We present app.build (https://github.com/neondatabase/appdotbuild-agent), an open-source framework that improves LLM-based application generation through systematic validation and structured environments. Our approach combines multi-layered validation pipelines, stack-specific orchestration, and model-agnostic architecture, implemented across three reference stacks. Through evaluation on 30 generation tasks, we demonstrate that comprehensive validation achieves 73.3% viability rate with 30% reaching perfect quality scores, while open-weights models achieve 80.8% of closed-model performance when provided structured environments. The open-source framework has been adopted by the community, with over 3,000 applications generated to date. This work demonstrates that scaling reliable AI agents requires scaling environments, not just models -- providing empirical insights and complete reference implementations for production-oriented agent systems.

The last decade has seen a remarkable rise in specialized database systems. Systems for transaction processing, data warehousing, time series analysis, full-text search, data lakes, in-memory caching, document storage, queuing, graph processing, and geo-replicated operational workloads are now available to developers. A belief has taken hold that a single general-purpose database is not capable of running varied workloads at a reasonable cost with strong performance, at the level of scale and concurrency people demand today. There is value in specialization, but the complexity and cost of using multiple specialized systems in a single application environment is becoming apparent. This realization is driving developers and IT decision makers to seek databases capable of powering a broader set of use cases when looking to adopt a new database. Hybrid transaction and analytical (HTAP) databases have been developed to try to tame some of this chaos.

XML is commonly supported by SQL database systems. However, existing mappings of XML to tables can only deliver satisfactory query performance for limited use cases. In this paper, we propose a novel mapping of XML data into one wide table whose columns are sparsely populated. This mapping provides good performance for document types and queries that are observed in enterprise applications but are not supported efficiently by existing work. XML queries are evaluated by translating them into SQL queries over the wide sparsely-populated table. We show how to translate full XPath 1.0 into SQL. Based on the characteristics of the new mapping, we present rewriting optimizations that dramatically reduce the number of joins. Experiments demonstrate that query evaluation over the new mapping delivers considerable improvements over existing techniques for the target use cases.

XML is commonly supported by SQL database systems. However, existing mappings of XML to tables can only deliver satisfactory query performance for limited use cases. In this paper, we propose a novel mapping of XML data into one wide table whose columns are sparsely populated. This mapping provides good performance for document types and queries that are observed in enterprise applications but are not supported efficiently by existing work. XML queries are evaluated by translating them into SQL queries over the wide sparsely-populated table. We show how to translate full XPath 1.0 into SQL. Based on the characteristics of the new mapping, we present rewriting optimizations that minimize the number of joins. Experiments demonstrate that query evaluation over the new mapping delivers considerable improvements over existing techniques for the target use cases.