React 19's useOptimistic Hook Makes Instant UI Feedback Simple to Build

·1 views

React 19 introduces the useOptimistic hook, designed to help developers implement optimistic UI patterns with minimal code and built-in automatic rollback. The hook works by immediately updating the user interface to reflect an expected outcome, then reconciling with the actual server response once it arrives. If the server returns an error, the UI automatically reverts to its previous state without extra handling logic. The hook must be used alongside React's startTransition API and pairs well with server actions for common interactions like likes, comments, and deletions. This approach eliminates the perceived lag from network round-trips, making applications feel faster even when server response times are unchanged.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

AI Dev Discovers 88% Accuracy Was Inflated by Train-Test Data Leak

A developer building an AI reasoning system called Origin celebrated hitting 88% accuracy on compositional reasoning probes, only to discover the next morning that 23 of 26 held-out test pairs had already appeared in training data. After fixing the data generators to enforce a strict train-test split and adding a verification tool to block overlapping runs, the honest accuracy dropped to 58% — well below the 60% threshold required to advance to the next development stage. Despite the setback, the real score was still roughly 16 times better than a baseline constant-prediction model. The incident was traced not to a single bug but to lax development discipline, symbolized by files labeled 'sandbox' that were actually being used in production. The developer responded by renaming files and scripts to reflect their true role, aiming to close the gap between informal habits and production-grade standards.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Use Python and Redis for Caching, Sessions, and Fast Data Structures

A technical guide published on DEV Community demonstrates how to integrate Redis with Python using the redis-py async client for building high-performance applications. Redis, an in-memory data store, supports a range of use cases including caching, session storage, leaderboards, and task queuing. The guide walks through practical code examples such as the cache-aside pattern for user profiles, atomic page view counters, and hash-based session management. It also covers sorted sets for real-time leaderboards and list-based task queues using blocking pop operations. The tutorial recommends installing the optional hiredis C parser, which can deliver two to five times faster protocol parsing compared to the default Python implementation.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why One Developer Chose a 16GB M1 Mac to Build an Autonomous AI Agent

A developer has spent three months running a self-operating AI agent on a 16GB M1 Mac, deliberately using small 9B-class models instead of powerful cloud alternatives like GPT or Claude Opus. The agent autonomously generates and posts social media content four times daily, all without human supervision. The author argues that building on small, resource-limited models forces genuine engineering discipline, since flaws such as silent context truncation and missing sampling parameters cannot be masked by a large model's inherent intelligence. In contrast, large cloud models absorb poorly designed prompts and architectural gaps, making it difficult for developers to distinguish their own design from the model's compensatory capability. The developer is preparing to publish a four-part technical series on lessons learned from hardening this constrained local setup.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Dev builds Chrome tab scheduler, reflects on human judgment vs. automation

A developer working on Tab Reminder, a Chrome extension for scheduling tabs to reopen at set times, drew parallels between their work and Alan Turing's legacy in computing. Building the scheduling feature required leveraging Chrome's alarms API to trigger background scripts that reopen tabs reliably, even after a browser restart. The developer found that while automation simplifies many tasks, user intent and context still require human-centered design decisions. The extension addresses a gap that fully automated tools may miss by offering a straightforward interface for tab scheduling. The project reinforced the view that developer tools should augment human capabilities rather than attempt to replace them.

0 comments Read more at DEV Community