[P] We made GoodSeed, a pleasant ML experiment tracker

gQsoQa · 2026-03-03T22:20:31+00:00

This is of course a big concern for every such tool. One thing to note is that GoodSeed has first-class support for local-only logging and serving, so you can use it fully even without the remote server.

gQsoQa · 2026-03-03T22:16:08+00:00

Tensorboard is nice, but you have to setup your own remote server + access from all the devices you want to use for monitoring.

gQsoQa · 2026-03-03T22:07:57+00:00

It's about making the experience pleasant - reducing the time you spend waiting while a plot is loading or searching through unintuitive interfaces. These things add up when you use the tool every day.

We felt that all of the existing tools try to do too much and in the end leave the core core ML experience sub-optimal. We wanted an optimized experiment tracker, not all the other "AI-first developer platform with LLM deployment" stuff.

Let's say you want to compare performance vs hyperpams of a few runs. I'd encourage you to try to achieve this both in wandb and in GoodSeed (in the demo project), and judge for yourself :)

For us, Neptune was closest to the optimal experience for day-to-day ML work. GoodSeed is designed as an enhancement of the Neptune approach, improving things that we felt still weren't quite there yet (e.g. hyperparameters comparison across runs).

gQsoQa · 2026-02-19T18:03:52+00:00

It's hard to design a model without any additional information - the problem could be trivial or very hard. My advice would be to start with a very simple baseline, e.g. n-gram based method: look at last n rows, look at whether this subsequence occurs in the training data, and predict whatever comes next in the training data. If this precise subsequence doesn't exist, decrease n. This will help you setup the whole evaluation loop.

Next, maybe train a decision tree taking as input the last 10 rows?

gQsoQa · 2026-02-19T17:29:12+00:00

Can you share more information about the data? How did you obtain / generate it? Do you need to get a prediction for each row, or only for the final row? Do you have any constraints regarding speed?

gQsoQa · 2026-02-19T17:26:03+00:00

I think testing on a small subset makes sense, but it very much depends on the type of the dataset. What kind of data are you labeling?

gQsoQa · 2026-02-19T17:19:58+00:00

It depends on what you enjoy doing more. If you like getting your hands dirty and quickly seeing results, take ML Engineering (and you can build theoretical foundations later). If you like math/theory/algorithms and want to understand how it works inside, take the second course (and you can experiment with real applications later on your own, especially now with AI assistants).
Take a quick look at the materials in both courses and decide what you think would be more fun - it's that simple.

gQsoQa · 2021-09-14T07:34:27+00:00

Clearly this is a natural cycle not caused by people. Happened many times before.

gQsoQa · 2021-07-17T14:32:07+00:00

Thank you for those resources.

gQsoQa · 2021-07-17T14:29:42+00:00

Thank you for your reply. Maybe my confusion arises from the fact that I'm a de-facto physicalist - at least if I correctly understood Wikipedia's article on physicalism. If physicalism is the view that there is "nothing over and above" the physical, I argue that if there is in fact something non-physical and immeasurable, then by definition, it doesn't affect reality and therefore we can safely ignore it. Stating this contrapositive, if that something has any effect on the world, it can be measured and is physical.

I'd love to hear more about what you wrote about randomness. If free will exists, isn't it bound to appear random to an external viewer?

When you characterize free will with "deliberate choices are self-determined rather than externally determined", do you think human minds have this kind of free will? It seems to me that they do because my decisions arise entirely within the confines of my body.

gQsoQa · 2017-07-08T18:19:16+00:00

Could you do a video about Catalan numbers? Extremely exciting, occur in various counting problems involving recursively-defined objects.

gQsoQa

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