Many times those updates are not properly tested, for example in one update the model selector got completely changed.
then next hotfix was pushed which restored original.
It's interesting this was submitted to HN over 15 times since it was published in February: https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
But this is the only submission that's had traction at all. Since the content is nearly the same for all submissions, it highlights how getting to the front page can be a bit random. (Though this is the only one that capitalized 'Leveraged' so maybe that's the secret)
Forcing readers to wade through an unceasing string of LLM clichés demonstrates the opposite of the point you’re trying to make—that the consumers of your work are worse off because you exercised no human judgment in creating it.
That's an insane level of throughput. What's a good baseline? Prior to agentic coding, whats the typical number of PRs engineers were expected to push? Maybe a 2-10?
Do people feel the software has gotten better in the last 6 months? The number of engs is prob the same so we should expect maybe 5x faster cycle in major software apps, but I don't see it. The AI apps do change very fast but given its a very new field, I'd expect as much. But outside of that, I don't see it.
You end up with about 3 lines added per commit, which is not ridiculous when you consider that most would be editions rather than full additions.
Here, we have 1500 PRs and 1M LOC, which is about 650 added LOC per PR. Remember, not 650 lines total in the PR, but +650 balance after additions-removals.
Fun questions for attentive readers:
- What does a project growing at a rate of one full firefox-codebase worth of LOC per year look like, a decade down the line?
- What does the line count say about the verbosity of the tool, and what does it say about outcomes that the purpose of the project isn't clearly disclosed?
- Do we have reasons to care about LOC in a world where we don't write code manually? What happens to token usage numbers when the codebase is significantly larger?
- If it was confirmed that LLM usage blows up your line count, what's the implication for codebases that want to return to manual coding after months of usage? (Say, because the tool gets expensive).
For some reason most of the uses of "agents" are to build yet other AI products, it's turtles all the way down. Maybe that says more about the field of harnesses than it does about the power of "agents".
Agents help a ton with the discovery, but the act of building a product needs a deeper level of thought and validation to make it actually better than what came before. So IMO what you see is people still learning what needs to be understood and crafted first hand to make a product better (including economics)
We’ll get there if more of us try
To what end and what would that even look like though? Enshittifying everything at maximum speed? The apps/platforms I use regularly - GitHub, Spotify, Google maps (just to name a few), have gotten noticeably shittier in recent times.
This almost reeks of "I've never cleaned up our code base because there is too much code, and didn't even bother having agents/LLM cleaning them up".
You almost never need a million lines of code - this includes your software, infra, testing and operational tools. You didn't ship the linux kernel in 3 weeks and you know it. The code is already speghetti and it achieve the basic functions OK but it will harder and harder to simplify and untangle and maintain.