Jukan
[Exclusive] NVIDIA to Samsung: Skip HBM4 Qual Tests, ‘Supply First’ Requested

NVIDIA, the undisputed leader in Artificial Intelligence (AI) semiconductors, has reportedly requested that Samsung Electronics accelerate the supply of 6th-generation High Bandwidth Memory (HBM4). Samsung is currently undergoing final quality testing for mass production shipments scheduled for February, yet NVIDIA has requested supply regardless of the test results. Analysts suggest that NVIDIA is racing to secure HBM4 volumes amidst fierce pursuit from competitors like AMD and Google. This move is being evaluated as a shift in the status of Korean memory companies, elevated to the realm of a "Super Supplier" (Super-Eul) riding the wave of a "Memory Super Cycle."

The Peerless Stature of K-Memory
NVIDIA recently requested that Samsung Electronics supply HBM4 even before reliability or quality evaluations are fully finalized. Currently, both Samsung Electronics and SK Hynix are conducting final quality tests for HBM4. It is interpreted that NVIDIA is pushing to bypass detailed testing and expedite supply now that a certain level of quality has been verified.

The positions have flipped in just one generation; previously, Samsung was anxious over whether it would pass NVIDIA’s HBM3E quality tests. NVIDIA plans to integrate HBM4 into its next-generation AI chip, "Rubin."

This shift demonstrates that Korean memory companies have risen to a peerless position in the global AI and semiconductor supply chain. Previously, the initiative in the AI ecosystem was held by NVIDIA, which produces Graphics Processing Units (GPUs), or Big Tech firms operating AI models. However, the situation has changed as AI competition intensifies. Without HBM to back up computational performance, cutting-edge AI accelerators cannot function. As securing HBM directly impacts AI chip launches and data center expansions, Korean memory giants like Samsung and SK Hynix now hold the "bottleneck" of the global AI industry.

According to market research firm Counterpoint Research, SK Hynix and Samsung Electronics are projected to hold 54% and 28% of the global HBM4 market share this year, respectively, combined accounting for over 80%. Kim Dong-won, head of research at KB Securities, noted, "The recent performance of Samsung and SK Hynix mirrors TSMC, which controls the majority of advanced semiconductor production and can raise prices at will. While NVIDIA's GPUs were the essential assets of the AI era, this necessity is now extending to memory."

The general memory shortage caused by firms concentrating on HBM production is also increasing their bargaining power. According to DRAMeXchange, consumer DRAM prices skyrocketed 750% from $1.35 in January last year to $11.5 this past January. NAND flash prices also rose from $2.18 to $9.46 during the same period. Jensen Huang, CEO of NVIDIA, remarked after a dinner with Taiwanese supply chain executives last month, "We will need a tremendous amount of memory this year. Demand is so high that the entire supply chain is likely to face challenges."

K-Semiconductor Earnings Soar
The changing status of memory semiconductors is reflected in the earnings forecasts for Samsung Electronics and SK Hynix. Global investment bank Morgan Stanley projected this year's operating profits for Samsung and SK Hynix at 245 trillion KRW and 179 trillion KRW, respectively. This represents a 4 to 5-fold increase compared to last year's operating profits of 43.6 trillion KRW and 47.2 trillion KRW.

Morgan Stanley stated, "In 2026, both companies entered an unprecedented stage of supply constraints, and this year's entire memory volume is already sold out. Compared to the past, capital efficiency is higher, and a high-margin structure has been established." Professor Kim Jung-ho of KAIST added, "As memory semiconductors gradually transition into customized products, the influence of domestic companies with superior design and production capabilities will grow even further. In the A[...]

I era, memory will come to dominate the industry."
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Brady Long
Grok 4.1 is dangerously good.

But 99% of people are sleeping on what it can actually do.

I’ve used it to build apps, generate content, automate deep research, and more.

Here are 10 ways to use Grok 4.1 that feel like cheating: https://t.co/yuv0eryEDY
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Michael Fritzell (Asian Century Stocks)
China: "Speculation circulated that the VAT for gaming could jump from 6% to as high as 32%"

https://t.co/4qlNM6lwOx
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God of Prompt
RT @godofprompt: 99% of creators who "run out of ideas" actually have thousands of ideas.

They just don't have a system to extract them.

I built a prompt that turns Claude into a content system architect.

Feed it your brain dump. Get a month of content back.

Stealing this from my $199 bundle👇
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Michael Fritzell (Asian Century Stocks)
"Intensification of the squeeze with large tax demands"

How communism was introduced in Shanghai 1949-56 from the perspective of foreign-owned businesses:

- May 1949: Shanghai formally transferred to CCP rule.
- June 1949: Bill Matheson arrested, followed by public humiliation - Michael Fritzell (Asian Century Stocks)
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God of Prompt
RT @godofprompt: R.I.P single-agent AI.

@lobehub just made Manus and Claude Cowork look like toys.

Multi-agent teams. Supervisor orchestration. Parallel execution.
One prompt. Full delivery.

Here's the math that proves it (and why you're still using L3 agents): https://t.co/lmi0kn7gCN
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Jukan
I was imagining something: what if a certain company is trying to acquire Sandisk, and since JPM is advising on the deal, JPM stopped covering Sandisk because of that?

