Michael Fritzell (Asian Century Stocks)
RT @nitinkinvest: I'm not entirely sure, but I've been monitoring this for about six months. There seems to be sudden notification fatigue that people are talking about. The analog watch does its job and respects boundaries.
I think smartwatch pricing is another factor. An analog watch is like permanent jewelry, whereas one has to continually upgrade to the latest Garmin. We've seen some switching to lower-priced smartwatches like the Zepp Helio band.
Quiet luxury and old money trends are also driving a pivot toward expensive analog watches as well I feel. Personally purchased an analog watch for the first time in 5 years.
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RT @nitinkinvest: I'm not entirely sure, but I've been monitoring this for about six months. There seems to be sudden notification fatigue that people are talking about. The analog watch does its job and respects boundaries.
I think smartwatch pricing is another factor. An analog watch is like permanent jewelry, whereas one has to continually upgrade to the latest Garmin. We've seen some switching to lower-priced smartwatches like the Zepp Helio band.
Quiet luxury and old money trends are also driving a pivot toward expensive analog watches as well I feel. Personally purchased an analog watch for the first time in 5 years.
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God of Prompt
RT @godofprompt: 🚨 Holy shit… Stanford just published a paper that questions whether we even need humans to study humans.
The title sounds like a joke:
“This human study did not involve human subjects.”
But it’s dead serious.
The researchers are asking a controversial question:
Can LLM simulations count as behavioral evidence?
Here’s the core idea.
Instead of recruiting thousands of participants, running surveys, and waiting weeks for results, they simulate people using large language models.
Not generic prompts.
But structured simulations where the model is assigned demographic traits, preferences, beliefs, and contextual constraints.
Then they test whether the simulated responses statistically match real-world human data.
And disturbingly… they often do.
Across multiple behavioral tasks, the LLM-generated “participants” reproduced known human patterns:
• Established psychological biases
• Preference distributions
• Decision-making trends
• Even demographic splits
Not perfectly. Not universally.
But far closer than most people would expect.
The key contribution of the paper isn’t “LLMs are human.”
It’s validation.
They systematically compare simulated outputs to ground-truth human datasets and evaluate alignment using statistical benchmarks.
When the distributions match, the simulation isn’t just storytelling.
It becomes empirical evidence.
That’s the uncomfortable shift.
If a sufficiently constrained LLM simulation reproduces real behavioral patterns, does it become a legitimate experimental proxy?
Because if the answer is yes, this changes everything:
• Behavioral economics
• Political science
• Market research
• Policy testing
• UX experimentation
You could prototype social interventions before deploying them in the real world.
You could stress-test messaging strategies across simulated demographics.
You could explore rare edge-case populations without recruitment bottlenecks.
But here’s where Stanford is careful.
The models don’t “understand” humans.
They reflect training data patterns.
They can amplify biases.
They can collapse under distribution shift.
And they can simulate plausibility without causality.
So the paper doesn’t claim replacement.
It argues for calibration.
LLM simulations can be useful behavioral instruments if validated against real data and bounded within known limits.
That’s the distinction.
Not synthetic humans.
Synthetic behavioral priors.
The wild part?
This paper forces academia to confront something bigger:
If large models encode large-scale behavioral regularities from the internet, they become compressed maps of human tendencies.
Not minds.
Maps.
And maps can be useful.
We’re moving from “AI as text generator” to “AI as behavioral simulator.”
The ethics, methodology, and epistemology implications are massive.
Because once simulation becomes statistically reliable, the bottleneck in social science shifts from data collection to model alignment.
And that might be the real revolution hidden in this paper.
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RT @godofprompt: 🚨 Holy shit… Stanford just published a paper that questions whether we even need humans to study humans.
The title sounds like a joke:
“This human study did not involve human subjects.”
But it’s dead serious.
The researchers are asking a controversial question:
Can LLM simulations count as behavioral evidence?
Here’s the core idea.
Instead of recruiting thousands of participants, running surveys, and waiting weeks for results, they simulate people using large language models.
Not generic prompts.
But structured simulations where the model is assigned demographic traits, preferences, beliefs, and contextual constraints.
Then they test whether the simulated responses statistically match real-world human data.
And disturbingly… they often do.
Across multiple behavioral tasks, the LLM-generated “participants” reproduced known human patterns:
• Established psychological biases
• Preference distributions
• Decision-making trends
• Even demographic splits
Not perfectly. Not universally.
But far closer than most people would expect.
The key contribution of the paper isn’t “LLMs are human.”
It’s validation.
They systematically compare simulated outputs to ground-truth human datasets and evaluate alignment using statistical benchmarks.
When the distributions match, the simulation isn’t just storytelling.
It becomes empirical evidence.
