Feature Flags Multiply Test Complexity — Here Is How to Manage It

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Feature flags help teams control deployments, but each flag doubles the number of code states that require testing, making exhaustive coverage impractical. Developers are advised to focus testing on flag combinations that share the same code paths, while ensuring every individual flag is verified in both its enabled and disabled states. Applications should also define and test a fallback behavior for when the flag service itself becomes unavailable, typically defaulting to the disabled state. Automating flag-state tests within CI pipelines — using mocks rather than live flag servers — is recommended to keep coverage consistent and avoid QA burnout. Documenting the expected behavior for each flag state serves as a shared specification that guides test writing, code review, and the eventual safe removal of flags.

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PostgreSQL 18 Introduces Incremental Backup Support via pg_basebackup

PostgreSQL 18 adds native incremental backup capability through the pg_basebackup utility, requiring WAL summarization to be enabled via the summarize_wal parameter in postgresql.conf. A full base backup is first created using pg_basebackup, which generates a backup_manifest file that serves as a reference point for subsequent incremental backups. Any database changes made after the full backup — such as new tables or inserted data — are captured exclusively in the incremental backup. The pg_combinebackup tool is then used to merge the full and incremental backups before restoring the database. This workflow significantly reduces storage and time costs compared to taking repeated full backups.

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ProgrammingDEV Community ·

How Trueforge, OneCLI, and Lightdash Are Shaping Production-Ready AI Agents in 2026

By 2026, building production-grade AI agents has evolved into a rigorous architectural discipline, moving well beyond simple chatbot wrappers. A five-layer 'Agency Stack' has emerged, covering interface, orchestration, execution, data memory, and observability. Failures seen in 2024 and 2025, including hallucinated code and unmonitored token spend, were largely traced to weak execution and observability layers. Three companies, Trueforge, OneCLI, and Lightdash, offer key lessons across developer tooling, local-first CLI agents, and structured data analytics respectively. Their approaches highlight the importance of sandboxed execution, deterministic verification loops, and fine-grained tool permissions in deploying reliable AI agents at scale.

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ProgrammingDEV Community ·

How to Build API Docs That Actually Help Developers Succeed

Effective API documentation requires more than listing endpoints — it must guide developers through a first successful request, exact reference details, and clear error recovery. A well-structured quickstart should provide credentials, a complete sample request, an expected response, and links to next steps. Reference pages must precisely document parameters, types, defaults, constraints, and response schemas so developers can scan without guessing. Guides and reference serve distinct purposes: guides walk users through tasks, while reference records stable, exact behavior — and both are needed even when OpenAPI-generated docs exist. Choosing a safe, simple list endpoint as the first example helps verify authentication, base URL, and response handling without risking production data.

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ProgrammingDEV Community ·

Beyond Authentication: Why API Authorization Failures Drive Most Security Breaches

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