Tootfinder

vibe main
yeet "vibez"
slay main() {
vibez.spill("Hello, World!")
}
https://simonwillison.net/2025/Sep/9/cursed/

LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
Linghan Huang, Peizhou Zhao, Huaming Chen
https://arxiv.org/abs/2510.10179 https://

LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
The rapid development of large language models (LLMs) has revolutionized software testing, particularly fuzz testing, by automating the generation of diverse and effective test inputs. This advancement holds great promise for improving software reliability. Meanwhile, the introduction of MOJO, a high-performance AI programming language blending Python's usability with the efficiency of C and C++, presents new opportunities to enhance AI model scalability and programmability. However, as a new l…

A Multilingual Python Programming Language
Saad Ahmed Bazaz, Mirza Omer Beg
https://arxiv.org/abs/2510.09591 https://arxiv.org/pdf/2510.09591

A Multilingual Python Programming Language
All widely used and useful programming languages have a common problem. They restrict entry on the basis of knowledge of the English language. The lack of knowledge of English poses a major hurdle to many newcomers who do not have the resources, in terms of time and money, to learn the English language. Studies show that people learn better in their own language. Therefore, we propose a language transpiler built on top of the Python programming language, called UniversalPython, which allows one…

PLSEMANTICSBENCH: Large Language Models As Programming Language Interpreters
Aditya Thimmaiah, Jiyang Zhang, Jayanth Srinivasa, Junyi Jessy Li, Milos Gligoric
https://arxiv.org/abs/2510.03415

PLSEMANTICSBENCH: Large Language Models As Programming Language Interpreters
As large language models (LLMs) excel at code reasoning, a natural question arises: can an LLM execute programs (i.e., act as an interpreter) purely based on a programming language's formal semantics? If so, it will enable rapid prototyping of new programming languages and language features. We study this question using the imperative language IMP (a subset of C), formalized via small-step operational semantics (SOS) and rewriting-based operational semantics (K-semantics). We introduce three ev…

RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code
Yubo Bai, Tapti Palit
https://arxiv.org/abs/2510.07604 https://arxiv.org/pdf/25…

RustAssure: Differential Symbolic Testing for LLM-Transpiled C-to-Rust Code
Rust is a memory-safe programming language that significantly improves software security. Existing codebases written in unsafe memory languages, such as C, must first be transpiled to Rust to take advantage of Rust's improved safety guarantees. RustAssure presents a system that uses Large Language Models (LLMs) to automatically transpile existing C codebases to Rust. RustAssure uses prompt engineering techniques to maximize the chances of the LLM generating idiomatic and safe Rust code. Moreove…

Regression Language Models for Code
Yash Akhauri, Xingyou Song, Arissa Wongpanich, Bryan Lewandowski, Mohamed S. Abdelfattah
https://arxiv.org/abs/2509.26476 https://

Regression Language Models for Code
We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the …

Senior Microsoft Product Manager Wendy Breiding discusses in this recent post how you can now customize your IDE to include agentic AI to your project that is focused on tasks related to a specific language or UI stack, in this case: C# and WinForms. The results have been positive when comparing these agents to previous more general approaches.
"Introducing Custom Agents for .NET Developers: C# Expert & WinForms Expert"

ChatGPT in Introductory Programming: Counterbalanced Evaluation of Code Quality, Conceptual Learning, and Student Perceptions
Shiza Andleeb, Brandon Kantorski, Jeffrey C. Carver
https://arxiv.org/abs/2510.00946

ChatGPT in Introductory Programming: Counterbalanced Evaluation of Code Quality, Conceptual Learning, and Student Perceptions
Background: Large language models (LLMs) such as ChatGPT are increasingly used in introductory programming courses to provide real-time code generation, debugging, and explanations. While these tools can boost productivity and code quality, concerns remain about over-reliance and potential impacts on conceptual learning. Objective: To investigate how ChatGPT access affects code quality, conceptual understanding, task completion times, and student perceptions in a CS1 course. Methods: We conduct…

{\L}ukasiewicz Logic with Actions for Neural Networks training
Ioana Leu\c{s}tean (University of Bucharest), Bogdan Macovei (University of Bucharest)
https://arxiv.org/abs/2509.13020

Łukasiewicz Logic with Actions for Neural Networks training
Based on the already known connection between multilayer perceptrons and Lukasiewicz logic with rational coefficients, we take a step forward in analyzing its training process using a three-sorted hybrid modal logic: a multilayer perceptron is a logical formula; the actions of the training process are modal operators; the training process is a sequence of logical deductions. Using the proof assistant and the programming language Lean 4, the algorithmic implementation of the training process is …

Evaluating Large Language Models for Code Translation: Effects of Prompt Language and Prompt Design
Aamer Aljagthami, Mohammed Banabila, Musab Alshehri, Mohammed Kabini, Mohammad D. Alahmadi
https://arxiv.org/abs/2509.12973

Evaluating Large Language Models for Code Translation: Effects of Prompt Language and Prompt Design
Large language models (LLMs) have shown promise for automated source-code translation, a capability critical to software migration, maintenance, and interoperability. Yet comparative evidence on how model choice, prompt design, and prompt language shape translation quality across multiple programming languages remains limited. This study conducts a systematic empirical assessment of state-of-the-art LLMs for code translation among C++, Java, Python, and C#, alongside a traditional baseline (Tra…