[ado__dev]

We're still very much in the early days.

Code AI tools today absolutely crush at creating proof of concept apps. You can test your idea and get market validation in days vs months.

They are getting better at medium/large codebases, but still have a ways to go before being super useful and it translating to a huge increase in productivity. Currently it's really good for helping with the menial tasks (creating docs, unit tests, understanding and onboarding) but not quite there yet when it comes to integrating gen ai code in large codebases, but it's only a matter of time.

[vunderba]

Not discounting your claim, but the fact that you work at a company (Sourcegraph) whose business is literally AI coding / coding assistants calls your objectivity somewhat into question.

I will agree that they're very useful for boilerplate tasks particularly around deployment (cloudformation, github actions, etc.)

[ado__dev]

Def don't take my word at face value. I'm just sharing my experience and knowledge, but the great thing about most of these tools is that many offer usable free tiers or very low-cost plans that don't require any sort of lock in. So try the tools yourself, for your use cases, and see if it's a fit or not.

Personally, I have seen a ton of change in the last 4-5 months, so if you haven't tried these tools recently, I encourage you to try them today and see what's possible.

[bamboozled]

This is such a wildly unsubstantiated claim. Where is the evidence of this happening ?

[ado__dev]

Which one?

I run DevRel at Sourcegraph and our AI coding assistant, Cody, is used by tons of individuals, small business, and large enterprises. I get to talk to a ton of customers and see how their adoption of AI is going. And it's certainly increasing and developers are finding a ton of value.

[bamboozled]

Finding value in a product does make development go from months to days , which is the unsubstantiated claim. Even your customers can talk shit to sometimes in order to curry favour for a discount.

[ado__dev]

Since gpt came out I have built tons of throwaway apps, plenty of specialized apps for side projects, and experimented with tons of ideas that I likely wouldn’t have if I didn’t have access to a tool to build it for me from just asking it to do it and explain what it did. Claude artifacts has been awesome for this. Cody when I actually want to build it out. I recommend trying it before you knock it.

[KronisLV]

> Since gpt came out I have built tons of throwaway apps, plenty of specialized apps for side projects, and experimented with tons of ideas that I likely wouldn’t have if I didn’t have access to a tool to build it for me from just asking it to do it and explain what it did.

GitHub Copilot, ChatGPT and Phind are all a bit like this for me - they both lower the barrier of entry and save me a lot of time for trivial algorithms and boilerplate code, in addition to helping me find things better than search engines sometimes do, especially when given a look at the code that I'm working with.

It might not be an order of magnitude difference in my case, but things that wouldn't have happened with the higher barrier of entry are now happening and that's quite the difference in of itself! I'm cautiously optimistic about LLMs and other forms of "AI". If nothing else, so far we basically have a more versatile form of IntelliSense, even if it's not always going to output correct code.

I wonder if some day it'll be feasible to feed in the entirety of a larger codebase and reason about it better than people who only know a part of it could.

[skydhash]

> If nothing else, so far we basically have a more versatile form of IntelliSense, even if it's not always going to output correct code.

That's the real issue for me. I remember learning programming and I either had not so good intelligence (Codeblocks, IDLE, Netbeans) or none at all (notepad++,...). This forces me to either follow the book attentively (and hunting down errata) or read the manual and getting explanations from forums or friends. When you're a beginner, uou need a good source of truth, not something that can be subtly wrong.

[DrillShopper]

Cool, so we're re-visiting the 90s with RAD tools, just this time charged with AI.

Everything old is new again

[csomar]

So you can give us a couple examples of apps you built with AI that are actually useful?

[ado__dev]

Many services that power https://videotap.com/ have recently been rewritten with the help of AI.

Recently I used Cody to rebuild the entire video processing pipeline and made it much more efficient and scalable, and I actually learned a ton about ffmpeg by pair programming with the AI. Now I'm building additional features into this app, mostly w/ just iterative prompting or chat-oriented programming to replace 3rd party services that are still in this pipeline and it's been a blast.

I've also used AI tools to really brush up on frameworks, like Laravel, that I haven't touched in a while, and it's been a great experience. Also started building a game w/ Godot and found AI super helpful there in walking me step by step. So for me it's been great.

[bamboozled]

Since gpt came out I have built tons of throwaway apps,

Throw away apps are the easiest to make. No scaling, no bug fixing, no long term maintenance considerations, no consequences for poor architecture, no need to consider data models, you just write some shit. Bravo.

[talldayo]

> They are getting better at medium/large codebases

If we are strictly speaking about the cutting-edge models like OpenAI's o1, their context is getting smaller, not larger.

[ado__dev]

I believe o1's context window will increase over time as well. It's a new model that takes a different approach compared to other ones, so testing it with less context seems logical and expanding the context window as it's quality is validated.

Even in a large code-base, you don't have to have the full context of every single file for every single question, can usually get away with half a dozen files, it's figuring out which ones to provide to the LLM to get the best response.