11–20 of 66 posts
Earlier quoted context omitted.
We have never been a blockchain company. However, I do believe there is one with a similar name.
Not just similar name but the same URL as well: https://twitter.com/getsuperblocks I presume you bought the domain name from them in that case.
I've been focused on similar concepts with my open source AI coding tool. My tool is a command line GPT chat tool. You can ask it to write or edit code in any git repo. It displays live diffs as the AI edits stream in and automatically integrates them into your source files. I think this is similar to what your article is describing?
Folks might want to check out `aider` if they want to do the style of AI coding you're showing, but on their own git repos.
This looks cool. I like that you're showing diffs and integrating the AI code and suggestions back into the original source code. I've been focused on similar concepts with my open source AI coding tool. My tool is a command line GPT chat tool. You can ask it to write or edit code in any git repo. It displays live diffs as the AI edits stream in and automatically integrates them into your source files. I think this i…
The fact that it's even willing to write a function which removes PII from a string is nonsense. No such function is possible.
And the comment for filterUsersTable is incredibly long, tedious, and unhelpful.
How does the SQL thing work? How does it know what my schema is? That's nowhere on screen.
The only believable example is the Salesforce one which anyone could do.
This might be a bit contrarian, but I don't really think there's product market fit here, at least for engineers. And if given in the hands of non-engineers, you'll need to have an engineer at least look over the code anyway. I haven't seen or heard of anyone that seriously uses ChatGPT to generate code and uses it in prod (apart from engagement-farming Twitter posts).
> Understanding someone else’s code for large internal engineering teams is often a challenge
Without proper context, this will fail miserably. Have you ever seen a corporate codebase before? It can be the definition of "wtf?" even for a seasoned developer that's been working there for years.
This AI-generated code trend reminds me of the "no-code" fad of the past decade.
This is neat, but, as with all projects in this vein, I fail to see the real value add. We've had ML-powered code generation for several years now (Copilot released publicly in late 2021), and apart from a cute novelty, there's no real use case I've found. ( Maybe except for generating tests, but when dealing with a novel code base, the models really can't figure out dependency injection, mocks, weird edge cases I'd…
I would agree with this in general. The main goal here is to optimize the "editor experience". When I personally code, I'll have ChatGPT pulled up on the side in case I need to reference it for speed. Some examples of how I might use it are:
- add some tests for me to review - take an existing piece of code and modify it in some way - help me think of ways in which I can write some code if I'm lazy that day.
This would generate code that after I iterate on it would need to be reviewed which speaks to your point, "you'll need to have an engineer at least look over the code anyway" which I agree with.
The main goal is to have it there as a resource to pair program with rather than trusting it without review/input.
This is neat, but, as with all projects in this vein, I fail to see the real value add. We've had ML-powered code generation for several years now (Copilot released publicly in late 2021), and apart from a cute novelty, there's no real use case I've found. ( Maybe except for generating tests, but when dealing with a novel code base, the models really can't figure out dependency injection, mocks, weird edge cases I'd…
The value added is that you don't send your source code to OpenAI and/or their parent org Microsoft.
This looks neat but the examples are terrible. The fact that it's even willing to write a function which removes PII from a string is nonsense. No such function is possible. And the comment for filterUsersTable is incredibly long, tedious, and unhelpful. How does the SQL thing work? How does it know what my schema is? That's nowhere on screen. The only believable example is the Salesforce one which anyone could do.
This is good feedback. I think having an ability to adjust verbosity is valuable. A comment version should maybe be more concise than the actual explanation.
> How does the SQL thing work? How does it know what my schema is? That's nowhere on screen.
When you configure integrations with Superblocks, we query your integrations metadata which we use to give the best answers here.
This is neat, but, as with all projects in this vein, I fail to see the real value add. We've had ML-powered code generation for several years now (Copilot released publicly in late 2021), and apart from a cute novelty, there's no real use case I've found. ( Maybe except for generating tests, but when dealing with a novel code base, the models really can't figure out dependency injection, mocks, weird edge cases I'd…
You're telling me you've never once pressed "TAB" to auto-fill the line or even several lines with Copilot? Because about half of the letters in my source code is now Copilot generated. The value added is that you don't send your source code to OpenAI and/or their parent org Microsoft.