✨Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective
📝 Summary:
This study investigated software developers' perspectives on Large Language Models, identifying benefits like improved workflow and entrepreneurship, alongside risks to personal well-being and reputation. It highlights key trade-offs and best practices for adopting LLMs in software development.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06428
• PDF: https://arxiv.org/pdf/2511.06428
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMs #SoftwareDevelopment #AIinDevelopment #DeveloperExperience #TechResearch
📝 Summary:
This study investigated software developers' perspectives on Large Language Models, identifying benefits like improved workflow and entrepreneurship, alongside risks to personal well-being and reputation. It highlights key trade-offs and best practices for adopting LLMs in software development.
🔹 Publication Date: Published on Nov 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06428
• PDF: https://arxiv.org/pdf/2511.06428
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLMs #SoftwareDevelopment #AIinDevelopment #DeveloperExperience #TechResearch
✨PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides
📝 Summary:
PPTAgent improves presentation generation with a two-stage approach that analyzes reference presentations to ensure structural and content consistency. It outperforms traditional methods across content, design, and coherence.
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.03936
• PDF: https://arxiv.org/pdf/2501.03936
• Github: https://github.com/icip-cas/PPTAgent
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Forceless/Zenodo10K
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AIPresentations #GenerativeAI #MachineLearning #NLP #TechResearch
📝 Summary:
PPTAgent improves presentation generation with a two-stage approach that analyzes reference presentations to ensure structural and content consistency. It outperforms traditional methods across content, design, and coherence.
🔹 Publication Date: Published on Jan 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.03936
• PDF: https://arxiv.org/pdf/2501.03936
• Github: https://github.com/icip-cas/PPTAgent
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Forceless/Zenodo10K
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AIPresentations #GenerativeAI #MachineLearning #NLP #TechResearch