Google is turning to nuclear energy to help power its AI drive. On Monday, the company said it will partner with the startup Kairos Power to build seven small nuclear reactors in the US. The deal targets adding 500 megawatts of nuclear power from the small modular reactors (SMRs) by the decade’s end. The first is expected to be up and running by 2030, with the remainder arriving through 2035.

It’s the first-ever corporate deal to buy nuclear power from SMRs. Small modular reactors are smaller than existing reactors. Their components are built inside a factory rather than on-site, which can help lower construction costs compared to full-scale plants.

Kairos will need the US Nuclear Regulatory Commission to approve design and construction permits for the plans. The startup has already received approval for a demonstration reactor in Tennessee, with an online date targeted for 2027. The company already builds test units (without nuclear-fuel components) at a development facility in Albuquerque, NM, where it assesses components, systems and its supply chain.

The companies didn’t announce the financial details of the arrangement. Google says the deal’s structure will help to keep costs down and get the energy online sooner.

“By procuring electricity from multiple reactors — what experts call an ‘orderbook’ of reactors — we will help accelerate the repeated reactor deployments that are needed to lower costs and bring Kairos Power’s technology to market more quickly,” Michael Terrell, Google’s senior director for energy and climate, wrote in a blog post. “This is an important part of our approach to scale the benefits of advanced technologies to more people and communities, and builds on our previous efforts.”

The AI boom — and the enormous amount of data center power it requires — has led to several deals between Big Tech companies and the nuclear industry. In September, Microsoft forged an agreement with Constellation Energy to bring a unit of the Three Mile Island plant in Pennsylvania back online. In March, Amazon bought a nuclear-powered data center from Talen Energy.

The newly launched Swarm framework from developers at OpenAI is an experimental tool designed to orchestrate networks of AI agents, and it’s been making waves in the tech community. Unlike other multi-agent frameworks, Swarm aims to provide a blend of simplicity, flexibility and control that sets it apart. Although still in its early stages, Swarm offers a fresh take on agent collaboration, with core concepts like “routines” and “handoffs” to guide agents through collaborative tasks.

While Swarm is not an official OpenAI product nor is intended as a production-ready tool, it provides valuable insights into the potential of multi-agent systems in enterprise automation. Its key focus is on simplifying agent interactions, which is achieved through the Chat Completions API. This stateless design means agents do not retain memory between interactions, contributing to Swarm’s simplicity but limiting its use for complex decision-making tasks that require contextual memory. 

Instead, developers need to implement their own memory solutions, which offer both challenges and opportunities for customization. This balance of simplicity and control is a major point of attraction for developers interested in learning about or building multi-agent orchestration systems.

A lightweight approach for developers

Swarm is distinct in its lightweight design, focusing on ease of understanding and implementation. This approach gives developers more granular control over execution steps and tool calls, making it easier to experiment with agent interactions and orchestrations. Compared to other frameworks like LangChain or CrewAI, Swarm’s stateless model is easier to grasp, which makes it accessible for those who are new to multi-agent systems.

However, the lack of built-in memory management is a noted limitation. To achieve more sophisticated agent behavior, developers must implement external memory solutions. Despite this, Swarm’s emphasis on transparency and modularity has been praised for enabling developers to tailor agent behaviors and extend the framework based on their needs

Guiding collaboration with routines and handoffs

At the heart of Swarm are the concepts of “routines” and “handoffs,” which are mechanisms designed to help agents carry out collaborative tasks in an organized manner. A routine is a set of instructions that agents follow to complete specific actions, while handoffs allow for seamless transitions between agents, each specializing in particular functions. 

This structured approach to agent interactions allows developers to create dynamic, multi-step processes where tasks are handled by the agent best suited for each step. Examples include customer service systems where triage agents manage initial contact before passing on specific queries to agents specialized in sales, support or refunds. This adaptability makes Swarm particularly useful for building applications that require multiple, specialized capabilities to work together.

