Ale𝕩ander Moore, CPA 🇺🇸

🎿 @ZephyrAdvGrp 🇺🇸 ‘Global macro is like a drug, views are mine. ♥ #lover of all asset classes, #alpha is always there if you can find it.’ 💎 $BTC 2013 🪙

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

📌 Follow the @Scobleizer aka Robert Scoble and be sure to check out his 𝕏 Lists.

🍄 https://t.co/MXC6j4MlYn 🏁

💡From topics and domains that generate economic productivity, spark hobbyist interests, emerging technology breakthroughs and the spectrum of hardware, software, and AR/VR or XR at the intersection of robotics.

💡Scoble's 𝕏 Lists are the closest thing to an intelligent + lived experienced knowledge base alpha database for science, technology, and creative discipline ontologies. These lists were recursive before most of the world knew what that word meant.

🔔His donation of countless years contributing to the timeline but also in spaces should earn him a Bell Award. He has certainly earned my nomination for quality content on X (and any social media platform for that matter). #bellaward @adiboaron94

🔔This award is unique in that it objectively looks at the creator, the individual content produced and the perceived value to the platform without consideration for follower or subscriber counts.

**I know he has a lot of followers which is a social proof in itself, but this is a different path into very high value small accounts.**

💡It's a great starting point for the average user, but a frontier of truly infinite possibilities if you have access to the 𝕏 API.

💡Real-time tracking, tagging, account IDs, organization, a data repository one could see as social value agnostic of bias from follower count. These can be a foundation for additional hierarchies of attributions and qualitative keywords; think of it like a game of ontological cross-domain 'connect the dots' if you will.

💡My main utility in being drawn to the library of lists (aside from an excellent source for inherently high quality 𝕏 Pro inputs) is the wide userbase that can find their own niche content in curating a project or customizing their 𝕏 user experience.

💎If 𝕏 were to have a graph database with @neo4j overlay to further dive into these lists, that would be a cool project and a feature I'd probably use often.
XR capabilities would be neat, obviously further the road along with 3D displays (with no glasses) to view the GraphQL-type relationships. @engineering @XCreators @premium

[Defense One @DefenseOne]

Software patches from Army hackathon going straight to troops in CENTCOM https://t.co/eIqWOUMZml

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

bro just can’t help himself 😂 https://t.co/NqdKf9i78Q

[NVIDIA RTX Spark @NVIDIARTXSpark]

The new Microsoft @Surface Laptop Ultra, powered by RTX Spark ⚡

Featuring:
• 15-inch PixelSense Ultra touchscreen + all-day battery life
• Largest-ever haptic trackpad on a Surface

Create, build, run AI agents locally and game with RTX.

Learn more: https://t.co/rfQwWivh4U

[Google Gemini @GeminiApp]

Join @GoogleDeepMind Principal Engineer @__apf__ to walk through how Gemini Spark helps simplify your daily workflows.

Powered by Gemini 3.5 Flash, Spark builds upon Gemini's ability to connect with @GoogleWorkspace apps like Docs and Gmail to execute complex tasks.

[NVIDIA @nvidia]

🏆 Cosmos 3 just topped 7 physical AI leaderboards.

NVIDIA Cosmos™ 3, the open omni-model for physical AI, ranks #1 across world generation, robot action policy, and industrial vision understanding.

🌎 World generation: Artificial Analysis, PAI-Bench, Physics-IQ, R-Bench
🤖 Robot policy: RoboLab
👁️ Vision: VANTAGE-Bench, TAR

One model. Every domain of physical AI.

Available now on Hugging Face ▶️ https://t.co/y9iJ4mO6zh

[NVIDIA @nvidia]

The agentic AI era is here.

From Taipei, Jensen Huang joined @satyanadella at #MSBuild to show how NVIDIA and @Microsoft are building it together, from Windows devices to AI factories at scale.

▶️ Watch the conversation: https://t.co/6mYc7uIoCI

[McKinsey & Company @McKinsey]

The Bay Area is home to 300+ unicorns and generates roughly $1.2T in annual GDP.

With San Francisco home to 25 of the world’s top 50 AI companies, the region’s challenge now is turning this moment of AI leadership into lasting advantage. https://t.co/BsRJt1Oq7h

[Google Labs @GoogleLabs]

🚨 NEW EXPERIMENT 🚨

Dreambeans is a new, experimental mobile app that uses Personal Intelligence to connect to your Google apps. Every day, it delivers collections of personalized stories, surfacing things you might otherwise miss, alongside topics that are relevant to you, to help you dive deeper into the things you care about most.

