I think AI is still nowhere near where it needs to be to provide business value in software development.
As an experiment, some time ago, I tried to build a TODO app entirely with AI prompts. I used a special serverless platform on the backend to store the data so that it would persist between page refreshes. I uploaded the platform's frontend components README file to the AI as part of the input.
Anyway, what happened is that it was able to create the TODO app quickly; it was mostly right after the first prompt and the app was storing and loading the TODOs on the server. Then I started asking for small changes like 'Add a delete button to the TODOs'; it got that right. Impressive!
All the code fit in a single file so I kept copying the new code and starting a new prompt to ask for changes... But eventually, in trying to turn it into a real product, it started to break things that it had fixed before and it started to feel like a game of whac-a-mole. Fixing one thing broke another and it often broke the same thing multiple times... I tried to keep conversations longer instead of starting a new one each iteration but the results were the same.
The article is about Cursor. You don't need all your code to fit in a single file. It sits in your IDE. You don't need it to create your app entirely. I just tell it what I need, and where, and it makes me 20x faster. It solves the problem you're describing exactly.
do you feel like it needs a major architectural change or breakthrough leap in abstraction before it can provide business value in software development, or would far larger context window sizes be enough for it to provide substantial value already?
For me it feels like it just starts to break after a certain length, but may not require a breakthrough new architecture to provide more value. Just larger context window sizes, so it can do the same thing it does on smaller pieces of code, on larger pieces of code, too.
Can be, yes. I think besides the context window size though, there is the issue that performance seems to decline as the input size increases. It can become a problem before you reach the context window size limits.
As an experiment, some time ago, I tried to build a TODO app entirely with AI prompts. I used a special serverless platform on the backend to store the data so that it would persist between page refreshes. I uploaded the platform's frontend components README file to the AI as part of the input.
Anyway, what happened is that it was able to create the TODO app quickly; it was mostly right after the first prompt and the app was storing and loading the TODOs on the server. Then I started asking for small changes like 'Add a delete button to the TODOs'; it got that right. Impressive!
All the code fit in a single file so I kept copying the new code and starting a new prompt to ask for changes... But eventually, in trying to turn it into a real product, it started to break things that it had fixed before and it started to feel like a game of whac-a-mole. Fixing one thing broke another and it often broke the same thing multiple times... I tried to keep conversations longer instead of starting a new one each iteration but the results were the same.