February 2026 Summaries

Mike Cann discusses the evolving guidance on using return validators in Convex queries and mutations, highlighting the shift from always recommending their use to a more nuanced approach. Initially, return validators were advised to mitigate TypeScript circular type issues and enforce runtime correctness, but the "always" rule led to problems such as verbosity, increased token usage, and potential schema errors due to the limitations of Large Language Models (LLMs). The article explains that while return validators are beneficial for enforcing exact runtime contracts, especially in scenarios like static codegen and OpenAPI generation, their blanket use can lead to inefficiencies and complications in refactoring. Therefore, the new guideline encourages relying on TypeScript types and inference by default, reserving return validators for specific cases where runtime exactness is essential. Convex offers a comprehensive backend platform for building full-stack AI projects, integrating functionalities like cloud functions, databases, and real-time updates.

Convex's approach to database performance emphasizes practicality over engaging in "bar chart wars" or synthetic benchmarks, which the team views as misleading and counterproductive. The company, with a strong background in building scalable systems, argues that comparing different database benchmarks is often futile due to the inherent differences in system designs and configurations, which lead to varied performance outcomes. Instead of focusing on theoretical maximum performance, Convex prioritizes scaling based on real customer needs and feedback, ensuring their platform is reliable, economical, and productive for actual use cases. To provide transparency, Convex plans to publish metrics based on real customer scenarios that reflect their platform's performance and cost, catering to future customer projections rather than competing in arbitrary benchmarks. This strategy aligns with their goal of delivering an effective and scalable platform for building full-stack AI projects while maintaining a clear focus on customer satisfaction and practical application.

Convex's marketing journey from 2022 to 2026 reflects a continuous exploration of messaging strategies to effectively communicate its platform's value to potential users. The company has experimented with various taglines, such as "The Reactive Backend for your Reactive App" and "The Global State Management Platform," each highlighting different aspects of its offerings, ranging from ease of backend architecture to seamless integration with frontend development. Despite initial challenges and mixed feedback, Convex aims to position itself as the ideal backend solution for AI projects, emphasizing strong typing with TypeScript, real-time updates, and a focus on scalable, modular app development. The marketing team acknowledges the importance of aligning messages with the target audience's needs and evolving brand identity, learning from past missteps and successes to refine their communication strategy. Convex's efforts include creating interactive experiences and leveraging feedback from enthusiasts, aiming to strike a balance between technical features and accessible messaging to attract new users.

Jashwanth Peddisetty's blog post details a method for integrating Convex with RunPod to handle GPU-intensive tasks serverlessly, focusing on a project that requires video compression and AI-based background removal. By using Convex functions to trigger GPU tasks on RunPod, the system processes tasks like background removal without waiting for API responses, effectively managing storage and processing costs. The workflow begins when a user uploads a video, triggering a Convex action that initiates a RunPod GPU job. This job processes the video, removes its background, and uploads the result back to Convex, updating the job's status in real-time. The blog provides a comprehensive guide on setting up Convex with Next.js and Convex Auth, creating a serverless API handler on RunPod, and demonstrates how to maintain a synchronized database through Convex mutations. The author emphasizes the flexibility of this approach, allowing for a variety of GPU-intensive tasks to be integrated seamlessly, with updates reflected instantly in the user interface, and highlights the cost-effectiveness of using serverless GPU endpoints for projects requiring heavy computation without maintaining dedicated servers.

The discussion revolves around the necessity of including Convex guidelines in AI model prompts, initiated when npm create convex@latest began shipping these guidelines to fill Convex's knowledge gaps compared to larger databases like Postgres or Express. The text details the "evals project," a series of rigorous tests that evaluate models' understanding of Convex, highlighting categories like fundamentals, data modeling, and actions. Despite advancements in models and increased availability of Convex data online, the need for guidelines remains debatable, as recent experiments showed mixed results regarding their impact on model performance. Particularly, guidelines seem to aid older or smaller models significantly, whereas top models perform well with or without them, albeit with slight performance differences. The text explores alternatives to the current guideline setup, such as shrinking the guidelines or adopting a retrieval-based index system that allows models to access documentation dynamically rather than relying on pre-loaded prompts. The author remains undecided on the best approach, emphasizing the importance of further experimentation and inviting community input.