Available starting today for eligible US-based Google AI Ultra users (+18), with an open waitlist found on our website below!

Learn more at https://t.co/jCdAzMHvOD

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

@googleaidevs best of both worlds 🏁

[Google AI Developers @googleaidevs]

We’re launching Gemma 4 12B: Our unified, encoder-free model that brings powerful multimodal intelligence straight to your laptop 🚀

The model bridges the gap between our mobile E4B model and larger 26B MoE models, packaging frontier-class reasoning and native audio into a highly optimized footprint, all under a permissive Apache 2.0 license.

Here’s what makes it unique:

+ Encoder-Less Architecture: We removed the multimodal encoders. The vision and audio inputs flow directly into the LLM backbone.
+ Agentic Performance (16GB VRAM): Run complex, multi-step workflows locally, with performance nearing our 26B model.

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

🏁 gemma brilliance

[Google Gemma @googlegemma]

Meet Gemma 4 12B!

A unified, encoder-free multimodal model designed to bring high-performance intelligence directly to your laptop, and released under an Apache 2.0 license.

Bridging the gap between edge efficiency and advanced reasoning. Here is what’s new with Gemma 4 12B: 👇

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

@McKinsey @quantumblack

[McKinsey & Company @McKinsey]

As AI transforms how we work, access to skills is becoming a key driver of economic opportunity.

Our Sustainable & Inclusive Growth Impact Report explores how closing skill gaps can help people thrive while building resilient, future-ready workforces. https://t.co/ytSHztcHdx

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

@CryptoStorm__ ladies can be pimps too

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

@playmatejaylene ooo pls post more pictures of NZ i’ve never been!

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

@sierracatalina this is signal purity.

[⚪️ sierra catalina @sierracatalina]

hey gentleman:

the great pussification is real.

do not let yourself fall victim
to society's confusion about your role.

[Ale𝕩ander Moore, CPA 🇺🇸 @Moore_Irish]

Check out ABC News Live's video! #TikTok https://t.co/qefJqAPehi

nanoplastics can cross the blood brain barrier… the average cadaver contained 7 grams of microplastics or the size of a plastic spoon.

brains with dementia contained higher than the average amount of microplastics but there is no definitive statistically causal link yet.

@ABC @tiktok_us @AFTDHope @alzheimerssoc @alzassociation

[NVIDIA @nvidia]

What does it take to build an AI factory ready for the agentic era?

80+ partners joined Jensen Huang at #NVIDIAGTC Taipei to show how NVIDIA MGX is making it possible.

With NVIDIA Vera Rubin, 800 VDC, and partners spanning systems, power, and cooling, NVIDIA MGX is turning modular design into deployed AI infrastructure for the agentic AI era. ⚡

#NVIDIAGTC

🔗 https://t.co/ZMw7LZzwBk

[How To AI @HowToAI_]

ByteDance has published a paper that should make every NVIDIA investor sweat.

They trained an AI that writes CUDA better than humans experts.

They call it CUDA Agent.

And it completely rewrites the economics of AI hardware.

They built a massive agentic reinforcement learning loop. The AI writes a kernel, compiles it, profiles the hardware, analyzes the bottlenecks, and rewrites the code until it's flawless.

It learned how to optimize memory access patterns and hardware tiling strategies that traditional compilers miss.

The results are staggering.

On the industry-standard KernelBench, CUDA Agent completely destroyed traditional compilers.

It delivered code that runs up to 3.2x faster than PyTorch's native execution.

On the hardest, most complex models, it beat the strongest proprietary models in the world—including Claude Opus 4.5 and Gemini 3 Pro, by 40%.

It didn't just match human experts. It started discovering optimizations that static compilers literally cannot see.

Here is why this is a massive threat to NVIDIA.

NVIDIA's dominance relies on the fact that CUDA is incredibly hard to master. Developers get locked in because optimizing code for other chips is too painful.

But if an AI agent can autonomously generate hyper-optimized hardware kernels...

You don't need a team of $500k a year CUDA engineers to build world-class infrastructure.

And if an AI can autonomously master CUDA, it can master AMD's ROCm. Or custom silicon.

The impenetrable software wall protecting NVIDIA's monopoly just got breached by a reinforcement learning loop.

If anyone can automatically squeeze maximum performance out of any chip...

Hardware becomes a commodity.