January 2026 Summaries

Inngest prioritized developer experience (DX) and embarked on a migration from Next.js to Tanstack Start due to challenges with cognitive overhead and slow load times in their existing framework. Initially adopting Next.js for its promise of eliminating blank loading states and providing streaming and nested layouts, the team found its workflow, optimized for dedicated frontend teams, to be cumbersome for their multi-role engineers. Attempts to improve performance with Turbopack and Next.js upgrades were insufficient, prompting the exploration of alternatives like Deno Fresh and React Router. Ultimately, Tanstack Start was chosen for its clear conventions and alignment with the team's previous positive experiences with Tanstack products. The migration process involved a direct transition, with AI assisting in conversion tasks, resulting in significantly improved local load times and developer satisfaction. The project, largely completed by one engineer, was relatively quick and painless, demonstrating the impact of prioritizing a streamlined DX.

In an effort to improve the usability and adoption rate of its SQL-based query tool, the company developed Insights AI by integrating AI to allow natural language queries, thus addressing the steep learning curve associated with understanding SQL and specific schema knowledge. The transition involved shifting from OpenAI's models to Anthropic's Claude for better context handling and instruction adherence, significantly enhancing query accuracy and response times. They also implemented checkpointing to drastically reduce latency from 15-30 seconds to 3-6 seconds per query and refined their prompt engineering, moving from vague system prompts to explicit, detailed instructions and examples, resulting in an improved success rate of 90% for queries. The development of Insights AI demonstrated the company's capability in building AI-powered features, increasing user engagement by making data querying accessible to non-technical users, and reducing frustration for those encountering errors in custom queries. The project highlighted the importance of prompt engineering, model selection, and infrastructure management, serving as a successful example of how dogfooding can refine a platform's capabilities for both internal and customer benefit.

Insights AI is a recently launched feature that enhances the Insights platform by allowing users to interact with data using natural language queries, thereby eliminating the need to write SQL. It processes requests in plain English, automatically generating and executing queries that comply with Insights' syntax constraints, which exclude certain SQL functions like COUNT(DISTINCT) and sub-selects. This innovation democratizes data access across teams, enabling product managers and support staff to obtain metrics and investigate issues without relying on engineering support, while also aiding SQL-proficient team members in speeding up their workflows. The AI assistant remembers previous interactions, allowing users to refine their queries iteratively and supports complex queries, bulk event inspection, and payload analysis without manual SQL crafting. Built on Inngest's infrastructure, including Realtime, Checkpointing, and AgentKit technologies, Insights AI is designed for real-time feedback and reliable orchestration and is now available on the Inngest dashboard, with user feedback encouraged via Discord.

Inngest has introduced a new feature called Checkpointing within its orchestration engine, designed to enhance workflow efficiency by achieving near-zero inter-step latency while maintaining durability. This innovation has resulted in a 50% increase in execution speed, exemplified by a reduction in the time to process Shopify orders from 18 seconds to 5 seconds, as reported by users. Checkpointing allows for a hybrid durable execution mode, where synchronous steps execute immediately and asynchronously if necessary, significantly reducing the latency usually caused by multiple HTTP requests between steps. The Checkpointing API is robust and idempotent, ensuring workflow state preservation and seamless execution even in the event of interruptions. The feature is compatible with serverless environments by incorporating a tunable maximum lifetime to manage serverless timeouts effectively. Now available in public beta, Checkpointing can be configured at both the client and function levels, offering enhanced performance without compromising the reliability and durability of Inngest’s existing framework.