Contentsquare vs Guardsquare

Contentsquare plugs behavioral data straight into Dust AI agents

Contentsquare's MCP connector for Dust gives joint customers live access to funnel data, friction scores, and revenue impact analysis inside AI agent workflows.

Brands Risk Invisibility in AI Search

MarTech reports that AI-referred traffic to brand websites grew 632% in about ten months, but many brand sites are effectively invisible to AI answer engines because they serve empty HTML shells (client-side rendering) while most AI crawlers do not execute JavaScript. Experts from Contentsquare and Gartner recommend server-side rendering and adding machine-readable layers (transcripts, alt text, structured metadata) so AI agents can parse visual assets. The article highlights rising bot traffic and identity gaps on sites, cites Forrester and Imperva data about AI research and bot volumes, and notes Gartner’s projection that up to $15 trillion of B2B spend could flow through AI agent exchanges, urging cross-functional engineering-marketing action and measurement changes.

• AI-referred traffic to brand websites increased 632% in roughly 10 months, according to Contentsquare.
• Many brand websites use client-side rendering (an empty HTML shell filled by JavaScript), and most AI crawlers do not execute JavaScript, leaving those sites invisible to AI answer engines.
• Forrester data cited: 51% of software buyers now start research in an AI chatbot, up from 29% the previous year.

Safeguarding LLM-Assisted Dev at Guardsquare

New blog post discussing Guardsquare's approach to using large language models (LLMs) in development, highlighting security considerations for a cybersecurity company handling sensitive IP.

Use P2C + EWMA for High-Throughput Java Routing

A technical article demonstrating that traditional round-robin load balancing performs poorly for high-concurrency Java virtual thread workloads. The author recommends using the Power-of-Two-Choices (P2C) sampling algorithm combined with an Exponentially Weighted Moving Average (EWMA) latency metric to compute a real-time health score per instance: Score = (Active Virtual Threads + 1) × EWMA Latency. The post includes a Java code snippet implementing a P2C selector and argues this approach reduces p99/p999 tail latency and avoids synchronization costs inherent to full-node scans or naive least-connections strategies.

• Java virtual threads allow microservices to handle ~50,000 concurrent requests per instance (as asserted by the author).
• Legacy round-robin load balancers can degrade p99 latency when servicing virtual-thread workloads due to head-of-line blocking.
• The recommended routing policy is Power-of-Two-Choices (P2C) sampling of two instances plus a health score computed as (Active Virtual Threads + 1) × EWMA Latency.

Community Poll: Do You Test AI Agents for Prompt Injection?

A Dev.to community post by Brij Purswani (published 2026-07-07) asks developers whether they test AI agents for prompt injection and adversarial inputs. The author, who builds security tools for AI agents, reports having spoken with roughly 200 developers and says most admitted they do not test for adversarial prompts. The post lists poll options (A: I test, B: I know I should, C: I didn't know, D: Not sensitive) and links to a quick scan tool (sec-ra.com) for testing agents. The piece is a discussion prompt rather than a technical guide or policy announcement.

• Author Brij Purswani published the post on Dev.to on 2026-07-07.
• The post asks whether developers test AI agents for adversarial inputs such as prompt injection, system prompt extraction, or unauthorized tool/data access.
• The author says he has talked to about 200 developers and that most answered 'no' to testing for adversarial inputs.