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nOps Raises $30M to Optimize AWS Customers’ Cloud Spend

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nOps lands $30M to optimize AWS customers’ cloud spend

Companies don’t necessarily have to produce groundbreaking technology to gain traction within the marketplace. Undercutting rivals could be enough to make a dent in a competitive industry. So can counting on connections to win customers who need a fast fix.

Operational Operations is an example of this. Like countless other vendors, nOps sells software designed to “optimize” the budgets that corporations spend on cloud services and products. Yet the corporate has managed to grow faster — and greater — than lots of its rivals, perhaps partially since it serves AWS customers exclusively.

nOps says its customer base has grown 450% prior to now 18 months and that it helps customers manage greater than $1.5 billion in AWS cloud spend. That clearly impressed investors; this month, nOps closed a $30 million Series A funding round led by PE firm Headlight Partners, bringing nOps’ total raised to $40.5 million.

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JT Giri, founder and CEO of nOps, began within the cloud industry as a network engineer and DevOps consultant. In 2012, he decided to put those skills to use and co-founded AWS-focused consulting firm nClouds. nOps began as a spin-off of nClouds in 2017, and after nClouds was acquired by Charles Thayne Capital in 2022, Giri dedicated himself to nOps full-time.

“There’s a growing pain in the cloud space,” Giri told TechCrunch. “As companies tighten budgets ahead of fiscal year 2025 planning, a solution that provides a comprehensive, automated view of cloud costs is critical.”

As Giri notes, for a lot of corporations, effective use of the cloud stays an aspiration, not a reality, especially as corporations invest increasingly in cloud-hosted AI projects. (Gartner projects According to a 2024 Statista report, spending on cloud services will reach $675.4 billion in 2024, up from $561 billion in 2023. questionnaire84% of organizations said managing cloud spend was a “significant” challenge for them due to obstacles related to governance, security, and technology expertise.

nOps addresses the obstacles to cloud optimization from several different perspectives. It generates dashboards and reports that show all of an organization’s AWS spend and mechanically handles tasks that may potentially get monetary savings. This includes steps like resource planning and “rightsizing,” stopping idle instances and containers, and dynamically adjusting storage volumes.

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One of nOps’ dashboards on cloud spending.
Image sources: Operational Operations

“nOps uses AI and machine learning to analyze compute needs and automatically optimize for performance, reliability, and cost,” Giri said. “For most of its products, nOps has a unique and flexible pricing structure where it doesn’t get paid until the customer saves money; nOps gets a percentage of the cost savings.”

Giri didn’t say where nOps stands when it comes to revenue, nor did he say exactly how many shoppers nOps has today. But he did suggest that the Series A round positions the startup well for the approaching months.

What’s next for nOps? Giri says the plan is to grow from 60 employees now to 80 by the top of the yr, and construct recent integrations with AWS products and open-source cost-optimization tools.

“In our experience processing over $1.5 billion in AWS cloud spend, 30% of cloud costs are wasteful and 20% are spent on-demand, the most expensive type of purchase, leaving organizations with a huge opportunity to reduce their monthly cloud costs,” Giri said. “nOps provides insight, identifies inefficiencies, and enables resource optimization through built-in automation or one-click changes.”

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This article was originally published on : techcrunch.com

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One of the last AI Google models is worse in terms of safety

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The Google Gemini generative AI logo on a smartphone.

The recently released Google AI model is worse in some security tests than its predecessor, in line with the company’s internal comparative test.

IN Technical report Google, published this week, reveals that his Flash Gemini 2.5 model is more likely that he generates a text that violates its security guidelines than Gemini 2.0 Flash. In two indicators “text security for text” and “image security to the text”, Flash Gemini 2.5 will withdraw 4.1% and 9.6% respectively.

Text safety for the text measures how often the model violates Google guidelines, making an allowance for the prompt, while image security to the text assesses how close the model adheres to those boundaries after displaying the monitors using the image. Both tests are automated, not supervised by man.

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In an e-mail, Google spokesman confirmed that Gemini 2.5 Flash “performs worse in terms of text safety for text and image.”

These surprising comparative results appear when AI is passing in order that their models are more acceptable – in other words, less often refuse to answer controversial or sensitive. In the case of the latest Llam Meta models, he said that he fought models in order to not support “some views on others” and answers to more “debated” political hints. Opeli said at the starting of this yr that he would improve future models, in order to not adopt an editorial attitude and offers many prospects on controversial topics.

Sometimes these efforts were refundable. TechCrunch announced on Monday that the default CHATGPT OPENAI power supply model allowed juvenile to generate erotic conversations. Opeli blamed his behavior for a “mistake”.

According to Google Technical Report, Gemini 2.5 Flash, which is still in view, follows instructions more faithfully than Gemini 2.0 Flash, including instructions exceeding problematic lines. The company claims that regression might be partially attributed to false positives, but in addition admits that Gemini 2.5 Flash sometimes generates “content of violation” when it is clearly asked.

