Nvidia bets on OpenClaw, but adds a security layer – how NemoClaw works


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ZDNET’s key takeaways 

  • Nvidia’s NemoClaw aims to make OpenClaw agents more secure.
  • OpenClaw agents are highly capable, but come with risks.
  • The company also launched a multi-lab open-source model coalition.

“What’s your OpenClaw strategy?” Nvidia CEO Jensen Huang asked rhetorically to the crowd at Nvidia GTC, the company’s annual AI conference, on Monday. 

The company is full steam ahead on AI agents — and it’s hoping its latest release can fix OpenClaw’s security problem. During the keynote, Huang announced Nvidia’s new NemoClaw stack, which is built to shore up the OpenClaw agent platform, the viral open-source assistant framework that has impressed users with its autonomous capabilities. 

Also: Why buying into Moltbook and OpenClaw may be Big Tech’s most dangerous bet yet

OpenClaw does not run its own model; what sets it apart is how it leverages the sometimes-differing strengths of Anthropic’s Claude and OpenAI’s ChatGPT, while running locally on a user’s device to take action on its own. That level of autonomous capability and access to user information also poses a significant security risk, which has been its primary drawback. 

Nvidia, however, believes OpenClaw is the foundation of personal AI. The company, which has been working with OpenClaw founder Peter Steinberger, referred to the agent platform as history’s most important software release during a Sunday briefing before the conference. Nvidia said NemoClaw can optimize OpenClaw for privacy and security using Nvidia’s Agent Toolkit, an open-source library for managing teams of AI agents. 

How it works 

NemoClaw installs Nvidia’s OpenShell, a new open-source runtime that keeps agents safer to use by enforcing an organization’s policy-based guardrails. OpenShell keeps models sandboxed, adds data privacy protections and additional security for agents, and makes them more scalable. 

“This provides the missing infrastructure layer beneath claws to give them the access they need to be productive, while enforcing policy-based security, network, and privacy guardrails,” Nvidia said in the announcement. The company built OpenShell with security companies like CrowdStrike, Cisco, and Microsoft Security to ensure it is compatible with other cybersecurity tools. 

Also: Is your AI agent a security risk? NanoClaw wants to put it in a virtual cage

Nvidia said NemoClaw can be installed in a single commandruns on any platform, and can use any coding agent, including Nvidia’s own Nemotron open model family, on a local system. Through a privacy router, it allows agents to access frontier models in the cloud, which unites local and cloud models to help teach agents how to complete tasks within privacy guardrails, Nvidia explained. 

Automating work 

Nvidia seems to be hoping that the additional security can make OpenClaw agents more popular and accessible, with less risk than they currently carry. The bigger picture here is how NemoClaw could give companies the added peace of mind to let AI agents complete actions for their employees, where they wouldn’t have previously. 

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Screenshot by Radhika Rajkumar/ZDNET

In the release, Nvidia noted that advancing enterprise AI agents will “speed a generational shift in software and knowledge work,” and that the next phase of enterprise software will be all about specialized agents. As my colleague Tiernan Ray explains, Nvidia’s new Vera Rubin infrastructure is meant to back up this agentic AI drive, and will drive down costs in the process, according to the company.

Also: Nvidia wants to own your AI data center from end to end

Huang said during the keynote that he believes OpenClaw arrived at exactly the right time for the software industry, and that it spells a new path: agents-as-a-service rather than software-as-a-service (SaaS). 

How to try NemoClaw

NemoClaw is currently in preview. Starting today, developers can access Nvidia’s Agent Toolkit and OpenShell here, use OpenShell with LangChain, or download it from GitHub directly to run locally. Enterprises can create and deploy AI agents via cloud providers like AWS, Google Cloud, and Microsoft Azure, among others. 

A new open-source initiative 

Nvidia also launched the Nemotron Coalition, a collaboration between several model developers and AI labs aimed at advancing open-source AI through shared resources and compute. 

Also: Why enterprise AI agents could become the ultimate insider threat 

The coalition includes Mira Murati’s startup Thinking Machines Lab, Perplexity, Cursor, and Mistral AI, among others. To start, Mistral and Nvidia will co-develop an open model trained on Nvidia DGXTM Cloud and open-source the result, which will also be the foundation for Nvidia’s forthcoming Nemotron 4 model family. Other coalition members will support the model with data and testing. 

“By combining forces, the coalition aims to accelerate progress on AI models, expanding intelligence beyond any single model and
strengthening a vibrant open ecosystem while making model development more efficient so organizations can build, specialize, and innovate on a shared, open foundation,” the company said.

Also: Why AI is both a curse and a blessing to open-source software – according to developers

The move is an emphatic investment in making cutting-edge AI models available to everyone. As with all open-source projects, the initiative should pool expertise to effectively democratize competitive AI tools that individual developers can then adapt further to their local contexts or use cases. 

“AI reaches its full potential when it works in every language and for every community,” said Pratyush Kumar, cofounder and CEO of Sarvam, another founding coalition member, in the release. “Open models make this possible by giving builders the freedom to adapt frontier capabilities to real-world needs.”





