Benchmarking coding agents on Databricks' multi-million line codebase(www.databricks.com)
53 pointsby tanelpoder9 hours ago8 comments
  • redmalang2 hours ago
    We have an internal proxy (that I've been meaning to open source for ages) that routes all llm usage at our company, which allows us to see data in realtime. Its been fascinating how rapidly Pi has been adopted. Moreover since its pretty hackable, we've been able to automatically aggregate context from pi sessions, which has resulted in Pi efficacy being higher as more people use it, putting in place a interesting virtuous loop. I didn't expect this outcome: for whatever reason I assumed proprietary harnesses fine tuned to work with a companies' models would work better? ps/random aside: there is something slightly off about Pi's edit command, we are planning to investigate this further and patch this as we have quite a few session traces now..
  • cpard3 hours ago
    This was mostly because Sonnet 5 worked longer and read more to get there, consuming 1.9x more tokens.

    I have experienced similar behavior between opus and haiku when benchmarking Dara engineering tasks. The “cheaper” model takes many more turns to figure out the task and this is without taking into account other important factors.

    Another interesting behavior that I observed is that Haiku tended to cheat more maybe because it was having a harder time to find the root cause of the problem.

    Benchmarking and evaluation of agentic systems is very interesting and if there’s one thing that someone should keep from the Databricks post is how important is for everyone to build and run their own.

  • lukaxan hour ago
    Could it be that users of Pi are more senior and know better how to prompt and that's why the pass rate is higher?
  • yodon3 hours ago
    I wish they'd do a follow-on post drilling into the impact of the programming language on cost-per-task, specifically looking at cost to complete tasks in mainstream strongly typed languages (eg. C#, TypeScript) vs dynamic languages (eg. Python, JavaScript). Does the additional verbosity of the language help or hurt cost per task?
    • trollbridge22 minutes ago
      I don't have hard data, but we have shifted to Rust and Swift (for frontend UI) for the bulk of our dev simply because it is a lot more predictable, easier for tool calls to edit, the build steps produce easier output for the agent to loop on, the tests are easier to write/get results from, etc., although I am mostly measuring this in time, not cost.

      Once the thing is rock-solid it's relatively easy to do a Swift->HTTP/HTML/CSS/React/TypeScript conversion.

  • falaki6 hours ago
    1) Many models are now competitive at the top tier, including open source. 2) GLM 5.2 in particular was a major step forward in open source coding agent performance, 3) Harnesses make a huge difference in cost-performance. 4) Cheaper per-token does not imply cheaper per-task.
    • falaki6 hours ago
      Also they suggest every company should build their own benchmark and repeat these tests with new models instead of relying on the SWE bench.
  • 3 hours ago
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  • vegetablefinger3 hours ago
    [flagged]
  • zkmon2 hours ago
    > Databricks’ multi-million line codebase

    The combined size of codebases for the underlying opensource products (Apache Spark etc) might be around 1M lines, I think. Why does the orchestration/management layer, that is "databricks", exceed the sizes of the core products?

    • trollbridge22 minutes ago
      Because if they're like previous places I worked out with proprietary commercial codebases, the code is old, has been around a long time, and trying to shrink it is not a priority.

      It's a good stress test for the LLM because it is not an "ideal" codebase.

    • ozgrakkurtan hour ago
      Old codebase, you always add code and never remove it. So it is expected to be like this.

      Deleting code is difficult and almost never makes sense afaik

      • tijs6 minutes ago
        Every line you delete is a line you no longer need to maintain. We aggressively prune old code in our apps and it has definitely helped with maintainability. For a mobile app it’s also code you don’t ship so that’s a nice bonus which I guess is not much of an argument on a backend codebase
    • appplication2 hours ago
      LoC isn’t a super helpful metric so I think the better question is why is the headline using it. I can say I’ve personally created about 200k LoC code in the last 5 years and most of that has some value. But it really doesn’t say might about how much value or really anything else meaningful.
      • InsideOutSanta35 minutes ago
        They're probably mentioning the size of the code base as an indicator for how difficult it is for an LLM to understand where and how to make changes.