Publish your data, AI techniques, and agentic engineering work on Towards Data Science

If you’re reading The New Stack, there’s a good chance you’ve already done something worth writing about: built an agent pipeline, shipped a model to production, or figured out how to make an LLM actually reliable in a real system. You just might not have written it up yet.

Let’s change that. This is an invitation to publish your work on Towards Data Science, one of the largest publications in the data and AI space and part of the same network of sites as The New Stack. It’s free to submit, and authors are compensated through the TDS Author Payment Program.

The overlap between what The New Stack authors know and what TDS readers need is enormous, and growing every day as AI engineering becomes inseparable from infrastructure engineering.

Towards Data Science has more than 675,000 monthly search clicks, more than 150,000 newsletter subscribers, and a social following of more than 950,000. But the number that matters most is the one you can’t measure: the trust our readers place in what we publish.

Every article is written by a practitioner, not a marketer. Our readers are data scientists, ML engineers, AI engineers, and analysts because they know the content is written by people who actually do the work.

Why it matters for The New Stack readers

If you’re already in the world of cloud infrastructure, DevOps, or agentic engineering, you likely touch data and ML systems more than you realize.

Perhaps you’ve built a multi-agent system using Claude and OpenClaw that handles real production workflows, designed a reliable tool-use layer for an LLM-powered pipeline, or benchmarked different frontier models to find the right tradeoff between reasoning quality and latency. Great — tell us about it. Maybe you’ve wired up an agentic framework to act on live data, built evaluation harnesses to catch model regressions before they ship, or figured out how to keep long-running agents on task without losing context. Publish your findings.

That’s exactly the kind of content TDS readers expect.

What works best on TDS

The articles that resonate most with our audience share a few things in common:

• Specificity over generality: “How I Built a Reliable Multi-Agent Pipeline with Claude and OpenClaw” will always outperform “An Introduction to Agentic AI.” Our readers want to learn from your particular experience.
• Genuine technical depth: Show the code, explain the trade-offs, and discuss what didn’t work. Practitioners can spot surface-level content immediately, and they’ll scroll right past it.
• A timely angle: Tie your piece to something current: a new model capability, a shift in how agents are being architected, a hard-won lesson from a recent project. Give readers a reason to care right now.
• Your own visuals. A clear architecture diagram or a well-labeled chart can turn a good article into a shareable one.

Here’s what to expect when you work with us:

• Word count: Articles typically range from 1,000 to 5,000 words; long enough to go deep, short enough to respect your reader’s time.
• Editorial support: Our editors review every article and work with you before anything goes live.
• Promotion: Every published article gets pushed across our social channels at least twice, plus homepage placement and a potential spot in our newsletter.
• Turnaround: Once your article is approved, it will be published within two days.

You can get paid, too

TDS runs an Author Payment Program. Once accepted, authors earn from the articles, and the more the work connects with readers, the higher the compensation. It’s a straightforward way to get rewarded for sharing what you know.

How to submit

The process is simple: Write your article, submit it through our submission form, and our editors will review it (usually within a week). We look at every submission for accuracy, clarity, and relevance. If your piece is accepted, it gets a homepage placement, social promotion, and could be selected for our newsletter.

Before you submit, you can review our full submission guidelines and FAQ for details on formatting, images, and content standards.

The bottom line

You’re already building the next generation of AI-powered systems. Publishing about them is how you turn that experience into something that helps thousands of other engineers while building your reputation in the process.

We’d love to read what you’ve got. Submit your article here.

The New Stack, Towards Data Science, and Roadmap.sh are owned and operated by Insight Media Group.

The post Publish your data, AI techniques, and agentic engineering work on Towards Data Science appeared first on The New Stack.