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CODETRICKS

(c) 2025 Kilian Singer

These little light tutorials are ment to show important tricks with programming and tools to be efficient in STEM (science, technology, engineering and mathamatics). This includes how to use cutting edge ai tools for programming, Retrieval-Augmented Generation, LLM finetuning, OCR for even math, and more. Learn how to self-host a server from home and use most advanced commercial computer algebra systems for free.

Note

Instead of browsing these files through the web I recommend going through the first >part and then browsing it through visual studio code. As a starter you can download >the first two parts here: codetricks

Then open the first part and start your journey...

Tool setup

01 - Welcome

02 - Revision Control with GIT

03 - Computer Algebra

04 - My webpage

04b - My webpage on raspberry pi

05 - AI Coding

06 - Local AI Coding

07 - Advanved AI Coding agent

08 - Literature Research with AI

09 - Jupterlab Programming

Learning 4 Programming languages at the same time: C++ ,C , Python and Javascript

P1.0 Setup

P2.1 Datentypen-Zahlen

[P2.2 Character and Boolean](./jupyternotebooks/P2.2-Character and Boolean.html)

[P2.3 Constants, References and strings](./jupyternotebooks/P2.3-Constants, References and strings.html)

P2.4 Scope

P3.1.1 Operators

P3.1.2 Typecasting

[P3.2 Binary operators](./jupyternotebooks/P3.2-Binary operators.html)

[P3.3 Assignment operators and miscelanousoperators.html](./jupyternotebooks/P3.3-assignment operators and miscelanousoperators.html)

[P4.1 Loops and Jumps - Keyboard.html](./jupyternotebooks/P4.1-Loops and Jumps - Keyboard.html)

[P4.2-Conditions and Loops.html](./jupyternotebooks/P4.2-Conditions and Loops.html)

[P5 Functions](./jupyternotebooks/P5 Functions.html)

[P6 Classes](./jupyternotebooks/P6 Classes.html)

[P7.1 Containers Array](./jupyternotebooks/P7.1 Containers Array.html)

[P7.2 Containers Map](./jupyternotebooks/P7.2 Containers Map.html)

[P8 Memory Management](./jupyternotebooks/P8 Memory Management.html)

[P9 Usefull libraries](./jupyternotebooks/P9 Usefull libraries.html)

RootCPP.html

RootPython.html

Using our new skills

In these chapters we will use our new skills to finetune large language models to specific tasks. I will also show you how to finetune Optical-Character-Recognition such that it can read and convert your handwritten lecture notes including math formulas into markdown, even if you have a terrible handwriting. Also I found the first speech recognition software that can transcribe my lecture with all the mixture of german, physics, english. I will show you how to get aroundt the memory demand that the original code had by segmenting your task. These are currently all highly sought programming tasks in companies. So have a look and add it to your skill portfolio.

10 - Finetuning Large Language Models

11 - Cutting edge Optical-Character-Recognition

12 - World's best speech recognition for free

Useful links

If you want to dive into the basics of neural networks have a look here (Niesen also coauthored a book about Quantum Information Theory...) http://neuralnetworksanddeeplearning.com/

Tip

Choose a small programming project > that challanges you a bit. This is > a learning paradigm called learning the hard way. Which basically means that you learn best by trying a problem which is not too easy. Many years ago I needed to learn about compiler design, that's when I realized that there is no better way to learning all the nits and bits of a programming language and also value its design descissions and beauty by writing a compiler yourself.


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