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Grid Down? Scientists Say Your EV Could Provide the Needed Backup Power

Grid Down? Scientists Say Your EV Could Provide the Needed Backup Power

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If we’re planning to expand electric vehicle (EV) usage, we may as well maximize their energy potential. That’s the intent behind the vehicle-to-grid (V2G) model—a proposal that taps into idle EVs at charging stations as a backup source for power grids.

A recent Utilities Policy paper outlines why, despite sounding like an ideal solution, V2G hasn’t been widely implemented. The study presents potential roadmaps for adopting V2G based on stakeholder interviews and market analyses. Of course, the EV industry has suffered major turmoil under the current administration, so it’s unlikely that the paper’s suggestions will reach D.C. anytime soon. But in a hopeful future, V2G could both boost grid efficiency and reduce ownership costs for EV drivers, the researchers said.

“V2G is the idea that your electric vehicle isn’t just a car; it’s a battery on wheels,” Serena Kim, the study’s corresponding author and a data scientist at North Carolina State University, told Gizmodo in an email. “Most EVs sit parked about 95% of the time. V2G technology allows that stored energy to flow back out of the vehicle and into the power grid, your home, or a building when it’s needed most, like during a hot summer afternoon when everyone cranks the air conditioning.”

The EV state of the union

According to the U.S. Department of Energy, electric vehicles could help the fuel economy, reduce air quality impacts, and contribute to a safer transportation system. Given these benefits, ramping up the U.S. EV industry became a policy cornerstone for the Biden administration.

Until Donald Trump took office, that is. Last year, Trump revoked many Biden-era mandates on EVs, including a 50% target for EVs in new vehicle sales, government funds for charging stations, and EV tax credits, according to Reuters. As a result, EV sales plummeted in the U.S., with major players such as Ford, GM, and Stellantis retracting previous investments in EVs, reported Inside Climate News.

On that bright note

That said, several experts told E&E News that EVs aren’t yet “dead” in the U.S., although they might be “in suspended animation for a year or maybe two,” said Alan Baum, an independent auto analyst. And the new study taps into this optimistic scenario that the EV industry will bounce back. Once it does, solid policy suggestions for something like V2G could really help that comeback. For the study, the researchers interviewed 42 industry stakeholders, including power utilities, manufacturers, local and state governments, school districts, and EV owners.

“We went into this study knowing that V2G faced barriers,” Kim said. “But what surprised me was the distribution of where those barriers concentrate.”

Overall, the greatest obstacle to V2G implementation in the U.S. was—unsurprisingly—institutional barriers, the paper noted. For instance, power utilities and EV owners want to see more V2G-capable vehicles, as well as more transparency in how the program would work and compensate them. On the other hand, inconsistency along state lines is making it challenging for automakers to plan and invest in EVs, according to the paper.

“That means the barriers are primarily about coordination and implementation, not about whether people believe V2G works or whether they think it’s worth something,” Kim added. “It suggests we don’t need to win a public opinion battle or wait for some technical breakthrough.”

In hopes of a comeback

At the same time, the findings indicate the challenge lies in “building the institutional scaffolding… that makes it easy and predictable for everyone involved to participate,” Kim said. The paper also notes some shortcomings of the work, including a relatively small sample size and a focus away from residential EV owners, whose motivations are “likely shaped by different information constraints, risk perceptions, and value trade-offs.”

As a result, V2G only exists in pilot settings. But these preliminary projects have delivered some meaningful lessons, Kim said. For instance, Dorchester, Massachusetts, and Boulder, Colorado, launched pilot V2G programs in 2023 and 2024, respectively. While these programs were small, they showed V2G geared toward lower-income communities boosted accessibility to electric vehicles for a broader population, she explained.

“These are solvable problems, but they need real attention,” Kim concluded.

Considering the current political landscape, the prospects for addressing those uncertainties in the near future are probably slim. But as Kim says, they’re solvable problems. But for the time being, EVs—and their potential benefits—might be “suspended in animation.”

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Anthropic said Thursday that an internal investigation uncovered three incidents in which its AI model Claude breached the systems of three organizations while conducting cybersecurity tests. The investigation, and disclosure, comes more than a week after OpenAI disclosed that one of its unreleased models breached Hugging Face’s systems during internal testing.

In all three cases, a Claude model reached the internet from within a testing environment while interacting with a third party and then gained unauthorized access to the live systems of these organizations, Anthropic said in a blog post, describing what it found and what the company plans to change to prevent this from happening again.

Anthropic said the OpenAI episode earlier this month prompted the company to conduct its own cybersecurity evaluation. It specifically looked for evidence that Claude had accessed the internet from within testing environments, which are designed to act as sandboxes and keep models isolated.

Among the 141,006 evaluation runs it reviewed, the AI lab found three incidents in which its model accessed the internet while interacting with Irregular, one of its third-party partners. Anthropic said the access traced back to a misconfiguration in the evaluation environment run with Irregular. It called this a “misunderstanding” between the two companies over whether the test setup had internet access, when in fact it did.

Anthropic said it isn’t placing blame and is “approaching the fixes as if the responsibility were ours alone,” while observing that Irregular is conducting its own separate investigation.

Because of that open connection, the model gained unauthorized access to the production infrastructure of three different organizations. Anthropic said the incidents involved three different Claude models: Opus 4.7, Mythos 5, and an internal research test model.

Notably, Anthropic said that in each of these cases “Claude was explicitly told by our prompt that it had no internet access.” It appears that the AI model assumed real-world systems to be part of the exercise it was asked to perform.

That assumption didn’t hold up uniformly, as it turns out. The three models didn’t behave the same way once evidence emerged that their targets were real, which Anthropic flagged as one of the more eye-opening findings Thursday.

