Comment by ronfriedhaber

11 hours ago

This is cool.

As far as I can tell, incremental the library aims to solve the problem of partially hydrating a computation graph when source data is altered. This approach is similar to the one pursued by (well designed) build systems and is common in the FP world. [2] This has many use cases and is very cool.

In addition, in the sphere of incremental computation, there exists Differential Dataflow, Timely Dataflow (adjacent), and DBSP. Systems like Feldera are built on DBSP. Materialize is lead by some DD guys.

Personally, I am pursuing an orthogonal approach specifically for the problem of financial data and financial workloads, There exists huge, very important problems to solve! [1]

[1] https://modolap.com

[2] Signals And Threads episode on the subject https://signalsandthreads.com/build-systems/

5 comments

ronfriedhaber

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valzam  10 hours ago

> https://modolap.com

Redirect to a 2k USD stripe payment with no explanation when clicking on the main callout button is a pretty baller move.

  • ronfriedhaber  9 hours ago

    One man's baller is another man's insufficiently baller.

    Email me for details, pricing & installations, or your target use case, would love to talk. In addition, if you have any feedback.

    ron at modolap dot com

    • foltik  6 hours ago

      Your site has a bunch of marketing copy, trademark symbols, and a link to pay you $2k/mo, but zero technical detail and is broken on mobile. It gives vibe coded.

      The benchmark of 2m black-scholes evals/sec is meaningless without additional context. Also seems a couple orders of magnitude slower than what I’d expect even for a single core. What exact transformations are being done, and what’s the throughput in GB/sec? Is it multi threaded? Benchmark against the equivalent query in kdb+ and ClickHouse?

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