Vibe gets to work.

VibeGame: Exploring Vibe Coding Games

Leanstral: Open-Source foundation for trustworthy vibe-coding

Remote agents in Vibe. Powered by Mistral Medium 3.5.

Introducing Mistral Medium 3. 5, remote coding agents in Vibe, plus new Work mode in Le Chat for complex tasks.

Vibe-driven model-based engineering

arXiv:2604. 10645v2 Announce Type: replace-cross Abstract: There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems.

Vibe Coding Ate My Homework: An evaluation of AI approaches to greenfield software engineering and programming

arXiv:2606. 18293v1 Announce Type: cross Abstract: Thanks to rapid developments in generative AI, we are in the midst of a paradigm shift that may change how we interact with computers forever.

Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks

The paper introduces SUSVIBES, a benchmark of 186 real‑world software engineering tasks where human programmers have committed vulnerable code. It evaluates 12 popular coding‑agent settings on these tasks and finds that all agents perform poorly in terms of security, with only 11.8% of solutions from SWE‑Agent with Claude 4 Sonnet being secure despite 57% being functionally correct. Attempts to mitigate security issues by adding vulnerability hints to the prompts do not improve results.

How to Shine as a Data Scientist in the Vibe Coding Era

Here's how to be the Data Scientist who thrives in a world where coding is a commodity. The post How to Shine as a Data Scientist in the Vibe Coding Era appeared first on Towards Data Science .

VibeJam: A User Study Platform for Web Development with Agents

arXiv:2608.29889v1 Announce Type: new Abstract: Programming with AI is increasingly agentic, users prompt LLMs to directly edit their code and review the changes, with adoption growing especially for...

The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, and Responsible Adoption

The study evaluates Vibe Coding, an AI‑led conversational programming paradigm that lets developers generate software via natural‑language interaction with large language models. In a mixed‑methods experiment with 30 participants, Vibe Coding improved development efficiency—reducing task completion time by 27% versus traditional coding and 12% versus AI‑assisted coding—while also yielding a good usability score (SUS = 71.4) and moderate cognitive workload (NASA‑TLX = 55.5). However, the gains came with trade‑offs: lower maintainability indices, higher security vulnerabilities, and themes of trust calibration, loss of control, and prompt‑engineering strategy emerged, leading the authors to propose a three‑pillar framework for responsible adoption.

A Guiding Framework for K-12 Teachers in Creating AI-powered Learning Technologies through Vibe Coding

arXiv:2607. 05406v1 Announce Type: cross Abstract: Large language models generate code from natural language prompts, enabling "vibe coding," which allows non-programmers to develop computational solutions.

Enabling Creative Exploration for Vibe Design Agents

The paper introduces a new inference architecture for vibe design agents that separates design exploration from implementation. By generating structured design specifications with typicality scores and selecting one for downstream generation, the method allows users to explore coherent UI alternatives without altering the underlying generation settings. Experiments on UI themes and visual-asset prompts show increased selection coverage and screenshot variation, with mixed preferences from an LLM judge and modest operational costs in a large online test.

Vibe Coding and Web Application Security: A Twin-Prompt Study

The study examines how adding a security-requirements section to prompts affects web applications generated by a large language model. Six distinct applications were produced twice—once with a baseline prompt and once with a security-aware prompt—yielding 12 programs. Analysis of these programs revealed 75 confirmed security findings, with the security-aware variants showing fewer issues (24 vs. 51) and no Critical or High severity problems.