Opus 4.7, the oldest of the three, recognized that it had reached a real production system in all four runs of that incident. In two of them, it rationalized that the real company must somehow be part of the exercise, but in all four, it kept attacking anyway, including pulling credentials and touching a database of production data. Mythos 5 also picked up on signs that it was on the real internet, but it talked itself back into believing it was still in a simulation, going on to publish a malicious software package to the public Python software registry PyPI, which was then downloaded and run by outside systems before being caught. Only the internal research test model, Anthropic’s newest, stopped on its own once it concluded the target was real.

In response, Anthropic said significant controls must be placed on these kinds of evaluations if powerful AI models are involved, echoing some sentiments within the cybersecurity community.

The company also noted that Claude was running without the additional safety monitoring and classifiers it deploys on generally available models — safeguards it said would have blocked the behavior, because the evaluations are designed to measure the underlying model’s raw capabilities.

Importantly, Anthropic said it found no evidence of any model “pursuing a goal of its own” and instead merely tried to complete the task it was asked to do.

Though comparisons between the mishaps of these fiercely competitive companies are inevitable, Anthropic in its blog post drew a clear distinction between its cybersecurity tests and those of OpenAI, noting where OpenAI’s model exploited an unknown software vulnerability to break out of its test environment, Anthropic’s models instead reached the internet through a path that had, by mistake, been left open.

Anthropic also drew a distinction between itself and OpenAI by noting that it discovered the incidents itself, through a proactive review, and that the two affected organizations it was able to reach hadn’t previously detected the activity or flagged it to Anthropic. (In contrast, Hugging Face detected the recent intrusion of its own systems first; it was only in the following days that OpenAI identified and disclosed that its own AI agent was the perpetrator.)

The company added that it’s now working with the independent evaluation group METR on a third-party review of the incidents.

OpenAI’s accidental breach of Hugging Face, which was the first verifiable case of an AI lab losing control of its model, has sparked a string of wildly differing reactions from the industry and politicians. This latest disclosure from Anthropic ensures the debate over AI models and security will continue.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Anthropic #models #breached #companies #security #tests #TechCrunchAnthropic,OpenAI">Anthropic says its own AI models breached three companies during security tests | TechCrunch Anthropic said Thursday that an internal investigation uncovered three incidents in which its AI model Claude breached the systems of three organizations while conducting cybersecurity tests. The investigation, and disclosure, comes more than a week after OpenAI disclosed that one of its unreleased models breached Hugging Face’s systems during internal testing. In all three cases, a Claude model reached the internet from within a testing environment while interacting with a third party and then gained unauthorized access to the live systems of these organizations, Anthropic said in a blog post, describing what it found and what the company plans to change to prevent this from happening again. Anthropic said the OpenAI episode earlier this month prompted the company to conduct its own cybersecurity evaluation. It specifically looked for evidence that Claude had accessed the internet from within testing environments, which are designed to act as sandboxes and keep models isolated. Among the 141,006 evaluation runs it reviewed, the AI lab found three incidents in which its model accessed the internet while interacting with Irregular, one of its third-party partners. Anthropic said the access traced back to a misconfiguration in the evaluation environment run with Irregular. It called this a “misunderstanding” between the two companies over whether the test setup had internet access, when in fact it did. Anthropic said it isn’t placing blame and is “approaching the fixes as if the responsibility were ours alone,” while observing that Irregular is conducting its own separate investigation. Because of that open connection, the model gained unauthorized access to the production infrastructure of three different organizations. Anthropic said the incidents involved three different Claude models: Opus 4.7, Mythos 5, and an internal research test model. Notably, Anthropic said that in each of these cases “Claude was explicitly told by our prompt that it had no internet access.” It appears that the AI model assumed real-world systems to be part of the exercise it was asked to perform. That assumption didn’t hold up uniformly, as it turns out. The three models didn’t behave the same way once evidence emerged that their targets were real, which Anthropic flagged as one of the more eye-opening findings Thursday. Opus 4.7, the oldest of the three, recognized that it had reached a real production system in all four runs of that incident. In two of them, it rationalized that the real company must somehow be part of the exercise, but in all four, it kept attacking anyway, including pulling credentials and touching a database of production data. Mythos 5 also picked up on signs that it was on the real internet, but it talked itself back into believing it was still in a simulation, going on to publish a malicious software package to the public Python software registry PyPI, which was then downloaded and run by outside systems before being caught. Only the internal research test model, Anthropic’s newest, stopped on its own once it concluded the target was real. In response, Anthropic said significant controls must be placed on these kinds of evaluations if powerful AI models are involved, echoing some sentiments within the cybersecurity community. The company also noted that Claude was running without the additional safety monitoring and classifiers it deploys on generally available models — safeguards it said would have blocked the behavior, because the evaluations are designed to measure the underlying model’s raw capabilities. Importantly, Anthropic said it found no evidence of any model “pursuing a goal of its own” and instead merely tried to complete the task it was asked to do. Though comparisons between the mishaps of these fiercely competitive companies are inevitable, Anthropic in its blog post drew a clear distinction between its cybersecurity tests and those of OpenAI, noting where OpenAI’s model exploited an unknown software vulnerability to break out of its test environment, Anthropic’s models instead reached the internet through a path that had, by mistake, been left open. Anthropic also drew a distinction between itself and OpenAI by noting that it discovered the incidents itself, through a proactive review, and that the two affected organizations it was able to reach hadn’t previously detected the activity or flagged it to Anthropic. (In contrast, Hugging Face detected the recent intrusion of its own systems first; it was only in the following days that OpenAI identified and disclosed that its own AI agent was the perpetrator.) The company added that it’s now working with the independent evaluation group METR on a third-party review of the incidents. OpenAI’s accidental breach of Hugging Face, which was the first verifiable case of an AI lab losing control of its model, has sparked a string of wildly differing reactions from the industry and politicians. This latest disclosure from Anthropic ensures the debate over AI models and security will continue. When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Anthropic #models #breached #companies #security #tests #TechCrunchAnthropic,OpenAI

Hugging Face’s systems during internal testing.

