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We present an algorithm that canonicalizes the algebraic representations of the topological semantics of machine knitting programs. Machine knitting is a staple technology of modern textile production where hundreds of mechanical needles are manipulated to form yarn into interlocking loop structures. Our semantics are defined using a variant of a monoidal category, and they closely correspond to string diagrams. We formulate our canonicalization as an Abstract Rewriting System (ARS) over words in our category, and prove that our algorithm is correct and runs in polynomial time. 

Climate change demands urgent action, yet understanding the environmental impact (EI) of everyday objects and activities remains challenging for the general public. While Life Cycle Assessment (LCA) offers a comprehensive framework for EI analysis, its traditional implementation requires extensive domain expertise, structured input data, and significant time investment, creating barriers for non-experts seeking real-time sustainability insights. Here we present the first autonomous sustainability assessment tool that bridges this gap by transforming unstructured natural language descriptions into in-context, interactive EI visualizations. Our approach combines language modeling and AI agents, and achieves >97% accuracy in transforming natural language into a data abstraction designed for simplified LCA modeling. The system employs a non-parametric datastore to integrate proprietary LCA databases while maintaining data source attribution and allowing personalized source management. We demonstrate through case studies that our system achieves results within 11% of traditional full LCA, while accelerating from hours of expert time to real-time. We conducted a formative elicitation study (N=6) to inform the design objectives of such EI communication augmentation tools. We implemented and deployed the tool as a Chromium browser extension and further evaluated it through a user study (N=12). This work represents a significant step toward democratizing access to environmental impact information for the general public with zero LCA expertise. 

Storage, organizing, and decorating are important aspects of home design. Buying commercial items for many of these tasks, this can be costly, and reuse is more sustainable. An alternative is a “home hack,” i.e., a functional assembly constructed from existing household items. However, coming up with such hacks requires combining objects to make a physically valid design, which might be difficult to test if they are large, require nailing or screwing to the wall, or if the designer has mobility limitations. We present a design and visualization system, FabHacks, for cre- ating workable functional assemblies. The system is based on a new solver-aided domain-specific language (S-DSL) called FabHaL. By analyzing existing home hacks shared online, we create a design abstraction for connecting household items using predefined con- nection types. We also provide a UI for designing hack assemblies that fulfill a given specification. FabHacks leverages a physics-based solver that finds the expected physical configuration of an assembly design. Our validation includes a user study with our UI, which shows that users can easily create assemblies and explore a range of designs. 

Reducing the environmental footprint of electronics and computing devices requires new tools that empower designers to make informed decisions about sustainability during the design process itself. This is not possible with current tools for life cycle assessment (LCA) which require substantial domain expertise and time to evaluate the numerous chips and other components that make up a device. We observe first that informed decision-making does not require absolute metrics and can instead be done by comparing designs. Second, we can use domain-specific heuristics to perform these comparisons. We combine these insights to develop DeltaLCA, an open-source interactive design tool that addresses the dual challenges of automating life cycle inventory generation and data availability by performing comparative analyses of electronics designs. Users can upload standard design files from Electronic Design Automation (EDA) software and the tool will guide them through determining which one has greater carbon footprints. DeltaLCA leverages electronics-specific LCA datasets and heuristics and tries to automatically rank the two designs, prompting users to provide additional information only when necessary. We show through case studies DeltaLCA achieves the same result as evaluating full LCAs, and that it accelerates LCA comparisons from eight expert-hours to a single click for devices with ~30 components, and 15 minutes for more complex devices with ~100 components.