Piotr Płoński

Working on data science tools in MLJAR,

[Piotr Płoński @papapepek]

Python notebooks are great for exploration.
Styled web apps are great for sharing results.
With Mercury, you can have both.

Custom Mercury styles are available on GitHub:
https://t.co/PA6HkDFFtD

[Piotr Płoński @papapepek]

You will find Mercury github repo at https://t.co/cmgbKmjM7l

[Piotr Płoński @papapepek]

You can easily turn Python notebooks to beautiful web apps with Mercury

[Piotr Płoński @papapepek]

im working on tutorial how to create web app from machine learning model, code is ready https://t.co/eLwfGdhqj6

[Piotr Płoński @papapepek]

I've updated @MLJARofficial github page, we have so many open source projects to make data science easier 🤓

You can follow us on GitHub https://t.co/JhpHQWUzDV

#DataScience #Python @github #OpenSource

[Piotr Płoński @papapepek]

@ChinaNipona i love Disney

[Piotr Płoński @papapepek]

@bijanbowen all look similar

[Piotr Płoński @papapepek]

@ayesha_fatiima dont, linux better

[Piotr Płoński @papapepek]

@araseb_ i dont want to

[Piotr Płoński @papapepek]

@tanujDE3180 cpp

[Piotr Płoński @papapepek]

@kritikakodes tenem

[Piotr Płoński @papapepek]

@NumeraiCoE @numerai subscribed! looking forward to this episode

[Piotr Płoński @papapepek]

@ritu_twts dont

[Piotr Płoński @papapepek]

@ritu_twts if you are going to code better than AI then yes

[Piotr Płoński @papapepek]

Custom styles in Mercury https://t.co/cmgbKmjM7l

[Sebastian Raschka @rasbt]

https://t.co/L4s34f28oX

[Piotr Płoński @papapepek]

Tabular Foundation Models are hot these days, but they can't beat Decision Trees in terms of explainability and interpretation. Checkmate ♟️

I'm working on a new release for our package for interactive tree visualization in Python 🐍 🌳

https://t.co/oabaYdRqLq

[Piotr Płoński @papapepek]

Tabular Foundation Models are hot these days, but they can't beat Decision Trees in terms of explainability and interpretation. Checkmate ♟️

I'm working on a new release for our package for interactive tree visualization in Python 🐍 🌳

https://t.co/oabaYdRqLq

[OpenAI @OpenAI]

The proof came from a general-purpose reasoning model, not a system built specifically to solve math problems or this problem in particular, and represents an important milestone for the math and AI communities.

https://t.co/a0DKbCAWZI

[will depue @willdepue]

just quick napkin math on how long this took (unless i missed where they said):
the published CoT summary is 111,145 tokens long. it's really hard to say how much they summarized, assume 3x-20x reduction in tokens?
and i'm assuming this is gpt-5.6 pro, so taking Artifical Analysis' benchmark of 51ms tok/sec at 100k input for gpt 5.5. underestimate prob hard to say
this seems a bit low so going to multiply all of this by 2x
then this probably took anywhere between 5 hours to 32 hours. so like $120 - $1000 in gpt 5.5 pro tokens
whole point is not that long for a result of this magnitude!