As AI coding assistants accelerate software development, one OWASP-backed open-source project is arguing that dependency security tooling still arrives too late to be truly useful.
CVE Lite CLI, a JavaScript and TypeScript dependency vulnerability scanner focused on local lockfile analysis, is positioning itself around a simple idea. Developers should see dependency risks while they are still writing code, not hours later inside a failing CI pipeline.
“What developers are missing is early feedback at the point where the dependency decision is made,” Sonu Kapoor, creator and maintainer of the project, told CSO. According to Kapoor, traditional CI-centric workflows often disconnect developers from the dependency choices that introduced risk in the first place.
CVE Lite CLI scans npm, pnpm, and Yarn lockfiles using OSV vulnerability data and claims to focus heavily on remediation guidance, including separating direct and transitive vulnerabilities, validating upgrade targets, and recommen
The backlash was inevitable. For the past year, Silicon Valley has been telling us that software development is on the verge of becoming a prompt-and-ship exercise. You know, just describe what you want and let an AI coding agent build it. Sure, maybe you could keep a few token senior engineers around to bless the output…or maybe not. I mean, Google’s Sundar Pichai says 75% of its new code is now AI-generated and reviewed by engineers, up sharply from earlier levels.
Hurray! Right??? Well…
The Wall Street Journal recently highlighted warnings from Mario Zechner and Armin Ronacher, two engineers behind core pieces of the popular OpenClaw AI agent, who argue that AI coding tools are flooding software with what they call “vibe slop.” Their complaint is that too many people are using AI to skip the parts of software development that actually matter: design, judgment, testing, ownership, and deep understanding of the system being changed.
This is worth taking seriously. When people who help
As intelligence itself becomes privatised by big tech, allowing your intellectual faculties to wither in service of inane bots seems a dangerous move
Long before the age of multi-billion-dollar AI companies promising to disrupt the field of software development, I was learning to code the hard way.
It was the mid-2000s, and I was a child with unmonitored access to the family computer. With the help of a basic text editor program, I learned how to make websites – first basic, then increasingly complex – from scratch. The results were never as beautiful or polished as in my imagination, but I could live with that, because I was learning a craft. The painstaking hours of debugging and poring over arcane documentation for projects that I eventually abandoned never felt wasted.
Wendy Liu is a writer based in San Francisco and the author of Abolish Silicon Valley
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AI tools are reshaping software development, enabling smaller teams to achieve more, but require careful integration to maximize benefits.
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Vitalik Buterin says AI‑assisted formal verification could be the “final form” of software, letting Ethereum ship ultra‑optimized code with machine‑checked proofs of correctness. Ethereum (ETH) co‑founder Vitalik Buterin has said that combining artificial intelligence with formal verification could become the…
The AI coding agent field in 2026 is more capable, more fragmented, and harder to benchmark than it looks. Claude Code leads on code quality at 87.6% SWE-bench Verified. GPT-5.5 tops Terminal-Bench at 82.7%. But the benchmark OpenAI itself declared contaminated in February 2026 is still being used to rank these tools — including by the labs publishing their own scores.
The post Best AI Agents for Software Development Ranked: A Benchmark-Driven Look at the Current Field appeared first on MarkTechPost.
Writing code has always been the most time- and resource-intensive task in software development. AI is changing that, and faster than most engineering organizations are prepared for. Tools like Claude Code and Cursor are already handling significant parts of code construction, freeing developers to spend more time on requirements, architecture, and design.
But that shift creates a new challenge nobody is talking about enough. As AI takes on the heavy lifting, the skills that matter most are moving upstream: how to provide the right context for a prompt, how to evaluate what the model produces, and how to understand a problem deeply enough that you can’t be fooled by a confident but wrong answer.
This piece explores those three skills and why developers who master them will have a significant edge over those who don’t.
Beyond coding: Mastering the art of the prompt
Software translation tools such as compilers and assemblers map a high-level description of code to a lower-level represent