In all three cases, a Claude model reached the internet from within a testing environment while interacting with a third party and then gained unauthorized access to the live systems of these organizations, Anthropic said in a blog post, describing what it found and what the company plans to change to prevent this from happening again.

Anthropic said the OpenAI episode earlier this month prompted the company to conduct its own cybersecurity evaluation. It specifically looked for evidence that Claude had accessed the internet from within testing environments, which are designed to act as sandboxes and keep models isolated.

Among the 141,006 evaluation runs it reviewed, the AI lab found three incidents in which its model accessed the internet while interacting with Irregular, one of its third-party partners. Anthropic said the access traced back to a misconfiguration in the evaluation environment run with Irregular. It called this a “misunderstanding” between the two companies over whether the test setup had internet access, when in fact it did.

Anthropic said it isn’t placing blame and is “approaching the fixes as if the responsibility were ours alone,” while observing that Irregular is conducting its own separate investigation.

Because of that open connection, the model gained unauthorized access to the production infrastructure of three different organizations. Anthropic said the incidents involved three different Claude models: Opus 4.7, Mythos 5, and an internal research test model.

Notably, Anthropic said that in each of these cases “Claude was explicitly told by our prompt that it had no internet access.” It appears that the AI model assumed real-world systems to be part of the exercise it was asked to perform.

That assumption didn’t hold up uniformly, as it turns out. The three models didn’t behave the same way once evidence emerged that their targets were real, which Anthropic flagged as one of the more eye-opening findings Thursday.

Opus 4.7, the oldest of the three, recognized that it had reached a real production system in all four runs of that incident. In two of them, it rationalized that the real company must somehow be part of the exercise, but in all four, it kept attacking anyway, including pulling credentials and touching a database of production data. Mythos 5 also picked up on signs that it was on the real internet, but it talked itself back into believing it was still in a simulation, going on to publish a malicious software package to the public Python software registry PyPI, which was then downloaded and run by outside systems before being caught. Only the internal research test model, Anthropic’s newest, stopped on its own once it concluded the target was real.

In response, Anthropic said significant controls must be placed on these kinds of evaluations if powerful AI models are involved, echoing some sentiments within the cybersecurity community.

The company also noted that Claude was running without the additional safety monitoring and classifiers it deploys on generally available models — safeguards it said would have blocked the behavior, because the evaluations are designed to measure the underlying model’s raw capabilities.

Importantly, Anthropic said it found no evidence of any model “pursuing a goal of its own” and instead merely tried to complete the task it was asked to do.

Though comparisons between the mishaps of these fiercely competitive companies are inevitable, Anthropic in its blog post drew a clear distinction between its cybersecurity tests and those of OpenAI, noting where OpenAI’s model exploited an unknown software vulnerability to break out of its test environment, Anthropic’s models instead reached the internet through a path that had, by mistake, been left open.

Anthropic also drew a distinction between itself and OpenAI by noting that it discovered the incidents itself, through a proactive review, and that the two affected organizations it was able to reach hadn’t previously detected the activity or flagged it to Anthropic. (In contrast, Hugging Face detected the recent intrusion of its own systems first; it was only in the following days that OpenAI identified and disclosed that its own AI agent was the perpetrator.)

The company added that it’s now working with the independent evaluation group METR on a third-party review of the incidents.

OpenAI’s accidental breach of Hugging Face, which was the first verifiable case of an AI lab losing control of its model, has sparked a string of wildly differing reactions from the industry and politicians. This latest disclosure from Anthropic ensures the debate over AI models and security will continue.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Anthropic #models #breached #companies #security #tests #TechCrunchAnthropic,OpenAI">Anthropic says its own AI models breached three companies during security tests | TechCrunch

Anthropic said Thursday that an internal investigation uncovered three incidents in which its AI model Claude breached the systems of three organizations while conducting cybersecurity tests. The investigation, and disclosure, comes more than a week after OpenAI disclosed that one of its unreleased models breached Hugging Face’s systems during internal testing.

In all three cases, a Claude model reached the internet from within a testing environment while interacting with a third party and then gained unauthorized access to the live systems of these organizations, Anthropic said in a blog post, describing what it found and what the company plans to change to prevent this from happening again.

Anthropic said the OpenAI episode earlier this month prompted the company to conduct its own cybersecurity evaluation. It specifically looked for evidence that Claude had accessed the internet from within testing environments, which are designed to act as sandboxes and keep models isolated.

Among the 141,006 evaluation runs it reviewed, the AI lab found three incidents in which its model accessed the internet while interacting with Irregular, one of its third-party partners. Anthropic said the access traced back to a misconfiguration in the evaluation environment run with Irregular. It called this a “misunderstanding” between the two companies over whether the test setup had internet access, when in fact it did.

Anthropic said it isn’t placing blame and is “approaching the fixes as if the responsibility were ours alone,” while observing that Irregular is conducting its own separate investigation.

Because of that open connection, the model gained unauthorized access to the production infrastructure of three different organizations. Anthropic said the incidents involved three different Claude models: Opus 4.7, Mythos 5, and an internal research test model.

Notably, Anthropic said that in each of these cases “Claude was explicitly told by our prompt that it had no internet access.” It appears that the AI model assumed real-world systems to be part of the exercise it was asked to perform.

That assumption didn’t hold up uniformly, as it turns out. The three models didn’t behave the same way once evidence emerged that their targets were real, which Anthropic flagged as one of the more eye-opening findings Thursday.

