Article URL: https://www.socialeurope.eu/europes-ultra-rich-could-fund-a-substantial-part-of-the-eus-budget Comments URL: https://news.ycombinator.com/item?id=49084515 Points: 19 # Comments: 11
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A locally-run home AI system in France operates on a two-tier model where sensitive personal data never leaves the home network. Its creator assigns every piece of information a clearance label — shareable (S0) or strictly private (S3) — determining what a more capable cloud AI is permitted to access. The cloud model is brought in only as a contractor for technical tasks like debugging, security audits, and code rewrites, working exclusively with non-personal, shareable data. The local AI handles all personal context — family activity, conversations, and documents — while the human owner acts as the final clearance authority over what crosses the boundary. The setup challenges the industry norm of consolidating all personal data into a single cloud assistant, arguing that capability and data custody can and should remain separate.
A developer has published a tutorial detailing how to build 'sec-watch', a roughly 600-line Node.js script that automates morning security checks for Cisco, Windows, and VMware environments. The tool consolidates data from the CISA Known Exploited Vulnerabilities catalog, Microsoft's MSRC patch feed, and Cisco's recommended software releases into a single dated HTML report. It requires no npm dependencies and runs on Node.js version 20 or higher, making it lightweight and easy to deploy. The script is designed to answer three key questions each morning: whether any tracked product is actively being exploited, what the latest Patch Tuesday covered, and whether Cisco software versions have drifted from recommended releases. A notable feature is its use of exit codes to signal to the scheduler whether any action is required, reducing the need for manual review.
Technical debt in property management refers to the buildup of software shortcuts, disconnected tools, and unupdated integrations that accumulate over time and quietly drain productivity. Businesses often rely on multiple mismatched systems for tasks like tenant screening, maintenance tracking, and rent collection, forcing staff to manually re-enter data across platforms. This fragmented setup not only wastes hours each week but also creates reliability risks, as outdated integrations can fail without warning. Most critically, scattered and inconsistently structured data makes it nearly impossible to deploy AI tools, which require clean, stable data pipelines to function reliably. Resolving technical debt through unified, well-integrated systems is therefore a prerequisite for property managers looking to leverage modern AI capabilities.
A developer has built and tested a cross-cloud currency conversion agent that connects an Amazon Bedrock AgentCore-hosted Strands Agents coordinator on AWS with a Google ADK agent running on Google Cloud Platform via the A2A v1.0 protocol. The experiment goes beyond basic connectivity checks, measuring latency, token overhead, and result accuracy across three evaluation modes: local MCP tool only, remote A2A delegation only, and a verified cross-check of both. Both cloud agents independently query the same Frankfurter daily exchange-rate data, ensuring any discrepancies reflect protocol or model behavior rather than data differences. Numeric agreement between agents is assessed strictly through code using relative difference calculations, avoiding any LLM-based judgment of results. The benchmark also compares AWS Bedrock AgentCore Runtime performance directly against a prior run on Microsoft Azure's Foundry platform, offering a rare empirical three-cloud comparison.
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