RNM

Moats for stock filtering playbook

Learning resources for trading

Unfortunately, blue sky is unavailable in Missippi because it’s too lazy to put in very important age verification for all users. Age verification is required for child safety.

Thoughts on this stock filtering playbook?

Is there anything you would change?

This article captured a moment of reckoning—a moment that called on Americans to confront their national identity. We need to acknowledge that while Trump may have been a catalyst, the divisions he exposed existed long before his presidency. Trump was just the first to amplify them for personal gain

I discuss the following AI Topics:

#ArtificialIntelligence
#MachineLearning
#AIInnovation
#AIResearch
#FutureOfAI
#OpenSourceAI
#BigTech
#AIForAll
#AIAccessibility
#DemocratizingAI
#AIModels
#ExponentialGrowth
#ComputeEfficiency
#DataScience
#AIApplications

18/ In the end, this tension will define the next decade of AI: Exponential costs versus exponential creativity. Centralized power versus decentralized ingenuity. What side of history will you be on?

17/ For big AI, the real challenge isn’t just reaching 82.5% accuracy—it’s staying relevant in a world where 75% accuracy at a fraction of the cost solves 90% of real-world problems.

16/ And this isn’t just about competition; it’s about access. Open-source AI lowers barriers, empowering researchers, startups, and even hobbyists to innovate. The ripple effects could transform industries we haven’t even imagined yet.

15/ The big question: Will the future of AI be defined by a few centralized giants or by a decentralized network of creators? History suggests that open, collaborative ecosystems tend to win in the long run.

14/ This is the same tension we’ve seen in other industries—like the shift from mainframe computing to personal computers or from monolithic software to agile, open-source solutions.

13/ The irony is that, as big AI spends billions to climb the last few rungs of the performance ladder, open-source innovators are building entirely new ladders. They’re asking: How can we do more with less? How can we democratize AI for the many, not just the few?

12/ The result? A divergence in AI paths:
• Big AI: Chasing state-of-the-art accuracy, but at astronomical costs and diminishing societal returns.
• Open-source: Unlocking widespread potential by solving smaller, more practical problems at scale.

11/ Meanwhile, big tech companies face mounting pressure. To justify their investments, they must deliver performance and profitability. Yet the public increasingly questions the ethics, environmental impact, and monopolistic tendencies of these AI giants.

10/ Open-source communities are already leading this charge. By sharing knowledge and tools, they reduce duplication of effort and spark creativity. A breakthrough in optimization or training efficiency by one developer can ripple across the entire ecosystem.

9/ Consider this: the next wave of AI innovation may not come from creating even bigger models but from rethinking how we use existing ones. Fine-tuning smaller models, creating task-specific architectures, or even exploring new computational paradigms could outpace brute-force scaling.

8/ In short, AI’s future won’t just be dominated by the biggest models or budgets. It will be defined by agility, efficiency, and collaboration. The age of open-source disruption has just begun.

7/ This divide could accelerate. As innovation at the top slows, smaller players will refine tools, find efficiencies, and create breakthroughs in areas overlooked by mega-models.

6/ Big AI, in contrast, may double down on enterprise solutions or specialized domains where incremental gains justify massive investment. Think healthcare, finance, or defense.

5/ This also shifts power dynamics. Open-source models are becoming increasingly competitive, offering high-quality tools at low cost. Democratized access could lead to a flood of niche applications—solutions optimized for specific tasks rather than general ones.

4/ Take aerospace as an analogy. Early innovations in flight were relatively cheap. But optimizing jet fuel efficiency by 1% now costs billions. AI is entering a similar phase where progress costs more than it yields.

3/ This creates an innovation gap. While open-source thrives on efficiency and collaboration, big AI faces diminishing returns. Every marginal improvement in accuracy costs more, but produces less real-world impact.

2/ The cost of scaling large models isn’t linear—it’s exponential. Compute demands skyrocket, data requirements grow unsustainably, and the hardware itself is a bottleneck. Moving from 75% to 85% accuracy might cost 100x more than the initial breakthrough.

The future of AI is about diminishing returns. 🧵

1/ Open-source models can reach “good enough” (e.g., 75% accuracy) with modest resources. Meanwhile, big tech needs to invest billions to push the frontier even slightly (e.g., from 80% to 82.5%). Why? Let’s break it down.

Ahhh that’s better.

Lot going on?

It’s getting highly suspect over there, is it not? All the big accounts are like, let’s be nice to everyone when they come back - as the tumble-weed rolls by.

Now imagine that by the time you’ve completed this monumental effort, the planet has warmed another 1.5°C. The cycle starts again: more relocation, more upheaval, and less time, and land, to adapt. This is the relentless pace of climate change—moving faster than we can respond.

Picture this: you’re tasked with relocating all the infrastructure that sustains your region’s primary industries—factories, farms, supply chains—400 kilometers closer to the poles to escape rising temperatures. The costs are staggering, the logistics overwhelming, and the disruption profound.

Some might say short term gain.
Some might say short term pain long term gain.
Some might say short term pain long term gain, longer term crippling pain.
Some might say..