Opus 4.7, the oldest of the three, recognized that it had reached a real production system in all four runs of that incident. In two of them, it rationalized that the real company must somehow be part of the exercise, but in all four, it kept attacking anyway, including pulling credentials and touching a database of production data. Mythos 5 also picked up on signs that it was on the real internet, but it talked itself back into believing it was still in a simulation, going on to publish a malicious software package to the public Python software registry PyPI, which was then downloaded and run by outside systems before being caught. Only the internal research test model, Anthropic’s newest, stopped on its own once it concluded the target was real.

In response, Anthropic said significant controls must be placed on these kinds of evaluations if powerful AI models are involved, echoing some sentiments within the cybersecurity community.

The company also noted that Claude was running without the additional safety monitoring and classifiers it deploys on generally available models — safeguards it said would have blocked the behavior, because the evaluations are designed to measure the underlying model’s raw capabilities.

Importantly, Anthropic said it found no evidence of any model “pursuing a goal of its own” and instead merely tried to complete the task it was asked to do.

Though comparisons between the mishaps of these fiercely competitive companies are inevitable, Anthropic in its blog post drew a clear distinction between its cybersecurity tests and those of OpenAI, noting where OpenAI’s model exploited an unknown software vulnerability to break out of its test environment, Anthropic’s models instead reached the internet through a path that had, by mistake, been left open.

Anthropic also drew a distinction between itself and OpenAI by noting that it discovered the incidents itself, through a proactive review, and that the two affected organizations it was able to reach hadn’t previously detected the activity or flagged it to Anthropic. (In contrast, Hugging Face detected the recent intrusion of its own systems first; it was only in the following days that OpenAI identified and disclosed that its own AI agent was the perpetrator.)

The company added that it’s now working with the independent evaluation group METR on a third-party review of the incidents.

OpenAI’s accidental breach of Hugging Face, which was the first verifiable case of an AI lab losing control of its model, has sparked a string of wildly differing reactions from the industry and politicians. This latest disclosure from Anthropic ensures the debate over AI models and security will continue.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Anthropic #models #breached #companies #security #tests #TechCrunchAnthropic,OpenAI
is*hosting for their Linux VPS hosting to get the job done. Flexible server solutions make it possible to configure resources, select a suitable location, and deploy the environment automatically.

The list of users who will find this helpful is extensive: DevOps engineers, web application developers, SaaS platforms, and many others. Using open-source tools helps them create a reliable, transparent, and scalable environment for development and operations.

DevOps is today one of the key approaches to creating and managing software products. It connects development, testing, and operations, helping teams work faster. Open-source tools make workflows easier and flexible. Open source is basically built to bend. You can customize your tools, stitch them together, and automate the boring stuff. Honestly, it’s a perfect fit for pretty much anything — web, mobile, cloud, data, or AI. 

It is fair to say that several specific DevOps components help automate processes, improve system stability, and shorten the time between product development and launch. These include:

  • application build automation;
  • containerization of production services;
  • server configuration management;
  • infrastructure health monitoring;
  • secure storage of credentials.

Less manual grunt work means fewer mistakes. Auto-deploys let you ship updates in a flash, while smart monitoring catches performance bottlenecks before they become fires. 

Scalable Infrastructure for DevOps Teams

In practice, any DevOps model requires a reliable and powerful infrastructure. Every project, even the smallest one, must have the ability to quickly scale resources as more and more users join or as technical requirements change. The server environment must support flexible power settings, rapid creation of new environments, and stable service operation.

International IaaS providers offer VPS/VDS, dedicated servers, VPN services, and additional infrastructure solutions. Users can configure key server parameters, including:

  • processing power;
  • amount of RAM;
  • drive type;
  • data center location.

A broad infrastructure geography makes it possible to select suitable locations for different projects and reduce access latency. Such opportunities are in demand in various fields. Online stores use scalable servers to handle large numbers of requests.

When choosing DevOps infrastructure, it is important to consider not only current challenges but also growth prospects. A flexible server platform should allow configuration changes without complex migrations. Among the most popular features are:

  • setting up CPU and RAM;
  • use of fast NVMe drives;
  • support for different operating systems;
  • connecting additional IP addresses;
  • automatic deployment of environments;
  • integration via API tools.

This model makes it easier for technicians and helps launch new projects faster. DevOps teams can create test environments, run experiments, and migrate applications between environments with minimal time investment.

Fundamentals of System Security and Resilience

As soon as digital products begin to evolve, securing the system as a whole becomes just as important as maintaining performance. Good companies must secure the users data, provide uninterrupted service, and minimize the risk of breakdowns.

It is exactly these advanced open-source tools that help not only manage the entire system but also monitor it clearly. Teams will be able to track changes, audit settings, and implement additional security measures. When building a secure DevOps environment, special attention is paid to the following aspects:

  • isolation of virtual servers;
  • data backup;
  • protecting network connections;
  • use of IPv4 and IPv6;
  • user access control.

KVM technology ensures effective isolation of virtual machines and stable operation of VPS servers. SSDs and NVMe drives enable faster processing, and the latest generation of Intel Xeon and AMD EPYC processors allows you to run resource-intensive applications.

How to Choose Infrastructure for Long-Term Development

Choose a server that actually grows with you. You want to scale smoothly without hitting hidden fees. Look for clear, pay-as-you-go pricing — it’s the smartest way for startups to get top-tier tech without breaking the bank. 

Flexible servers work for almost any project. Building an edtech app? Marketing tools? Analytics? AI? To summarize, good infrastructure is the secret sauce to keep your product growing smoothly.

When you need a comprehensive strategy, keep in mind that it can only be based on a successful combination of elements. This includes automation, open-source tools, and high-quality server infrastructure. Together, they create a secure and scalable DevOps environment. These technologies help developers build products faster, monitor all critical processes, and manage resources as efficiently as possible.

