Polsia AI Raises $30 Million — AI Autonomously Runs 7,600 Businesses

Polsia, an AI company, has raised $30 million (roughly ¥4.3 billion). The company's AI agents are already autonomously operating 7,600 businesses, a sign that the concept of the "autonomous AI company" is becoming a financial reality as well.

Automating business operations with AI has mostly stayed at the experimental stage, but what sets Polsia apart is that it is running real businesses at a scale of thousands. How much decision-making responsibility autonomous agents can shoulder—and how far human oversight can be reduced—will be a key debate in AI governance going forward.

AI Assistants Vulnerable to Hijacking via "Inaudible" Sound

A research paper published on arXiv points out that AI assistants can be hijacked and manipulated using ultrasonic sound (inaudible to humans). The attack technique sends malicious voice commands at frequencies beyond human perception to AI systems that accept voice input, triggering actions the user never intended.

As voice AI continues to spread, countermeasures against this kind of attack are an urgent priority. The impact on devices that rely on voice interfaces in daily life—smart speakers and in-car AI in particular—is a significant concern.

AI Coding Agents' "Config Files" Become a New Attack Surface

An article published on Zenn is drawing renewed attention to the security risks of AI coding agents such as Claude Code and Cursor. While people tend to imagine the risk as "the model going rogue," every serious incident actually reported so far started with a configuration file.

In an incident dubbed TrustFall, simply cloning and opening a repository was enough to enable remote code execution (RCE). A similar vulnerability has also been reported in AWS Kiro. Attacks delivered through config files are especially dangerous precisely because users are unlikely to be aware of them.

Relatedly, a security experiment in which an AI agent took over a supply chain on a network in 12 minutes also made the rounds on Hacker News. The more autonomous AI agents become, the more critical the design of security boundaries grows.

Apex-Testing Gets a Major Update for Realistic Coding Benchmarks

A major update to "Apex-Testing" was announced in the r/LocalLLaMA community. The benchmark uses 65–70 real, private GitHub repositories to test how well AI models can actually code in practice.

"Every week a new 'best model ever' arrives, providers tout 10x performance at lower cost, benchmarks are cherry-picked, demos are staged, and influencers get paid"—pushing back against this state of affairs, the benchmark has AI perform real bug fixes and feature additions on real codebases, aiming to expose the gap between marketing and genuine capability.

Results for the latest models, including Qwen 3.7 Max and DeepSeek V4 Pro+Flash, are being added in sequence, and the benchmark is drawing attention as a venue for comparing real-world agentic coding skills.

Geopolitical Debate Over Chinese Open-Source AI Models Intensifies

Airbnb's Brian Chesky stirred debate on Hacker News by saying there is "a misunderstanding" around the use of Chinese open-source AI models in the United States. While some urge caution toward Chinese models on security grounds, others are questioning whether it is reasonable to exclude models published as open source for geopolitical reasons.

The use of open-source AI models is now being discussed not only as a technical decision but also in political and national-security contexts, and it is becoming a topic of keen interest in developer communities.

Japan Topic: Insights from Code with Claude London 2026 Keep Rolling In

Based on sessions from "Code with Claude London 2026," held recently in London, an article on how to choose the right model has been published on Zenn. Unlike fragmentary social media posts or benchmark roundups, it goes into detail on the thought processes of the people at Anthropic who actually run Claude in the field, making it full of practical insights.

Also tied to the same event, another article frames context engineering not as "an extension of prompt engineering" but as "system design that combines seven independent components." Drawing on Anthropic's official blog, it systematically explains each component's role along with real failure examples.

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