Data Science at Home

VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies. Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.

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Two AI experts who actually love the technology explain why chasing AGI might be the worst thing for AI's future—and why the current hype cycle could kill the field we're trying to save. Want to dive deeper? Head to datascienceathome.com for detailed show notes, code examples, and exclusive deep-dives into the papers we discuss.

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Mark Brocato built Mockaroo—the tool that taught millions of developers how to fake data. Now, as Head of Engineering at Tonic.ai, he's building the AI agent that's making his own creation obsolete. In this episode, we explore why static test data can't survive the AI era, what it means to "negotiate" datasets with an agent instead of scripting them, and whether we're heading toward a future where sandbox environments vanish entire...

Most companies don't have an AI problem. They have a decision-making problem. Matt Lea, founder of Schematical and CloudWarGames, has spent nearly 20 years helping tech leaders ship smarter.

In this conversation, he breaks down when AI actually makes sense, where AWS costs spiral out of control, and why your "cool demo" keeps dying before launch. If you're tired of AI hype and ready for straight answers, hit play. Join the co...

LLMs generate text painfully slow, one low-info token at a time. Researchers just figured out how to compress 4 tokens into smart vectors & cut costs by 44%—with full code & proofs! Meanwhile OpenAI drops product ads, not papers. We explore CALM & why open science matters. 🔥📊

Sponsors

This episode is brought to you by Statistical Horizons  At Statistical Hori...

VortexNet uses actual whirlpools to build neural networks. Seriously. By borrowing equations from fluid dynamics, this new architecture might solve deep learning's toughest problems—from vanishing gradients to long-range dependencies. Today we explain how vortex shedding, the Strouhal number, and turbulent flows might change everything in AI.

Sanjoy Chowdhury reveals AI's hidden weakness: while systems can see objects and hear sounds perfectly, they can't reason across senses like humans do. His research at University of Maryland College Park, including the Meerkat model and AVTrustBench, exposes why AI recognizes worried faces and thunder separately but fails to connect them—and what this means for self-driving cars and medical ...

This episode exposes the uncomfortable truth: most defense tech startups are just software engineers cosplaying as military innovators, creating fragmented solutions that Pentagon doesn't need. Not now, at least.

References

War On The Rocks: https://warontherocks.com/2025/08/ukraine-isnt-the-model-for-winning-the-innovation-war/

LinkedIn: https://www.linkedin.com/in/jonasrsinger/

Spotify: https://tr.ee/Omy_1X8k1U

Apple Podcas...

A nostalgic dive into the rise and fall of true hacker culture - from MIT's curious tinkerers to today's hustle-obsessed "founders." Plus, why IRC was peak internet and what we lost when convenience killed community. For anyone who misses when coding was about elegance, not exits.RetryClaude can make mistakes. Please double-check responses.

Interesting link

https://www.twitch.tv/tsoding/about

Sponsors

DSH is proudly spon...

We were promised robot butlers and got Roombas that cry under the couch. In this brutally honest (and slightly hilarious) episode, Francesco dives into why the robot revolution fizzled, why your dishwasher still needs you, and how robotics became more YouTube circus than household helper. Spoiler: It's not the tech – it's us.

Sponsors

DSH is proudly sponsored by Amethix Technologies. At the intersection of ethics and e...

The brutal truth about why Silicon Valley is blowing billions on glorified autocomplete while pretending it's the next iPhone.

We're diving deep into the AI investment circus where VCs who can't code are funding companies that barely understand their own technology. From blockchain déjà vu to the "ChatGPT wrapper" economy—this episode will make you ...

From the viral article "Tech's Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything" on my newsletter at https://defragzone.substack.com/p/techs-dumbest-mistake-why-firing

here are my thoughts about AI replacing programmers...

🎙️ Sponsors

• AGNTCY — The open source collective building the Internet of Agents 🌐 https://www.agntcy.org

In this episode of Data Science at Home, we explore the fascinating world of neuromorphic computing — a brain-inspired approach to computation that could reshape the future of AI and robotics. The episode breaks down how neuromorphic systems differ from conventional AI architectures like transformers and LLMs, diving into spiking neural networks (SNNs), their benefits in energy efficiency and real-time processing, and their...

This episode explores the invisible battlespace of cyber and electronic warfare, where AI takes center stage. From autonomous hacking bots to smart jamming and adversarial attacks on machine learning models, we uncover how modern conflicts are increasingly fought with code, not bullets — and why the future of warfare may be decided by algorithms.

Building multi-agent software is hard — agent-to-...

Swarming the Battlefield explores how artificial intelligence is revolutionizing combat through coordinated drone swarms. We dive into the algorithms that enable autonomous collaboration, from decentralized decision-making to real-time target allocation, and examine real-world implementations shaping modern battle strategies. This episode uncovers how these intelligent agents turn the chaos of the battlefield into a synch...

In this gripping follow-up, we dive into how AI is transforming kinetic operations—from identifying a threat to executing a strike.

🔍 Highlights from this episode:

• How AI compresses the OODA loop (Observe, Orient, Decide, Act)

• The spectrum of autonomy: human-on-the-loop vs. human-out-of-the-loop

• Real-world systems like loitering munitions (Switchblade, Harpy) and Selective Ground Response AI (SGR-AI)

• The ethical and le...

Welcome to DSH/Warcoded

We explore how AI is transforming ISR (Intelligence, Surveillance, Reconnaissance)—from satellite imagery to drone feeds. In this episode:

🔍 Computer vision for target ID 📡 Predictive surveillance & pattern-of-life modeling 🧠 LLMs for SIGINT & OSINT intelligence briefings 🌍 Real-world examples: Ukraine, Gaza & more

Listen now and see how machines are learning to see, predict, and infor...

🎙️ In this episode of Data Science at Home, we sit down with Kenny Vaneetvelde, the mastermind behind Atomic Agents, a groundbreaking framework redefining AI development.

🔍 Discover how atomicity simplifies complex AI systems, why modularity matters more than ever, and how Atomic Agents is eliminating hidden assumptions and redundant complexity in AI workflows.