We’ve talked a lot about the emergence of AI technology, like ChatGPT. We’ve discussed how our own security engineers use AI during penetration testing and how something like ChatGPT interacts with reverse engineering code. AI technology like this has changed the world in many ways, every industry has felt its impact. Cyrex’ own cybersecurity has changed to accommodate its assistance during penetration testing and other services. 

All of the news on the topic seems to focus on ChatGPT alone, so we thought it best to explore other solutions. Read on to learn more about other AI solutions and how the future of AI technology might look!  

ChatGPT Today

ChatGPT is very impressive, we’ll never contend that. But it is a general model, it doesn’t specialise in any particular subject. And as this world of AI technology grows, we see more specified solutions entering the world. Many are open source which is allowing for great strides in innovation and experimentation.

While ChatGPT is based on the OpenAI language model, ChatGPT was never open source. And with the arrival of GPT-4 and its upgraded capacity for language, images, and videos, this innovation has happened behind closed doors. As it stands, we’ll never know what tricks they’re using to stand so far ahead. However, as a jack of all trades, it is great as a general tool but will lose out to specific alternatives. Let’s discuss some of them.  

Phind, the AI Search Engine

Phind is focused on tech and it’s meant for developers. Its ability to assist and help its users learn and grow their tech skills is impressive. Unlike ChatGPT, Phind will give references to the content it has drawn from. It’s very similar to ChatGPT in its interface but it’s like combining ChatGPT with a Google search. It will show you where it sourced its data and show its references. This is a huge step up from the personal verification needed in ChatGPT’s results.

It delivers its results in natural language and the model was created from specific parts of the web which were common grounds for developers. It’s not limited to just development questions and information, but developers in need of assistance will find a particularly fantastic tool in Phind.  

GitHub Copilot, AI Code Assistant

We’ve discussed how useful ChatGPT is for code assistance. How it is able to analyse code, detect potential vulnerabilities, and even suggest payloads for testing security. ChatGPT struggles with extensive lengths of text and code, which is where something like GitHub Copilot enters the scene.

A product of GitHub and owned by Microsoft, just like OpenAI and ChatGPT. They upgraded to the GPT-4 recently as well, meaning the Microsoft suite of AI assistants looks quite strong! As an AI code assistant, it is very capable of completing functions you’re actively writing by recognising patterns and previously seen code. This makes it very effective for boilerplating.

Of course it can do everything ChatGPT is able to do but it will go beyond its sister-application’s capacity. GitHub Copilot is built entirely for code and coding. It has no other elements in its language model to worry about. Its knowledge is code and functions and because it’s made for this purpose, it has no limit of function lengths. Those familiar with ChatGPT and GitHub will find a familiar and useful tool in GitHub Copilot  

Other AI Assistants

Snyk.io is another AI co-pilot/code assistant that has emerged onto the scene. It’s a security product for developers which includes real-time code checking for error or potential vulnerabilities. It’s almost like a grammar or language tool but for coding. 

Mintlify is an AI tool for generating documentation from the code you have created. It can read and understand your code and generate the laborious documentation that most developers loath doing! More documentation means more transparency and that assists in discovering errors or issues. More AI written code and documentation means more security!

Test automation for functional QA. While there’s no bespoke tool for this job, there’s a few rising names in the field. Adding AI to the world of QA means simplifying and streamlining the manual testing of all the endpoints. It’s already possible in some ways with tools like ChatGPT but it can’t replace humans yet!

FAQ

Q: How do AI solutions like Phind and GitHub Copilot address the limitations of general models like ChatGPT, especially in terms of providing specialized assistance for developers and code-related tasks?

A: AI solutions like Phind and GitHub Copilot offer specialized assistance tailored to specific domains, addressing the limitations of general models like ChatGPT. Phind, for instance, focuses on providing tech-related assistance for developers and offers references to sourced content, enhancing credibility and trust. GitHub Copilot, on the other hand, is designed exclusively for code-related tasks, leveraging its deep understanding of programming languages and patterns to offer tailored code suggestions and completions. By specializing in their respective domains, these AI solutions provide more targeted and accurate assistance compared to general-purpose models.

Q: Are there any notable differences in the training data or methodologies used by AI assistants like Phind, GitHub Copilot, and Snyk.io compared to general-purpose models like ChatGPT, and how do these differences impact their capabilities and effectiveness?

A: The training data and methodologies employed by AI assistants like Phind, GitHub Copilot, and Snyk.io differ from those used for general-purpose models like ChatGPT. These specialized AI solutions often leverage domain-specific datasets and training techniques to enhance their understanding and performance within their respective domains. For example, GitHub Copilot may utilize vast repositories of code from GitHub to train its models, while Snyk.io might analyze code for security vulnerabilities using specialized algorithms and data sources. These differences in training data and methodologies contribute to the unique capabilities and effectiveness of each AI assistant in addressing specific tasks and challenges faced by developers and security professionals.

Q: Can you provide insights into the potential challenges or drawbacks associated with relying on AI-driven solutions for tasks such as code assistance, security checking, and documentation generation, and how can businesses mitigate these risks when adopting such technologies?

A: While AI-driven solutions offer numerous benefits in terms of efficiency and productivity, there are potential challenges and drawbacks to consider. One challenge is the risk of reliance on AI for critical tasks such as code assistance, security checking, and documentation generation. AI models, while powerful, may still have limitations and may not always provide accurate or comprehensive results. Additionally, there may be concerns regarding data privacy and security, especially when sensitive code or information is involved. To mitigate these risks, businesses should implement robust validation and verification processes, supplement AI-driven solutions with human expertise and oversight, and adhere to best practices for data protection and cybersecurity.

We hope you enjoyed this look at the emerging AI tools and how they are changing the technological world we live in. If you’re interested in the gold-standard of cybersecurity and load testing solutions for both traditional and Web3 applications or games, don’t hesitate to contact the Cyrex team.