NVIDIA Accelerates EDA Workloads with Its In-House CPU "Vera"

On July 27, NVIDIA announced that it is accelerating the design process for its next-generation CPUs and GPUs using "Vera," its in-house CPU architecture. Working with Cadence and Synopsys, the two leaders of the EDA industry, the company has optimized major electronic design automation applications for Vera. The plan is to have Vera handle critical workloads that depend on CPU rather than GPU performance, such as logic simulation, formal verification, and parts of digital implementation.

In chip design, pre-silicon verification and iteration take years. That makes the choice of CPU architecture directly tied to development pace and the breadth of the design space. By folding Vera into the EDA workflows for next-generation CPU and GPU development, NVIDIA aims to shorten design cycles and expand the scope of exploration at the same time. It is a characteristically NVIDIA deepening of vertical integration—feeding the company's own strengths back into its own processes.

A Proposal to Make Brain Waves the Next Training Data for "Physical AI"

In a column reported by TechCrunch on July 26, reading human brain waves is floated as a candidate for the next training data for "Physical AI," now that YouTube videos alone are no longer enough. Frontier physical AI models already demand multi-angle footage and dense annotation, and researchers have reportedly begun exploring the direction of adding human cognitive signals themselves as training data.

Commercialization is likely still a long way off, but the fact that research aimed at directly feeding "human intent" into model inputs is now being discussed publicly is significant. It suggests the data strategy for embodied AI has advanced another stage.

Quebec Scraps Its Public-Sector AI and Automation Projects

On July 26, the Canadian province of Quebec abruptly abolished the AI and automation projects it had been pursuing in the public sector. Local media CTV News reported the story, with the detailed reasons laid out in the coverage, and it sends a strong message about rethinking AI adoption in public services.

On public-sector AI use, there had already been earlier debates such as the EU AI Act and state-level regulation in the US. But rolling back already-deployed projects is a considerably stronger step, and it could influence other governments as well.

Minimax-M3 Gains Vision Support in llama.cpp

According to a report posted on July 26 to r/LocalLLaMA, the local LLM community, vision (image input) support for Minimax-M3 has been merged into the mainline llama.cpp. This opens the way to running Minimax-M3 multimodally in local environments.

Multimodal support for open models has been advancing rapidly in Gemma and Qwen too. Integration into llama.cpp is a practical milestone in the sense that it broadens use in CLI and edge environments.

NEC Generates High-Fidelity 3D Models from Smartphone Video in as Little as One Minute

On July 27, NEC announced that it has developed technology that generates high-fidelity 3D models in as little as about one minute from video shot with general-purpose cameras such as smartphones. The company's proprietary AI automatically detects and removes people and transient obstacles in the footage and reconstructs the background from the surrounding frames.

3D scanning has typically assumed specialized equipment and a stationary subject. If a model can be produced just by walking around with an ordinary camera, potential applications—facility management, real estate, VR content production, education, and more—could expand dramatically.

4B-Class Open Models Close In on o3 on a Medical Licensing Exam

According to an experimental report posted on July 26 to r/MachineLearning, two 4B-class open models, Gemma4-E4B and Qwen3.5-4B, achieved 77% accuracy on the Swedish medical licensing exam (MedQA-SWE) without any additional training. With reasoning mode enabled, Qwen3.5-4B climbs to 87%, closing in on the 88% recorded by o3.

What's interesting is that even when the prompt, question, and answer choices are all in Swedish, Qwen3.5-4B runs its reasoning process in English. Swedish is estimated to make up only about 1% of LLM training data, but by routing through English-based reasoning capabilities, the model has effectively overcome a specialized multilingual task. It is also another sign that the bar for small models keeps rising on a timescale of months.

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