#Building #Secure #Scalable #DevOps #Environment #OpenSource #ToolsDevOps">Building a Secure and Scalable DevOps Environment Using Open-Source Tools We can all see how the newest digital products require flexibility and the ability to scale quickly together with the business. At the same time, they must cope with growing workloads. That’s why teams choose is*hosting for their Linux VPS hosting to get the job done. Flexible server solutions make it possible to configure resources, select a suitable location, and deploy the environment automatically. The list of users who will find this helpful is extensive: DevOps engineers, web application developers, SaaS platforms, and many others. Using open-source tools helps them create a reliable, transparent, and scalable environment for development and operations. DevOps is today one of the key approaches to creating and managing software products. It connects development, testing, and operations, helping teams work faster. Open-source tools make workflows easier and flexible. Open source is basically built to bend. You can customize your tools, stitch them together, and automate the boring stuff. Honestly, it’s a perfect fit for pretty much anything — web, mobile, cloud, data, or AI.  It is fair to say that several specific DevOps components help automate processes, improve system stability, and shorten the time between product development and launch. These include: application build automation; containerization of production services; server configuration management; infrastructure health monitoring; secure storage of credentials. Less manual grunt work means fewer mistakes. Auto-deploys let you ship updates in a flash, while smart monitoring catches performance bottlenecks before they become fires.  Scalable Infrastructure for DevOps Teams In practice, any DevOps model requires a reliable and powerful infrastructure. Every project, even the smallest one, must have the ability to quickly scale resources as more and more users join or as technical requirements change. The server environment must support flexible power settings, rapid creation of new environments, and stable service operation. International IaaS providers offer VPS/VDS, dedicated servers, VPN services, and additional infrastructure solutions. Users can configure key server parameters, including: processing power; amount of RAM; drive type; data center location. A broad infrastructure geography makes it possible to select suitable locations for different projects and reduce access latency. Such opportunities are in demand in various fields. Online stores use scalable servers to handle large numbers of requests. When choosing DevOps infrastructure, it is important to consider not only current challenges but also growth prospects. A flexible server platform should allow configuration changes without complex migrations. Among the most popular features are: setting up CPU and RAM; use of fast NVMe drives; support for different operating systems; connecting additional IP addresses; automatic deployment of environments; integration via API tools. This model makes it easier for technicians and helps launch new projects faster. DevOps teams can create test environments, run experiments, and migrate applications between environments with minimal time investment. Fundamentals of System Security and Resilience As soon as digital products begin to evolve, securing the system as a whole becomes just as important as maintaining performance. Good companies must secure the users data, provide uninterrupted service, and minimize the risk of breakdowns. It is exactly these advanced open-source tools that help not only manage the entire system but also monitor it clearly. Teams will be able to track changes, audit settings, and implement additional security measures. When building a secure DevOps environment, special attention is paid to the following aspects: isolation of virtual servers; data backup; protecting network connections; use of IPv4 and IPv6; user access control. KVM technology ensures effective isolation of virtual machines and stable operation of VPS servers. SSDs and NVMe drives enable faster processing, and the latest generation of Intel Xeon and AMD EPYC processors allows you to run resource-intensive applications. How to Choose Infrastructure for Long-Term Development Choose a server that actually grows with you. You want to scale smoothly without hitting hidden fees. Look for clear, pay-as-you-go pricing — it’s the smartest way for startups to get top-tier tech without breaking the bank.  Flexible servers work for almost any project. Building an edtech app? Marketing tools? Analytics? AI? To summarize, good infrastructure is the secret sauce to keep your product growing smoothly. When you need a comprehensive strategy, keep in mind that it can only be based on a successful combination of elements. This includes automation, open-source tools, and high-quality server infrastructure. Together, they create a secure and scalable DevOps environment. These technologies help developers build products faster, monitor all critical processes, and manage resources as efficiently as possible. #Building #Secure #Scalable #DevOps #Environment #OpenSource #ToolsDevOps

for their Linux VPS hosting to get the job done. Flexible server solutions make it possible to configure resources, select a suitable location, and deploy the environment automatically.

The list of users who will find this helpful is extensive: DevOps engineers, web application developers, SaaS platforms, and many others. Using open-source tools helps them create a reliable, transparent, and scalable environment for development and operations.

DevOps is today one of the key approaches to creating and managing software products. It connects development, testing, and operations, helping teams work faster. Open-source tools make workflows easier and flexible. Open source is basically built to bend. You can customize your tools, stitch them together, and automate the boring stuff. Honestly, it’s a perfect fit for pretty much anything — web, mobile, cloud, data, or AI. 

It is fair to say that several specific DevOps components help automate processes, improve system stability, and shorten the time between product development and launch. These include:

  • application build automation;
  • containerization of production services;
  • server configuration management;
  • infrastructure health monitoring;
  • secure storage of credentials.

Less manual grunt work means fewer mistakes. Auto-deploys let you ship updates in a flash, while smart monitoring catches performance bottlenecks before they become fires. 

Scalable Infrastructure for DevOps Teams

In practice, any DevOps model requires a reliable and powerful infrastructure. Every project, even the smallest one, must have the ability to quickly scale resources as more and more users join or as technical requirements change. The server environment must support flexible power settings, rapid creation of new environments, and stable service operation.

International IaaS providers offer VPS/VDS, dedicated servers, VPN services, and additional infrastructure solutions. Users can configure key server parameters, including:

  • processing power;
  • amount of RAM;
  • drive type;
  • data center location.

A broad infrastructure geography makes it possible to select suitable locations for different projects and reduce access latency. Such opportunities are in demand in various fields. Online stores use scalable servers to handle large numbers of requests.

