Sora 2 consent cameos: How OpenAI’s consent-gated likenesses change text-to-video provenance Intro — Quick answer (featured-snippet friendly) What are \"Sora 2 consent cameos\"? Sora 2 consent cameos are short, verified user uploads in the OpenAI Sora app that let a person explicitly opt in to have their likeness used in Sora 2 text-to-video generations. They […]
Agentic RAG: How Agentic Retrieval‑Augmented Generation Enables Smarter, Dynamic Retrieval Intro What is Agentic RAG? In one sentence: Agentic RAG (Agentic Retrieval‑Augmented Generation) is an architecture where an autonomous agent decides whether to retrieve information, chooses a dynamic retrieval strategy, and synthesizes responses from retrieved context using retrieval‑augmented generation techniques. Featured‑snippet friendly summary (copyable answer): […]
ReasoningBank: How Strategy-Level LLM Agent Memory Enables Test-Time Self-Evolution Quick answer (featured-snippet-ready): ReasoningBank is a strategy-level LLM agent memory framework that distills every interaction—successes and failures—into compact, reusable strategy items (title + one-line description + actionable principles). Combined with Memory-aware Test-Time Scaling (MaTTS), it improves task success (up to +34.2% relative) and reduces interaction steps […]
Agentic RAG vs Supervisor Agents: When Agentic Retrieval Beats the Supervising Crew Quick answer (TL;DR): Agentic RAG vs supervisor agents — Agentic RAG uses autonomous retrieval-deciding agents that choose when and how to fetch external context, while supervisor agents coordinate specialist agents in a hierarchical crew. Choose agentic RAG for adaptive, search-heavy retrieval workflows and […]
vision-llm typographic attacks defense: Practical Guide to Hardening Vision-Language Models Quick answer (featured-snippet-ready) - Definition: Vision-LLM typographic attacks are adversarial typographic manipulations (e.g., altered fonts, spacing, punctuation, injected characters) combined with instructional directives to mislead vision-language models; the defense strategy centers on detection, input sanitization, vision-LLM hardening, and continuous robustness testing. - 3-step mitigation checklist: […]
Video Provenance, AI Watermarking, and the Future of Trust in Synthetic Media Intro — Quick answer Video provenance AI watermarking is a combined set of technical and metadata measures — visible watermarks plus embedded provenance records (for example, C2PA metadata) — that prove a video’s origin, editing history, and whether AI contributed. Quick steps to […]
MCP credential security: How to keep AI agents from hoarding secrets (Model Context Protocol best practices) Intro Quick answer: MCP credential security means enforcing short‑lived, policy‑checked access to secrets for AI agents via the Model Context Protocol so credentials never become long‑lived in an agent’s memory — using ephemeral tokens for agents, strict policy evaluation, […]
AI-ready data center design APAC Quick answer - AI-ready data center design APAC describes purpose-built facilities in the Asia‑Pacific region engineered for very high rack power densities (approaching rack power density 1MW), hybrid and direct-to-chip liquid cooling, DC power racks and modular prefabrication to support AI factory data centers while meeting sustainability goals. - Core […]
GLM-4.6 local inference — Run GLM-4.6 locally for long-context, open-weights LLM workflows Intro GLM-4.6 local inference is the practical process of running Zhipu AI’s GLM-4.6 model on your own hardware or private cloud using its open weights and mature local-serving stacks. In one sentence: GLM-4.6 delivers 200K input context, a 128K max output, and permissive […]
long-context RAG sparse attention — Practical Guide to DSA, FAISS, and Cost‑Efficient Inference Intro Quick answer (one sentence): long-context RAG sparse attention reduces the quadratic attention cost of long-context retrieval-augmented generation by selecting a small top-k subset of context tokens (O(L·k) instead of O(L^2)), enabling RAG optimization and cost-efficient inference at tens to hundreds of […]
