







People pick languages differently with LLMs.
chad/whichlang
What programming language do LLMs default to when you don't tell them? A small benchmark.
Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via...
Despite their impressive capabilities, multilingual large language models (MLLMs) frequently exhibit inconsistent behaviour when the prompt's language changes. While such adaptation is generally...

Wolfram LLM Benchmarking Project
Results from Wolfram's ongoing tracking of LLM performance. The benchmark is based on a Wolfram Language code generation task.

Best LLM for Coding 2026 | AI Coding Model Rankings & Benchmarks
Which AI model writes the best code? We rank every major LLM — open and closed source — across SWE-bench, HumanEval, LiveCodeBench, and Terminal-Bench coding benchmarks. Compare the best LLMs for coding, software engineering, and programming.

The Return of Language-Oriented Programming | Middle of Nowhere
I’ve been wondering what LLMs mean for language design and implementation. Some believe that, because language models are obviously trained on existing content, they are inherently less capable of assisting users with new programming languages. Intuitively this makes sense. However:
The Case Against LLMs as Rerankers
Authors: Apoorva Joshi, Zhenmei Shi, Akshay Goindani, Hong LiuResearch Leads: Zhenmei Shi, Akshay Goindani, Hong Liu Large language models are increasingly being used for a broad range of tasks, in…

Beyond Standard LLMs
Linear Attention Hybrids, Text Diffusion, Code World Models, and Small Recursive Transformers

Copyrightability of LLM-generated code: Can we license “vibe code” into Free Software? - FSFE
The use of large language models (“LLMs”) has exploded in recent years, including in the generation of source code. But even as their usage gains popularit...

Translating non-trivial codebases with Claude
I don’t think it’s [me writing about LLMs] likely to happen anytime soon: I prefer to write about things that I’m excited about.

MCP servers vs. skills: Choosing the right context for your AI | Red Hat Developer
Large language models (LLMs) are efficient general-purpose tools, but they work much better when you give them the right context

Magicoder: Empowering Code Generation with OSS-Instruct
We introduce Magicoder, a series of fully open-source (code, weights, and data) Large Language Models (LLMs) for code that significantly closes the gap with top code models while having no more...

Writing Code Is Easy. Reading It Isn’t.
Writing code is easy. Once you have a solution in mind, and have mastered the syntax of your favorite programming language, writing code is easy. Having an LLM write entire functions for you? Even eas
Large language model
A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots.[1] Biased or inaccurate training data can make an LLM's output less reliable.[2]
Statistical Foundations of LLM-based A/B Testing: A Surrogacy...
Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost. We...

I just came across this paper arxiv.org/abs/2509.22908 which coins the term "vericoding" for using LLMs to generate formally verified code, and presents benchmark results for several languages
A benchmark for vericoding: formally verified program synthesis
arxiv.orgMartin Kleppmann
New blog post! ✨ I argue that AI will make formal verification go mainstream. martin.kleppmann.com/2025/12/08/ai-formal-verifica… Three reasons for this: 1. LLMs are getting increasingly good at writing proofs using proof assistants. This will make formal verification vastly cheaper than it's been to date.