In brief

• In late summer 2025, a publicly developed large language model (LLM) will be released — co-created by researchers at EPFL, ETH Zurich, and the Swiss National Supercomputing Centre (CSCS).

• This LLM will be fully open: This openness is designed to support broad adoption and foster innovation across science, society, and industry.

• A defining feature of the model is its multilingual fluency in over 1,000 languages.

Last year, China generated 834 terawatt-hours of solar power.

Which is more than the G7 countries generated, and more than the US and EU combined. In fact the only country group that generates more solar power than China is the OECD, all 38 countries of it.

Data: @ember-energy.org

Source: https://bsky.app/profile/nathanielbullard.com/post/3lsbbsg6ohk2j

At the request of NATO and Poland, a Royal Australian Air Force E-7A Wedgetail aircraft will deploy to Europe in August to help protect a vital international gateway for humanitarian and military assistance into Ukraine.

This includes new targeted financial sanctions and travel bans on 37 individuals, and targeted financial sanctions on seven entities.

These sanctions target Russia’s defence, energy, transport, insurance, electronics and finance sectors, as well as promulgators of Russian disinformation and propaganda.

During the Summit, Australia also signed an agreement with the NATO Support and Procurement Organisation (NSPO). The NSPO Agreement strengthens Australia’s cooperation with NATO partners on capability acquisition, systems support and logistics.

Under the agreement, the Australian Defence Force will be able to gain access to collaborative procurement efforts with NATO partners.

The new partnership also unlocks wider opportunities for Australian defence industry to access new markets across NATO supply chains.

It looks like AI has followed Crypto chip wise in going CPU > GPU > ASIC

GPUs, while dominant in training large models, are often too power-hungry and costly for efficient inference at scale. This is opening new opportunities for specialized inference hardware, a market where startups like Untether AI were early pioneers.

In April, then-CEO Chris Walker had highlighted rising demand for Untether’s chips as enterprises sought alternatives to high-power GPUs. “There’s a strong appetite for processors that don’t consume as much energy as Nvidia’s energy-hungry GPUs that are pushing racks to 120 kilowatts,” Walker told CRN. Walker left Untether AI in May.

Hopefully the training part of AI goes to ASIC's to reduce costs and energy use but GPU's continue to improve inference and increase VRAM sizes to the point that AI requires nothing special to run it locally

Sorry team flipped the URL’s around to prevent overflow from lemmy.world users

https://fly.io/blog/youre-all-nuts/

Could the fuel powering F1 next season also run your car? | The Business | ABC NEWS

https://www.youtube.com/watch?v=0yVBvsbOZ-0

Devstral is an agentic LLM for software engineering tasks built under a collaboration between Mistral AI and All Hands AI 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this benchmark.

It is finetuned from Mistral-Small-3.1, therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from Mistral-Small-3.1 the vision encoder was removed.

Learn more about Devstral in our blog post. Key Features:

Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents.

lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use.

Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.

Context Window: A 128k context window.

Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.

Learn more about Devstral in our blog post.

https://mistral.ai/news/devstral