Minnesota Passes the Nation's First Ban on AI Nudify Apps

According to Ars Technica, Minnesota has passed a bill that fully bans "nudify apps"—applications that use AI to generate nude images of people. It becomes the first state in the nation to enact this kind of regulation, and developers of violating apps face fines of up to $500,000 (roughly 75 million yen).

As deepfake technology has advanced, apps that generate nude images of people without their consent have become a serious social problem. Minnesota's move could serve as a model for other states and is expected to influence the regulatory debate at the federal level as well. Regulation of AI-generated content is always contested against freedom of expression, but this bill is notable for clearly targeting non-consensual nude generation.

A 103B-Token Usenet Corpus Is Released

The r/MachineLearning community on Reddit has shared details of a dataset that organizes Usenet archives from 1980 to 2013 into 103.1 billion tokens. It covers 4.08 million posts across 18,347 newsgroups, spanning 33 years of continuous data.

The processing pipeline includes deduplication, removal of binary data, handling of quoted text, and anonymization of email addresses (SHA-256 hashing), with Meta's fasttext LID-176 used for language detection. 96.6% of the corpus is English, and more than 100 languages are represented.

The dataset is drawing attention as pretraining data for large language models. Usenet is a valuable resource that accumulated technical discussions from the internet's early days, and discussions from the 1980s and 90s in particular contain information that is hard to obtain from other web data.

Making Computer-Use Agents More Efficient: A Step-Level Optimization Approach

A paper featured on Hugging Face introduces a new approach to improving the efficiency of computer-use agents. The method, called "Step-level Optimization," uses a small model by default at each GUI step instead of always invoking a large VLM (vision-language model), switching to the large model only when progress stalls or milestones need to be verified.

On the OSWorld and WebArena benchmarks, the method reportedly recovers most of the performance of approaches that always use the large model while drastically cutting large-model usage, latency, and cost. It is an important piece of research for resolving the trade-off between computational cost and performance in putting AI agents to practical use.

AI's Water Use Is Lower Than Commonly Believed

An analysis from the California Water Blog has sparked discussion on Hacker News, picking up 58 points. It argues that actual AI water consumption is lower than widely believed. While water use by large data centers is certainly a challenge, the article contends that the issue is often discussed without the context of comparisons to agriculture and other industries.

The article emphasizes that debates over AI's environmental impact tend to be driven by emotion, and that data-based assessment is needed. It also offers lessons on how AI water use should be positioned from the perspective of California's water policy.

HN Discussion: "Billions Poured into LLM Code Generation—Where Is the UX?"

A question posed on Hacker News has drawn significant discussion: "It's May 2026, every app and SaaS is fully up to date, so where is the software revolution LLMs were supposed to deliver?"

The poster notes that none of the everyday apps from banks, insurers, delivery services, or utilities feel like they offer "groundbreaking features"—if anything, the quality of some giant companies' apps has declined. They ask: "What has the billions of dollars invested in AI actually produced?"

While it is true that AI coding tools are boosting developer productivity, the prevailing view is that it will still take time for those benefits to reach the end-user experience. The gap between productivity gains and improved user experience appears to be wider than imagined.

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