Welcome to the Grid

•

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Welcome to the
Grid

How we got here
The problem with distributed
load testing is the multitude of
man-draulic tasks

☑ setup AWS account
☑ ec2-start-instances
☑ PROVISION BOXES
☑ FLAP AROUND WITH RMI
☑ SCP scripts out
☑ SSH AND START MONITORS
☑ START LOAD TEST
☑ FIX NETWORK CONNECTIVITY ISSUES
☑ RE-START LOAD TEST
☑ TAIL RESULTS
☑ SCP RESULTS
☑ PARSE RESULTS
☑ OPEN EXCEL ...

“Less time testing, more time setting up”

Gridinit

How we got here
Most solutions that tackle this
problem are cost prohibitive

Gridinit

How we got here
Many solutions dictate crazy
licensing or time based constraints

Gridinit

How we got here
Whilst other solutions simply
suffer from NIH syndrome

Gridinit

We’re different
we deliver the best available products

Gridinit

Simple & Affordable
in an easy to use platform that won’t blow your budget

Gridinit

Gridinit is

A collection of
semi autonomous nodes
capable of running load tests
in the

Gridinit

Gridinit should

be test tool agnostic,
index and share results,
survive the vagaries of the cloud

Gridinit

Gridinit nodes are

loosely coupled, disposable resources

deployed on a grid network
in multiple geographic regions

Gridinit

Gridinit nodes scale

out quickly in less than 5 minutes

Gridinit

Gridinit reports

are comprehensive yet concise

Gridinit results

available in different formats

Gridinit

Gridinit likes smart solutions

writing test plans in Ruby

Gridinit

Gridinit prefers simple ideas

sharing data amongst threads

Gridinit

Gridinit offers flexible pricing

On-Demand. No long term
commitments. Pay as you go.

Host-Your-Own. Monthly
subscription. Your own AWS account.

Enterprise. Monthly subscription.
Different deployment and support
options.

Gridinit

Gridinit Load Testing for Everyone.
locate: Melbourne, Australia
email: support@gridinit.com
blog: blog.gridinit.com
follow: @GRIDinit

Gridinit

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Welcome to the Grid

1. Welcome to the Grid

2. How we got here The problem with distributed load testing is the multitude of man-draulic tasks ☑ setup AWS account ☑ ec2-start-instances ☑ PROVISION BOXES ☑ FLAP AROUND WITH RMI ☑ SCP scripts out ☑ SSH AND START MONITORS ☑ START LOAD TEST ☑ FIX NETWORK CONNECTIVITY ISSUES ☑ RE-START LOAD TEST ☑ TAIL RESULTS ☑ SCP RESULTS ☑ PARSE RESULTS ☑ OPEN EXCEL ... “Less time testing, more time setting up” Gridinit

3. How we got here Most solutions that tackle this problem are cost prohibitive Gridinit

4. How we got here Many solutions dictate crazy licensing or time based constraints Gridinit

5. How we got here Whilst other solutions simply suffer from NIH syndrome Gridinit

6. We’re different we deliver the best available products Gridinit

7. Simple & Affordable in an easy to use platform that won’t blow your budget Gridinit

8. Gridinit is A collection of semi autonomous nodes capable of running load tests in the Gridinit

9. Gridinit should be test tool agnostic, index and share results, survive the vagaries of the cloud Gridinit

10. Gridinit nodes are loosely coupled, disposable resources deployed on a grid network in multiple geographic regions Gridinit

11. Gridinit nodes scale out quickly in less than 5 minutes Gridinit

12. Gridinit reports are comprehensive yet concise

13. Gridinit results available in different formats Gridinit

14. Gridinit likes smart solutions writing test plans in Ruby Gridinit

15. Gridinit prefers simple ideas sharing data amongst threads Gridinit

16. Gridinit offers flexible pricing On-Demand. No long term commitments. Pay as you go. Host-Your-Own. Monthly subscription. Your own AWS account. Enterprise. Monthly subscription. Different deployment and support options. Gridinit

17. Gridinit Load Testing for Everyone. locate: Melbourne, Australia email: support@gridinit.com blog: blog.gridinit.com follow: @GRIDinit Gridinit