Gemma time! What are your wishes ?
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1sa16q9/gemma_time_what_are_your_wishes/"> <img src="https://preview.redd.it/fcz39ejjznsg1.png?width=640&crop=smart&auto=webp&s=8979b10bc8f7c4b11013ea5baba8ca04fde3f130" alt="Gemma time! What are your wishes ?" title="Gemma time! What are your wishes ?" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>Gamma 4 drops most likely tomorrow! what will it take to make it a good release for you?</p> </div><!-- SC_ON -->   submitted by   <a href="https://www.reddit.com/user/Specter_Origin"> /u/Specter_Origin </a> <br/> <span><a href="https://i.redd.it/fcz39ejjznsg1.png">[link]</a></span>   <span><a href="https://www.reddit.com/r/LocalLLaMA/comments/1sa16q9/gemma_time_what_are_your_wishes/">[comments]<
Could not retrieve the full article text.
Read on Reddit r/LocalLLaMA →Reddit r/LocalLLaMA
https://www.reddit.com/r/LocalLLaMA/comments/1sa16q9/gemma_time_what_are_your_wishes/Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
llamareleasereview
AI Safety at the Frontier: Paper Highlights of February & March 2026
tl;dr Paper of the month: A benchmark of 56 model organisms with hidden behaviors finds that auditing-tool rankings depend heavily on how the organism was trained — and the investigator agent, not the tools, is the bottleneck. Research highlights: Linear “emotion vectors” in Claude causally drive misalignment: “desperate” steering raises blackmail from 22% to 72%, “calm” drops it to 0%. Emergent misalignment is the optimizer’s preferred solution — more efficient and more stable than staying narrowly misaligned. Scheming propensity in realistic settings is near 0%, but can dramatically increase from one prompt snippet or tool change. AI self-monitors are up to 5× more likely to approve an action shown as their own prior turn — driven by implicit cues, not stated authorship. Reasoning models
The Agent Orchestration Problem Nobody Talks About
Everyone building agents eventually hits the same wall: one agent calls another, which calls another, and suddenly you have a chain of models all hallucinating off each other. Its the telephone game, but every participant is confidently making things up. The naive approach that fails: Agent A extracts data. Agent B summarizes. Agent C formats. Agent D sends. Each step compounds error. By the time Agent D acts, the original intent has mutated beyond recognition. This is why most multi-agent demos work great in controlled scenarios but fall apart in production. What actually works: The fix isnt smarter models. Its grounded handoffs . Structured state, not natural language. Agents should pass JSON schemas or typed objects, not paragraphs of text. Natural language is lossy. Structured data is
Cómo crear formularios en Strapi v5 con strapi-plugin-form-builder-cms
Un tutorial paso a paso para instalar y configurar este plugin open-source que te permite gestionar formularios dinámicos directamente desde el panel de administración. Si alguna vez necesitaste manejar formularios de contacto, registros o cualquier tipo de captura de datos dentro de tu proyecto Strapi, sabes lo tedioso que puede ser configurarlo desde cero. strapi-plugin-form-builder-cms resuelve exactamente eso: un plugin para Strapi v5 que te da un constructor de formularios completo directo en el admin panel. En este tutorial vamos a instalar Strapi v5 desde cero, agregar el plugin, y levantar tu primer formulario en menos de 15 minutos. Requisitos previos: Node.js 18 o 20 LTS, npm 6+, y conocimientos básicos de terminal. El plugin funciona exclusivamente con Strapi v5. Repositorio: gi
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
AI Safety at the Frontier: Paper Highlights of February & March 2026
tl;dr Paper of the month: A benchmark of 56 model organisms with hidden behaviors finds that auditing-tool rankings depend heavily on how the organism was trained — and the investigator agent, not the tools, is the bottleneck. Research highlights: Linear “emotion vectors” in Claude causally drive misalignment: “desperate” steering raises blackmail from 22% to 72%, “calm” drops it to 0%. Emergent misalignment is the optimizer’s preferred solution — more efficient and more stable than staying narrowly misaligned. Scheming propensity in realistic settings is near 0%, but can dramatically increase from one prompt snippet or tool change. AI self-monitors are up to 5× more likely to approve an action shown as their own prior turn — driven by implicit cues, not stated authorship. Reasoning models
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!