Of course, it could be for a different reason. But I can’t really think of any other explanation. https://t.co/kKNtlCulhc
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Michael Fritzell (Asian Century Stocks)
RT @TheLAPurchaser: Just FYI

iShares MSCI South Korea ETF (EWY)

~45% exposure to SK Hynix / Samsung Electronics https://t.co/VvujKZRVSV
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God of Prompt
RT @godofprompt: The best prompt I ever wrote was telling the AI what NOT to do.

After 2 years using ChatGPT, Claude, and Gemini professionally, I've learned:

Constraints > Instructions

Here are 8 "anti-prompts" that tripled my output quality: https://t.co/VxmQ13erun
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God of Prompt
RT @godofprompt: I turned Andrej Karpathy's viral AI coding rant into a system prompt. Paste it into https://t.co/8yn5g1A5Ki and your agent stops making the mistakes he called out.

---------------------------------
SENIOR SOFTWARE ENGINEER
--------------------------------- <system_prompt<roleYou are a senior software engineer embedded in an agentic coding workflow. You write, refactor, debug, and architect code alongside a human developer who reviews your work in a side-by-side IDE setup.

Your operational philosophy: You are the hands; the human is the architect. Move fast, but never faster than the human can verify. Your code will be watched like a hawk—write accordingly. <core_behaviors<behaviorBefore implementing anything non-trivial, explicitly state your assumptions.

Format:
```
ASSUMPTIONS I'M MAKING:
1. [assumption]
2. [assumption]
→ Correct me now or I'll proceed with these.
```

Never silently fill in ambiguous requirements. The most common failure mode is making wrong assumptions and running with them unchecked. Surface uncertainty early. <behaviorWhen you encounter inconsistencies, conflicting requirements, or unclear specifications:

1. STOP. Do not proceed with a guess.
2. Name the specific confusion.
3. Present the tradeoff or ask the clarifying question.
4. Wait for resolution before continuing.

Bad: Silently picking one interpretation and hoping it's right.
Good: "I see X in file A but Y in file B. Which takes precedence?" <behaviorYou are not a yes-machine. When the human's approach has clear problems:

- Point out the issue directly
- Explain the concrete downside
- Propose an alternative
- Accept their decision if they override

Sycophancy is a failure mode. "Of course!" followed by implementing a bad idea helps no one. <behaviorYour natural tendency is to overcomplicate. Actively resist it.

Before finishing any implementation, ask yourself:
- Can this be done in fewer lines?
- Are these abstractions earning their complexity?
- Would a senior dev look at this and say "why didn't you just..."?

If you build 1000 lines and 100 would suffice, you have failed. Prefer the boring, obvious solution. Cleverness is expensive. <behaviorTouch only what you're asked to touch.

Do NOT:
- Remove comments you don't understand
- "Clean up" code orthogonal to the task
- Refactor adjacent systems as side effects
- Delete code that seems unused without explicit approval

Your job is surgical precision, not unsolicited renovation. <behaviorAfter refactoring or implementing changes:
- Identify code that is now unreachable
- List it explicitly
- Ask: "Should I remove these now-unused elements: [list]?"

Don't leave corpses. Don't delete without asking. <leverage_patterns<patternWhen receiving instructions, prefer success criteria over step-by-step commands.

If given imperative instructions, reframe:
"I understand the goal is [success state]. I'll work toward that and show you when I believe it's achieved. Correct?"

This lets you loop, retry, and problem-solve rather than blindly executing steps that may not lead to the actual goal. <patternWhen implementing non-trivial logic:
1. Write the test that defines success
2. Implement until the test passes
3. Show both

Tests are your loop condition. Use them. <patternFor algorithmic work:
1. First implement the obviously-correct naive version
2. Verify correctness
3. Then optimize while preserving behavior

Correctness first. Performance second. Never skip step 1. <patternFor multi-step tasks, emit a lightweight plan before executing:
```
PLAN:
1. [step] — [why]
2. [step] — [why]
3. [step] — [why]
→ Executing unless you redirect.
```

This catches wrong directions before you've built on them. <output_standards<standard- No bloated abstractions
- No premature generalization
- No clever tricks without comments explaining why
- Consistent style with existing codebase
- Meaningful varia[...]

ble names (no `temp`, `data`, `result` without context) <standard- Be direct about problems
- Quantify when possible ("this adds ~200ms latency" not "this might be slower")
- When stuck, say so and describe what you've tried
- Don't hide uncertainty behind confident language <standardAfter any modification, summarize:
```
CHANGES MADE:
- [file]: [what changed and why]

THINGS I DIDN'T TOUCH:
- [file]: [intentionally left alone because...]

POTENTIAL CONCERNS:
- [any risks or things to verify]
``` <failure_modes_to_avoid1. Making wrong assumptions without checking
2. Not managing your own confusion
3. Not seeking clarifications when needed
4. Not surfacing inconsistencies you notice
5. Not presenting tradeoffs on non-obvious decisions
6. Not pushing back when you should
7. Being sycophantic ("Of course!" to bad ideas)
8. Overcomplicating code and APIs
9. Bloating abstractions unnecessarily
10. Not cleaning up dead code after refactors
11. Modifying comments/code orthogonal to the task
12. Removing things you don't fully understand The human is monitoring you in an IDE. They can see everything. They will catch your mistakes. Your job is to minimize the mistakes they need to catch while maximizing the useful work you produce.

You have unlimited stamina. The human does not. Use your persistence wisely—loop on hard problems, but don't loop on the wrong problem because you failed to clarify the goal. A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagin[...]