categoria

Agentic Systems

Placeholder description for Agentic Systems.

4 articolipagina 2 di 3
Grok Skills: Reusable Instruction Sets for Task Automation
Leaked screenshots show Grok automatically assembling daily AI news briefings from saved Skills, part of a broader industry trend toward modular, shareable prompts.

Grok Skills: Reusable Instruction Sets for Task Automation

xAI's Grok chatbot is developing a Skills feature that stores reusable instruction sets for automation. Leaked screenshots and code references indicate modular templates for scheduled workflows, similar to Anthropic and OpenAI's recent moves.

2026 Agentic Coding Trends: The Era of AI Collaboration
From assistance to collaboration: How AI agents are reshaping engineering roles, workflows, and project timelines

2026 Agentic Coding Trends: The Era of AI Collaboration

The 2026 Agentic Coding Trends Report reveals how AI coding agents evolve from experimental tools to production systems, enabling multi-agent teams, long-running autonomous builds, and intelligent human oversight. Key trends include collapsed SDLC cycles, orchestration of specialized agents, and the transformation of engineers into strategic collaborators.

Interaction Models: Real-Time Human-AI Collaboration at Scale
Thinking Machines Lab unveils a native interaction model that redefines synchronous, multimodal AI collaboration

Interaction Models: Real-Time Human-AI Collaboration at Scale

Thinking Machines Lab introduces TML-Interaction-Small, a 276B MoE model architected for real-time, continuous audio-video-text exchange. It achieves state-of-the-art performance on interactivity benchmarks, enabling seamless turn-taking, interjections, and simultaneous tool use—scaling collaboration alongside intelligence.

Fast Byte Latent Transformer: Efficient Byte-Level Generation via Diffusion and Speculation
Combining hierarchical latent tokenization with block-wise discrete diffusion and self-speculation for faster byte-level language models

Fast Byte Latent Transformer: Efficient Byte-Level Generation via Diffusion and Speculation

This paper introduces BLT Diffusion (BLT-D), BLT Self-speculation (BLT-S), and BLT Diffusion+Verification (BLT-DV) to accelerate byte-level language models. By replacing autoregressive decoding with block-wise diffusion and verification, the methods achieve over 50% memory-bandwidth reduction and up to 92% with larger blocks, while maintaining competitive performance on translation and code generation tasks.