Ciro Santilli (@cirosantilli) - Newly followed articles

Many IDEs are also implementing this now (e.g. VS Code, Eclipse. Most free IDE GIt implementations are still crap, but that is the future, because you want to edit, view history, edit, view history, commit, edit.

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Git tips / git log --graph by

Ciro Santilli 37 Updated 2025-07-16

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For the strong.

git log --abbrev-commit --decorate --graph --pretty=oneline master HEAD

Output:

* b4ec057 (master) 5
* 0b37c1b 4
| * fbfbfe8 (HEAD -> my-feature) 7
| * 7b0f59d 6
|/
* 661cfab 3
* 6d748a9 2
* c5f8a2c 1

If we also add the --simplify-by-decoration, which you very often want want on a real repository with many commits:

* b4ec057 (master) 5
| * fbfbfe8 (HEAD -> my-feature) 7
|/
* c5f8a2c 1

As we can see, this removes any commit that is neither:

under a branch or tag
at the intersection of too branches or tags

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Git tips / git rebase moves commits one by one by

Ciro Santilli 37 Updated 2025-07-16

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In order to solve conflicts, you just have to understand what commit you are trying to move where.

E.g. if from:

5 master
|
4 7 my-feature HEAD
| |
3 6
|/
2
|
1

we do:

git rebase master

what happens step by step is first 6 is moved on top of 5:

6on5 HEAD
|
5 master
|
4                 7 my-feature
|                 |
3                 6
|                 |
2-----------------+
|
1

and then 7 is moved on top of the new 6:

7on5 HEAD
|
6on5
|
5 master
|
4                 7 my-feature
|                 |
3                 6
|                 |
2-----------------+
|
1

All good? so OK, let's move the my-feature to the new 7:

7on5 my-feature HEAD
|
6on5
|
5 master
|
4
|
3
|
2
|
1

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Git tips / The key to solve conflicts: see the two conflicting diffs by

Ciro Santilli 37 Updated 2025-07-16

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The key to solve conflicts is:

You have to understand what are the two commits that touched a given line (one from master, one from features), and then combine them somehow.

Or in other words, at every rebase conflict we have something like:

master-commit    feature-commit
|                |
|                |
base-commit------+
|
|

Therefore there are 2 diffs that you have to understand and reconcile:

base-commit to master-commit
base-commit to feature-commit

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Git tips / Conflict resolution tool by

Ciro Santilli 37 Updated 2025-07-16

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gnuplot command line hello world by

Ciro Santilli 37 Updated 2025-07-16

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askubuntu.com/questions/277363/gnuplot-not-showing-the-graph-window/683073#683073

CLI hello world:

gnuplot -p -e 'p sin(x)'

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Julian Togelius by

Ciro Santilli 37 Updated 2025-07-16

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https://web.archive.org/web/20241005224059im_/https://engineering.nyu.edu/sites/default/files/styles/square_large_620_2x/public/2019-05/julian-togelius.png?h=6a0cab5b&itok=HKFEZIB_

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Pipeline stall by

Ciro Santilli 37 Updated 2025-07-16

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Brodmann area by

Ciro Santilli 37 Updated 2025-07-16

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Allen Adult Human Brain by

Ciro Santilli 37 Updated 2025-07-16

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atlas.brain-map.org/atlas?atlas=265297126

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y86.js.org by

Ciro Santilli 37 Updated 2025-07-16

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The good:

slick UI! But very hard to read characters, they're way too small.
attempts to show state diffs with a flash. But it goes by too fast, would be better if it were more permanent
Reverse debugging

The bad:

educational ISA
unclear what flags mean from UI, no explanation on hover. Likely the authors assume knowledge of the Y86 book.

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WebRISC-V by

Ciro Santilli 37 Updated 2025-07-16

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webriscv.dii.unisi.it/

The good:

The bad:

Clunky UI
circuit diagram doesn't show any state??

