Bruno leads lightweight Postman alternatives in 2026 for cost and migration ease

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A technical comparison published on DEV Community evaluates five lightweight API clients as alternatives to Postman, focusing on pricing and migration effort. Bruno, a fully free and open-source tool, is recommended as the top choice because it stores API collections as local text files compatible with Git version control. It requires no account registration, works entirely offline, and supports importing Postman collections in just a few steps. Other tools reviewed include Hoppscotch, Insomnia, HTTPie Desktop, and Thunder Client, each suited to different team setups and protocol needs. The guide also provides a six-step migration walkthrough from Postman to Bruno, covering export, installation, import, and team deployment via Git.

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Rust-Based Glob Matcher Zeromatch Offers 2x Speed Boost Over Picomatch in One-Shot Use

A developer has released zeromatch, a Rust-based JavaScript native addon designed to overcome performance bottlenecks in glob pattern matching found in tools like bundlers and file watchers. Unlike the widely used picomatch library, which compiles patterns into regular expressions and depends on V8 for execution, zeromatch uses a bytecode virtual machine with optimized fast paths for common patterns. Benchmarks show zeromatch is roughly twice as fast as picomatch in one-shot matching scenarios, though it underperforms in cached single-match workloads due to the latency introduced by the JavaScript-Rust Foreign Function Interface boundary. The library is API-compatible with picomatch, but developers are advised to test their specific patterns carefully, as edge cases in handling escaped characters or complex negations may behave differently. Zeromatch is positioned as a targeted solution for performance-critical one-shot matching rather than a universal replacement for picomatch.

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ProgrammingDEV Community ·

CTE vs Temporary Tables: How to Choose the Right SQL Tool for Your Query

CTEs and temporary tables both handle intermediate datasets in SQL, but they differ significantly in behavior and best use cases. A CTE is a named subquery that exists only for the duration of a single SQL statement, making it ideal for improving query readability and supporting recursion. Temporary tables, by contrast, physically store data in the database's temporary storage and persist throughout a session, allowing indexes to be created and results to be reused multiple times. For large datasets or complex stored procedures requiring multiple reads of the same data, temporary tables generally offer better performance. Developers are advised to use CTEs for clean, readable queries and temporary tables when performance and data reuse are the priority.

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ProgrammingDEV Community ·

AI Agent System Screens 310 Resumes and Books Interviews in Under an Hour

A recruiter used an AI multi-agent system called SoloEngine to fill an on-site operations engineer role in Beijing, completing in roughly 12.5 hours what would typically take days of manual effort. The system comprised three coordinated agents: a Lead Agent to plan and oversee, a Resume Collection Agent to source and gather applications across platforms, and a Screening Agent to evaluate and score candidates. Within 12 hours, the collection agent had autonomously gathered 310 resumes, which the screening agent then analysed, scored, and exported to a structured Excel file in 30 minutes. The top six candidates were shortlisted with detailed strength-and-weakness breakdowns, and the Lead Agent subsequently coordinated interview scheduling by confirming times and sending confirmation emails automatically. The recruiter noted the entire pipeline was configured in approximately three minutes on SoloEngine's canvas interface.

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ProgrammingDEV Community ·

How CLAUDE.md and AGENTS.md Rule Files Improve AI Code Quality in FastAPI Projects

A 2026 guide published on DEV Community explains how rule files like CLAUDE.md and AGENTS.md help control the behavior of LLM-based AI coding agents in FastAPI projects. Without precise instructions, agents tend to produce plausible but incorrect code, including misuse of async functions, dependency injection errors, and missing response models. Tools such as Claude Code, Cursor, and the OpenAI Agents SDK automatically load these files from the project root and treat them as generation constraints. The guide recommends keeping rule files under 300 lines, using verifiable and specific directives rather than vague instructions like 'write good code', and explicitly listing prohibited actions. Best practices also include separating project conventions from agent behavior policies and leaving change history to version control rather than annotating rule files directly.

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