🚨 Who Really Controls Nuclear Power?

🇺🇸 US: 97 GW (94 reactors)

🇫🇷 France : 63 GW

🇨🇳 China: 55 GW

🇷🇺 Russia: 27 GW

🇰🇷 South Korea: 26 GW

🇯🇵 Japan: 13 GW

🇨🇦 Canada: 13 GW

$QMCO

One of the most overlooked AI infrastructure stories on the market.

Everyone talks about:

$NVDA
$SMCI
$HPE
$DELL

But AI doesn’t run on compute alone.

It needs:

→ storage
→ data management
→ information infrastructure

That’s where $QMCO operates.

Technically:

- Reclaimed 200MA
- Broke a 1-year downtrend
- Volume exploding
- OBV rising
- TTM Squeeze positive
- Momentum accelerating

The biggest moves often start when a stock transitions from:

“forgotten”

to

“discovered.”

$QMCO is beginning to look like one of those charts.

2026 IS THE YEAR OF MEMORY

• $SNDK +516%
• SK Hynix +245%
• $MU +208%
• Samsung +147%

AI is becoming memory-first as models and agents need longer context, faster retrieval and more data kept closer to compute.

Local AI hardware = capacity × bandwidth × software stack

- Capacity tells you what fits
- Bandwidth tells you how hard the box can breathe
- The software stack tells you how much of the spec sheet you can actually cash out.

Hardware by Memory Bandwidth
- Mac Studio M3 Ultra: up to 512GB @ 819 GB/s
- RTX PRO 6000 Blackwell: 96GB @ 1792 GB/s
- RTX 5090: 32GB @ 1792 GB/s
- RTX 4090: 24GB @ 1008 GB/s
- RX 7900 XTX: 24GB @ 960 GB/s
- Radeon PRO W7900: 48GB @ 864 GB/s
- AMD Radeon AI PRO R9700: 32GB @ 640 GB/s
- Intel Arc Pro B65: 32GB @ ~608 GB/s
- Tenstorrent Wormhole n300: 24GB @ 576 GB/s
- Tenstorrent Blackhole p150: 32GB @ 512 GB/s + 800G
- MacBook Pro M5 Max: 460-614 GB/s
- MacBook Pro M5 Pro: 307 GB/s
- DGX Spark: 128GB @ 273 GB/s (coherent + CUDA)
- Mac mini M4 Pro: 273 GB/s
- Ryzen AI Max / Strix Halo: ~256 GB/s (~96GB usable GPU)
- MacBook Air M5: 153 GB/s
- Snapdragon X2 Elite: 152-228 GB/s
- Intel Lunar Lake: 136 GB/s
- Snapdragon X Elite: 135 GB/s
- Mac mini M4: 120 GB/s
- Arc Pro B60: 24GB @ ~456 GB/s

Verdict

- GPUs are still the bandwidth kings

- Apple wins: stupid amounts of memory, don’t want to shard across GPUs
- Apple loses: when raw tokens/sec & concurrency matter more

- DGX Spark: coherent memory + NVIDIA stack

- Strix Halo / Ryzen AI Max: first real x86 unified-memory contender

- Tenstorrent: fully OSS stack, excited to see this mature

Fitting ≠ serving

Even if it fits, you still pay for
- bandwidth during decode
- KV cache growth
- dequantization
- batching + concurrency
- scheduler quality
- framework overhead

The only mental model that matters:

1. What must fit?
2. What bandwidth tier do I need?
3. What software stack can actually deliver it?

In short:
- NVIDIA → fastest raw speed
- Apple Studio M3 Ultra → biggest one-box memory
- Strix Halo → first real x86 unified
- DGX Spark → coherent NVIDIA dev appliance
- AMD / Intel Arc → rising alternatives
- Tenstorrent → fully opensource stack

Do ask: “which bottleneck am I buying?”

Not: “which hardware is best?”

$SUGP Watching this one closely into the open.

🔹 Float under 500K shares
🔹 Borrow fee 132.76%
🔹 7000 shares available to short at huge fees
🔹 Volume expansion pre-market

Recent developments:
✅ Revenue grew 5.6% YoY in FY2025
✅ Secured multiple government & infrastructure projects in Hong Kong

✅ Continued adoption of AI-assisted security screening technology across transportation and cargo screening applications

With a razor-thin float, improving business execution and exposure to AI-enabled security technology, $SUGP is a name on my radar today.

$SUGP New contract win worth noting.

The company announced the installation of a next-generation cross-border security and high-speed vehicle clearance system.

This isn't consumer tech.

This is critical infrastructure.

As governments continue investing in smarter border security, transportation screening and automated clearance systems, $SUGP continues expanding its footprint in the sector.

With a sub-500K float, this remains one of the more interesting security technology names on my watchlist today.

