csvtools by Ciro Santilli 40 Updated 2025-07-16
A fast version of a somewhat subset of csvkit, written in C.
Build failed with undefined reference to pcre_config on Ubuntu 23.04: github.com/DavyLandman/csvtools/issues/18
Unfortunately it is lacking some basic options, like optional header + selecting column by index on csvgrep (though csvcut has it). The project seems kind of dead.
Also unclear if it allows to filter + print only selected columns.
jq by Ciro Santilli 40 Updated 2025-07-16
Yet another awk-like domain-specific language to do things from the CLI in a ridiculously short humber of character? Oh yes.
Jacquard machine by Ciro Santilli 40 Updated 2025-07-16
Video 1.
A Jacquard loom in action by NationalMuseumsScotland (2016)
Source. The lack of audio is unsettling!
Ciro Santilli was contributing to this, when CommonMark left private mode and killed it, thus wasting many hours of Ciro's time.
CommonMark by Ciro Santilli 40 Updated 2025-07-16
CommonMark is a good project. But its initial release method was not very nice, they first developed everything behind closed doors with the big adopters like GitHub and Stack Overflow, and only later released the thing read, thus wasting the time of people who were working on alternative in the meanwhile, e.g. github.com/karlcow/markdown-testsuite which Ciro contributed to: Ciro Santilli's minor projects.
Let's run on this Imagenet10 subset called Imagenette.
First ensure that you get the dummy test data run working as per MLperf v2.1 ResNet.
Next, in the imagenette2 directory, first let's create a 224x224 scaled version of the inputs as required by the benchmark at mlcommons.org/en/inference-datacenter-21/:
#!/usr/bin/env bash
rm -rf val224x224
mkdir -p val224x224
for syndir in val/*: do
  syn="$(dirname $syndir)"
  for img in "$syndir"/*; do
    convert "$img" -resize 224x224 "val224x224/$syn/$(basename "$img")"
  done
done
and then let's create the val_map.txt file to match the format expected by MLPerf:
#!/usr/bin/env bash
wget https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
i=0
rm -f val_map.txt
while IFS="" read -r p || [ -n "$p" ]; do
  synset="$(printf '%s\n' "$p" | cut -d ' ' -f1)"
  if [ -d "val224x224/$synset" ]; then
    for f in "val224x224/$synset/"*; do
      echo "$f $i" >> val_map.txt
    done
  fi
  i=$((i + 1))
done < <( sort map_clsloc.txt )
then back on the mlperf directory we download our model:
wget https://zenodo.org/record/4735647/files/resnet50_v1.onnx
and finally run!
DATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 cpu --accuracy
which gives on P51:
TestScenario.SingleStream qps=164.06, mean=0.0267, time=23.924, acc=87.134%, queries=3925, tiles=50.0:0.0264,80.0:0.0275,90.0:0.0287,95.0:0.0306,99.0:0.0401,99.9:0.0464
where qps presumably means "querries per second". And the time results:
446.78user 33.97system 2:47.51elapsed 286%CPU (0avgtext+0avgdata 964728maxresident)k
The time=23.924 is much smaller than the time executable because of some lengthy pre-loading (TODO not sure what that means) that gets done every time:
INFO:imagenet:loaded 3925 images, cache=0, took=52.6sec
INFO:main:starting TestScenario.SingleStream
Let's try on the GPU now:
DATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 gpu --accuracy
which gives:
TestScenario.SingleStream qps=130.91, mean=0.0287, time=29.983, acc=90.395%, queries=3925, tiles=50.0:0.0265,80.0:0.0285,90.0:0.0405,95.0:0.0425,99.0:0.0490,99.9:0.0512
455.00user 4.96system 1:59.43elapsed 385%CPU (0avgtext+0avgdata 975080maxresident)k
TODO lower qps on GPU!
OpenAI Gym by Ciro Santilli 40 Updated 2025-07-16
Development ceased in 2021 and was taken up by a not-for-profit as Farama Gymnasium.

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
  2. local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:
    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 2.
    You can publish local OurBigBook lightweight markup files to either https://OurBigBook.com or as a static website
    .
    Figure 3.
    Visual Studio Code extension installation
    .
    Figure 4.
    Visual Studio Code extension tree navigation
    .
    Figure 5.
    Web editor
    . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
    Video 4.
    OurBigBook Visual Studio Code extension editing and navigation demo
    . Source.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
  4. Infinitely deep tables of contents:
    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
    Descendant pages can also show up as toplevel e.g.: ourbigbook.com/cirosantilli/chordate-subclade
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact