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Towards Data Science @towardsdatascience.com · 5h

Making hundreds of API calls sequentially can take forever. In this this article, @taupirho.bsky.social explains how httpx and asyncio can complete the same tasks in a fraction of the time, with a practical test case you can replicate.

Beyond Requests: Why httpx is the Modern HTTP Client You Need (Sometimes) | Towards Data Science

A comprehensive comparison of these two Python libraries

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Towards Data Science @towardsdatascience.com · 8h

See practical code examples for building better AI agent tools. Eivind Kjosbakken's new article contrasts bad and good tool definitions to illustrate key principles for developers.

How to Build Tools for AI Agents | Towards Data Science

Learn how to design and build effective tools to be used by AI Agents

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Reposted by Towards Data Science

taupirho.bsky.social @taupirho.bsky.social · 9h

In my latest article for the @towardsdatascience.com blog, I do a deep dive comparison between the two leading HTTP libraries, requests and httpx. I compare the runtimes of the two in typical use-cases and show you when -and when not - to use one over the other
towardsdatascience.com/beyond-reque...

Beyond Requests: Why httpx is the Modern HTTP Client You Need (Sometimes) | Towards Data Science

A comprehensive comparison of these two Python libraries

towardsdatascience.com

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Towards Data Science @towardsdatascience.com · 10h

Turn your struggles into a breakthrough for others. 🚀 The articles our community loves most are those that take a messy technical experience and shape it into a clear, approachable guide.

Submit your article today ➡️ bit.ly/TDSContributor

Towards Data Science @towardsdatascience.com · 11h

Chinmay Kakatkar demonstrates how the concept of Eulerian paths can be used for algorithmic music composition. Discover how techniques from bioinformatics and logistics can unlock new possibilities in creative fields.

Eulerian Melodies: Graph Algorithms for Music Composition | Towards Data Science

Conceptual overview and an end-to-end Python implementation

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Reposted by Towards Data Science

roadmap.sh @roadmapsh.bsky.social · 21h

Your chance to watch the future of learning 🚀

@kamrify.bsky.social shows you how the AI Tutor works, detailing the quick story of its creation and giving a deep-dive demo. Discover how to effortlessly generate custom learning and have a tutor dedicated only to you.

Use AI to Supercharge Your Learning on roadmap.sh

In this session, I'll walk you through our AI Tutor, share the quick story of how and why we built it, then jump into the demo. You will get the chance to ask questions at the end. Come see how to…

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Towards Data Science @towardsdatascience.com · 13h

When we build an app, it’s only natural to want to share it. Ivo Bernardo walks you through a short tutorial on how to deploy your own @hf.co Space. If you want to highlight your work and applications, this is a strong option.

Showcasing Your Work on HuggingFace Spaces | Towards Data Science

Building an app is exciting - but sharing it is where the real value kicks in. Back when Heroku offered a free tier, deploying demos was effortless. Those days are gone, and finding a simple, free…

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Towards Data Science @towardsdatascience.com · 15h

Samir Saci breaks down his playbook for learning JavaScript through @n8n.io Code Node examples, showing how data scientists can quickly build workflows without heavy documentation.

From Python to JavaScript: A Playbook for Data Analytics in n8n with Code Node Examples | Towards Data Science

Learn the basics of JavaScript through tiny n8n Code node snippets for sales data analytics

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Towards Data Science @towardsdatascience.com · 1d

See why a well-intentioned optimization can sometimes hurt performance. Ryan Pégoud's new article shares a surprising benchmark result from a Triton GEMM kernel and discusses the importance of profiling.

Learning Triton One Kernel at a Time: Matrix Multiplication | Towards Data Science

Tiled GEMM, GPU memory, coalescing, and much more!

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Towards Data Science @towardsdatascience.com · 1d

Learn to translate complex model metrics into clear business KPIs. This new article from Kristopher McGlinchey teaches how to frame your results in terms of money saved, generated, or time saved.

Building A Successful Relationship With Stakeholders | Towards Data Science

Show your value by moving beyond the technical

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Reposted by Towards Data Science

dmitryontech.bsky.social @dmitryontech.bsky.social · 2d

How to Build Effective Agentic Systems with LangGraph in @towardsdatascience.com

towardsdatascience.com/how-to-build...

How to Build Effective Agentic Systems with LangGraph | Towards Data Science

Create AI workflows with agentic frameworks

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Reposted by Towards Data Science

taupirho.bsky.social @taupirho.bsky.social · 1d

In my recent article on @towardsdatascience.com , I dive into how you can call Mojo directly from Python to supercharge your code without leaving the Python comfort zone. Read it for FREE using this link.
towardsdatascience.com/python-can-n...

Python Can Now Call Mojo | Towards Data Science

Boost your runtimes with lightning-fast Mojo code

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Reposted by Towards Data Science

roadmap.sh @roadmapsh.bsky.social · 1d

Want to stand out as a junior developer? 🙋 Knowing #SQL is non-negotiable. With our SQL course, you’ll...

✔️ Go from SELECT to complex joins
✔️ Practice in an integrated IDE
✔️ Earn a certificate to share with employers

Start learning → roadmap.sh/courses/sql

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Towards Data Science @towardsdatascience.com · 1d

🌟 Welcome Nidhin Karunakaran Ponon! 🌟 Nidhin dives into practical guardrails for AI systems — what they are, why they matter, and how to create them in your own workflows.

Want to see your work featured next? Submit your article and join our community of paid TDS contributors: bit.ly/TDSContributor

How To Build Effective Technical Guardrails for AI Applications | Towards Data Science

Exploring the most practical guardrails to implement at ground level

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Towards Data Science @towardsdatascience.com · 1d

Struggling with unreliable AI predictions for equipment failure? It's often due to a lack of specific training data. Illia Smoliienko's debut TDS article explains why this makes human analysts essential in predictive maintenance.

Why AI Still Can’t Replace Analysts: A Predictive Maintenance Example | Towards Data Science

Learn about the limitations of AI in analytics through the example of bearing vibration data analysis

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Towards Data Science @towardsdatascience.com · 1d

Discover why a programmer's core function becomes more valuable, not less, in the age of AI. This new article by Elisha Rosensweig and Eitan Wagner explores how the process of coding is one of discovery, making crucial decisions that AI's statistical approach cannot replicate.

Human Won’t Replace Python | Towards Data Science

Why vibe-coding is not a step up from "classic" coding — and why it matters

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Towards Data Science @towardsdatascience.com · 1d

Reduce development friction and standardize project setup across your team. Elena Jolkver's new article walks you through Cookiecutter, a tool for creating project templates. Learn how to define a reusable structure, automate boilerplate, and generate fully-formed projects with a single command.

How to Spin Up a Project Structure with Cookiecutter | Towards Data Science

If you’re anything like me, “procrastination” might as well be your middle name. There’s always that nagging hesitation before starting a new project. Just thinking about setting up the project…

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Towards Data Science @towardsdatascience.com · 1d

Eivind Kjosbakken explains how to leverage LangGraph to build effective agentic systems, simplifying AI workflows while managing tool-calling and agent states.

How to Build Effective Agentic Systems with LangGraph | Towards Data Science

Create AI workflows with agentic frameworks

towardsdatascience.com

Towards Data Science @towardsdatascience.com · 1d

AI tools & vibe coding have hidden tradeoffs. Here's how to use them wisely and why open source is a great secret weapon.

By Clara Chong

How to Level Up Your Technical Skills in This AI Era | Towards Data Science

AI tools & vibe coding have hidden tradeoffs. Here's how to use them wisely and why open source is a great secret weapon.

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Towards Data Science @towardsdatascience.com · 1d

Are you using #DeepSeek?

Matthew Gunton dives into how they formulated their policy optimization equations to allow for Reinforcement Learning.

Exploring DeepSeek's R1 Training Process | Towards Data Science

Open-Source Intelligence on Par with Proprietary Models

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Towards Data Science @towardsdatascience.com · 2d

Mitchell Telatnik explores why traditional cyber risk models fall short and how applying some light statistical tools such as probabilistic modeling offers a better way forward.

Why Most Cyber Risk Models Fail Before They Begin | Towards Data Science

The case for quantitative thinking in cyber risk

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Towards Data Science @towardsdatascience.com · 2d

The only way for businesses to keep up and move fast enough is with open source. Read @anacondainc.bsky.social Chief Product and Technology Officer Laura Sellers's full article FREE now.

Why Open Source is No Longer Optional — And How to Make it Work for Your Business | Towards Data Science

To stay ahead, open source is the only way.

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Towards Data Science @towardsdatascience.com · 2d

Explore a complete, end-to-end workflow for temporal graph fraud detection. Erika G. Gonçalves walks through loading data, building a time-stamped graph, and preparing subgraphs for training.

No Peeking Ahead: Time-Aware Graph Fraud Detection | Towards Data Science

How to implement leak-free graph fraud detection

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Towards Data Science @towardsdatascience.com · 2d

Understand how ROC curves and AUC help you go beyond accuracy with visuals and examples.

By Nikhil Dasari

ROC AUC Explained: A Beginner’s Guide to Evaluating Classification Models | Towards Data Science

Understand how ROC curves and AUC help you go beyond accuracy with visuals and examples.

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Towards Data Science @towardsdatascience.com · 2d

In this follow-up piece, Murtaza Ali dives into visual encoding channels, offering a clear, non-technical look at how great data visualizations communicate meaning.

Data Visualization Explained (Part 2): An Introduction to Visual Variables | Towards Data Science

A non-technical and accessible guide to the underlying concept behind visual design: visual encoding channels

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