$LASE - Did you listen?

$BB Building AI was Phase One.

Deploying AI into the physical world may be Phase Two.

Autonomous vehicles.

Industrial automation.

Medical devices.

Critical infrastructure.

As intelligence moves beyond the cloud, trusted operating systems become increasingly important.

qnx.software/en

The 2023 article on $SPCX using $BB QNX was about its past role in Falcon rockets. While SpaceX now runs mostly custom Linux, QNX remains trusted for real time systems.Stronger proof: In early 2026, NASA added official support for QNX SDP 8.0 to its core Flight System (cFS), used on 40+ missions. BlackBerry helped make it happen showing QNX is still a top choice for safety critical space tech.

Honestly speaking, im just going to keep adding to my Blackberry position as I think Physical AI itself and the core mechanisms is going to shift at a rapid space and anything under $15 on $BB is a add for me.

See you monday for market openings :)

etd.gsfc.nasa.gov/capabilities/core-flight-system/news/nasas-cfs-expands-operating-system-support-with-addition-of-qnx/

$SPCX IS USING $BB QNX real time OS??

Why is no one talking about this?

Humanoid Robotics supply chain.

While most operating systems are built as massive monolithic code structures, where a failure in one area can cascade, BlackBerry $BB QNX is fundamentally different at its core.

Its unique value lies in its microkernel architecture. Instead of containing all OS services within the kernel, QNX places them in tiny, isolated 'modules.' If a file system or network driver crashes, it doesn't bring down the entire system—the OS simply restarts that specific service module in milliseconds, preserving device integrity and safety-critical functions. This microkernel design is why QNX is chosen for the world’s most critical deployments, from robotic medical devices to autonomous vehicle platforms.

#BB #QNX #Microkernel #EmbeddedSystems #IoT #SafetyCritical #AutonomousVehicles

How to never hit your Claude rate limits ever again.

It took me months to uncover these tips.

I have now been using Claude for 4+ hours/day with ZERO rate limit interpretations.

You'll want to save this

Google just shrank 31GB of AI memory down to 4GB. Same search. Faster than the industry standard. No training required.

This is not a model improvement. This is not a new architecture.

→ It's a compression algorithm that makes the hardware problem smaller.

Right now, running serious AI locally means serious RAM. Most machines can't do it. Most phones can't do it. Most edge devices can't do it.

Turbovec quietly changes that math. A 10 million document search engine that used to need a server now fits on a laptop.

Nobody is talking about this because it shipped as a GitHub repo, not a press release.

The models get the headlines. The infrastructure is where the shift actually happens.

github.com/RyanCodrai/turbovec

The market is crashing, panic is spreading.

At this very moment, Trump’s team suddenly announced: they may acquire U.S. shares of artificial intelligence companies!

This is not a coincidence, but a signal.

History has repeatedly proven: Whenever you follow President Trump in buying stocks, it often marks the beginning of great wealth.

June Investment Advice

Absolutely no conflicts of interest here, right?

Who gets the next government bailout... I mean "investment"

AI has completely restructured how I invest - and I'm making some big portfolio adjustments because of it.

Here's the ultimate playbook to investing in the AI gold rush

$GOOG The market keeps debating whether the AI trade is over.

Meanwhile the world’s largest technology companies continue securing massive amounts of compute capacity.

AI isn’t slowing down.

It’s industrializing.

Compute.

Power.

Memory.

Datacenters.

Infrastructure.

The next phase may be significantly bigger than the first.

If you missed the $NVDA trade, don't worry. The next big AI trade is literally sitting right in front of you.

I just compiled 60+ AI companies currently printing multiples - all in one place.

If you want massive gains from this sector, bookmark this

First came AI models.

Then came AI assistants.

The next phase may be AI ecosystems.

OpenAI’s reported ChatGPT overhaul points toward something much bigger:

• AI agents
• Search
• Coding
• Images
• Third-party integrations

The companies that control the ecosystem may ultimately control the value.

This is no longer just a race for better models.

It’s becoming a race for distribution, engagement and daily utility.

The AI story keeps expanding.

$LFVN caught my attention.

#2 Trending on StockTwits.
Massive volume.
Major trendline breakout.
Back above the 200-day moving average.

After spending months in a downtrend, the chart suddenly looks very different.

When a stock breaks structure and traders start paying attention at the same time, things can get interesting fast.

Worth watching next week. 👀

$LFVN #Stocks #Momentum #Trading

6 Best ML Libraries Top Quant Traders Use to Exploit Model Bias on Polymarket (Full Quant Stack)

Most bots on Polymarket still operate as simple reactive systems - they treat public forecasts as the ultimate truth

Top quant traders have moved far beyond that. Instead of relying on public models and third-party forecasts, they build meta-models that actively hunt for and exploit the systematic biases and weaknesses in those forecasts

Here’s the actual list of libraries that top quants are using right now:

1. LightGBM - The Go-To Workhorse

• Extremely fast gradient boosting with leaf-wise tree growth
• Handles hundreds of features effortlessly (raw forecasts + historical bias corrections + sentiment + volume)
• Repo: github.com/lightgbm-org/LightGBM

2. XGBoost - The Reliable Veteran

• Classic gradient boosting with strong regularization
• Excellent GPU support and best-in-class SHAP explainability
• Repo: github.com/dmlc/xgboost

3. HistGradientBoosting (scikit-learn)

• Powerful histogram-based boosting built directly into sklearn
• Best choice for fast prototyping and experimentation
• Repo: github.com/scikit-learn/scikit-learn

4. RandomForest (also sklearn)

• Solid baseline to quickly check if your features actually have signal
• Repo: github.com/scikit-learn/scikit-learn

5. TabNet

• Neural network architecture designed specifically for tabular data
• Great at combining text embeddings (news/tweets) with numerical features
• Repo: github.com/dreamquark-ai/tabnet

6. River

• Best library for online/incremental learning
• The model learns and adapts in real time - essential for fast-moving events
• Repo: github.com/online-ml/river

Recommended 2026 Stack: LightGBM (main model) + XGBoost (stacking) + River (online adaptation)

All of these libraries work great with SHAP - so you don’t just get a probability, you actually see which model bias you’re currently profiting from

Bookmark this post so you don’t lose it!

$IREN $NBIS $MRVL $HUT $CRWV | Zurich Insurance expands data center underwriting offerings to major markets across Europe and South America.

The strategic move targets a projected $134 billion global digital infrastructure premium pool.

$IBM $IONQ $HON $QBTS $RGTI | Microsoft cut the quantum computing timeline in half, now targeting a scalable, commercial machine by 2029.

This leap follows the debut of the Majorana 2 chip, featuring qubits 1,000 times more reliable than predecessors.