denfromufa

I am working on strict subset of extremely fast nutty Python subset Pystachy, that also compiles itself via LLVM:

https://github.com/den-run-ai/pystachy

This is very cool, are all these command-line tools open-source?

If you can define this as a feature request to pdfmini, please submit it on github, e.g. drag-and-drop flow builder

My wife hit a wall trying to upload a hefty PDF - every “shrink” tool we tried barely compressed the size, and some even made it larger! Frustrated by the state of PDF compressors (looking at you, Adobe), I turned to LLMs - Claude, Deepseek, and Gemini came up short, but OpenAI’s o4-mini saved the day with a perfect solution. That inspired me to build pdfmini: a tiny, open‑source, client‑side HTML app that crushes PDF sizes right in your browser!!! No installs, no fees, zero privacy worries - all your data stays on your machine.

Try pdfmini now:

https://den-run-ai.github.io/pdfmini/

Source code for pdfmini:

https://github.com/den-run-ai/pdfmini

I would appreciate if stackoverflow integrated something like a REPL or replit in their Q&A to reproduce example easily (maybe even CI?). For Python it would actually be very easy with backends such as Google Colab or even built-in ChatGPT Code Interpreter.

The highlight of this event was running with Jeff at Rice University before his talk:

https://x.com/JeffDean/status/1756319820482592838?s=20

Can you please tell more about your ML stack?

Can you please tell more about the ML stack?

When are you expecting applied scientist position to open?

VirtualBuddy is still experimental

Does Rosetta still work in the virtualized MacOS in Parallels?

Does Rosetta still work in the virtualized MacOS when using Apple’s virtualization framework?

This looks like a better open-source option:

https://github.com/KhaosT/MacVM

Anyone using Parallels to virtualize MacOS on M1 Macs?

One of the most important things not mentioned in the article is sleep. Not enough sleep and your diet will change. Not enough sleep and your body will not finish recovering overnight. Read the book “Why we sleep”. It will change your life.

P.S. I do marathon training and glycogen storing as well as activating the fat burning are essential!

@jph00 where do I find lstm/gru/seq2seq layers for time-series sequence predictions (not text)? Also interested in autoencoder implementations. The fast.ai docs search does not really work for this. What do you think about other notable APIs built on top of pytorch such as Pyro and AllenNLP?

AGPL would be a restriction if you need to deploy this model on top of PlaidML in production. It is still very useful during the training time after which the neural network can be offloaded into production framework such as tensorflow.

The results are income do not reflect the location, which is really important in US.

is there open-source implementation on github?

Main features added to IPython:

- Show function signature in the terminal while completing.

- Assignments can trigger the display mechanism.

- IPdb can be called recursively.

- Support for system-wide configuration.

- Built-in support for Progress Bar.

More details here:

http://ipython.readthedocs.io/en/stable/whatsnew/version6.ht...

Main features added to Jupyter Notebooks:

- Internationalization.

- Shutdown server from command-line.

More details here:

https://medium.com/jupyter-blog/jupyter-notebook-5-1-0-ddc8b...

Will this work with CFFI?

Strange that no one has mentioned xonsh here:

http://xon.sh

I was really surprised how efficient numpy arrays were at finding all combinations of the pieces. The backtracking is not using recursion, but instead a stack. This makes it more verbose to code and read, but easier to understand. I would like to make this notebook more interactive with ipywidgets and Unicode characters, so stay tuned ;)

wow, this is insightful generalization

I have a jupyter notebook about a similar game "Penguins on Ice", solved with rotation of numpy arrays:

https://nbviewer.jupyter.org/gist/denfromufa/9a5e1fdeaf611dc...

If the deep learning network has enough layers, then can't it start incorporating "abstract" ideas common to any learning task? E.g. could we re-use some layers for image/speech recognition & NLP?

State of visualization in Python by Jake Vanderplas:

https://speakerdeck.com/jakevdp/pythons-visualization-landsc...

also worth mentioning here llvmlite & numba, peachpy:

http://llvmlite.pydata.org/en/latest

http://numba.pydata.org

https://github.com/Maratyszcza/PeachPy