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Recent Reviews


Spotify aims to provide a consistent listening experience that uses minimal data. As a result, your audio quality might be less than ideal, especially if you’re using a pair of high-fidelity headphones or high-end speakers. Here’s how to fix that.

Switch audio streaming quality to Very High or Lossless

The default audio streaming quality in both the mobile and desktop Spotify apps is set to Automatic, which usually keeps the audio quality at Normal, which is only 96 Kbps. Even though Spotify uses the Ogg Vorbis codec, which is superior to MP3, OGG files exhibit slight (but noticeable) digital noise, poor bass detail, dull treble, and a narrow soundstage at 96 Kbps.

Even worse, Spotify is aggressive about adjusting the automatic bitrate. Even though 4G is more than fast enough to stream high-quality OGG files, even with a weak signal, Spotify may still drop the quality to Low, which has a bitrate of just 24 Kb/s. You will notice such a sharp drop in quality, even on a pair of bottom-of-the-barrel headphones.

To rectify this, open the Spotify app, tap your user image, open “Settings and privacy,” and tap the “Media Quality” menu. Once there, set Wi-Fi streaming quality and cellular streaming quality to “Very high” or “Lossless.”

I recommend setting cellular streaming quality to Very high and reserving Lossless for Wi-Fi, since lossless streaming is very data-intensive. One hour of streaming lossless files can take up to 1GB of data, as well as a good chunk of your phone’s storage, because Spotify caches files you’re frequently streaming. Besides, you’ll struggle to notice the difference unless you’re listening to music on a wired pair of high-end headphones or speakers; wireless connection just doesn’t have the bandwidth needed to convey the full fidelity of Spotify lossless audio.

You might opt for High quality if you have a capped data plan, but I recommend doing so only if you stream hours upon hours’ worth of music every single day over a cellular network. For instance, I burn through about 8 GB of data per month on average while streaming about two hours of very high-quality music over a cellular network each day.

Illustration of a headphone with various music icons around.


How Audio Compression Works and Why It Can Affect Your Music Quality

Feeling the squeeze when listening to your favorite song?

Set audio download quality to Very high or Lossless

If you tend to download songs and albums for offline listening, you should also set the audio download quality to “Very high” or “Lossless.” This setting is located just under the audio streaming quality section.

The audio download quality menu in Spotify's mobile app.

If you’ve got enough free storage on your phone, opt for the latter, but if you’d rather save storage space, set it to Very high. You’ll hardly hear the difference, but lossless files are about five times larger than the 320 Kb/s OGG files Spotify offers at its Very high quality setting, and they can quickly fill up your phone’s storage.

Adjust video streaming quality at your discretion

The last section of the Media quality menu is Video streaming quality. This sets the quality of video podcasts and music videos available for certain songs. Since I care about neither, I set it to “Very high” on Wi-Fi and “Normal” on cellular, but you should tweak the two options at your discretion because songs sound notably better at higher video streaming quality levels.

If you often watch videos over cellular and have unlimited data, feel free to toggle video quality to very high.

Make sure Data Saver mode is disabled

Even if your audio quality is set to Very high or Lossless, Spotify will switch to low-quality streaming if the app’s Data saver mode is enabled. This option is located in the Data saving and offline menu. Open the menu, then set it to “Always off,” or choose “Automatic” to have Spotify’s Data Saver mode kick in alongside your phone’s Data Saver mode.

You can also enable volume normalization and play around with the built-in equalizer

Spotify logo in the center of the screen with an equalizer in front. Credit: Lucas Gouveia / How-To Geek

Last but not least, there are two additional features you can play with to improve your listening experience. The first is volume normalization, which sets the same loudness for every track you’re listening to. This can be handy because different albums are mastered at different loudness levels, with newer music usually being louder.

Since I’m an album-oriented listener, I keep the option disabled. I can just play an album and set the audio volume accordingly, and I don’t really mind louder songs when listening to playlists, artists, or song radios.

But if you can’t stand one song being quiet and the next rattling the windows, visit the Playback menu, enable “Volume normalization,” and set it to “Quiet” or “Normal.” The “Loud” option can digitally compress files, and neither Spotify nor I recommend using it. This also happens with “Quiet” and “Normal,” since both adjust the decibel level of the master recording for each song, but the compression level is much lower and extremely hard to notice.

Before I end this, I should also mention that you can access the equalizer directly from the Spotify app, where you can fine-tune your music listening experience or pick one of the available equalizer presets. If your phone has a built-in equalizer, Spotify will open it; if it doesn’t, you can use Spotify’s. On my phone (a Samsung Galaxy S21 FE), I can only use One UI’s built-in equalizer.

To open the equalizer, open “Playback,” then hit the “Equalizer” button. Now you can equalize your audio to your heart’s content.


Adjusting just a few settings can have a drastic impact on your Spotify listening experience. If you aren’t satisfied with Spotify’s sound quality, make sure to adjust the audio before jumping ship. You should also check the sound quality settings from time to time, as Spotify can reset them during app updates.​​​​​​​

Three phones with a Spotify screen and the logo in the center.


These 8 Spotify Features Are My Favorite Hidden Gems

Look for these now.



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