3 Simple & Effective Bias Correction Methods for Your Polymarket Weather Trading Bot (Decaying Average + MOS + Kalman Filter)

If you're building a trading bot for weather markets, one of the most common mistakes is jumping straight into U-Net, XGBoost, and other deep learning models

In reality, many top-performing bots start with classical methods. They’re simple, fast to implement, and already remove a large portion of systematic error right from the start

Here are three solid baseline methods you should implement first

1. Decaying Average (Exponential Smoothing)

The most popular and easiest method. You take the model’s past errors and gradually reduce the influence of older data

Formula:

Bias_today = α × Bias_yesterday + (1-α) × Current_error

Usually α is set between 0.95 and 0.98. Excellent at capturing seasonal and regional biases. Almost zero computational cost

2. MOS - Model Output Statistics (Linear Regression)

A meteorology classic that still works extremely well. You train a simple linear model to correct the raw forecast:

Corrected_Temp = a × Raw_Forecast + b + c × Hour + d × Season + ...

You can add cloud cover, wind, land type, etc.
Works very well for fixing the diurnal cycle (day vs night temperatures)

3. Kalman Filter (Adaptive Filter)

The most dynamic of the three. It continuously updates the bias estimate in real time, taking into account how much “trust” the model should give to new observations

Formula:

Bias_new = Bias_old + Kalman_Gain × (Current_error - Bias_old)

The more stable the model, the smaller the Kalman Gain, and vice versa. Adapts excellently to season changes and model updates (GFS/ECMWF)

Why These Methods Are a Must-Have at the Beginning

• They remove most of the systematic bias (often 60-80% of what’s possible)
• Easy to implement and test
• Very low risk of overfitting

Good bias correction doesn’t have to be complicated

These three methods form an excellent foundation on which you can already make consistent money on 1°C bins on Polymarket

$AIAI The breakout hasn’t happened yet.

That’s exactly why I’m interested.

Most traders chase after the move.

The best opportunities usually appear during compression.

Current setup:

✅ Tightening triangle
✅ Higher lows
✅ Sellers losing control
✅ TTM Squeeze ON
✅ Breakout level near $13.30

Meanwhile the company continues building at the intersection of:

AI + Blockchain + Digital Infrastructure + Autonomous Systems

A clean break above resistance could quickly bring $15+ back into play.

The chart is coiling.

The narrative is growing.

Now we wait for confirmation. 🚀

#AIAI #AIStocks #Blockchain #SmallCaps #Trading #BreakoutAlert

$SUGP Update

The story remains the same, but the volume just became impossible to ignore.

🔥 Over 11M shares traded

🔥 Float remains under 500K shares

🔥 Borrow fee recently above 130%

🔥 No shares available to short reported earlier

Technically:

✅ Buyers continue defending the recent lows

✅ Higher-low structure remains intact

✅ OBV has exploded higher, signaling aggressive accumulation

✅ Volume is now multiple times larger than the entire float

The chart is still in recovery mode and needs additional confirmation.

However, this is exactly the type of action traders monitor in micro-float names when a stock begins transitioning from capitulation to accumulation.

On the fundamental side, the company recently announced it was awarded a next-generation cross-border security and high-speed vehicle clearance system installation project, expanding its presence in critical security infrastructure and advanced screening systems.

For now, the key is whether buyers can continue building above the recent base and turn today’s volume into a sustained trend rather than a one-day event.

Definitely one of the more interesting micro-float charts on my screen today.

$NEE $CEG $PLUG $VST $BE | Google signs a 3 year Bring Your Own Capacity (BYOC) contract with virtual power plant operator Voltus.

The novel program aggregates up to 100MW of decentralized energy resources across the US PJM grid region.

$CBRS $MU $INTC $ASML $ANET | Cerebras plans extensive data center collaborations to position the group as the primary alternative to Nvidia hardware.

Management uses the existing Amazon and OpenAI supply deals as blueprints for expansion.

Google just released the most powerful high-performance multimodal LLM.

Gamma 4 12B is live now, and you need to read this.

It runs for FREE locally, powered by state-of-the-art multimodal intelligence.

Build offline voice apps, vision assistants, agentic workflows, and more.

AI is starting to build AI.

Anthropic just published one of the clearest signals yet.

Claude now writes 80%+ of Anthropic's production code.

Their engineers are merging 8x more code per day than in 2024.

That sounds like a productivity stat.

It' actually much bigger.

For most of AI history, humans did the whole loop:

- write code
- run experiments
- debug systems
- interpret results
- decide what to try next

Now Claude is taking over more of that loop.

At Anthropic, Claude can already:
→ write and edit code
→ run code
→ delegate work to agents
→ debug incidents
→ optimize experiments
→ review code before merge
→ catch bugs humans missed

One example was insane:

Claude shipped 800+ fixes that reduced a class of API errors by 1,000x.

