March 2026 Summaries
14 posts from Weave
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Mar 26, 2026
1,117 words in the original blog post.
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Mar 25, 2026
1,046 words in the original blog post.
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Mar 24, 2026
1,167 words in the original blog post.
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Mar 23, 2026
898 words in the original blog post.
In the 2026 engineering landscape, accurately measuring the impact of AI tools on teams is crucial for demonstrating their return on investment and value. Traditional metrics like the "acceptance rate" fall short, as they do not capture the quality or effectiveness of AI-assisted code. To address this, a comprehensive balanced scorecard approach is recommended, which evaluates AI usage across four layers: utilization, productivity, quality, and developer experience. Utilization metrics examine who is using AI tools and how frequently, while productivity metrics measure tangible output improvements against a pre-AI baseline. Quality metrics assess the impact on code quality and technical debt, and qualitative insights focus on developer sentiment and experiences with AI tools. By employing a multifaceted framework, engineering leaders can move beyond superficial metrics to gain a deeper understanding of AI's true value and optimize its integration into their teams.
Mar 18, 2026
1,129 words in the original blog post.
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Mar 17, 2026
1,346 words in the original blog post.
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Mar 16, 2026
1,098 words in the original blog post.
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Mar 13, 2026
1,566 words in the original blog post.
In March 2026, as AI tools for coding have become commonplace, engineering leaders seek tangible data to assess their effectiveness, moving beyond anecdotal evidence to evaluate the return on investment (ROI) and refine strategies. Tools like Claude Code and Cursor offer built-in analytics that provide insights into AI usage, with Cursor focusing on developer interactions within the IDE and Claude emphasizing broader business outcomes like ROI and overall adoption. Cursor Analytics tracks metrics such as feature usage and AI suggestion acceptance rates, while Claude Code Analytics focuses on lines of code accepted and spend over time, offering different perspectives on AI tool impact. However, both platforms have inherent limitations, as their analytics are siloed and fail to provide a comprehensive view of AI's impact on overall engineering performance. To overcome this, the use of an engineering intelligence platform like Weave is suggested, allowing integration of disparate data sources to form a unified view of AI's true impact on metrics that matter to the business, such as cycle time, bug rates, and feature delivery efficiency.
Mar 12, 2026
1,311 words in the original blog post.
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Mar 11, 2026
1,034 words in the original blog post.
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Mar 09, 2026
1,049 words in the original blog post.
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Mar 06, 2026
1,192 words in the original blog post.
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Mar 04, 2026
1,069 words in the original blog post.
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Mar 02, 2026
1,211 words in the original blog post.