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product#code generation📝 BlogAnalyzed: Jan 4, 2026 08:18

AI-Assisted Code: Fast Implementation, Slow Results? Identifying and Fixing 'AI Code Smells'

Published:Jan 4, 2026 07:37
1 min read
Qiita AI

Analysis

The article highlights a critical issue in AI-assisted development: the potential for increased initial velocity to be offset by increased debugging and review time due to 'AI code smells.' It suggests a need for better tooling and practices to ensure AI-generated code is not only fast to produce but also maintainable and reliable.
Reference

“生成AIで実装スピードは上がりました。(自分は入社時からAIを使っているので前時代のことはよくわかりませんが...)”

PermalinkQiita AI
Paper#llm🔬 ResearchAnalyzed: Jan 3, 2026 08:55

Training Data Optimization for LLM Code Generation: An Empirical Study

Published:Dec 31, 2025 02:30
1 min read
ArXiv

Analysis

This paper addresses the critical issue of improving LLM-based code generation by systematically evaluating training data optimization techniques. It's significant because it provides empirical evidence on the effectiveness of different techniques and their combinations, offering practical guidance for researchers and practitioners. The large-scale study across multiple benchmarks and LLMs adds to the paper's credibility and impact.
Reference

“Data synthesis is the most effective technique for improving functional correctness and reducing code smells.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:29

Specification and Detection of LLM Code Smells

Published:Dec 19, 2025 19:24
1 min read
ArXiv

Analysis

This article likely focuses on identifying and addressing problematic patterns (code smells) in code generated or used by Large Language Models (LLMs). The research probably explores methods to define these smells and develop techniques to automatically detect them, potentially improving the quality and maintainability of LLM-related code.

Key Takeaways

    Reference

    “”

    PermalinkArXiv
    Research#llm👥 CommunityAnalyzed: Jan 3, 2026 06:40

    Anthropic’s paper smells like bullshit

    Published:Nov 16, 2025 11:32
    1 min read
    Hacker News

    Analysis

    The article expresses skepticism towards Anthropic's paper, likely questioning its validity or the claims made within it. The use of the word "bullshit" indicates a strong negative sentiment and a belief that the paper is misleading or inaccurate.

    Key Takeaways

    Reference

    “Earlier thread: Disrupting the first reported AI-orchestrated cyber espionage campaign - <a href="https://news.ycombinator.com/item?id=45918638">https://news.ycombinator.com/item?id=45918638</a> - Nov 2025 (281 comments)”

    PermalinkHacker News