Google Research Releases TabFM 1.0.0, a Zero-Shot Foundation Model for Tabular Data
On July 2, Google Research released TabFM 1.0.0, a zero-shot foundation model for tabular data, on Hugging Face. It supports both classification (up to 10 classes) and regression, and its defining feature is that it makes predictions in a single inference pass — just feed the training data as context, with no fine-tuning or hyperparameter search required.
The architecture adopts alternating column-wise and row-wise attention, with a 24-layer ICL Transformer deriving predictions from patterns in the training data. On the TabArena evaluation benchmark (51 datasets), the model reportedly outperformed hyperparameter-tuned gradient-boosted trees despite running in a zero-shot setting.
The model was trained on hundreds of millions of synthetic datasets, allowing it to learn diverse data patterns while avoiding privacy concerns. It is licensed for non-commercial use (TabFM Non-Commercial License v1.0), with source code available under Apache 2.0.
AI Data Center Firm Crusoe in Talks to Raise $3 Billion — Valuation on Track to Triple
According to a Bloomberg report on July 2, Crusoe, which operates data centers for AI, is in talks to raise approximately $3 billion. If the round closes, the company's valuation is expected to reach around $30 billion — a threefold jump from the roughly $10 billion valuation of October 2025.
Crusoe holds contracts to supply AI compute capacity to Meta and Oracle, and builds and operates the large-scale data centers that power AI systems. While the final valuation has not been locked in, reports indicate that investors predominantly expect it to land in the $30 billion range.
The move is a signal that investor appetite for AI infrastructure remains strong, and it reinforces confidence in the durability of data center demand.
Adobe Demonstrates "Agentic Site," Where Websites Assemble Themselves for Each Visitor
At the AI Engineer World's Fair (AIEWF), Adobe Principal Scientist Carlos Sanchez demonstrated a concept called the "Agentic Site" — a web experience that interprets a visitor's intent, retrieves relevant material from existing content, and assembles a personalized page in real time.
Discussing this approach, which he calls an "audience of one," Sanchez said: "Most people can't even imagine generating a page on the fly, but this isn't the future — it's the present." Current inference costs are estimated at one to two cents per page, and are expected to get even cheaper over time.
The demo included an example in which a coffee machine website reassembled itself for a visitor interested in camping, swapping in outdoor-oriented copy and product selections. Adobe uses existing content as the grounding corpus, so the LLM is designed not to fabricate content from scratch.
Paper Published on a Self-Propagating AI Worm That Runs on Local LLMs
According to an arXiv paper that gained traction in Reddit's r/LocalLLaMA community, researchers have built a self-propagating AI worm that runs entirely on open-weight local models. The work is drawing attention for demonstrating an AI worm that propagates autonomously in a fully local environment, without relying on external APIs.
As open-source models become more widespread, AI security risks in local environments are emerging as a new source of concern. Details of the research are available on arXiv.
AI Tool Consumes 37 Terabytes of SSD Writes — Burning Through Its Lifespan in 21 Days
According to ITmedia, a flaw in an AI tool's implementation caused 37 terabytes of writes to an SSD in just 21 days, dramatically shortening the drive's lifespan. The cause is believed to be a bug on the tool's side, and users of the affected tool are advised to check their systems as soon as possible.
Disk I/O load from AI development tools tends to be overlooked, but the community is paying growing attention to it as a practical issue that directly affects long-term hardware lifespan. Given that similar concerns had already been raised about tools such as Codex CLI, storage efficiency in AI tools is a problem that clearly needs addressing.
5 Patterns in Which AI-Generated Code "Rotted" Over Six Months — Real Examples Shared on Qiita
A Qiita article titled "AIに8割書かせて半年運用してみて実際に腐った書き方を5つ" (5 Coding Patterns That Actually Rot After Letting AI Write 80% of the Code and Running It for Six Months) was recently published. It identifies the following specific anti-patterns:
Adding piles of meaningless comments that merely restate the code
Premature generalization and abstraction
Leaving giant single functions untouched
Swallowing errors silently
Other practices that reduce maintainability
All of these are patterns AI tends to produce, and the warning that leaving them unchecked makes the code unmaintainable is a practical lesson worth heeding as AI coding tools become ubiquitous.