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Kudos to databricks, I also find it interesting that such different companies (Stripe, Ramp, Databricks) are all building the exact same internal tools.
I think building companies is going to look more generic in the future because intelligence is an API now.
This approach seems fundamentally predicated on being able to evaluate coding agents on your own code by having domain specific evals. With that knowledge, you can trust the routing logic is actually improving/maintaining perf while reducing costs. Without the insight into agent performance, any changes like this feel like a gamble to save $$ at the cost of developer productivity I'm actually working on building gene…
In large user populations like at Databricks I think the ultimate answer will come from experimentation instead of offline evals. We are already doing this in small groups, exposing them to new candidate models and then measuring per-developer cost and perceived quality changes.
First time hearing of Omnigent. Anyone have experience using it?
I haven't gotten a chance to test the multi-agent capabilities, but the DeepSeek Flash prices are so low that I probably will soon.
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This was more true a few months ago but Fable has improved the situation considerably. Also just remember - minimalist code looks and feels great but customers do not read your code. I have caught myself many times providing "corrections" to abstractions that were already ~fine, just not perfect. The average SWE costs $200/hr. Careful you don't burn $50 worrying about code that will likely be rewritten or can be bett…
> The average SWE costs $200/hr. And this is how I find out I'm woefully underpaid.
Surprisingly pragmatic and info packed article.. Kudos to databricks, I also find it interesting that such different companies (Stripe, Ramp, Databricks) are all building the exact same internal tools. I think building companies is going to look more generic in the future because intelligence is an API now.
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> I work at a small startup How does a “small startup” end up with a multi million line “legacy” codebase? Something not mathing
> How does a “small startup” end up with a multi million line “legacy” codebase? Easy! The output of 6 months ago Opus! Which seemed so wonderful at the time.
First the mofos force you to use AI then they become stingy about it. An AI-edited post by the way.
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How is this not true? Taking a Senior SWE @ ~$200K, even just the base salary cost / 2080 working hours is $100/hr. Fully loaded employer cost + accounting for non-coding time gets you to upper 100s easily. Even for a junior making $100K, I have a hard time believe their time is worth less than $75/hr or so. Edit: Fine, "Senior" is not "Average". But naive salary is not the true numerator.
I have a hard time believe their time is worth less than $75/hr or so. In many places in Europe it is.
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Something isn't clear about the size of your codebase here and the level of reliability your customers expect, as a reader of your comments. Clarity there will help. My observation has been: - Initial greenfield work by an LLM is fast and very effective with minimal or no human oversight. - Subsequent work ends up being over engineered and very verbose. Assumptions are made that aren't suited to the problem at hand (…
> unguided LLM usage Why aren't you guiding your LLM usage? Is that what I said - to spam it and not guide anything? Or to have a careful workflow where you agree on design and maximize your human judgement/leverage? > any state that's not explicitly being tested and verified in QA loops As opposed to before, when engineers perfectly reasoned about code behavior from first principals and QA was unnecessary?
Design is too expensive, we do agile. QA too expensive, we fire all of them, and claim devops is the now, which allows us to fire the Ops team too, 100% ownership from deisng to ops on devs.
One person with an agent can replace all these teams. Yeah mo profits.