AI Magic Reading

AI Magic Reading

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Generative Agents: Interactive Simulacra of Human Behavior
Generative Agents: Interactive Simulacra of Human Behavior
Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this...
We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. I
We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior.
#literature#read#important
·arxiv.org·
Generative Agents: Interactive Simulacra of Human Behavior
Access your homelab from anywhere with a YubiKey and mutual TLS
Access your homelab from anywhere with a YubiKey and mutual TLS
By combining YubiKey’s smart card support with mutual TLS client certificates, hardware-bound private keys, and device attestation, you can expose your homelab to the internet in a way that carries very low security risk.
·smallstep.com·
Access your homelab from anywhere with a YubiKey and mutual TLS
The Smol Training Playbook
The Smol Training Playbook

This might seem like an odd way to start an “LLM training guide”. But many failed training projects didn’t fail because of bad hyperparameters or buggy code, they failed because someone decided to train a model they didn’t need. So before you commit to training, and dive into how to execute it, you need to answer two questions: why are you training this model? And what model should you train? Without clear answers, you’ll waste months of compute and engineering time building something the world already has, or worse, something nobody needs

Here’s a simple test: spend a few days building on top of Qwen3, Gemma3, or another current SOTA model. Can you reach your performance goals through prompting, tool-use, or post-training? If not, it’s probably time to train your own.

A change is derisked when testing shows it either improves performance on your target capabilities, or provides a meaningful benefit (e.g. faster inference, lower memory, better stability) without hurting performance beyond your acceptable tradeoffs.

·huggingface.co·
The Smol Training Playbook
LLM Embeddings Explained: A Visual and Intuitive Guide
LLM Embeddings Explained: A Visual and Intuitive Guide
Explore how language models convert text into meaningful representations through interactive visualizations. No input required; simply browse the guide to understand embeddings.
·huggingface.co·
LLM Embeddings Explained: A Visual and Intuitive Guide
UncheatableEval
UncheatableEval
This application allows users to compare different language models based on their compression efficiency metrics like compression ratio, bits per character, and bits per byte. Users can filter resu...
·huggingface.co·
UncheatableEval
Introduction
Introduction
a vendor and technology agnostic open source automation software for your home
·openhab.org·
Introduction
Distinguishing Autonomous AI Agents from Collaborative Agentic Systems: A Comprehensive Framework for Understanding Modern Intelligent Architectures
Distinguishing Autonomous AI Agents from Collaborative Agentic Systems: A Comprehensive Framework for Understanding Modern Intelligent Architectures
We characterize AI Agents as specialized, tool-enhanced systems leveraging foundation models for targeted automation within constrained environments. Conversely, Agentic AI represents sophisticated multi-entity frameworks where distributed agents exhibit emergent collective intelligence through coordinated interaction protocols.
computing can be traced to pioneering work in distributed artificial intelligence and multi-agent
Computer Use system’s architecture implements continuous feedback loops where the agent receives objectives, formulates action plans, executes specific operations, evaluates outcomes, and iterates until successful task completion. This operational pattern demonstrates how contemporary AI Agents can effectively utilize existing software ecosystem
Domain specialization provides several advantages including improved accuracy within target domains, reduced computational overhead through focused processing, and enhanced interpretability through simplified decision logic. However, this characteristic also imposes limitations on cross-domain generalization and adaptability to novel problem types outside the agent’s specialized expertise.
This responsiveness extends beyond simple pattern matching to include contextual interpretation, preference learning, and behavioral refinement through experience accumulation. Ad
·arxiv.org·
Distinguishing Autonomous AI Agents from Collaborative Agentic Systems: A Comprehensive Framework for Understanding Modern Intelligent Architectures