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Phaser.js by

Ciro Santilli 37 Updated 2025-07-16

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Likely the best JavaScript 2D game engine as of 2023.

github.com/photonstorm/phaser
phaser.io/
phaser.io/examples/v3.85.0/games contains the demo games

Uses Matter.js as a physics engine if enabled. There's also an alternative (in-house?) "arcade" engine: photonstorm.github.io/phaser3-docs/Phaser.Physics.Arcade.ArcadePhysics.html but it appears to be simpler/less robust (but also possibly faster).

TODO any 2D first person examples a bit like Ciro's 2D reinforcement learning games?

The examples are present under:

git clone https://github.com/photonstorm/phaser3-examples

but note that that repo is huge, about 4.5 GiB on local disk, as is has tons of assets.

The demos also include a Monaco-editor based sandbox mode where you can edit code directly on the web and see the game update which is a really sweet addition.

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Matter.js by

Ciro Santilli 37 Updated 2025-07-16

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To run the demos locally, tested on Ubuntu 22.10:

git clone https://github.com/liabru/matter-js
cd matter-js
git checkout 0.19.0
npm install
npm run dev

and this opens up the demos on the browser.

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activatedgeek/LeNet-5 by

Ciro Santilli 37 Updated 2025-07-16

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github.com/activatedgeek/LeNet-5

This repository contains a very clean minimal PyTorch implementation of LeNet-5 for MNIST.

It trains the LeNet-5 neural network on the MNIST dataset from scratch, and afterwards you can give it newly hand-written digits 0 to 9 and it will hopefully recognize the digit for you.

Ciro Santilli created a small fork of this repo at lenet adding better automation for:

extracting MNIST images as PNG
ONNX CLI inference taking any image files as input
a Python tkinter GUI that lets you draw and see inference live
running on GPU

Install on Ubuntu 24.10 with:

sudo apt install protobuf-compiler
git clone https://github.com/activatedgeek/LeNet-5
cd LeNet-5
git checkout 95b55a838f9d90536fd3b303cede12cf8b5da47f
virtualenv -p python3 .venv
. .venv/bin/activate
pip install \
  Pillow==6.2.0 \
  numpy==1.24.2 \
  onnx==1.13.1 \
  torch==2.0.0 \
  torchvision==0.15.1 \
  visdom==0.2.4 \
;

We use our own pip install because their requirements.txt uses >= instead of == making it random if things will work or not.

On Ubuntu 22.10 it was instead:

pip install
  Pillow==6.2.0 \
  numpy==1.26.4 \
  onnx==1.17.0 torch==2.6.0 \
  torchvision==0.21.0 \
  visdom==0.2.4 \
;

Then run with:

python run.py

This script:

does a fixed 15 epochs on the training data
it then uses the trained net from memory to check accuracy with the test data
then it also produces a lenet.onnx ONNX file which contains the trained network, nice!

It throws a billion exceptions because we didn't start the Visdom server, but everything works nevertheless, we just don't get a visualization of the training.

The terminal outputs lines such as:

Train - Epoch 1, Batch: 0, Loss: 2.311587
Train - Epoch 1, Batch: 10, Loss: 2.067062
Train - Epoch 1, Batch: 20, Loss: 0.959845
...
Train - Epoch 1, Batch: 230, Loss: 0.071796
Test Avg. Loss: 0.000112, Accuracy: 0.967500
...
Train - Epoch 15, Batch: 230, Loss: 0.010040
Test Avg. Loss: 0.000038, Accuracy: 0.989300

And the runtime on Ubuntu 22.10, P51 was:

real    2m10.262s
user    11m9.771s
sys     0m26.368s

One of the benefits of the ONNX output is that we can nicely visualize the neural network on Netron:

Figure 1.
Netron visualization of the activatedgeek/LeNet-5 ONNX output
. From this we can see the bifurcation on the computational graph as done in the code at:
`output = self.c1(img) x = self.c2_1(output) output = self.c2_2(output) output += x output = self.c3(output)`
This doesn't seem to conform to the original LeNet-5 however?

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Ciro Santilli @cirosantilli 37