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2026-10-01
The custom setup couldn't beat DeepSeek's own list price.
A Go dev builds a RAG system in Go to answer questions on latest Go docs using embeddings and prompt context.
We used Multislice Training to run the world’s largest LLM distributed training job on a compute cluster of 50,944 Cloud TPU v5e chips.
We've routed decisions on LLM confidence since 2023, and calibration is what we most hope OpenAI's new Decisions API gets right. The model underneath, GPT-6 Luna, isn't there yet: on 3,600 reasoning problems its 99% meant 68%, and by five inference steps it was at a coin flip.
Frontier models can now produce novel historical knowledge, but in a really weird way
Abstract page for arXiv paper 2609.37725: Context Language Models
Last week Google introduced Gemini Pro 1.5, an enormous upgrade to their Gemini series of AI models. Gemini Pro 1.5 has a 1,000,000 token context size. This is huge—previously that …
LLM coding evals suggest static-vs-dynamic language efficiency claims don't hold; mainstream, popular languages tend to do better on real tasks.
[...] Put these pieces together and you have the two halves of a worm: a payload that hijacks the agent, and an agent that will carry the payload to the …
2026-09-30
The article discusses the challenges of learning complex programming languages like Rust and how the use of Large Language Models (LLMs) can help b...
Analysis of Google's Gemini 4 Argon (High) and comparison to other AI models across key metrics including quality, price, performance (tokens per second & time to first token), context window & more.
Ant Group’s InclusionAI has launched Ling-3.1-flash, a language model with 560 billion total parameters and about 25 billion activated for each token. The
Nine philosophers explore the various issues and questions raised by the newly released language model, GPT-3, in this edition of Philosophers On, guest edited by Annette Zimmermann. […]
GPT-6.1 Sol replaces GPT-6 Sol after just 7 days. It scores 1 point below GPT-6 Astra in the Intelligence Index at less than one quarter of the Cost per Task
Retrieval Augmented Generation (RAG) is a technique for adding extra “knowledge” to systems built on LLMs, allowing them to answer questions against custom information not included in their training data. …