Bears are back at it again claiming Apple buying Chinese memory chips will crush the memory industry and here's why they're completely wrong (Save this).

CXMT makes the kind of memory that goes in your iPhone and MacBook, standard, commodity chips that any consumer device needs.

The memory that actually matters for AI is something completely different.

It's called High-Bandwidth Memory, a highly specialized chip that is custom-engineered for AI servers and physically stacked directly on top of Nvidia GPUs.

It is one of the most technically complex products in the semiconductor industry, and only three companies on earth can produce it at scale.

CXMT is not one of them.

CXMT's most ambitious goal right now is to begin certifying HBM3, the previous generation for mass production by end of year.

The Big 3 are already shipping HBM3E, the generation after that, to hyperscaler customers today.

That gap doesn't close quickly. because HBM development requires years of co-engineering directly with customers like Nvidia, Google and Microsoft, embedded in their chip design process 18 to 24 months before a product ships.

CXMT has never shipped a single HBM chip to a Western hyperscaler.

They have no supply relationships, lack co-development history, and still do not have a certified product in the generation that actually matters.

When CXMT potentially wins some MacBook memory business from Apple, (very low probability of US approving this) it is not taking anything from the part of the memory market that actually drives valuations.

It is like worrying that a budget airline winning a short-haul route will hurt Boeing's order book for long-haul widebody jets.

They are different product, customer and market entirely.

Everyone thinks Apple buying cheap Chinese memory chips is a threat to Micron and the memory industry and it's not even close (Save this).

Dylan of Semi-Analysis is absolutely right and once you understand why, the whole narrative falls apart.

CXMT is not sitting on a warehouse full of unsold chips waiting for Apple to show up.

Every gigabyte it produces is already committed to Chinese domestic demand, phones, laptops and consumer electronics made and sold inside China.

There is no flood of cheap supply hitting the global market and there is no price war being waged against Samsung, SK Hynix or Micron.

Now look at the chart I attached from Semi-Analysis, from 2022 through mid-2025, all four companies CXMT, Samsung, SK Hynix and Micron were priced at roughly the same depressed level, around $0.20–0.50 per gigabyte.

That was the commodity DRAM downcycle, everyone was getting hurt equally bu then in late 2025, three of those lines broke sharply upward.

Micron, SK Hynix, and Samsung are all projected to reach $2.70–$2.90 per gigabyte by end of 2027.

CXMT is also rising but its green line tops out around $2.10, a full 25–30% below where the Big 3 are headed.

That gap is not a sign that CXMT is catching up but rather a sign that the Big 3 are selling a completely different and far more valuable product, High Bandwidth Memory, the specialized chips that go inside every Nvidia AI GPU.

HBM generates 3–5x more revenue per wafer than standard commodity DRAM, CXMT cannot make it at any meaningful scale.

So while Apple potentially buys some cheap commodity DRAM from a Chinese supplier, Micron is busy repricing its entire product mix into the highest margin memory product ever made.

Those are two completely separate stories and the market is confusing them.

Apple buying CXMT chips does not touch Micron's HBM revenue and on top of all of that, there is an extremely low probability the US government actually approves this.

CXMT is on the Pentagon's Chinese Military Company blacklist for alleged ties to the People's Liberation Army.

The Trump administration has been tightening semiconductor export controls throughout 2025 and 2026, Apple is lobbying hard, but the regulatory environment is working directly against them.

The most likely outcome is that this never gets approved at all.

$OKLO $CCJ $SMR $CEG $LEU | American Fusion prepares a five megawatt (MW) Texatron preproduction engine for independent testing following final component assembly.

The upcoming campaign aims to gather verified scientific diagnostic data under Texas state regulatory oversight.

$MU $SNDK $WDC $INTC $ASML | Sources report that China's CXMT secures a multiyear server memory supply contract with Tencent worth around $3 billion.

The massive chip pipeline stabilizes essential component access for cloud networks amid a global DRAM shortage.

$SPCX $RKLB $VOYG $PL $ASTS | A new US think tank report warns that China now leads the United States in GPS style satellite navigation and remote sensing.

The strategic shift also places Beijing ahead in orbital warfare and advanced counterspace weapons.

$MP $UUUU $UAMY $USAR $LAC $CRML | The EU pitches Brazil an advanced critical minerals deal, promising domestic refining infrastructure and tech transfers.

