Create

Sign in to ReadmeX

or

How we handle your data: Privacy Policy

ReadmeX

Your AI reading companion

What would you like to catch up on?

Pick a briefing, or ask your own question.

Your community and people briefings stay personal to you.

gpt-5.6-luna

AI overview

Sign in and the AI will write an overview from our coverage.

Headlines · 2

  1. Simon Willison releases llm 0.37

    Simon Willison announced llm 0.37, a new version of his command-line tool and Python library for accessing large language models. The release adds gpt-6.1-sol and gpt-6.1-terra, introduces an `llm openai endpoint add` command for configuring additional models that use OpenAI-compatible endpoints, changes the default model to gpt-6-luna, and expands embedding support to handle binary content with different content types. Willison said the release represents around two months of accumulated features and fixes.

    Simon Willison's Weblog · 🔥 0
  2. HybridAgent paper mixes latent-space thinking with text tool calls

    A commentary published by Huxiu AI reports that a paper submitted to arXiv on October 13 (2610.18861v1), with authors drawn from Stanford, UIUC, Together AI and Meta, proposes HybridAgent, which lets a model decide within a single generation whether to reason in latent space without emitting text or to act in text for tool calls and outward delivery. The design rests on three parts: HybridLink, a residual adapter of roughly 10M parameters on a 4B model that switches between text and latent modes; a continuous working memory that carries mixed context across turns; and a pseudo-text rendering of latent reasoning that monitors can read. The authors self-report 2.7–3.7x cost savings, 1.8–2.6x speedup and a 14B model beating GPT-5.6-Luna by about 14.5 points on the single-point GAIA benchmark, and the article states none of these figures has been independently replicated.

    虎嗅 AI · 🔥 0

Experience and discussion from the community

Share my gpt-5.6-luna experienceAsk about gpt-5.6-luna

Recursive Evolution•Florian•

EnvOpt: Making Environments More Suited to Intelligent Agents

When an intelligent agent handles enterprise tasks, it often spends considerable effort on preparatory work: finding APIs, browsing directories, parsing spreadsheets, and determining which script generated a particular table. Change the question, and all that preparation gets repeated. Models can certainly keep getting stronger, but if many tasks take place in the same workspace, we can ask anothe…

HarnessEnvEvolutionEnvOpt

1 / 9
arxiv.orghttps://arxiv.org/abs/2611.07372