211–220 of 267 posts
I would be really curious to hear from devs at Databricks what the experience of development is like internally. I work at a small startup with essentially unlimited AI spend budget - the entire point is that I should be turning to it at every opportunity since our human labor is so expensive relative to tokens. So generally it's like: - Spend most time prioritizing/discussing what to do. - Once that's agreed, use Fa…
What I take from this is that models are already commoditized, and it’s pretty clear nobody has a moat: routing for the models, they can be swapped whenever new models are released, AI labs will have to continue to run on the treadmill non stop or be replaced. Long term I cannot imagine that business will be high margin. Routing for the harness, so anything that differentiate a provider vs another isn’t exposed to th…
Do you think Anthropic or OpenAI will eventually try to crack down on routing harnasses? Provide a more vertically integrated experience? They are already trying ro ship hardware products.
Earlier quoted context omitted.
They may have forked something
Definitely possible, but up thread they wrote: >”Have you worked at many startups?” In response to a question about a legacy codebase at a startup. That implies that they think whatever they are doing is common. And forking a multi million line codebase and heavily developing it isn’t common for startups.
Earlier quoted context omitted.
I agree with your larger point. Though I think it's pretty natural to wonder how the poster ended up with a multi million line codebase.
2M lines of code is 15 people committing ~26k lines of code per year (~100 lines per working day) for 5 years. 15 people is a pretty small startup, what if this is a 50-person startup? Doesn't seem like that much to me, depending on what you're building and the size of your team.
15 devs putting out 400k LOC a year into the same codebase is not normal at all. I’ve never seen anything close to that kind of rate of growth across that number of people.
And 50 engineers is not even remotely close to a small startup. I have worked at a startup that had 50 engineers after a few years, but it was a multi billion dollar unicorn.
I think the important aspects are making individual developers responsible for their costs and restricting the most expensive models when costs are too high. That way, people have a chance to learn when they tend to use the most expensive models for trivial tasks.
1. Thinking about how to optimize AI spend is one more thing to think about when I have too many things to think about already;
2. If there’s even a minuscule chance that me selecting down the model results in a task getting delayed because for that particular thing the model happened to be “too dumb”, I’m just not going to do that. Because I’m evaluated for what I ship and saying, “oh, not happening this week because I downgraded to Haiku for the exploration phase, and that resulted in a faulty plan, so it slipped a day” just doesn’t cut it.
3. Why should I spend time thinking about costs, and risk being slower because of that, if the next guy doesn’t?
Earlier quoted context omitted.
If software has become a mass-produced commodity then seems to me the software business will become a much more finance focused business You will really have to weigh the cost of making the software against the expected revenue.
Since software is still a winner-takes-all market, the mass-production property of software doesn't really matter. In such markets, what you produce is either worth nothing or worth millions of dollars. For as long as it's the case that well-constructed code (with or without LLM help) is more likely to be in the latter category, the economics of software don't really change. Even before LLMs, you could've commissione…
This could not be further from the truth. It might be true if the software works without supervisors in a B2C scenario where everything is self explanatory and nothing can go wrong, movie streaming services for example, and even these have tons of variants and multiple big players racking up billions in market share. So even there "winner-takes-all" does not apply. It gets even worse in B2B software, as there is SO MUCH software thats commonly used and needs tons of supervising by experienced devs. If we had a "winner-takes-all" market a lot more software would already be dead. And a lot of companies would have way less options when adopting new software for something.
I would be really curious to hear from devs at Databricks what the experience of development is like internally. I work at a small startup with essentially unlimited AI spend budget - the entire point is that I should be turning to it at every opportunity since our human labor is so expensive relative to tokens. So generally it's like: - Spend most time prioritizing/discussing what to do. - Once that's agreed, use Fa…
>Auto-fix bugs and CI until green + thermonuclear review skill x3. Gotta love this loop, I have it running while I'm asleep all the time.