When choosing DevOps infrastructure, it is important to consider not only current challenges but also growth prospects. A flexible server platform should allow configuration changes without complex migrations. Among the most popular features are:

  • setting up CPU and RAM;
  • use of fast NVMe drives;
  • support for different operating systems;
  • connecting additional IP addresses;
  • automatic deployment of environments;
  • integration via API tools.

This model makes it easier for technicians and helps launch new projects faster. DevOps teams can create test environments, run experiments, and migrate applications between environments with minimal time investment.

Fundamentals of System Security and Resilience

As soon as digital products begin to evolve, securing the system as a whole becomes just as important as maintaining performance. Good companies must secure the users data, provide uninterrupted service, and minimize the risk of breakdowns.

It is exactly these advanced open-source tools that help not only manage the entire system but also monitor it clearly. Teams will be able to track changes, audit settings, and implement additional security measures. When building a secure DevOps environment, special attention is paid to the following aspects:

  • isolation of virtual servers;
  • data backup;
  • protecting network connections;
  • use of IPv4 and IPv6;
  • user access control.

KVM technology ensures effective isolation of virtual machines and stable operation of VPS servers. SSDs and NVMe drives enable faster processing, and the latest generation of Intel Xeon and AMD EPYC processors allows you to run resource-intensive applications.

How to Choose Infrastructure for Long-Term Development

Choose a server that actually grows with you. You want to scale smoothly without hitting hidden fees. Look for clear, pay-as-you-go pricing — it’s the smartest way for startups to get top-tier tech without breaking the bank. 

Flexible servers work for almost any project. Building an edtech app? Marketing tools? Analytics? AI? To summarize, good infrastructure is the secret sauce to keep your product growing smoothly.

When you need a comprehensive strategy, keep in mind that it can only be based on a successful combination of elements. This includes automation, open-source tools, and high-quality server infrastructure. Together, they create a secure and scalable DevOps environment. These technologies help developers build products faster, monitor all critical processes, and manage resources as efficiently as possible.

#Building #Secure #Scalable #DevOps #Environment #OpenSource #ToolsDevOps">Building a Secure and Scalable DevOps Environment Using Open-Source Tools

We can all see how the newest digital products require flexibility and the ability to scale quickly together with the business. At the same time, they must cope with growing workloads. That’s why teams choose is*hosting for their Linux VPS hosting to get the job done. Flexible server solutions make it possible to configure resources, select a suitable location, and deploy the environment automatically.

The list of users who will find this helpful is extensive: DevOps engineers, web application developers, SaaS platforms, and many others. Using open-source tools helps them create a reliable, transparent, and scalable environment for development and operations.

DevOps is today one of the key approaches to creating and managing software products. It connects development, testing, and operations, helping teams work faster. Open-source tools make workflows easier and flexible. Open source is basically built to bend. You can customize your tools, stitch them together, and automate the boring stuff. Honestly, it’s a perfect fit for pretty much anything — web, mobile, cloud, data, or AI. 

It is fair to say that several specific DevOps components help automate processes, improve system stability, and shorten the time between product development and launch. These include:

  • application build automation;
  • containerization of production services;
  • server configuration management;
  • infrastructure health monitoring;
  • secure storage of credentials.

Less manual grunt work means fewer mistakes. Auto-deploys let you ship updates in a flash, while smart monitoring catches performance bottlenecks before they become fires. 

Scalable Infrastructure for DevOps Teams

In practice, any DevOps model requires a reliable and powerful infrastructure. Every project, even the smallest one, must have the ability to quickly scale resources as more and more users join or as technical requirements change. The server environment must support flexible power settings, rapid creation of new environments, and stable service operation.

International IaaS providers offer VPS/VDS, dedicated servers, VPN services, and additional infrastructure solutions. Users can configure key server parameters, including:

  • processing power;
  • amount of RAM;
  • drive type;
  • data center location.

A broad infrastructure geography makes it possible to select suitable locations for different projects and reduce access latency. Such opportunities are in demand in various fields. Online stores use scalable servers to handle large numbers of requests.

When choosing DevOps infrastructure, it is important to consider not only current challenges but also growth prospects. A flexible server platform should allow configuration changes without complex migrations. Among the most popular features are:

  • setting up CPU and RAM;
  • use of fast NVMe drives;
  • support for different operating systems;
  • connecting additional IP addresses;
  • automatic deployment of environments;
  • integration via API tools.

This model makes it easier for technicians and helps launch new projects faster. DevOps teams can create test environments, run experiments, and migrate applications between environments with minimal time investment.

Fundamentals of System Security and Resilience

As soon as digital products begin to evolve, securing the system as a whole becomes just as important as maintaining performance. Good companies must secure the users data, provide uninterrupted service, and minimize the risk of breakdowns.

It is exactly these advanced open-source tools that help not only manage the entire system but also monitor it clearly. Teams will be able to track changes, audit settings, and implement additional security measures. When building a secure DevOps environment, special attention is paid to the following aspects:

  • isolation of virtual servers;
  • data backup;
  • protecting network connections;
  • use of IPv4 and IPv6;
  • user access control.

KVM technology ensures effective isolation of virtual machines and stable operation of VPS servers. SSDs and NVMe drives enable faster processing, and the latest generation of Intel Xeon and AMD EPYC processors allows you to run resource-intensive applications.

How to Choose Infrastructure for Long-Term Development

Choose a server that actually grows with you. You want to scale smoothly without hitting hidden fees. Look for clear, pay-as-you-go pricing — it’s the smartest way for startups to get top-tier tech without breaking the bank. 

Flexible servers work for almost any project. Building an edtech app? Marketing tools? Analytics? AI? To summarize, good infrastructure is the secret sauce to keep your product growing smoothly.

