JetBrains vs npm

AI Reduces Entry-Level Hiring in Software Development

A JetBrains survey of over 15,000 developers shows 90% use AI coding agents at least weekly. A Stanford study 'Canaries in the Coal Mine' reports a 20% decline in employment for software developers aged 22-25 in the US since late 2022, while experienced developers see growth. This trend worries the author, Dr. Volker Oshege, chairman of viadee, as it threatens the future talent pipeline. He argues that AI shifts bottlenecks to review capacity and guardrails, requiring human judgment. He advocates for mixed teams, buddy systems, and continuous training to ensure the workforce of 2036. The article is an opinion piece, not a news report, and contains no specific corporate announcements or financial data.

• A JetBrains survey found 90% of developers use AI coding agents at least weekly.
• A Stanford study shows a 20% employment decline for software developers aged 22-25 in the US since late 2022.
• The article is authored by Dr. Volker Oshege, chairman of viadee Unternehmensberatung AG.

A New Agentic Experience: JetBrains Air in IDEs – EAP Now Open

Last week, we shared our point of view on where we think agentic development is headed and introduced JetBrains' answer to it: JetBrains Air, an open system of products for agentic development. To…

Air Teams: Bring Your Best Agentic Workflows to the Whole Team – and Automate Repeatable Work

JetBrains announced Air Teams on September 28, 2026, as the team layer for agentic development, providing humans and agents with shared context, environments, tools, and instructions. Air Teams includes Automations for recurring tasks, shared cloud environments, cloud tasks that run independently of a developer's laptop, and team projects with roles and credits. It is available immediately to JetBrains business customers, with plans to expand access to individual customers later.

• JetBrains announced Air Teams on September 28, 2026, as the team layer for agentic development.
• Air Teams is immediately available to JetBrains business customers; individual customer access is planned later.
• Air Teams includes four components: Automations, shared cloud environments, cloud tasks, and team projects.

Why npm and pnpm audit miss vulnerabilities

This technical article explains why npm and pnpm audit commands can produce conflicting results and miss vulnerabilities. It clarifies that audit is a network request to the registry's audit endpoint, relying solely on the GitHub Advisory Database. The piece identifies four structural gaps: single-source dependency, silent failure without network, npm-ecosystem exclusivity, and lack of reachability analysis or inventory output. The differences between npm and pnpm audit stem from how each resolves the dependency tree and the timing of data. The author recommends a more robust approach: using lockfile-based scanning with aggregated open advisory data like OSV, and producing CycloneDX SBOMs for durable coverage. The article is technical and applicable to developers concerned with supply chain security.

• npm audit and pnpm audit are network requests to a registry endpoint, not local scanners.
• Advisory data for npm and pnpm audit comes from the GitHub Advisory Database.
• The commands can disagree due to differences in dependency tree resolution, devDependencies inclusion, and timing.

10 AI Coding Actions Developers Must Always Review

A developer describes how they use AI to generate code but enforces strict review rules. The article lists ten specific actions the author never allows an AI coding assistant to perform without human verification — including running terminal commands blindly, installing unknown packages, exposing .env secrets, writing authentication or security logic without review, running database migrations immediately, making large project-wide edits, merging code they can't explain, trusting AI-generated tests automatically, letting AI make security decisions alone, and deploying straight to production. The author recommends a simple review workflow (generate, read, understand, test, review diff, then merge) and emphasizes that humans remain responsible for the final result.

• The author uses AI to help generate components, write APIs, fix bugs, explain errors, create tests, refactor code, write SQL, and plan features.
• They list 10 things not to let AI do without checking, including running terminal commands blindly, installing unknown packages, and touching .env secrets carelessly.
• The author warns against running AI-generated database migrations or deploying AI-written code straight to production without review and testing.

Practical Guide to React Performance

This technical guide summarizes practical, high-impact techniques for improving React application performance. It advises measuring with the React Profiler and browser performance tools before optimizing, avoiding unnecessary re-renders by stabilizing referential identity (useMemo, useCallback, React.memo), and using code-splitting and lazy loading to ship less JavaScript. The guide recommends moving heavy work to the server with React Server Components (preferring server components for data fetching and static content, and client components for interactivity), and optimizing images and fonts via responsive sizes, modern formats, and lazy loading. The author emphasizes that fixing a small set of common patterns resolves most real-world React performance problems.

• Measure before optimizing using the React Profiler and the browser's performance panel.
• Avoid unnecessary re-renders by stabilizing derived data and callbacks with useMemo and useCallback and by using React.memo for expensive children.
• The article recommends installing a single dependency to let the React Compiler handle memoization: npm install babel-plugin-react-compiler.