7 time-saving Linux commands

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7 time-saving Linux commands

$ fortune | rev!ti deteled dna ,ti daer resu-repus eht tub ,liam dah uoY The shuf command allows you to randomize the lines in a file. Here’s a sample file and the results of shuffling its contents. $ cat namesBillDannyDorothyJimJoanneJohnMartyNancySandraStewart$ shuf namesJimNancyJoanneMartySandraDorothyStewartBillDannyJohn Each time you run shuf, the output should be different. When you just […]

$ fortune | rev
!ti deteled dna ,ti daer resu-repus eht tub ,liam dah uoY

The shuf command allows you to randomize the lines in a file. Here’s a sample file and the results of shuffling its contents.

$ cat names
Bill
Danny
Dorothy
Jim
Joanne
John
Marty
Nancy
Sandra
Stewart

$ shuf names
Jim
Nancy
Joanne
Marty
Sandra
Dorothy
Stewart
Bill
Danny
John

Each time you run shuf, the output should be different.

When you just need some random text for testing a script, these commands can help you out.

6. Using the watch command to monitor changes

The watch command allows you to run a command repeatedly and watch for the output changes you might be expecting to see. By default, it runs every 2 seconds, but you can change the interval by using the -n option. The command below uses the -d option to highlight changes in the command output.

$ watch -d free -m
Every 2.0s: free -m fedora: Fri Feb 14 15:52:23 2025

total used free shared buff/cache available
Mem: 3784 1121 819 135 1844 2190
Swap: 3783 77 3706

7. Using the tree command to view directory contents in an user-friendly way

The tree command provides a file listing that displays directories and files in a tree-like display, giving you a quick but useful view of what’s inside some particular directory. Notice how the indentation makes the structure of the directory easy to understand.

$ tree bin
|----- backup
|----- FindFiles
|----- NOTES
|      |----- report_formats
|      |----- meeting notes
|----- scripts

Wrap-up

Some commands can make seeing what you’re looking for on the command line a lot easier. Just don’t forget to turn the more complex commands into aliases to make them easier to use.

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His projections account for recent advances in AI and data center efficiency, he says. For example, the open-source AI model from Chinese company DeepSeek seems to have shown that an LLM can produce very high-quality results at a very low cost with some clever architectural changes to how the models work. These improvements are likely to be quickly replicated by other AI companies. “A lot of these companies are trying to push out more efficient models,” says Fung. “There’s a lot of effort to reduce costs and to make it more efficient.” In addition, hyperscalers are designing and building their own chips, optimized for their AI workloads. Just the accelerator market alone is projected to reach $392 billion by 2029, Dell’Oro predicts. By that time, custom accelerators will outpace commercially available accelerators such as GPUs. The deployment of dedicated AI servers also has an impact on networking, power and cooling. As a result, spending on data center physical infrastructure (DCPI) will also increase, though at a more moderate pace, growing by 14% annually to $61 billion in 2029. “DCPI deployments are a prerequisite to support AI workloads,” says Tam Dell’Oro, founder of Dell’Oro Group, in the report. The research firm raised its outlook in this area due to the fact that actual 2024 results exceeded its expectations, and demand is spreading from tier one to tier two cloud service providers. In addition, governments and tier one telecom operators are getting involved in data center expansion, making it a long-term trend.

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2025 playbook for enterprise AI success, from agents to evals

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

OpenAI’s ChatGPT explodes to 400M weekly users, with GPT-5 on the way

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Medical training’s AI leap: How agentic RAG, open-weight LLMs and real-time case insights are shaping a new generation of doctors at NYU Langone

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Patient data records can be convoluted

Identity is the breaking point — get it right or zero trust fails

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here.

Milliseconds to breach: How patch automation closes attackers’ fastest loophole

This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here.

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