When you need a comprehensive strategy, keep in mind that it can only be based on a successful combination of elements. This includes automation, open-source tools, and high-quality server infrastructure. Together, they create a secure and scalable DevOps environment. These technologies help developers build products faster, monitor all critical processes, and manage resources as efficiently as possible.

#Building #Secure #Scalable #DevOps #Environment #OpenSource #ToolsDevOps

The Department of Defense labeled AI lab Anthropic a supply-chain risk after the company refused to agree to allow the Pentagon to use its models for autonomous weapons systems and domestic surveillance. It seems the company isn’t just winning the moral argument on this front, but the legal one, too. According to Axios, the judge hearing Anthropic’s challenge to the Trump administration’s designation has thus far not found the Pentagon’s case to be particularly compelling.

Per the report, U.S. District Judge Rita Lin wasted little time pouring cold water on the federal government’s position. “I don’t see additional evidence from the government really justifying what it did. If anything, it seems like the record, in some ways, has gotten worse for the government,” she reportedly said.

She also did not seem convinced by the Pentagon’s apparent argument that it was concerned Anthropic would mess with its model to prevent the military from using it how they intended. “I don’t see evidence that Anthropic could alter the model after it was delivered or flip some kind of kill switch,” she said, per Axios.

This doesn’t come as a major surprise, given Judge Lin’s general vibe since this case landed in her court. Back in March when Anthropic first brought its challenge to the government’s designation, the judge said that “it looks like an attempt to cripple Anthropic.” She ultimately granted a temporary injunction on the government’s attempt to basically blacklist the AI firm.

You’ll recall the origin of this whole fight came earlier this year when negotiations between Anthropic and the Department of Defense fell through after the company refused to agree to a deal that would have allowed the Pentagon to use its AI models for “all lawful purposes.” Anthropic reportedly sought to clarify that it would not include using the model to launch weapons without human involvement or to perform surveillance on American citizens. The Pentagon was unwilling to agree to honor Anthropic’s redlines.

Under a normal administration, that would likely just lead to the government moving on to another company. Under the Trump administration, though, refusal to comply makes you an enemy of the country. Trump and company moved to label Anthropic a supply chain risk, a designation typically reserved for foreign adversaries, not domestic companies. Getting slapped with that tag meant the rest of the federal government would have to stop doing business with Anthropic, which would be a blow to the company.

Instead, the whole thing is in limbo—though it’s not looking great for the administration. Of course, they could just cancel the contracts and do business with less scrupulous AI companies. There are plenty willing to abandon their beliefs for a nice, fat contract, after all.

#Pentagons #Case #Anthropic #IsntAnthropic,artifical intelligence,Department of Defense,Lawsuit,pentagon">The Pentagon’s Case Against Anthropic Isn’t Going Well The Department of Defense labeled AI lab Anthropic a supply-chain risk after the company refused to agree to allow the Pentagon to use its models for autonomous weapons systems and domestic surveillance. It seems the company isn’t just winning the moral argument on this front, but the legal one, too. According to Axios, the judge hearing Anthropic’s challenge to the Trump administration’s designation has thus far not found the Pentagon’s case to be particularly compelling. Per the report, U.S. District Judge Rita Lin wasted little time pouring cold water on the federal government’s position. “I don’t see additional evidence from the government really justifying what it did. If anything, it seems like the record, in some ways, has gotten worse for the government,” she reportedly said. She also did not seem convinced by the Pentagon’s apparent argument that it was concerned Anthropic would mess with its model to prevent the military from using it how they intended. “I don’t see evidence that Anthropic could alter the model after it was delivered or flip some kind of kill switch,” she said, per Axios. This doesn’t come as a major surprise, given Judge Lin’s general vibe since this case landed in her court. Back in March when Anthropic first brought its challenge to the government’s designation, the judge said that “it looks like an attempt to cripple Anthropic.” She ultimately granted a temporary injunction on the government’s attempt to basically blacklist the AI firm. You’ll recall the origin of this whole fight came earlier this year when negotiations between Anthropic and the Department of Defense fell through after the company refused to agree to a deal that would have allowed the Pentagon to use its AI models for “all lawful purposes.” Anthropic reportedly sought to clarify that it would not include using the model to launch weapons without human involvement or to perform surveillance on American citizens. The Pentagon was unwilling to agree to honor Anthropic’s redlines. Under a normal administration, that would likely just lead to the government moving on to another company. Under the Trump administration, though, refusal to comply makes you an enemy of the country. Trump and company moved to label Anthropic a supply chain risk, a designation typically reserved for foreign adversaries, not domestic companies. Getting slapped with that tag meant the rest of the federal government would have to stop doing business with Anthropic, which would be a blow to the company. Instead, the whole thing is in limbo—though it’s not looking great for the administration. Of course, they could just cancel the contracts and do business with less scrupulous AI companies. There are plenty willing to abandon their beliefs for a nice, fat contract, after all. #Pentagons #Case #Anthropic #IsntAnthropic,artifical intelligence,Department of Defense,Lawsuit,pentagon

Judge Rita Lin wasted little time pouring cold water on the federal government’s position. “I don’t see additional evidence from the government really justifying what it did. If anything, it seems like the record, in some ways, has gotten worse for the government,” she reportedly said.

She also did not seem convinced by the Pentagon’s apparent argument that it was concerned Anthropic would mess with its model to prevent the military from using it how they intended. “I don’t see evidence that Anthropic could alter the model after it was delivered or flip some kind of kill switch,” she said, per Axios.

This doesn’t come as a major surprise, given Judge Lin’s general vibe since this case landed in her court. Back in March when Anthropic first brought its challenge to the government’s designation, the judge said that “it looks like an attempt to cripple Anthropic.” She ultimately granted a temporary injunction on the government’s attempt to basically blacklist the AI firm.

