[terabytest]

> FastRender may not be a production-ready browser, but it represents over a million lines of Rust code, written in a few weeks, that can already render real web pages to a usable degree

I feel that we continue to miss the forest for the trees. Writing (or generating) a million lines of code in Rust should not count as an achievement in and of itself. What matters is whether those lines build, function as expected (especially in edge cases) and perform decently. As far as I can tell, AI has not been demonstrated to be useful yet at those three things.

[mejutoco]

100%. An equivalent situation would be:

Company X does not have a production-ready product, but they have thousands of employees.

I guess it could be a strange flex about funding but in general it would be a bad signal.

[azornathogron]

Absolutely.

I think some of these people need to be reminded of the Bill Gates' quote about lines of code:

“Measuring programming progress by lines of code is like measuring aircraft building progress by weight.”

[bflesch]

SLOC was a bad indicator 20 years ago and it is today. Don't tell them - once they realize it's a red flag for us they will use some other metric, because they fight for our attention.

[embedding-shape]

> because they fight for our attention.

Not only that, they straight up pay people to just share and write about their thing: https://i.imgur.com/JkvEjkT.png

Most of us probably knew this already, the internet had paid content for as long as I can remember, but I (naively perhaps) thought that software developers and especially Hacker News was more resilient to it, but I think all of us have to get better at not trusting what we read, unless it's actually substantiated.

[ksynwa]

Line count also becomes less useful of a metric because LLM generated code tends to be unnecessarily verbose.

[agumonkey]

Makes me wonder what would happen if you introduce cyclomatic complexity constraints in your agents.md file.

[signatoremo]

I think you missed the point. From the blog post:

To test this system, we pointed it at an ambitious goal: building a web browser from scratch. The agents ran for close to a week, writing over 1 million lines of code across 1,000 files [...]

Despite the codebase size, new agents can still understand it and make meaningful progress. Hundreds of workers run concurrently, pushing to the same branch with minimal conflicts.

The point is that the agents can comprehend the huge amount of code generated and continue to meaningfully contribute to the goal of the project. We didn't know if that was possible. They wanted to find out. Now we have a data point.

Also, a popular opinion on any vibecoding discussion is that AI can help, but only on greenfield, toy, personal projects. This experiment shows that AI agents can work together on a very complex codebase with ambitious goals. Looks like there was a human plus 2,000 agents, in two months. How much progress do you think a project with 2,000 engineers can achieve in the first two months?

> What matters is whether those lines build, function as expected (especially in edge cases) and perform decently. As far as I can tell, AI has not been demonstrated to be useful yet at those three things.

They did build. You can give it a try. They did function as expected. How many edge cases would you like it to pass? Perform decently? How could you tell if you didn't try?