141–150 of 269 posts
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exactly. Usually legacy code forms when people lose context and confidence in parts of the codebase due to staff turnover etc and ppl avoid touching or enhancing those parts for long periods. Six months is a short time to accrue that much tech debt, its enough time where most of the people who created that "legacy" are probably still around. As you said indicates bigger problems.
So basically any LLM codebase of sufficient size is immediately legacy.
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"startup" and "legacy codebase" are diametrically opposed concepts. And if you're saying (based on your other comments) that a 6 month window is enough to create a legacy codebase...that indicates a serious lack of experience or understanding as to what a legacy codebase is, or why they exist.
Man, so many people in this thread just arguing pointless semantics, making weirdo absolutist (and incorrect) statements. Accept that other people may ascribe different meanings/interpretations to words than you, and that if your reading of their statement doesn't make sense to you, perhaps you are simply reading it wrong. Trying to hold someone else to your definition of words suits what purpose exactly? Are you jus…
But who cares? The point is any codebase over a few years old with lots of customers and a big surface area has lots of code, much of it "legacy" from the standpoint of a guy in 2026.
There are a surprising number of articles like this along the lines of, "we started using AI tools and ended up spending millions per year". On what planet do people start paying for things without keeping an eye on the costs and no-one notices until you have spent a crazy amount? I don't understand. You are either paying a fixed amount which you are happy about in-advance or you are PAYG in which case you would ball…
What didn't happen is any analysis of cost/benefit up front. Many of the corporate decisions around AI have seemed characterized by companies blindly copying each other.
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…
you should probably be doing this discussion work along with Fable 5. It will give good feedback if you're working on the correct things.
> Come up with QA plan and have Codex Computer Use execute on it
QA plan should be part of the above "design + plan", not after it. The implementer needs to be able to fully test before publishing a PR. This is true whether humans or agents are writing the code.
> Manually spot check the final result (usually a sizable diff, thousands of lines, complete feature E2E, etc)
unfortunately this is not really scalable with amount of code agents can produce, so you need independent (fresh context) agent reviewers to help. Ideally they only escalate to a human when really stuck.
> I probably spend like $80 a day at least
at a small startup you should be on the $200/month plan(s).
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.
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…
$80 sounds extremely low for what you're describing - are you on API token plans? I have had some $3,000 token days - even without Fable. I don't see how this is sustainable. My personal 20x plans get so much usage for so cheap. The consumer subsidies are crazy, but alas I can't use them for work.
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In our team's experience, the product of agents is generally The Homer (1). It does work, but it's vastly overengineered. When I personally want tight code, I have to spend a considerable amount of time adjusting it manually: - It needs to be trimmed down. In my experience, at least one agent I use struggles to produce minimalist designs, and it's very frustrating - I need to consider whether there are solutions base…
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.
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 commissioned a half-assed clone of any app you wanted from a 3rd world consultancy for a few thousand dollars. LLMs are basically Bangalore-as-API.
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How long has your startup been around? I’ve worked at plenty of startups over the past 20 years. Including one that was still calling themselves a startup 10 years out. The org I work at now was a startup before my tech giant employer acquired them. We have a very bloated and very profitable 8 year old codebase that is barely 500k LOC. I’ve never seen a startup with a multi million line legacy codebase.
They may have forked something
>”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.
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.
Thank you for the feedback. We wrote this because after discussing with some of our peer companies, I realized everyone was roughly doing similar things. And I thought it would be good for someone to just systematically write down what those are so that others can try out the techniques if they find them useful.
Have you tried measuring Gemini? now that you have the router it should be a simple task. Thanks!