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Many end-user programming tasks require programmatically processing JSON, wrangling it from one format to another or building interactive applications atop it. But end-users are impeded by the indirectness and steep learning curve of textual code. We present Sculpin, a direct-manipulation environment supporting a broad range of JSON-transformation tasks. A user of Sculpin transforms JSON data step by step, recording a program in the process. Sculpin makes three design commitments to ensure directness and versatility: (1) steps are small and precise, not inferred; (2) steps are general-purpose and open to re-appropriation; (3) steps operate on JSON itself, rather than on a limited intermediate representation. To support these commitments, Sculpin introduces a mechanism of sculptable selections: the user can direct their action by guiding a selection on top of the data through small steps like generalization and hierarchical navigation. Sculpin also extends JSON with embedded interface elements like form inputs and buttons, allowing applications to be sculpted incrementally from source data. We demonstrate the breadth and directness of Sculpin in use-cases ranging from wrangling data to building applications. We evaluate Sculpin through a heuristic analysis, situating it in a broad space of programming systems and surfacing limitations such as difficulties editing preexisting programs. 

LLM chains enable complex tasks by decomposing work into a sequence of subtasks. Similarly, the more established techniques of crowdsourcing workflows decompose complex tasks into smaller tasks for human crowdworkers. Chains address LLM errors analogously to the way crowdsourcing workflows address human error. To characterize opportunities for LLM chaining, we survey 107 papers across the crowdsourcing and chaining literature to construct a design space for chain development. The design space covers a designer’sobjectivesand thetacticsused to build workflows. We then surfacestrategiesthat mediate how workflows use tactics to achieve objectives. To explore how techniques from crowdsourcing may apply to chaining, we adapt crowdsourcing workflows to implement LLM chains across three case studies: creating a taxonomy, shortening text, and writing a short story. From the design space and our case studies, we identify takeaways for effective chain design and raise implications for future research and development. 

Dynamically Interactive Visualization (DIVI) is a novel approach for orchestrating interactions within and across static visualizations. DIVI deconstructs Scalable Vector Graphics charts at runtime to infer content and coordinate user input, decoupling interaction from specification logic. This decoupling allows interactions to extend and compose freely across different tools, chart types, and analysis goals. DIVI exploits positional relations of marks to detect chart components such as axes and legends, reconstruct scales and view encodings, and infer data fields. DIVI then enumerates candidate transformations across inferred data to perform linking between views. To support dynamic interaction without prior specification, we introduce a taxonomy that formalizes the space of standard interactions by chart element, interaction type, and input event. We demonstrate DIVI's usefulness for rapid data exploration and analysis through a usability study with 13 participants and a diverse gallery of dynamically interactive visualizations, including single chart, multi-view, and cross-tool configurations.