VibeGame: Exploring Vibe Coding Games

Vibe gets to work.

The unified agent for long-horizon productivity and coding, launching with Work and Code modes. Plus, a new Vibe VS Code extension.

Leanstral: Open-Source foundation for trustworthy vibe-coding

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.

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.

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.

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 .

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.

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.

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.

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.

I Vibe-Coded an App in Just Two Hours (And Regretted It the Next Day)

The article recounts a rapid, two‑hour app development experience that led to regret the following day. The author highlights a costly mistake that cost $52, suggesting readers can avoid a similar error. The narrative serves as a cautionary tale about hasty coding and its financial repercussions.

From Feelings to Metrics: Understanding and Formalizing How Users Vibe-Test LLMs

arXiv:2604. 14137v3 Announce Type: replace-cross Abstract: Evaluating LLMs is challenging, as benchmark scores often fail to capture models' real-world usefulness.