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Presentation . 2026
License: CC BY
Data sources: Datacite
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Presentation . 2026
License: CC BY
Data sources: Datacite
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You don't need to use AI to be a good software engineer

Authors: Zacharias, Anissa;

You don't need to use AI to be a good software engineer

Abstract

Anissa is a software engineer in the Computational and Information Systems Lab (CISL) at NSF NCAR. Her background is in computational mathematics and science and her current work focuses on creating computational and visualization tools for earth systems science on the GeoCAT team. With claims like “large language models are revolutionizing software development” everywhere, it might feel developers need to integrate LLM products into their workflows or risk falling behind. As we navigate questions about how to use AI responsibly, the case for rejection is often overlooked. This talk will focus on the inherent limitations of LLM products by examining the foundational computational concepts behind modern AI and present a case for rejecting these tools in scientific software development.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green