Build Your Dream App 10x Faster: The AI Advantage for Busy Startups
Applications like ChatGPT and GitHub Copilot can help developers work more efficiently. However, there are some downsides.
Titus Capilnean and Josh Levine created Private Market Labs to deal with the issues of business sales.
The goal was to create an online platform that connects buyers and brokers more easily and efficiently.
The creators explored no-code platforms and other methods to help speed up the process of developing an easily accessible viable product.
But when ChatGPT was on the scene, they considered it as the better choice.
The creators used GitHub Copilot, Microsoft’s AI-assisted coding platform.
“We built the current version of our app in React, with the help of ChatGPT and Copilot,” Capilnean said in a statement.
“We would not have been able to build so much, so quickly, and with a lean team, without the use of these tools.”
A.I.-assisted programming tools are among the few noteworthy uses of generative AI.
According to Microsoft, Copilot has attracted more than one million paying members and has 37,000 enterprises as clients.
“We’ve seen a lot of startups adopting applications like Copilot, and it’s becoming a standard part of the developer toolkit,” said Todd Graham, managing partner of Microsoft’s corporate venture capital fund, M12.
“These tools are seen as partners that complement developers, not replace their jobs.” Let’s look at how AI-assisted programming tools might help your business grow:
Test the tools
While Copilot and ChatGPT dominate the market, there are various additional options available.
It’s worthwhile to look at some of them. There may be significant differences in ease of use, cost, security, and functionality.
Here are some of the various artificial intelligence-assisted programming tools.
- AWS CodeWhisperer
- Google’s Duet AI for Developers
- Tabnine
- Replit A.I.
- Cody
- Cursor
How to Use the Tools
They frequently come with development environments such as Visual Studio Code, Neovim, and JetBrains. This is essential since the developer will remain in the flow.
In other words, you won’t be distracted by having to copy and paste code from another software.
There are several methods for creating code with an A.I.-assisted tool.
You can use a comment to direct the large-language model to do a task, such as creating a function or class.
This may even be depending on the program’s overall dependencies. Next, you can connect with the codebase.
You can use the tool to suggest improvements, resolve issues, or explain the code.
Some tools may even assist with document creation, code review, and unit testing.
They are often best suited to hard and boring operations. This means that the developer will have more time to spend on vital tasks.
Understand the limitations
While AI-assisted programming tools are effective, they are far from perfect.
The fundamental generative A.I. might generate code that is wrong, verbose, or unmaintainable.
After all, the models are trained on vast volumes of publicly available codebases. However, some of it is poor quality.
Models are also pre-trained. This suggests that the training data has a cut-off point.
As a result, the tools are unlikely to be updated with the most recent framework and library versions.
Next, there is a learning curve for artificial intelligence-assisted programming tools. Prompt engineering is a difficulty.
In certain cases, changing a prompt might be time-consuming and may not provide what you want.
Finally, the tools pose security issues. The code that follows could have bugs.
Bottom line: Always check the code generated by a tool.
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