AI is speeding up coding, but production is still hitting bottlenecks—a paradox that requires intelligent solutions.

As AI makes code easier to produce, the strongest engineering leaders are teaching teams to protect the codebase from unnecessary complexity.

Resilient infrastructure, advanced fraud detection, and rigid compliance requirements drive the critical need for custom ...

Lemon.io's 2026 rate report, based on real contracts with 2,500+ vetted developers, shows that senior software developer ...

Cybersecurity researchers at Kaspersky have identified more than 250,000 potential security misconfigurations across GitHub ...

A former software manager at Wisk Aero, Boeing’s autonomous air taxi subsidiary, has accused Wisk of rushing software testing ...

QA expert Daniil Khudenko explains how structured quality systems improve release stability, risk management, and scalability ...

A recent — and likely massive — breach at Novo Nordisk, where attackers reportedly gained an initial foothold using a single GitHub access token, underscores how code repositories and developer ...

Gartner's SSCS report frames the market around third-party software risk, AI components, SBOM life cycle management and auditable delivery pipelines.

The real challenge starts after the code is written The most visible part of the AI boom is easy to recognize. A developer types a prompt, and something functional appears on the screen. A feature ...

Softjourn approaches legacy system modernization with a code audit led by senior engineers who assess architecture, ...