The strategic framework counters extraction heavy models favored by global competitors like Washington and Beijing.

Anthropic's internal loop engineering playbook just got leaked.

And it's the most valuable AI guide I've read all year.

This guide is packed with info, and there are five things you need to know if you want to maximize your AI productivity with loops (save this):

1. You should structure every loop around these 5 principles:

• Discovery → Let the agent find its own work (CI fails, issues, commits)
• Handoff → Give every task its own isolated git worktree
• Verification → Never let the generator grade its own work
• Persistence → Always write state to disk (markdown or board)
• Scheduling → Run it on a timer so it works while you sleep

2. Separate Generator from Evaluator (most important rule)

Use two agents: one writes, the other is a skeptical judge who assumes the code is broken.

Make the evaluator act (run tests, click buttons, take screenshots) - this is what actually stops bad output.

3. Build with these 6 parts:

• Automations (the timer)
• Worktrees (safe parallelism)
• Skills (permanent project knowledge)
• Connectors (talk to GitHub, Linear, etc.)
• Sub-agents (generator + evaluator)
• Memory (state files that survive between runs)

4. Things you must watch out for with loop engineering:

• Verification debt (use verification agents)
• Losing understanding of your own codebase (start fresh if needed)
• Token costs exploding (solution below)
• Cognitive surrender (don't stop thinking because “the loop handles it”)

5. Solving token costs

Loop engineering can be crazy expensive.

I recommend you use an 80/20 "barbell" approach to loop engineering.

For your most complex tasks that require the best intelligence, use expensive models (Opus).

For the remaining 80% of your tasks (the gruntwork), use cheap, open-source models within the Claude Code harness (GLM-5.2 is great for code execution).

Save these 5 rules so you don't forget them.

The harsh truth is that respect usually comes after you've become strong enough, valuable enough, and successful enough to stop asking for it.

You become impossible to stop when you invest in what no one can steal from you. Your mindset, your character, and the person you become.

The open internet was the training ground for modern AI.

Billions of texts, forum posts, code repositories, Wikipedia articles, scientific papers, blogs, and discussions: a global knowledge space built collectively over decades.

This free knowledge has now been transferred into artificial intelligence.

And that is precisely why AI must not disappear entirely behind proprietary walls.

Closed source may currently be leading when it comes to the most powerful models. But if access to intelligence is increasingly regulated, restricted, and controlled geopolitically, then open source is no longer just a technical ideal. It is a question of power.

The open internet made AI possible.

Now open source must ensure that AI remains open.

This is the most powerful way to use Hermes.

Spinning up multiple subagents to complete work.

Rather than working sequentially, Hermes fans out across multiple agents at once - then cross-verifies and merges the results.

It's like having an entire team from a single prompt

Here's crybaby Dario Amodei trying to get the US government to ban China's open source models, because those models have caught up to Anthropic's models now. (GLM 5.2, for example...)

Does this guy EVER stop whining?

This idiot 🤡 doesn't care about his reputation at all because he can just pocket those 1T valuation for his team 🤑 It is disgusting 😂😂😂

Rockstar games after the preorders of GTA VI

POV: you’re in a room where everyone has experienced and survived that phase of life that most don’t make it past

#BTC/USDT H2 #Indicator #Signal #Trading #GlobalOneAI
Globalone.ch #Prompt

Use the peekaboo style like Mike Tyson 🥊

🚨EU exports to the US just collapsed by over 30% in a single year.

Trump's tariff war is showing up in the hard data now.

This maps the EU's top trade partners in Q1 2026, in billions of euros.

On the import side, China dominates.

The EU bought €145.3B in Chinese goods.

That's nearly 1/4 of all EU imports, far ahead of the US at €85.9B.

The export side is where the shock lives.
The US is still the top destination at €119.4B.

But exports to America have fallen over 30% from Q1 2025.
A massive, fast decline.

That's the tariff war landing.
European goods are getting priced out of the US market.

The UK ranks 2nd for EU exports at €88.7B, then Switzerland at €57.2B.

And note the imbalance with China.

The EU imports €145B but exports just €47.6B back.

5 countries make up over half of all EU foreign trade.

The bloc's fortunes ride on a handful of relationships.

Watch the EU-US export line.

If it keeps falling, the transatlantic trade rupture is real, and Europe needs new buyers fast.