You’ll recall the origin of this whole fight came earlier this year when negotiations between Anthropic and the Department of Defense fell through after the company refused to agree to a deal that would have allowed the Pentagon to use its AI models for “all lawful purposes.” Anthropic reportedly sought to clarify that it would not include using the model to launch weapons without human involvement or to perform surveillance on American citizens. The Pentagon was unwilling to agree to honor Anthropic’s redlines.

Under a normal administration, that would likely just lead to the government moving on to another company. Under the Trump administration, though, refusal to comply makes you an enemy of the country. Trump and company moved to label Anthropic a supply chain risk, a designation typically reserved for foreign adversaries, not domestic companies. Getting slapped with that tag meant the rest of the federal government would have to stop doing business with Anthropic, which would be a blow to the company.

Instead, the whole thing is in limbo—though it’s not looking great for the administration. Of course, they could just cancel the contracts and do business with less scrupulous AI companies. There are plenty willing to abandon their beliefs for a nice, fat contract, after all.

#Pentagons #Case #Anthropic #IsntAnthropic,artifical intelligence,Department of Defense,Lawsuit,pentagon">The Pentagon’s Case Against Anthropic Isn’t Going WellThe Pentagon’s Case Against Anthropic Isn’t Going Well The Department of Defense labeled AI lab Anthropic a supply-chain risk after the company refused to agree to allow the Pentagon to use its models for autonomous weapons systems and domestic surveillance. It seems the company isn’t just winning the moral argument on this front, but the legal one, too. According to Axios, the judge hearing Anthropic’s challenge to the Trump administration’s designation has thus far not found the Pentagon’s case to be particularly compelling. Per the report, U.S. District Judge Rita Lin wasted little time pouring cold water on the federal government’s position. “I don’t see additional evidence from the government really justifying what it did. If anything, it seems like the record, in some ways, has gotten worse for the government,” she reportedly said. She also did not seem convinced by the Pentagon’s apparent argument that it was concerned Anthropic would mess with its model to prevent the military from using it how they intended. “I don’t see evidence that Anthropic could alter the model after it was delivered or flip some kind of kill switch,” she said, per Axios. This doesn’t come as a major surprise, given Judge Lin’s general vibe since this case landed in her court. Back in March when Anthropic first brought its challenge to the government’s designation, the judge said that “it looks like an attempt to cripple Anthropic.” She ultimately granted a temporary injunction on the government’s attempt to basically blacklist the AI firm. You’ll recall the origin of this whole fight came earlier this year when negotiations between Anthropic and the Department of Defense fell through after the company refused to agree to a deal that would have allowed the Pentagon to use its AI models for “all lawful purposes.” Anthropic reportedly sought to clarify that it would not include using the model to launch weapons without human involvement or to perform surveillance on American citizens. The Pentagon was unwilling to agree to honor Anthropic’s redlines. Under a normal administration, that would likely just lead to the government moving on to another company. Under the Trump administration, though, refusal to comply makes you an enemy of the country. Trump and company moved to label Anthropic a supply chain risk, a designation typically reserved for foreign adversaries, not domestic companies. Getting slapped with that tag meant the rest of the federal government would have to stop doing business with Anthropic, which would be a blow to the company. Instead, the whole thing is in limbo—though it’s not looking great for the administration. Of course, they could just cancel the contracts and do business with less scrupulous AI companies. There are plenty willing to abandon their beliefs for a nice, fat contract, after all. #Pentagons #Case #Anthropic #IsntAnthropic,artifical intelligence,Department of Defense,Lawsuit,pentagon

The Department of Defense labeled AI lab Anthropic a supply-chain risk after the company refused to agree to allow the Pentagon to use its models for autonomous weapons systems and domestic surveillance. It seems the company isn’t just winning the moral argument on this front, but the legal one, too. According to Axios, the judge hearing Anthropic’s challenge to the Trump administration’s designation has thus far not found the Pentagon’s case to be particularly compelling.

Per the report, U.S. District Judge Rita Lin wasted little time pouring cold water on the federal government’s position. “I don’t see additional evidence from the government really justifying what it did. If anything, it seems like the record, in some ways, has gotten worse for the government,” she reportedly said.

She also did not seem convinced by the Pentagon’s apparent argument that it was concerned Anthropic would mess with its model to prevent the military from using it how they intended. “I don’t see evidence that Anthropic could alter the model after it was delivered or flip some kind of kill switch,” she said, per Axios.

This doesn’t come as a major surprise, given Judge Lin’s general vibe since this case landed in her court. Back in March when Anthropic first brought its challenge to the government’s designation, the judge said that “it looks like an attempt to cripple Anthropic.” She ultimately granted a temporary injunction on the government’s attempt to basically blacklist the AI firm.

You’ll recall the origin of this whole fight came earlier this year when negotiations between Anthropic and the Department of Defense fell through after the company refused to agree to a deal that would have allowed the Pentagon to use its AI models for “all lawful purposes.” Anthropic reportedly sought to clarify that it would not include using the model to launch weapons without human involvement or to perform surveillance on American citizens. The Pentagon was unwilling to agree to honor Anthropic’s redlines.

Under a normal administration, that would likely just lead to the government moving on to another company. Under the Trump administration, though, refusal to comply makes you an enemy of the country. Trump and company moved to label Anthropic a supply chain risk, a designation typically reserved for foreign adversaries, not domestic companies. Getting slapped with that tag meant the rest of the federal government would have to stop doing business with Anthropic, which would be a blow to the company.

Instead, the whole thing is in limbo—though it’s not looking great for the administration. Of course, they could just cancel the contracts and do business with less scrupulous AI companies. There are plenty willing to abandon their beliefs for a nice, fat contract, after all.

#Pentagons #Case #Anthropic #IsntAnthropic,artifical intelligence,Department of Defense,Lawsuit,pentagon

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