TTP are actually third parties connections to bank accounts generally mediated by platform like Plaid. They are used to get data and analyze it.

we are quite bad at promotion :D

Our platform is for free, friendly debate. If someone supports Trump, that's fine with us. What's not fine is the horde of automated bots designed to distort debate and perception.

Thx! It's totally free for early users

i think wasm = core of edge computing for web2

WTF

If you want to play with it, it's free: https://datastripes.com

i was stitching together a few CSVs recently and realized most of the options were either full sql environments or giant BI platforms. i just wanted to clean some columns, join a few tables, and make quick charts without spinning up something heavy. so i built a drag-and-drop flow where you connect nodes for cleaning, merging, and visualization. it's still pretty minimal, but i've used it to prep data for side projects and even generate podcast scripts from datasets. curious if others have hit the same pain point or if i'm overfitting to my own workflow.

Good question. Let's start saying that Datastripes isn't trying to replace Matlab/Octave/etc. It's closer to a more lightweight scenario builder that runs entirely in the browser. So instead of coding a model, you drop data in, build a flow (filters, regressions, clustering, projections), then tweak inputs and watch the charts update live. We started with the idea of no backend, no install, runs in WASM/WebGPU. Obv, this strategy cannot be a one-size-fits-all, but it covers a lot of use cases in business and educational domains.

It's less about heavy numerical libraries, more about quick "what if" exploration for non-technical users or teams that don't want to set up an environment. Think smaller/faster, but also easier to share where data stays local.

Aso potential for some biomedical research use cases too!

Bit easier setup / use than customized spreadsheet with query links to database.

And if the analysis could be extremely computationally intensive, consider an LSTM approach. Datastripes effectively addresses this problem by using WASM to build, train, and run the model in a near-native environment, but heavier models or larger datasets could introduce significantly more challenges.