Towards Data Science

The world's leading publication for data science and artificial intelligence professionals.

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[Towards Data Science @TDataScience]

Threat modeling where the LLM can't invent a CVE, and code computes the risk score

By Daniel Nweke

https://t.co/UJ4VNhJJWt

[Towards Data Science @TDataScience]

From benchmarks and current pricing to its underlying architecture, @BenjaminNweke11 recently shared a helpful introduction to Gemini 4 Argon. https://t.co/xMqF1GObzL

[Towards Data Science @TDataScience]

"Rather than repeat the theory from my earlier article, I wanted to ask a more useful question: what happens if I run exactly the same code on both versions?"

@taupirho takes the Python 3.15 JIT for a spin. https://t.co/TtJnuFBckB

[Towards Data Science @TDataScience]

What changes once an LLM comes with a million-token output window?

@BenjaminNweke11 walks us through the nitty-gritty details of using Google's new Gemini 4 Argon model. https://t.co/xMqF1GObzL

[Towards Data Science @TDataScience]

For his debut TDS article, Devashish Thakar presents an accessible deep dive on conformal prediction and its practical use cases. https://t.co/kMLbnP2eig

[Towards Data Science @TDataScience]

In a thorough and engaging walkthrough of a recent computer vision project, Florian Trautweiler unpacks the process behind synthetic-to-real transfer leveraging frozen DINOv3 features. https://t.co/Bd1VGdExap

[Towards Data Science @TDataScience]

"A better model follows advice more often but not always. Setting the temperature to zero makes its answers more repeatable, not more obedient."

In the latest installment of his Towards Spec-Driven Test Automation series, @GA4198498563411 weighs the benefits and drawbacks of using skills vs. an MCP server. https://t.co/xxlbLA5vZu

[Towards Data Science @TDataScience]

Switching to a cheaper model should... lower your overall costs, right?

@nhu_hoang Nhu Hoang explains why that's not always the case, and stresses the importance of verifying the hidden costs of LLM routing. https://t.co/9hnP91ETBI

[Towards Data Science @TDataScience]

Learn how to turn scattered requiremenets into deployable agents by following along Doster Esh's clear, actionable guide. https://t.co/OKPFF47kCD

[Towards Data Science @TDataScience]

On-premise RAG without a GPU by Hubert García Gordon

https://t.co/qVYbXHCVHt

[Towards Data Science @TDataScience]

Writing at the intersection of machine learning, computer vision, and medical AI, Renish Joshi shares a follow-up article zooming in on sensor-reduction testing, feature ranking, and other pragmatic factors to consider when working with electroencephalograms (EEG). https://t.co/q5l1RcBv38

[Towards Data Science @TDataScience]

We're thrilled to welcome Renish Joshi to TDS with an incisive debut article, focusing on the challenges of participant-aware evaluation in the context of EEG models. https://t.co/XH4kmiCcgh

[Towards Data Science @TDataScience]

"So, let’s talk a bit about the allegations against https://t.co/gbBBuNWCUZ, the technical reasons for them, and how they could have been mitigated."

Stephanie Kirmer invites us to examine the technical choices that can lead to AI-related safety failures. https://t.co/1rWfvZEYuG

[Towards Data Science @TDataScience]

For a fresh, Bayesian look at the Monty Hall problem, don't miss Samit Ganguly's accessible explainer. https://t.co/sTh5S0pXeU

[Towards Data Science @TDataScience]

Can two charts be accurate and nonetheless contradict each other?

The short answer is yes. The longer answer is what you'll find in Sara A. Metwalli's excellent exploration of data storytelling (and its pitfalls). https://t.co/2Wh2EkFo9N

[Towards Data Science @TDataScience]

"The claim, then, is not that the industry's gate is wrong. It is that the gate is structurally incapable of seeing a specific, measurable failure, and that seeing it costs two numbers and an afternoon of measurement."

Maxim Podstrechnyy's debut TDS article zooms in on the number your quantization gate doesn't have. https://t.co/VQR9YGSYPY

[Towards Data Science @TDataScience]

From cloning code from GitHub to Python virtual environments and Docker, Jarom Hulet presents an accessible new series on the must-know elements of deploying data science tools. https://t.co/QQAwrGrOKJ

[Towards Data Science @TDataScience]

Leveraging Codex to build a claim-checker for his writing sounded like a great idea to @BenjaminNweke11. The execution proved trickier. https://t.co/0hdAlbz9z7

[Towards Data Science @TDataScience]

To what extent do you "own" the code an AI coding assistant generated at your request? @emmimalpa's new deep dive explores this question from a pragmatic, measurable angle. https://t.co/rbTDcilPmH

[Lambert Leong, PhD @leonglambert]

Jev will change the future of agentic builds