Birbla

Join the discussion

Write your take first — we'll ask for email only when you're ready to publish.

Sign in
  • Hacker News
  • The tool gave me advice and code to do what I asked... but when I used the "export analysis" it did NOT include the code. It was simply an overview.

    It would be more useful for the export to have an option (or by default) to include everything from the session.

    by canada_dry
  • Problem space is very interesting. Sounds like most of the work will be the data handling which is an evergreen problem
    by alansaber
  • Great product and congratulations on the launch. Who is the target user vs customer? On the surface, and I may be wrong here, this feels like a LLM layered on top of a typical AutoML structure eg: TPOT, Caret. Is that the correct mental model for a tool like this? And if so, do you see a similar problem that these tools faced in broader adoption at companies?
    by abhishekbasu
  • In the demo, you didn’t show the process of cleaning and labeling data, does your product do that somehow, or do you still expect the user to provide that after connecting the data source.
    by tnt128
  • > Each agent focuses on its domain: data analysis, feature engineering, model selection, deployment, and so on

    Sounds very practical in real-world use cases. I trained a ML model couple months ago, I think it's a good case to test this product.

    by liqilin1567
  • Product seems cool. But can you help me understand if what you are doing is different from the following: > you put a prompt > Plexe glorifies that prompt into a bigger prompt with more specific instructions (augmented by schema definitions, intent and whatnot) > plug it into the provided model/LLM > .predict() gives me the output (which was heavily guardrailed by the glorified prompt in the step 2)
    by brightstar18
  • Amazing! Great work. Congratulations on launch.

    Few questions: 1. Can it work with tabular data, images, text and audio? 2. Data preprocessing code is deployed with the model? 3. Have you tested use cases when ML model was not needed? For example, you can simply go with average. I'm curious if agent can propose not to use ML in such case. 4. Do you have agent for model interpretation? 5. Are you using generic LLM or have your own LLM tuned on ML tasks?

    by pplonski86
  • Sounds interesting! I'm trying to train a model but it's still "processing" after a bit but fine-tuning takes a while I get it. I'm having trouble understanding how it's inferring schema. I used a sample dataset and yet the sample inference curl uses a blank json?

    curl -X POST "XXX/infer" \ -H "Content-Type: application/json" \ -H "x-api-key: YOUR_API_KEY" \ -d '{}'

    How do I know what the inputs/outputs are for one of my models? I see I could have set the response variable manually before training but I was hoping the auto-infer would work.

    Separately it'd be ideal if when I ask for models that you seem to not be able to train (I asked for an embedding model as a test) the platform would tell me it couldn't do that instead of making me choose a dataset that isn't anything to do with what I asked for.

    All in all, super cool space, I can't wait to see more!

    I'm a former YC founder turned investor living in Dogpatch. I'd love to chat more if you're down!

    by sinanuozdemir

Explore Birbla archives