This document details how to run Google's Gemma 4 models locally, including the E2B, E4B, 26B-A4B, and 31B variants. Gemma 4 is a family of open models supporting over 140 languages and up to 256K context, available in both dense and MoE configurations. The E2B and E4B models support image and audio input. These models can be run locally on your device and fine-tuned using Unsloth Studio. The document outlines hardware requirements, recommended settings, and best practices for prompting and multimodal use, including guidance on context length and thinking mode.
This Hugging Face page details the Gemma 4 31B-it model, an open-weights multimodal model created by Google DeepMind. Gemma 4 can process both text and image inputs, generating text outputs, with smaller models also supporting audio. It comes in various sizes (E2B, E4B, 26B A4B, and 31B) allowing for deployment on diverse hardware, from phones to servers.
The model boasts a context window of up to 256K tokens and supports over 140 languages. It utilizes dense and Mixture-of-Experts (MoE) architectures, excelling in tasks like text generation, coding, and reasoning. The page provides details on model data, training, ethics, usage, limitations, and best practices, along with code snippets for getting started with Transformers.
The model boasts a context window of up to 256K tokens and supports over 140 languages. It utilizes dense and Mixture-of-Experts (MoE) architectures, excelling in tasks like text generation, coding, and reasoning. The page provides details on model data, training, ethics, usage, limitations, and best practices, along with code snippets for getting started with Transformers.
This GitHub repository, "agentic-ai-prompt-research" by Leonxlnx, contains a collection of prompts designed for use with agentic AI systems. The repository is organized into a series of markdown files, each representing a different prompt or prompt component.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
The future of work is rapidly evolving, and a new skill set is emerging as highly valuable: building and managing "agent workflows." These workflows involve leveraging AI agents – autonomous software entities – to automate tasks and processes. This isn't simply about AI replacing jobs, but rather about augmenting human capabilities and creating new efficiencies.
The article highlights how professionals who can orchestrate these agents, defining their goals, providing necessary data, and monitoring their performance, will be in high demand. This requires a shift in thinking from traditional task execution to workflow design and management. The ability to do so is becoming a key differentiator in the job market, essentially becoming a "career currency."
The article highlights how professionals who can orchestrate these agents, defining their goals, providing necessary data, and monitoring their performance, will be in high demand. This requires a shift in thinking from traditional task execution to workflow design and management. The ability to do so is becoming a key differentiator in the job market, essentially becoming a "career currency."
Meta’s new “semi-formal reasoning” technique boosts LLM accuracy for code tasks (review, bug detection, patching) by having the AI reason through code instead of running it. This involves stating assumptions, tracing steps, and drawing conclusions – a structured process that improves results (up to 93% accuracy) and lowers computing costs.
This paper introduces Natural-Language Agent Harnesses (NLAHs) – a new approach to AI agent harness design. NLAHs use editable natural language, improving portability and study, unlike traditional code-embedded harnesses. The authors also present the Intelligent Harness Runtime (IHR) and demonstrate viability through coding/computer-use benchmarks.
CAID is a new multi-agent framework for software engineering tasks. It improves accuracy and speed by using a central planner, isolated workspaces for concurrent work, and test-based verification—inspired by human developer collaboration with tools like Git. Evaluations show CAID significantly outperforms single-agent approaches.
This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
A-Evolve, a new framework developed by Amazon researchers, aims to revolutionize the development of agentic AI systems. It addresses the current bottleneck of manual tuning by introducing an automated evolution process. Described as a potential "PyTorch moment" for agentic AI, A-Evolve moves away from hand-tuned prompts towards a scalable system where agents improve their code and logic iteratively.
The framework centers around an ‘Agent Workspace’ with components like manifest files, prompts, skills, tools, and memory. A five-stage loop—Solve, Observe, Evolve, Gate, and Reload—ensures stable improvements. A-Evolve is modular, allowing for "Bring Your Own" approaches to agents, environments, and algorithms, and has demonstrated State-of-the-Art performance on benchmarks like MCP-Atlas and SWE-bench Verified.
The framework centers around an ‘Agent Workspace’ with components like manifest files, prompts, skills, tools, and memory. A five-stage loop—Solve, Observe, Evolve, Gate, and Reload—ensures stable improvements. A-Evolve is modular, allowing for "Bring Your Own" approaches to agents, environments, and algorithms, and has demonstrated State-of-the-Art performance on benchmarks like MCP-Atlas and SWE-bench Verified.
json-render is a generative UI framework that allows developers to create dynamic, personalized user interfaces from prompts using AI. It focuses on reliability and predictability by utilizing predefined components and actions. The framework streamlines UI development through a three-step process: defining a catalog of components, letting AI generate JSON based on prompts, and then instantly rendering the UI as the JSON streams in.
Key features include guardrails to constrain AI output, streaming for progressive rendering, support for both React and React Native, data binding capabilities, and the ability to export generated UIs as standalone React code. This enables rapid prototyping and the creation of unique interfaces with minimal runtime dependencies.
Key features include guardrails to constrain AI output, streaming for progressive rendering, support for both React and React Native, data binding capabilities, and the ability to export generated UIs as standalone React code. This enables rapid prototyping and the creation of unique interfaces with minimal runtime dependencies.
