Greptile Blog

When I first came across the idea of vibe coding, my first reaction was that this was taking things too far. Isn't it enough that your LLM can help you do - autocomplete - suggest possible solutions to a problem you've taken the time to understand - helps you spend less time reading documentation and instead help guide your approach and sometimes even helps you identify obscure apis that could help you get shit done - help you review your code - come up with multiple designs for a solutions - evaluate multiple designs you come up with for trade-offs - help you understand your problem better and the available apis - write a prototype of some piece of code

I feel like LLMs are already doing quite a lot. I spend less time rummaging through documentation or trying to remember obscure api's or other pieces of code in a software project. All I need is a strong mental model about the project and how things are done.

There is a lot of obvious heavy lifting that LLMs are doing that I for one am not able to take for granted.

For people facing constraints similar to those in a resource constrained economic environment, the benefits of any technology that helps them spend less time doing work that doesn't deliver value is immediately visible/obvious/apparent.

It is no longer an argument about whether it is a hype or something, it is more about how best to use it to achieve your goals. Forget the hype. Forget the marketing of AI companies - they have to do that to sell their products…

> Where are the "code-review" agents at?

OpenAI's Codex Cloud just added a new feature for code review, and their new GPT-5-Codex model has been specifically trained for code review: https://openai.com/index/introducing-upgrades-to-codex/

Gemini and Claude both have code review features that work via GitHub Actions: https://developers.google.com/gemini-code-assist/docs/review-github-code and https://docs.claude.com/en/docs/claude-code/github-actions

GitHub have their own version of this pattern too: https://github.blog/changelog/2025-04-04-copilot-code-review-now-generally-available/

There are also a whole lot of dedicated code review startups like https://coderabbit.ai/ and https://www.greptile.com/ and https://www.qodo.ai/products/qodo-merge/

Greptile | Software Engineer (junior, senior, staff)| San Francisco ONSITE | https://greptile.com

Greptile is building AI agents that catch bugs in pull requests. Over 2,000 teams including Brex, Whoop, and Substack use Greptile to review nearly 1B lines of code every month.

We're a team of ~20 in San Francisco, working on things like better agent evals and sandbox execution environments.

We've raised $30M to date, including our recent Series A led by Benchmark.

Stack: Typescript

Open roles: greptile.com/careers

Salary ranges: $140k-270k base (depending on seniority) + $40-100k/yr equity

you know everything is made up right? And yet it just works. I too use a confidence score in an bug finder app, Github seems to use them in copilot reviews, people will use them until it is shown not to work anymore.

on the other hand this post https://www.greptile.com/blog/make-llms-shut-up says that it didn't work in their case:

> Sadly, this also failed. The LLMs judgment of its own output was nearly random. This also made the bot extremely slow because there was now a whole new inference call in the workflow.

I use opus for coding and codex for views. I trigger the reviews in each work task with a review skill that calls out to codex[0]

I don't need anything more complicated than that and it works fine - also run greptile[1] on PR's

[0] https://github.com/nc9/skills/tree/main/review

[1] https://www.greptile.com/