The engineer said a human would have taken 4 years.

Claude did it in April.

The scary part is not that Claude writes code.

The scary part is that Claude is getting better at the work around the code:
> figuring out what went wrong
> testing fixes
> choosing next steps
> running experiments
> checking its own outputs

Anthropic says Claude went from about 3x speedup on one internal optimization task in 2025 to about 52x with Mythos Preview in 2026.

That is not normal tool improvement.

That is compounding.

The current human advantage is still taste and judgment:

What problem should we solve?
Which result matters?
Which direction is worth pursuing?
When should we stop?

But the "doing" part is getting automated fast.

This is the real shift:

Humans used to build AI.

Now humans increasingly steer agents that build AI.

And if that loop closes completely, you get recursive self-improvement:

AI systems designing, testing, and improving their own successors.

Anthropic says we are not there yet.

But they also say institutions are not ready if it comes sooner than expected.

The AI race is no longer just about better chatbots.

It's about who can build the fastest machine for improving intelligence itself.

Hermes agent is extremely powerful.

Set it up once, and you have a 24/7 mini employee working for you while you sleep.

The new desktop app has made it easier than ever to do this.

🇺🇸 #Elonmusk explains why SpaceX is finally going PUBLIC: a massive growth phase.

The plan? Over 100,000 satellites in orbit, V3 birds with 100X the bandwidth, plus AI DATA CENTERS in space.

Profitable since 2015.

The growth phase is just beginning.

🚨 Only 4% of SpaceX shares will be made available as the free float, making the $SPCX IPO likely to be EXTREMELY volatile.

Ahead of this, Goldman Sachs says they expect SpaceX revenue to 100X by 2030.

Prepare for liftoff.

Elon Musk on Terafab:

"It's worth noting that there's not a single high volume computer memory fab in America right now, zero. There's one being built by Micron, but that will not reach volume production until I believe 2028 and there's something built in New York, but they are in, I think, 2029 and 2030, and this is a tiny fraction of the memory that's needed, and in fact, even if you take the best case assumptions of the memory makers and the logic makers, it is not enough to meet the demand that is anticipated, which is why you're seeing stocks of like Micron go to, I think, 1.2 trillion, or some quite high number, so there's just clearly a need for AI logic memory and packaging, AI computers, essentially, that is far beyond what even the best case assumptions of the existing fabricators can do, and that's why we need to do the Terafab. It seems essential, otherwise we will not, there will not be enough chips."

Elon Musk on building data centers in Space:

"We don't think this is a particularly difficult thing to do. In fact, we think it's easier than our communication satellites. The Starlink V3 communication satellite is an incredibly complex machine. The AI data center would be much simpler by comparison, because it's really just solar power plus radiator basic equipment for operating satellite, and then the laser links, which would connect to the Starlink communications constellation and then back to the ground the connection would happen no matter what the weather is because once you connect to the Starlink communication constellation the Starlink communicates the ground with frequencies that are cloud penetrating, so that in fact even roof penetrating some degree, so you would always be able to close link with the data centers."

Google’s next “debugging” project is not about code.

It involves millions of lab-bred mosquitoes, AI-powered sorting, and a plan to fight disease without heavy pesticide use.

A massive underground battery project in Switzerland could become part of Europe’s AI infrastructure future.

The strategic issue is clear: as AI factories, data centers, electrified transport, and renewable grids expand together, long-duration energy storage may become one of Europe’s most important infrastructure layers.

In the Swiss canton of Aargau, FlexBase Group has commenced construction on what will become the world’s most powerful underground battery installation at Laufenburg, selecting Invinity Energy Systems as its core technology partner for a 2.1 gigawatt-hour (GWh) Vanadium Redox Flow Battery system that will discharge up to 1.2 gigwatts into the Swissgrid network within milliseconds. Switzerland could lead in redox flow battery breakthroughs. 

Located in Laufenberg, the Star of Europe energy interchange on the German-Swiss border, the vanadium redux flow battery technology is designed to be far more than a battery installation. 
The excavators for the project have already broken ground on two soccer fields of surface area. Twenty-seven meters down. And a price tag measured in billions that Europe’s energy establishment is watching with the focused attention of an industry that has been waiting for this specific proof of concept. 

As renewable power redefines Europe’s electricity networks, Swiss developer FlexBase is combining long-duration energy storage, AI infrastructure, and research facilities to capture surplus renewable energy and deliver it when demand rises. 

Meta’s AI race is hitting another delay.

The company has reportedly pushed back the release of its Muse Spark AI model API for developers, even as it tries to compete with OpenAI, Anthropic, and Google.

For developers, timing is becoming part of the trust problem.