This week has been a big one for messaging privacy. The news broke on Monday that the Editor-in-Chief for The Atlantic magazine was accidentally added to a Signal group where members of the US government were talking about highly classified military actions. The report kicked off a firestorm of Congressional hearings about the nature of data sharing and privacy for not only government officials but members of the defense and intelligence community. This has also raised questions about the way that those same officials will often circumvent policy to facilitate communications. While the nature of the group and their discussion topic is highly political in nature let’s focus on the communications aspect. Why did they use Signal? How can we be sure it’s safe? And what does this mean for government agencies that still want to create backdoors into secure protocols? This and more on the Tech Field Day News Rundown.

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1:13 – Employers Target Engineers with LLM Skills

The explosive growth in enterprises wanting to board the Generative AI train has highlighted the shortage of expertise in building and operating LLM based applications. A recent study by LinkedIn showed AI skills to be in high demand and short supply. Maybe we need an AI agent to build AI applications for us? Will we always need humans with new skills?

Read More: Employers target engineers with LLM skills


4:09 – Google Acquires Wiz for $32 Billion after Previous Offer Falls Through

Google has successfully agreed to acquire cloud security firm Wiz for $32 billion, following a failed $23 billion attempt last July, which we covered at the time here on the Rundown. This acquisition is a strategic move to strengthen Google Cloud’s security offerings, positioning it to better compete with Amazon Web Services and Microsoft Azure. By leveraging Wiz’s technology, Google aims to enhance the security of its cloud infrastructure and applications, thereby attracting more enterprise customers.

Read More: Google Agrees to Buy Cloud Security Firm Wiz for $32 Billion


8:33 – AI Applications are putting Cloud Workloads at Risk

A study by Tenable suggests that AI applications have a higher-than-average rate of security vulnerabilities. The baseline rate of 59% of cloud applications containing critical security vulnerabilities is worrying enough, but Tenable found 72% AI applications have critical security vulnerabilities.

Read More: AI is putting your cloud workloads at risk


12:09 – SoftBank to Acquire Ampere

Ampere is getting amped up thanks to a purchase agreement from SoftBank. The deal is an all-cash transaction valued at $6.5 billion. Ampere has been covered on the Rundown in the past due to their development of an ARM-based server chip that was commercially viable. SoftBank has been in the news recently as part of the announced Project Stargate $500 billion investment in AI infrastructure in the US.

Read More: SoftBank seals $6.5 billion deal for chip designer Ampere


16:47 – Cloud FinOps is driving application repatriation

We hear that the public cloud is a very cost-effective place to fail but can be an expensive place to succeed. The shock of a large charge for a new cloud application is well known and often followed by a real challenge analysing the cloud providers bill to link cost to value. A whole new category of Cloud FinOps applications have grown up and are sometimes driving the repatriation of applications to on-premises data centers.

Read More: Workload repatriations persist as CIOs improve cloud cost management


23:27 – Dynamo is the NVIDIA operating system for your AI Factory

GTC was full of hardware talk but one new piece of software aims to make AI workloads even faster. NVIDIA Dynamo is the name for a new operating system aimed at reducing latency with AI requests and optimizing both translation and return of results. Dynamo is a suite that will optimize requests from various inferencing engines such as TensorRT and SGLang across a large number of GPUs concurrently. Dynamo is also designed to work with frameworks like PyTorch so you don’t need to recode everything and they’ve released the code on GitHub or as a prebuilt container image.

Read More: Introducing NVIDIA Dynamo, A Low-Latency Distributed Inference Framework for Scaling Reasoning AI Models


27:16 – Just How Secure Is Signal?

This week has been a big one for messaging privacy. The news broke on Monday that the Editor-in-Chief for The Atlantic magazine was accidentally added to a Signal group where members of the US government were talking about highly classified military actions. The report kicked off a firestorm of Congressional hearings about the nature of data sharing and privacy for not only government officials but members of the defense and intelligence community. This has also raised questions about the way that those same officials will often circumvent policy to facilitate communications. While the nature of the group and their discussion topic is highly political in nature let’s focus on the communications aspect. Why did they use Signal? How can we be sure it’s safe? And what does this mean for government agencies that still want to create backdoors into secure protocols?


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