Comment by kgilpin | Hacker News Reader

Comment by kgilpin | Hacker News Reader

When AI is used properly, it’s doing one of two things:

1) Making non-controversial fixes that save time and take cognitive load off the developer. The best example is when code completion is working well.

2) It’s making you smarter and more knowledgeable by virtue of the suggestions it makes. You may discard them but you still learn something new, and having an assistant brainstorm for you enables a different mode of thinking - idea triage - that can be fun, productive, useful and relaxing. People sometimes want completion to do this also, but it’s not well suited to it beyond teaching new language features by example.

The article makes an interesting assertion that AI tools “fail to scale” when the user has to remember to trigger the feature.

So how can AI usefully suggest design-level and conceptual ideas in a way that doesn’t require a user “trigger”? Within the IDE, I’m not sure. The example given of automated comment resolution is interesting and “automatic”, but not likely to be particularly high level in nature. And it also occurs in the “outer flow” of code review. It’s the “inner flow” that’s the most interesting to me because it’s when the real creativity is happening.

> So how can AI usefully suggest design-level and conceptual ideas in a way that doesn’t require a user “trigger”?

I'm just guessing here. But maybe make it part of some other (already natural and learned) trigger made by the user.

I'm thinking part of refactoring. Were your AI is not only looking at the code, but the LSP, recent git changes (both commit and branch name), which code files you've been browsing.

And if you want to make it even more powerful. I guess also part of your browser history will be relevant (even if there is privacy concerns)

> So how can AI usefully suggest design-level and conceptual ideas in a way that doesn’t require a user “trigger”? Within the IDE, I’m not sure.

https://upload.wikimedia.org/wikipedia/en/d/db/Clippy-letter...

Clippy was on the right path at the wrong time, the issue wasn't the concept of an assistant that watches your work and provides help, the issue was it could detect a letter, offer help but the help it gave you was just a bunch of shallow formatting suggestions without context to the actual work.

An actual assistant that can preempt what you need and create it before you get there with a 95% success rate will not feel like Clippy.

Clippy from MSFT? this is where the techies really lose perspective.. you see, its not just a computer, a computer company, and a user.. Real life includes social systems with social contract, and the relationship of the user's logs, records and autonomy to the "master" of the economic relationship. Microsoft has made it clear that surveilling the user and restricting autonomy is as valuable or more valuable from a business perspective than the actual actions performed, AND at the same time, entire industry product lines are designed to trivialize and factor out personal skill and make job performance more like replaceable piece-work.

Lastly, people are more or less aware of these other dynamics at work. Yet, people are people and respond to other social cues. The sheer popularity of something being novel, cute or especially "cool" does move the needle about adoption and implementation. Clippy was a direct marketing response to Apple getting "cool" ratings with innovative GUI elements.

While I agree with some of your points about corporate data practices and the potential for technology to deskill labor, I'm not sure I see the direct connection with Clippy. Clippy, as limited as it was, seems like a poor example of these larger concerns.

Furthermore, saying "techies really lose perspective" comes across as dismissive and judgmental. It's important to remember that people are drawn to technology for various reasons, and many are deeply concerned about the ethical implications of what they build. Painting an entire group as lacking awareness isn't helpful or accurate.

If we want to have a productive conversation about the impact of technology, we need to avoid generalizations and engage with each other respectfully, even when we disagree.

>Clippy was a direct marketing response to Apple getting "cool" ratings with innovative GUI elements

Honestly think Clippy predated that, came out in 1996 for Office 97, Macs were only on System 7.5 back then while I think you're thinking of the early MacOS X era which was was 6-7 years later.

Not an AI expert but this sounds a lot like code review.

Most people are using systems to facilitate code review these days (we aren't sitting around boardrooms with printouts these days) so I wonder if there is a way to use the code review data streams combined with diffs to train AI to "do code reviews"?

That sounds like what is mentioned in the article

As long LLMs are seen as patterned knowledge aggregator then work as intended. They hallucinate answers mostly because they try to interpolate data or there is some pattern that it didn't catch due to lack of context or training.

They're really good for type / name finding and boiler plate generation. For larger suggestions as your pointed out they're too wrong to be used as is. They can give good ideas, especially if guided with comments, but usually at this stage I just use Phind.

"When AI is used properly" is a very very loaded statement. Especially since evidence shows code is one of the worst things its good at, it' a lot better at other tasks.

every expert i talk to says: „LLMs are not good for my domain, but for $other_domain“

Put another way: "LLMs are great when I don't have lots of experience in the domain but want to do something in that domain. Otherwise, my own brain works better"

Id argue it's the opposite. If you have extremely deep knowledge you can constrain the problem and get a great answer. i.e. boilerplate for X. Boilerplate is only boilerplate because you have a certain level of knowledge.

Code is one of the only things LLMs ARE good at.

At this point, asking an LLM to "implement feature X please" is not going to give you great results. however, unless you can type at 600 wpm, an LLM doing extremely trivial boilerplate code completion is a godesend.

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