Comment by lysecret | Hacker News Reader

Comment by lysecret | Hacker News Reader

Congrats on the launch. I'm quite impressed.

Here are my unordered thoughts.

So it seems a lot like an improved colab with a deployment stage. Which sounds good to me, it will be much more expensive than colab though.

I like the pitch of SWEs doing ML instead of pitching towards Data Scientists. As Data Scientist turned SWE I still miss the Jupyter like cell based execution. (you said it exists but I couldn't find it.)

In general I'm quite sceptical when it comes to online IDEs. However, for Text and Image based models it might be enough (since you don't need tooo much code).

There might be a valid niche between colab on the one side and building it yourself with AWS cli on the other.

I wonder though, in your target market it really isn't such a big deal to spin up a rest api, there are no lambdas with GPU though (but this should be a matter of time). Or use something like AWS batch for remote training. It will come down to: Is it more convenient to code in your IDE and you handle Lambda, Batch Docker and CD. Or do you code in your own IDE and you have to handle this stuff yourself.

Wish you all the best!

Hi, thanks for checking it out! You bring up some great points.

When it comes to SWEs doing ML, our goal to bring together a bunch of apps that get people most of the way to something they can bring into production.

I appreciate your skepticism of online IDEs, and it’s unlikely we’ll ever completely replace notebooks or whatever IDEs people prefer hacking on locally. Instead, we’d like to take a hybrid approach, in which our online IDE is sufficient for making minor tweaks to a model after its in production, but the brunt of development can still happen locally, in which changes would be pushed to Git and synced with our online IDE.

It’s true that SWEs can deploy their own APIs, but that feels like an unnecessary annoyance to take for granted. At a high level you’re just setting up an API, but really you’re also going to setup some versioning system and a Docker file and monitoring, and all of that adds up to a lot of cognitive overhead.

BTW - the Jupyter-like cell execution can be turned on by clicking the “Interactive Mode” button on the bottom right corner of the IDE.

Makes a lot of sense! It really is true that ML models kind of live in its own little world with their training loop and should interact with the rest through a rest api. And now and then new data gets added for training, as well as the api should change a bit, maybe we tweak the labels. You managed to encapsulate that part. I might port one of our text model to it to try it out :)

Would love to hear how it goes! Reach out if you have any questions - we've ported over quite a few text models.

One other quick question. Have you built the ide yourself or are you using a provider? It looks pretty cool.

The core code editor itself uses Monaco (the same thing under the hood of VSCode), but everything else is custom (i.e. file browser, language server, syntax highlighting, tabs, etc.)

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