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Mises à jour des produits, actualités et conseils et astuces des logiciels de surveillance les plus utiles.
octobre 2, 2025
How AI Engineers Are Using Agentic RAG and FAISS Indexing to Build Dynamic Retrieval Strategies That Actually Scale

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): […]

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octobre 2, 2025
What No One Tells You About Test‑Time Scaling: The Dangerous Tradeoffs Between MaTTS, Raw Trajectories, and Strategy‑Level Memory

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 […]

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octobre 2, 2025
The Hidden Truth About Agentic RAG vs Supervisor Agents: When Multi‑Agent Orchestration Breaks Your Roadmap

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 […]

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octobre 1, 2025
The Hidden Truth About Instructional Directives Vulnerability: How Typographic Attacks Are Silently Hijacking Vision‑LLMs

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: […]

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octobre 1, 2025
What No One Tells You About Video Provenance: The Dark Side of Visible Watermarks, Revocable Cameos, and Synthetic Media Policy

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 […]

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octobre 1, 2025
The Hidden Truth About AI Agents and Secret Exfiltration: MCP, Ephemeral Credentials, and Least‑Privilege You Can’t Ignore

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, […]

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octobre 1, 2025
Why AI-Ready Data Center Design in APAC Is About to Change Everything: Prepare for 1MW Rack Power, Direct-to-Chip Cooling and Grid Risk

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 […]

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octobre 1, 2025
What No One Tells You About Running 200K‑Token Models Locally — Licensing, Costs, and MIT Risks

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 […]

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octobre 1, 2025
What No One Tells You About Building Cost‑Efficient RAG Pipelines with Sparse Attention: Warm‑up, Indexer, and Decode‑Time Caveats

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 […]

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octobre 1, 2025
Why Sora 2 Consent Cameos Are About to Rewrite Text-to-Video Ethics — And What Creators Must Do Now

Sora 2 consent cameos: what they are and why they matter Intro — Quick answer (featured-snippet friendly) Sora 2 consent cameos let verified users upload a one-time video-and-audio recording to opt in to having their likeness used in Sora-generated scenes. In short: Sora 2 consent cameos = consent-gated AI control that lets creators permit or […]

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