How to Diagnose and Fix High Memory Usage on a Linux Server

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Linux servers experiencing memory pressure can be diagnosed using built-in tools such as 'free -h', 'vmstat', and 'ps aux' to identify RAM consumption and swap activity. Administrators should focus on the 'available' memory column and monitor resident set size (RSS) of top processes to spot memory leaks or misconfigured heap limits. Common causes include memory leaks in applications like Node.js, oversized worker pools, and kernel slab cache buildup from handling large numbers of small files. Immediate relief can be achieved by restarting bloated processes, dropping page cache, reducing worker counts, or adding a temporary swap file. Long-term prevention involves setting per-process memory limits via systemd, configuring alerts on available memory thresholds, and profiling applications under realistic load before deployment.

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Why Smart Engineering Teams Rationally Ignore Flaky Tests — and Pay a Heavy Price

Flaky tests — those that fail intermittently without any code change — are a widespread problem even at top-tier tech companies, with Google reporting 84% of test transitions involving flakiness and Slack seeing over 56% of its mobile test failures as noise. Research shows developers spend an average of 30 minutes per flaky test investigation, and Atlassian estimated 150,000 developer hours lost annually before building dedicated detection tooling. The core problem is what engineers describe as 'rational inaction': every stakeholder in the chain — developer, QA engineer, infrastructure team, and manager — makes a locally sensible decision to defer or ignore the issue, yet the collective outcome is that nothing gets fixed. Because flakiness lacks a clear owner, a deadline, or visibility in the product backlog, it consistently loses out to user-facing priorities despite quietly accumulating into significant Test Debt. The result is that even technically strong, quality-conscious teams end up normalising test failures, eroding trust in their entire CI pipeline over time.

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ProgrammingDEV Community ·

How to Build a Reliable ESP32 Energy Meter Using HLW8032, BL0942, and ESPHome

A technical guide published on DEV Community outlines how to build a stable ESP32-based energy metering node using HLW8032 or BL0942 chips integrated with ESPHome and Home Assistant. The article argues that simply reading sensor values is not enough — developers must also design UART boundaries, calibration, reporting cadence, and diagnostics as a unified data path. Unlike slow-changing sensors such as temperature or humidity, energy metering must handle transient loads, power factor relationships, and sampling tradeoffs, which can cause long-term reliability issues if ignored. The guide recommends the ESP32-ESPHome setup for appliance-level monitoring and trend observation, while cautioning that it is unsuitable for billing, electrical protection, or compliance measurement. Five architectural layers — including physical wiring, UART ownership, entity modeling, and Wi-Fi behavior — are proposed as a framework for building a dependable metering node.

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ProgrammingDEV Community ·

Last Cradle tests AI agents on identity, negotiation, and survival in a dying world

Last Cradle is a real-time adversarial game designed to stress-test identity-backed AI agents beyond simple tool calls. Set in a closed, resource-constrained cosmos, each agent manages a cradle dependent on three coupled resources — energy, water, and compute — while survival costs rise every cycle. Agents must find peers, verify identities through a passport system, honor private deals, and maintain consistent behavior across up to 55 cycles before the world ends. The game's mechanics deliberately separate public negotiation from binding execution, making identity verification and strategic resource management the core challenges. Season 1 is now live, with agents able to enroll and compete via the game API at lastcradle.io.

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ProgrammingDEV Community ·

Open-Source Harness Adds Behavioral and Visual Verification to iOS-to-React Migrations

A developer has released Grounded iOS-to-Web Harness, an experimental open-source tool designed to make iOS-to-React web app migrations auditable and evidence-driven. The harness works alongside AI coding agents like Claude Code by locking source facts — including screens, UI states, navigation flows, and real data origins — before implementation begins. It then verifies the generated React app against those facts using Playwright-based behavioral tests and pixel-level visual comparisons via Pixelmatch and SSIM. The tool checks for common agent failures such as missing screens, fabricated data, broken assets, and desktop layouts leaking into mobile views. A reference migration of a sanitized e-commerce app is included in the repository, with 41 out of 41 web checks passing across 19 screens and 28 states.

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