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“Of course, there is a tension between (after instructions) on sensitive topics and violations of security policy, which is reflected in our assessment,” we read in the report.

The results from Meepmap, reference, which can examine how models react to sensitive and controversial hints, also suggest that Flash Gemini 2.5 is much less willing to refuse to reply controversial questions than Flash Gemini 2.0. Testing the TechCrunch model through the AI ​​OpenRoutter platform has shown that he unsuccessfully writes essays to support human artificial intelligence judges, weakening the protection of due protection in the US and the implementation of universal government supervisory programs.

Thomas Woodside, co -founder of the Secure AI Project, said that the limited details given by Google in their technical report show the need for greater transparency in testing models.

“There is a compromise between the instruction support and the observation of politics, because some users may ask for content that would violate the rules,” said Woodside Techcrunch. “In this case, the latest Flash model Google warns the instructions more, while breaking more. Google does not present many details about specific cases in which the rules have been violated, although they claim that they are not serious. Not knowing more, independent analysts are difficult to know if there is a problem.”

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Google was already under fire for his models of security reporting practices.

The company took weeks to publish a technical report for the most talented model, Gemini 2.5 Pro. When the report was finally published, it initially omitted the key details of the security tests.

On Monday, Google published a more detailed report with additional security information.

(Tagstotransate) Gemini

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This article was originally published on : techcrunch.com
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Aurora launches a commercial self -propelled truck service in Texas

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The autonomous startup of the Aurora Innovation vehicle technology claims that it has successfully launched a self -propelled truck service in Texas, which makes it the primary company that she implemented without drivers, heavy trucks for commercial use on public roads in the USA

The premiere appears when Aurora gets the term: In October, the corporate delayed the planned debut 2024 to April 2025. The debut also appears five months after the rival Kodiak Robotics provided its first autonomous trucks to clients commercial for operations without a driver in field environments.

Aurora claims that this week she began to freight between Dallas and Houston with Hirschbach Motor Lines and Uber Freight starters, and that she has finished 1200 miles without a driver to this point. The company plans to expand to El Paso and Phoenix until the top of 2025.

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TechCrunch contacted for more detailed information concerning the premiere, for instance, the variety of vehicles implemented Aurora and whether the system needed to implement the Pullover maneuver or the required distant human assistance.

The commercial premiere of Aurora takes place in a difficult time. Self -propelled trucks have long been related to the necessity for his or her technology attributable to labor deficiencies in the chairman’s transport and the expected increase in freigh shipping. Trump’s tariffs modified this attitude, not less than in a short period. According to the April analytical company report from the commercial vehicle industry ACT researchThe freight is predicted to fall this yr in the USA with a decrease in volume and consumer expenditure.

Aurora will report its results in the primary quarter next week, i.e. when he shares how he expects the present trade war will affect his future activity. TechCrunch contacted to learn more about how tariffs affect Auror’s activities.

For now, Aurora will probably concentrate on further proving his safety case without a driver and cooperation with state and federal legislators to just accept favorable politicians to assist her develop.

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At the start of 2025, Aurora filed a lawsuit against federal regulatory bodies after the court refused to release the appliance for release from the protection requirement, which consists in placing warning triangles on the road, when the truck must stop on the highway – something that’s difficult to do when there isn’t a driver in the vehicle. To maintain compliance with this principle and proceed to totally implement without service drivers, Aurora probably has a man -driven automotive trail after they are working.

(Tagstranslate) Aurora Innovation

This article was originally published on : techcrunch.com
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Sarah Tavel, the first woman of the Benchmark GP, goes to the Venture partner

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Eight years after joining Benchmark as the company’s first partner, Sarah Tavel announced that she was going to a more limited role at Hapeure Venture.

In his latest position as a partner Venture Tavel will proceed to invest and serve existing company boards, but may have more time to examine “AI tools on the edge” and fascinated with the direction of artificial intelligence, she wrote.

Tavel joined Benchmark in 2017 after spending a half years as a partner in Greylock and three years as a product manager at Pinterest. Before Pinterest, Tavel was an investor in Bessemer Venture Partners, where she helped Source Pinterest and Github.

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Since its foundation in 1995, the benchmark intentionally maintained a small team of six or fewer general partners. Unlike most VC corporations, wherein older partners normally receive most of the management and profits fees, the benchmark acts as an equal partnership, and all partners share fees and returns equally.

During his term as a general partner of Benchmark, Tavel invested in Hipcamp on the campsite, chains of cryptocurrency intelligence startups and the Supergreaty cosmetic platform, which was purchased by Whatnot in 2023. Tavel also supported the application for sharing photos of Paparazhi, which closed two years ago, and the AI ​​11x sales platform, about which TechCrunch wrote.

(Tagstotransate) benchmark

This article was originally published on : techcrunch.com
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