That’s the uncomfortable shift.
If a sufficiently constrained LLM simulation reproduces real behavioral patterns, does it become a legitimate experimental proxy?
Because if the answer is yes, this changes everything:
• Behavioral economics
• Political science
• Market research
• Policy testing
• UX experimentation
You could prototype social interventions before deploying them in the real world.
You could stress-test messaging strategies across simulated demographics.
You could explore rare edge-case populations without recruitment bottlenecks.
But here’s where Stanford is careful.
The models don’t “understand” humans.
They reflect training data patterns.
They can amplify biases.
They can collapse under distribution shift.
And they can simulate plausibility without causality.
So the paper doesn’t claim replacement.
It argues for calibration.
LLM simulations can be useful behavioral instruments if validated against real data and bounded within known limits.
That’s the distinction.
Not synthetic humans.
Synthetic behavioral priors.
The wild part?
This paper forces academia to confront something bigger:
If large models encode large-scale behavioral regularities from the internet, they become compressed maps of human tendencies.
Not minds.
Maps.
And maps can be useful.
We’re moving from “AI as text generator” to “AI as behavioral simulator.”
The ethics, methodology, and epistemology implications are massive.
Because once simulation becomes statistically reliable, the bottleneck in social science shifts from data collection to model alignment.
And that might be the real revolution hidden in this paper.
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Offshore
Video
Startup Archive
Jensen Huang explains his decision to start NVIDIA as a parent with young children
Jensen was 30 years old when he quit his job at LSI Logic to co-found NVIDIA in 1993. Asked how he made this decision as a young parent at the time, he responds:
“I believed in [my co-founders], and I believed in myself… Even though we had a family and our kids were young — they were just one and two — and that could cause us to be quite risk averse, I was never concerned about being able to do something else if it didn’t work out. And so I felt like I wasn’t risking anything. Maybe that’s too careless by some other standards, but I really believed it. I believed that we weren’t putting our family in harm’s way. And if things didn’t work out, there’ll be an even better job for me somewhere, someday… Lori and I were young and it wasn’t a decision that was difficult per se. It was probably even less than a dinner conversation. Maybe even less than that.”
Jensen offers the following advice to the Berkeley students in the audience:
“All of you are young and bright, and there’s so much opportunity out there. I genuinely don’t believe that when you make a decision to start a company or join a startup that it’s a horribly difficult life decision. The only thing that really matters, in my estimation, is are you going to love the people that you work with? Are you going to love the work that you’re going to do? Are you going to love it so much that all the pain and suffering that’s going to come your way — which I promise you will be lots: setbacks, disappointments, the list of bad days — you’ll be able to keep carrying on. So long as you love the work that you do, you’ll be able to keep carrying on. That’s really it. That’s 100% of the wisdom.”
Video source: @BerkeleyHaas (2023)
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Jensen Huang explains his decision to start NVIDIA as a parent with young children
Jensen was 30 years old when he quit his job at LSI Logic to co-found NVIDIA in 1993. Asked how he made this decision as a young parent at the time, he responds:
“I believed in [my co-founders], and I believed in myself… Even though we had a family and our kids were young — they were just one and two — and that could cause us to be quite risk averse, I was never concerned about being able to do something else if it didn’t work out. And so I felt like I wasn’t risking anything. Maybe that’s too careless by some other standards, but I really believed it. I believed that we weren’t putting our family in harm’s way. And if things didn’t work out, there’ll be an even better job for me somewhere, someday… Lori and I were young and it wasn’t a decision that was difficult per se. It was probably even less than a dinner conversation. Maybe even less than that.”
Jensen offers the following advice to the Berkeley students in the audience:
“All of you are young and bright, and there’s so much opportunity out there. I genuinely don’t believe that when you make a decision to start a company or join a startup that it’s a horribly difficult life decision. The only thing that really matters, in my estimation, is are you going to love the people that you work with? Are you going to love the work that you’re going to do? Are you going to love it so much that all the pain and suffering that’s going to come your way — which I promise you will be lots: setbacks, disappointments, the list of bad days — you’ll be able to keep carrying on. So long as you love the work that you do, you’ll be able to keep carrying on. That’s really it. That’s 100% of the wisdom.”
Video source: @BerkeleyHaas (2023)
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Offshore
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DAIR.AI
RT @omarsar0: How good are AI agents at long-horizon CLI programming?
Not very. Leading agents succeed less than 20% of the time.
LongCLI-Bench introduces a benchmark of 20 complex tasks spanning building from scratch, adding features, fixing bugs, and refactoring code, all executed through command-line interfaces.
Failures typically occur early in task execution. Self-correction provides minimal improvement.
