Tutorials
For professionals engaged with the cutting edge of artificial intelligence and data methodologies, particularly those leveraging tools like Claude and OpenClaw, there's a significant opportunity to disseminate their knowledge. Many in this field are actively involved in developing agent pipelines, deploying production models, or ensuring the dependability of large language models within complex systems. This expertise, often gained through hands-on experience, is highly valuable and deserves a broader audience.
This platform extends a warm invitation to these innovators to contribute their work to Towards Data Science (TDS), a prominent publication in the AI and data domains, affiliated with The New Stack. Submissions are welcome free of charge, and authors are compensated through a dedicated payment program. The synergy between the knowledge base of contributors to The New Stack and the interests of TDS readers is substantial, reflecting the increasingly intertwined nature of AI engineering and infrastructure development. TDS boasts an impressive reach, with hundreds of thousands of monthly search engagements, a large subscriber base for its newsletter, and a vast social media following. Crucially, its reputation is built on the trust its readership places in its content, which is consistently authored by active practitioners rather than marketing specialists. This ensures that the articles provide genuine insights from individuals who are deeply involved in the work they describe.
For those immersed in cloud infrastructure, DevOps, or agentic engineering, the intersection with data and machine learning systems is often more profound than initially perceived. Whether you've architected a multi-agent system for production workflows, designed robust tool-use layers for LLM-driven pipelines, or meticulously benchmarked advanced models to balance reasoning quality and latency, your experiences are precisely what the TDS community seeks. Similarly, if you've integrated agentic frameworks with live data, developed evaluation mechanisms to preempt model regressions, or found ways to maintain the focus of long-running agents, your findings are highly relevant. Articles that thrive on TDS prioritize specificity over broad generalizations, offering concrete examples like 'How I Built a Reliable Multi-Agent Pipeline with Claude and OpenClaw' over general introductions to agentic AI. They demand genuine technical depth, including code snippets, discussions of trade-offs, and candid acknowledgments of challenges. A timely perspective, connecting the content to recent advancements, architectural shifts, or lessons learned from current projects, also enhances an article's appeal. Furthermore, incorporating original visuals such as architectural diagrams or well-structured charts can significantly increase an article's impact and shareability. TDS offers editorial guidance to refine submissions, promotional efforts across various channels, and a swift publication process once an article is approved. Authors also have the opportunity to earn compensation, with remuneration tied to the article's engagement with the readership, providing a direct incentive for sharing valuable insights.
The path to publication is designed to be straightforward: authors draft their articles, submit them through a dedicated portal, and await review by editors who assess for accuracy, clarity, and relevance. Accepted pieces receive prominent placement and promotion, potentially reaching a wide audience through newsletters and social media. This process allows engineers to transform their practical experiences in building advanced AI systems into knowledge that benefits countless peers, simultaneously enhancing their professional standing. The opportunity to contribute to a vibrant community and foster collective learning is immense, and your unique contributions are eagerly anticipated.