Addressing limitations: The role of state and memory

Despite its promising features, Swarm’s lack of internal support for state and memory limits its effectiveness in complex decision-making based on past interactions. For instance, in a sales scenario, a stateful system would allow agents to track customer history across interactions—a capability that Swarm, in its current form, does not provide.

The release of Swarm has also sparked ethical discussions about its potential impact on the workforce and the broader implications of AI-driven automation. While Swarm aims to make sophisticated multi-agent systems more accessible, its capability to replace human tasks raises concerns about job displacement and fairness. Security experts have also highlighted the need for robust safeguards to prevent misuse or malfunction within these autonomous agent networks.

However, the decision to open-source Swarm has created an opportunity for community-driven development, potentially leading to novel uses and improvements. As developers experiment with Swarm, they contribute to the growing understanding of how multi-agent orchestration can be leveraged to solve real-world problems, particularly in enterprise environments where automation can drive efficiency and allow human workers to focus on more strategic initiatives.

Peanut, a social networking app for women, is launching a new feature to help users track their pregnancies. The new Peanut Track tool leverages seven years of data from questions asked on the social network and uses AI to anticipate questions that women have during different stages of their pregnancy. 

Founded in 2017, Peanut is an online community that is designed to connect women throughout all stages of their lives, from puberty to menopause. Now, the company is giving women personalized and anticipatory guidance tailored to each stage of pregnancy. 

Up until now, Peanut has been focused on community and helping women connect with other women over shared experiences. With this new feature, Peanut is taking its first steps into the utility element of its service by helping women track their pregnancies individually. 

“We’ve always been about community,” Peanut CEO and founder Michelle Kennedy told TechCrunch. “But, what about the times when I actually am not looking to engage with someone else, but am looking to source information. It’s really about serving you content which we know you’re going to ask because all these other women across the seven years that we have been going have been asking the same.”

Peanut Track offers content about what to expect during their current stage of pregnancy and what they should be thinking about in terms of well-being. The app will surface content written by gynecologists. 

In addition, users can access all of the traditional metrics when it comes to pregnancy. For instance, Peanut Track will tell you if your baby is currently the size of a peach or a pumpkin. Plus, the feature will tell you what sorts of symptoms you may be experiencing. 

Peanut Track stays true to Peanut’s core mission of community, as it also connects users with other women who are at the same stage of pregnancy as them. 

The feature was requested by users, as Kennedy says Peanut is in a position where it can talk to its user base about what they want to see on the platform, and then work with them to implement their vision. 

Following Peanut Track, the startup plans to launch Peanut Grow next year to help women anticipate the early stages of motherhood by tracking development and milestones. 

“You can track your feed and your poops and all the other things that you have to do when you have a newborn,” Kennedy explained. “But also, what about you? How are you doing? How’s your mood? That kind of tracking is very important, particularly when you think about postpartum depression, loneliness — all of those other elements. I think it’s really important because what’s the point in telling someone, yes, we diagnose you as lonely without giving them the solution of ‘here are other women who are in exactly the same situation as you, and this too shall pass,’” she said.

Peanut has raised $32 million in funding to date. The company raised $10 million in a funding round in August 2022 that helped Peanut get to profitability. Before that, the company closed a $12 million Series A in May 2020.

The show, based on a series of books by Hugh Howey, is about a community of 10,000 people living in an underground silo that’s intended to protect them from dangerous conditions aboveground. If you’ve been meaning to see the first season and haven’t yet, you probably shouldn’t watch this new trailer; as you might have guessed, it has quite a few mysteries that are fun to experience for yourself.

AMD has announced the Radeon PRO V710, a new GPU designed for access via Microsoft Azure, the exclusive cloud partner, as part of the company’s family of visual cloud GPUs.

The V710 is available today in private preview, but anyone hoping to use it for a home PC setup will be disappointed, as the V710 features a single-slot design and passive cooling.