But human-agent collaboration through plan guidance and interactive input substantially enhances performance.
Why does it matter?
The benchmark highlights that for real-world programming tasks, the path forward isn't fully autonomous agents. It's human-agent collaboration with structured oversight.
Paper: https://t.co/oTNUEnvb1j
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
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RT @omarsar0: How good are AI agents at long-horizon CLI programming?
Not very. Leading agents succeed less than 20% of the time.
LongCLI-Bench introduces a benchmark of 20 complex tasks spanning building from scratch, adding features, fixing bugs, and refactoring code, all executed through command-line interfaces.
Failures typically occur early in task execution. Self-correction provides minimal improvement.
But human-agent collaboration through plan guidance and interactive input substantially enhances performance.
Why does it matter?
The benchmark highlights that for real-world programming tasks, the path forward isn't fully autonomous agents. It's human-agent collaboration with structured oversight.
Paper: https://t.co/oTNUEnvb1j
Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX
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Offshore
Video
Brady Long
It’s been a day and I still can’t believe this is real.
I was getting tired of all these bullsh*t AI tools.
We don’t need more dashboards.
We need fewer steps between idea → execution.
That’s exactly what Lemon does... https://t.co/S3Mo04dgLC
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It’s been a day and I still can’t believe this is real.
I was getting tired of all these bullsh*t AI tools.
We don’t need more dashboards.
We need fewer steps between idea → execution.
That’s exactly what Lemon does... https://t.co/S3Mo04dgLC
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
Offshore
Video
Moon Dev
now my 6 opus's and i will be monitored by an ai agent who will cook up projects
and send those projects will be executed autonomously by my 5 openclaws
things are accelerating... https://t.co/kkdgT739S1
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now my 6 opus's and i will be monitored by an ai agent who will cook up projects
and send those projects will be executed autonomously by my 5 openclaws
things are accelerating... https://t.co/kkdgT739S1
tweet
Offshore
Photo
Jukan
"Targeting 50% Semiconductor Operating Margin"… Samsung Restructures Product Portfolio
Samsung Electronics' DS (Device Solutions) division, which oversees the semiconductor business, is restructuring its product portfolio with a target of achieving operating margins above 50%. This is a strategic move to dramatically improve profitability, which had been sluggish through the first half of last year, with efforts to maximize operating profit expected to continue in line with rising memory prices.
According to industry sources on the 18th, Samsung Electronics' DS division has decided to pursue a product portfolio strategy aimed at substantially boosting profitability. The core objective is securing a 50% operating margin. The essence of the plan is to shift the center of gravity in production and sales toward products with operating margins of 50% or above.
This is a highly unusual undertaking. Many companies employ product mix strategies that optimize by segment and compose portfolios around high-value-added products. However, setting a specific operating margin target signals the need for an aggressive overhaul of product strategy.
An industry insider familiar with the matter said, "The focus will be on concentrating capabilities around high-margin products, with products below 50% operating margin seeing production cuts or exclusion from the core portfolio. Production line adjustments and sales strategy revisions could follow accordingly."
Attention is on whether Samsung's 1c DRAM — the 10nm-class 6th-generation DRAM for which the company is aggressively ramping capacity — will become a prime example. Final product allocation could vary depending on yield and profitability.
Yield — the proportion of usable chips from production — is directly tied to operating profit. Higher yields mean higher operating margins, but until stable yields are secured, it is critical to use product mix strategies to lift profitability.
The 1c DRAM yield is currently reported at around 60%, still short of the stable range of 80–90%. As a result, DRAM allocation is expected to be differentiated by application — HBM, server, smartphone, and PC.
Server DRAM currently commands operating margins above 50%, driven by strong demand. HBM, on the other hand, is expected to carry lower margins for now when factoring in finished-product yields. The likely approach is to prioritize server DRAM supply to boost profitability first, then increase HBM production weighting once yields stabilize.
Another industry source noted, "Given that Samsung's DRAM and HBM supply volumes could be adjusted, the market impact will be significant," adding that "price changes will be inevitable depending on supply volumes."
For NAND, production is expected to shift toward the latest, most profitable products — primarily 8th-generation (V8) and 9th-generation (V9) NAND. Samsung is currently ramping V8 NAND utilization rates while accelerating the transition to V9 to expand production capacity. Conversion investment from legacy generations to V9 is progressing rapidly.
In the System LSI (foundry) segment, the company is expected to focus on securing orders at the 4nm, 5nm, and 8nm nodes, where stable yields and assured profitability can be achieved. For the leading-edge 2nm node, the near-term priority is expected to be stabilizing yields and advancing the technology.
