chad/whichlang

Fast and Hard Code

People pick languages differently with LLMs.

Wolfram LLM Benchmarking Project

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

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:

distil labs — Replace LLMs with Custom Small Language Models

Train and deploy custom small language models that are faster, cheaper, and just as accurate as LLMs.

leobeeson/llm_benchmarks

A collection of benchmarks and datasets for evaluating LLM.

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.

Wikipedia:LLM-assisted translation

This guideline applies to machine translation tools that include a large language model ("LLM"). Assume that it applies to any online translation tool unless you have confirmed there is no LLM element.

llm-wiki

llm-wiki · GitHub

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...

Introducing any-llm: A unified API to access any LLM provider

When it comes to using LLMs, it’s not always a question of which model to use: it’s also a matter of choosing who provides the LLM and where it is deployed. Today, we announce the release of any-llm, a Python library that provides a simple unified interface to access the most popular providers.

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]

The /llms.txt file – llms-txt

A proposal to standardise on using an /llms.txt file to provide information to help LLMs use a website at inference time.

Defeating Nondeterminism in LLM Inference

Reproducibility is a bedrock of scientific progress. However, it’s remarkably difficult to get reproducible results out of large language models. For example, you might observe that asking ChatGPT the same question multiple times provides different results. This by itself is not surprising, since getting a result from a language model involves “sampling”, a process that converts the language model’s output into a probability distribution and probabilistically selects a token. What might be more surprising is that even when we adjust the temperature down to 0This means that the LLM always chooses the highest probability token, which is called greedy sampling. (thus making the sampling theoretically deterministic), LLM APIs are still not deterministic in practice (see past discussions here, here, or here). Even when running inference on your own hardware with an OSS inference library like vLLM or SGLang, sampling still isn’t deterministic (see here or here).

The Myth of Deterministic Software

How I've come to terms with LLM non-determinism in software and abandoned the comforting lies we've been telling ourselves about traditional software.