Dedy Kredo of Qodo on GPT-5's code review capabilities and enterprise AI adoption

Graphite Code review for the Age of AI. Graphite helps teams on GitHub ship higher quality software faster. You can get started for free at graphite.dev. And let's bring in our next guest. How are you doing? Welcome to the stream.

Welcome.

Oo, very clean background. I know it's probably virtual, but whatever you got going on looks fantastic. You look great. How are you doing? Are you excited about GPT5?

Oh, I'm so excited. It's It's awesome.

It's actually like everybody's talking about the coding capabilities,

please. But no one is really talking about the code review capabilities and I'm going to talk about that today.

Yeah. Yeah. Break it down. Um how are you using it right now?

Yeah. So we just enabled it in our platform. Um it's uh the default model for both our ID plugin, our CLI, our Git plugin. Um and um yeah, we're using it to generate very high quality code reviews, catch bugs before they hit production, help enterprises verify that their code is aligned with their best practices. Mhm.

See, it's super exciting. I can share my screen and show a few things if like that makes sense.

You can everything you share will be live. It'll be a little bit please. Um but I I I want to know also while you're getting that set up, I want to know about um what changes materially do you think happened in GPT5 specifically for code and code review? Do you think there's more data going into the model, more data going into the pre-training, post-training? Anything else? any anything that you're noticing that you're like, "Oh, there's a specific upgrade here. They must have done something to get there."

Yeah. Yeah. I think it's a great point. So, I think it's all of the above. So it's scaling of both the like the pre-training but probably a lot of the reinforcement learning y

um and basically using that at scale to verify that uh code gets generated in high quality and then also um basically catching bugs like and and when you do it with reinforcement learn learning you have the the actual ground truth. So once you scale that you can get the model to be um to basically be a lot better at that. How how steep is the power law right now in uh in just programming languages? Is it basically all Python, JavaScript and then uh like a really hard fall-off or is it actually important for uh coding models if they want to be adopted widely to be like truly multi- language and get all the way down into the long tail of like the Rust and the and you know C and all the different languages that are out there.

Yeah. Yeah, for sure. It's important to I mean the majority of the market is in the JavaScript, TypeScript, Python

um like the majority of the early adopters I would say but then when you get to enterprise use cases you get a lot of .NET you get a lot of Java and the models are pretty getting pretty good at those uh languages as well. Um, for sure.

How are you excited about um I mean how do you think about the difference between like the improvements to GPT5 from the consumer's perspective versus at the API level? Um I always found it a little confusing that chat GPT was available as an API and you could interface with the chat I believe you could interface with the chat GPT model via the API. Um, and and there's a little bit of like a line blurring there, but are there features that you think are are cruff and you want to kind of rip out for an a API use case or do you just say, "Hey, give us the kitchen sink and we'll we'll we'll work from there and it's actually helpful to have, you know, a coding model that can still have a web browser."

Yeah. Yeah. I think basically it's a lot about uh we consume the model through the API and it's really the same model that drives the consumer product. M um but as the for us since our use cases are a lot aboutic use cases

the more the model gets better at using tools um and gets better at um kind of listening to very very specific instructions following instructions is critical for the enterprise use cases um because for us unlike the border market we believe that for enterprises you need to have um very specific um agents that are defined with specific set of instructions prompts and tools and permissions. Um, and the more the models get trained with that type of environment, the better they end up serving the the enterprise market, which is really where we're focused on.

Um, my my question is um I wonder like you you said like very specific instructions are important. Uh, when are we going to get an agent that I can just turn loose in a codebase and say like just go improve it? like just go hunt around do like rewrite that like like when you get a good open- source contributor on a team that just becomes nerd sniped by the project that you're building on. They will just go around and find little ways to improve this documentation needs to be a little better. Let's rewrite this test case over here. Let's add a little bit more, you know, functionality to this class or function. Um how far are we from that? Yeah, I think the models are getting u better and better at that part of basically kind of running loose in a codebase. Yeah.

Um but they do need the guardrails in place

and this is kind of where we're focused on like the a lot of the talk in the market is around the code generation side. Um you know let the agent loose and give it a task and it will just going to go around and run for hours and do and and do it. uh what we're seeing is that the real challenge is now shifting towards how do I verify that the code is aligned with the best practices? How do I make sure that it's well tested, well reviewed um doesn't break anything um you know so that that's I think the next frontier and really developers going forward are not going to write a lot of the code um by by hand. They're mo spend they're going to spend most of the their time reviewing code and that's the next frontier and that's what we're talk really like are here to tackle

very cool anything else Jordy

no well thank you so much for joining giving us some extra context on the GP GPT5 launch we will talk to you soon have a great rest of your day and thank you for joining

cheers thanks cheers

talk to you soon uh and let me tell you about profound get your brand mentioned on chatbt that seems more relevant than reach millions of consumers who are using AI to discover new products and brands. I forgot to ask about this. We'll have to come back to this, but I want to know if

the found powers MongoDB, Indeed, Mercury, Docyign, Zapier, RAMP,

row, Goolan, Workable, Majuri, Sleep, US Bank, Chime, Clay.

Okay. Okay, we get it. Um,

they got some logos. There is this question of like okay uh even if you're even if you're like okay GPT5 is more incremental than re more of a more of a an evolution than a revolution. It's like okay well then let's talk about how it affects every other business and every other aspect of the economy. What should you be focusing on? Um and and is like do the do any of the updates from GPT4 to GPT5 change how you're