubin timeline pulled forward!!
Some sources believe that China export bans have enabled NVDA to leverage suppliers that previously served China-bound volumes for global product development, pulling Rubin's schedule forward by 3-6 months.
Some would even not be surprised if Rubin shipments begin by end of 2Q26.
$NVDA
Evercore out with a 4Q25 AI Channel Checks, saying $NVDA Vera Rubin appears to be ahead of schedule https://t.co/CqWOUnl5Q8 tweet
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Benjamin Hernandez😎
Office property delinquencies hit a record high last month, deepening stress in commercial real estate. Banks and REITs could face renewed pressure as traders reassess credit risk exposure.
$KRE $SLG $VNO https://t.co/QZzi6ogjD1
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Office property delinquencies hit a record high last month, deepening stress in commercial real estate. Banks and REITs could face renewed pressure as traders reassess credit risk exposure.
$KRE $SLG $VNO https://t.co/QZzi6ogjD1
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The Transcript
RT @TheTranscript_: Apple is the chill guy of tech when it comes to capex
$AAPL CEO: "So in general, it's hard to read into the CapEx and draw any conclusions. And so I think I would just say there's going to be some ebbs and flows in CapEx. Last year, remember, we did build out our private cloud compute environment. And so we did have CapEx spending related to that in our results in December."
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RT @TheTranscript_: Apple is the chill guy of tech when it comes to capex
$AAPL CEO: "So in general, it's hard to read into the CapEx and draw any conclusions. And so I think I would just say there's going to be some ebbs and flows in CapEx. Last year, remember, we did build out our private cloud compute environment. And so we did have CapEx spending related to that in our results in December."
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Moon Dev
Don’t let all the other devs gaslight you
The only successful way to use openclaw is with Mac
99% of them haven’t done a direct comparison like I have
This is just the start of my farm https://t.co/GwSiSqZtki
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Don’t let all the other devs gaslight you
The only successful way to use openclaw is with Mac
99% of them haven’t done a direct comparison like I have
This is just the start of my farm https://t.co/GwSiSqZtki
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Javier Blas
The oil ministers of Iran and Russia met today.
Contrary to popular belief, Moscow and Tehran are now bitter rivals in the oil market as the size of the black market for crude shrinks. Both compete to supply China.
(My earlier @Opinion take is below)
https://t.co/lZ94rzxzKz
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The oil ministers of Iran and Russia met today.
Contrary to popular belief, Moscow and Tehran are now bitter rivals in the oil market as the size of the black market for crude shrinks. Both compete to supply China.
(My earlier @Opinion take is below)
https://t.co/lZ94rzxzKz
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God of Prompt
Voice-to-action is where the agent layer is heading.
Most people underestimate how much productivity leaks through input friction. Typing, tab switching, copy pasting between apps. None of that is actual work. It's overhead.
The teams solving that friction layer will own the next wave of AI productivity.
Same core principle we've built around for years: the quality of your input determines the quality of your output.
Worth watching.
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Voice-to-action is where the agent layer is heading.
Most people underestimate how much productivity leaks through input friction. Typing, tab switching, copy pasting between apps. None of that is actual work. It's overhead.
The teams solving that friction layer will own the next wave of AI productivity.
Same core principle we've built around for years: the quality of your input determines the quality of your output.
Worth watching.
Think it. Say it. Done.
The average person spends 3 hours typing + switches 1,000 tabs per day.
That ends today.
Meet Lemon: The first voice-to-action AI agent that turns your voice commands into finished tasks.
RT + Comment "Lemon" to get free access for 30 days.
(must be following so I can DM you) tweet
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Chips & SaaS
RT @StockSavvyShay: Evercore says $NVDA Vera Rubin timeline has been pulled forward by 3–6 months.
China export bans freed up supplier capacity, opening the door to shipments as early as end-Q2 2026. https://t.co/ruYBsSPhIW
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RT @StockSavvyShay: Evercore says $NVDA Vera Rubin timeline has been pulled forward by 3–6 months.
China export bans freed up supplier capacity, opening the door to shipments as early as end-Q2 2026. https://t.co/ruYBsSPhIW
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Benjamin Hernandez😎
Semi Sector: The Structural Support
While $MASI jumps on the Danaher acquisition, Big Tech is holding the index floor. Semiconductors remain the preferred vehicle for institutional capital flowing back into growth today.
$NVDA $MSFT $AMD $MU $PLTR
Watching the breakout. 💻🔥 https://t.co/qnOpuEmWJO
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Semi Sector: The Structural Support
While $MASI jumps on the Danaher acquisition, Big Tech is holding the index floor. Semiconductors remain the preferred vehicle for institutional capital flowing back into growth today.
$NVDA $MSFT $AMD $MU $PLTR
Watching the breakout. 💻🔥 https://t.co/qnOpuEmWJO
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DAIR.AI
Training tool-use agents with RL requires diverse, executable environments.
But these environments barely exist.
This new research introduces Agent World Model (AWM), a fully synthetic pipeline that generates executable agentic environments at scale. Starting from high-level scenario descriptions, it synthesizes database schemas, tool interfaces exposed via MCP, and verification code, all backed by SQL databases for consistent state transitions.
It presents 1,000 unique environments spanning finance, travel, retail, social media, and more, with 35,062 tools and 10,000 tasks paired with verification code. Each environment supports parallel isolated instances for large-scale RL.
Scalable, executable environment synthesis is the missing piece for agentic RL. AWM demonstrates that agents trained exclusively in synthetic environments can generalize to out-of-distribution real-world benchmarks.
Paper: https://t.co/Hp2PpGsBzw
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
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Training tool-use agents with RL requires diverse, executable environments.
But these environments barely exist.
This new research introduces Agent World Model (AWM), a fully synthetic pipeline that generates executable agentic environments at scale. Starting from high-level scenario descriptions, it synthesizes database schemas, tool interfaces exposed via MCP, and verification code, all backed by SQL databases for consistent state transitions.
It presents 1,000 unique environments spanning finance, travel, retail, social media, and more, with 35,062 tools and 10,000 tasks paired with verification code. Each environment supports parallel isolated instances for large-scale RL.
Scalable, executable environment synthesis is the missing piece for agentic RL. AWM demonstrates that agents trained exclusively in synthetic environments can generalize to out-of-distribution real-world benchmarks.
Paper: https://t.co/Hp2PpGsBzw
Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
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