This strategic push is interpreted as an effort to reverse the operating profit decline that persisted through the first half of last year. Samsung recorded single-digit operating margins through Q2 last year. While margins began recovering from Q3, they still trail SK Hynix — Samsung's Q4 operating margin was 37.27% versus SK Hynix's 58.39%.
On top of this, the strategy incorporates efforts to maximize the earnings structure in response to the semiconductor supercycle. With AI infrastructure investment expanding, memory supply is failing to keep pace with demand, keeping prices on a sustained upward trajectory. Anal[...]
"Targeting 50% Semiconductor Operating Margin"… Samsung Restructures Product Portfolio
Samsung Electronics' DS (Device Solutions) division, which oversees the semiconductor business, is restructuring its product portfolio with a target of achieving operating margins above 50%. This is a strategic move to dramatically improve profitability, which had been sluggish through the first half of last year, with efforts to maximize operating profit expected to continue in line with rising memory prices.
According to industry sources on the 18th, Samsung Electronics' DS division has decided to pursue a product portfolio strategy aimed at substantially boosting profitability. The core objective is securing a 50% operating margin. The essence of the plan is to shift the center of gravity in production and sales toward products with operating margins of 50% or above.
This is a highly unusual undertaking. Many companies employ product mix strategies that optimize by segment and compose portfolios around high-value-added products. However, setting a specific operating margin target signals the need for an aggressive overhaul of product strategy.
An industry insider familiar with the matter said, "The focus will be on concentrating capabilities around high-margin products, with products below 50% operating margin seeing production cuts or exclusion from the core portfolio. Production line adjustments and sales strategy revisions could follow accordingly."
Attention is on whether Samsung's 1c DRAM — the 10nm-class 6th-generation DRAM for which the company is aggressively ramping capacity — will become a prime example. Final product allocation could vary depending on yield and profitability.
Yield — the proportion of usable chips from production — is directly tied to operating profit. Higher yields mean higher operating margins, but until stable yields are secured, it is critical to use product mix strategies to lift profitability.
The 1c DRAM yield is currently reported at around 60%, still short of the stable range of 80–90%. As a result, DRAM allocation is expected to be differentiated by application — HBM, server, smartphone, and PC.
Server DRAM currently commands operating margins above 50%, driven by strong demand. HBM, on the other hand, is expected to carry lower margins for now when factoring in finished-product yields. The likely approach is to prioritize server DRAM supply to boost profitability first, then increase HBM production weighting once yields stabilize.
Another industry source noted, "Given that Samsung's DRAM and HBM supply volumes could be adjusted, the market impact will be significant," adding that "price changes will be inevitable depending on supply volumes."
For NAND, production is expected to shift toward the latest, most profitable products — primarily 8th-generation (V8) and 9th-generation (V9) NAND. Samsung is currently ramping V8 NAND utilization rates while accelerating the transition to V9 to expand production capacity. Conversion investment from legacy generations to V9 is progressing rapidly.
In the System LSI (foundry) segment, the company is expected to focus on securing orders at the 4nm, 5nm, and 8nm nodes, where stable yields and assured profitability can be achieved. For the leading-edge 2nm node, the near-term priority is expected to be stabilizing yields and advancing the technology.
This strategic push is interpreted as an effort to reverse the operating profit decline that persisted through the first half of last year. Samsung recorded single-digit operating margins through Q2 last year. While margins began recovering from Q3, they still trail SK Hynix — Samsung's Q4 operating margin was 37.27% versus SK Hynix's 58.39%.
On top of this, the strategy incorporates efforts to maximize the earnings structure in response to the semiconductor supercycle. With AI infrastructure investment expanding, memory supply is failing to keep pace with demand, keeping prices on a sustained upward trajectory. Anal[...]
Offshore
Jukan "Targeting 50% Semiconductor Operating Margin"… Samsung Restructures Product Portfolio Samsung Electronics' DS (Device Solutions) division, which oversees the semiconductor business, is restructuring its product portfolio with a target of achieving…
ysts note that the goal is to maximize profitability by reshaping the product portfolio while demand remains strong.
The new strategy is expected to remain effective through this year. The industry expects memory prices to continue rising through year-end. Signs of a moderation in the uptrend are unlikely to emerge until after the new manufacturing lines currently being prepared by memory makers come fully online toward year-end.
A Samsung Electronics spokesperson said, "We plan to pursue a product portfolio strategy to enhance profitability, but we cannot confirm specific figures."
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The new strategy is expected to remain effective through this year. The industry expects memory prices to continue rising through year-end. Signs of a moderation in the uptrend are unlikely to emerge until after the new manufacturing lines currently being prepared by memory makers come fully online toward year-end.
A Samsung Electronics spokesperson said, "We plan to pursue a product portfolio strategy to enhance profitability, but we cannot confirm specific figures."
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