Vibe Machine Learning: Using GenAI for ML, AI and R&D
Last Updated on June 22, 2026 by Editorial Team
Author(s): Artem Shelamanov
Originally published on Towards AI.
Vibe Machine Learning: Using GenAI for ML, AI and R&D
The rise of AI tools has affected many people across different areas of IT. But the field that has been affected the most is, without a doubt, software engineering. Over the past few decades, programmers around the world have created an enormous amount of open-source code, projects, guides, documentation, Stack Overflow/Reddit discussions, and other data that can be used to train AI models.
The author argues that while “vibe coding” tools are strong for software engineering, they’re often a poor fit for ML/AI R&D due to coding-oriented prompts and added “bloat” that wastes valuable context. Based on their experience, they recommend using Pi instead, highlighting its minimal prompts, lack of unnecessary agent features, full customization, and ability to avoid provider lock-in, and noting that it performs well in real evaluation-style science tasks. They then walk through practical use cases—initial research, rapid prototyping, training iteration support (with careful metric/seed control), bug search and code issue resolution, and data analysis/reporting—emphasizing speed gains but also warning that agents introduce tech debt, may hallucinate, and can optimize for misleadingly “good-looking” results. The conclusion frames AI agents as fast junior collaborators whose outputs must be verified by the engineer.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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