I'm open to any software or IT position. Furthermore, I graduated with a bachelor's degree in computer science in April 2023, and I'm currently based in Central Europe.
I'm skilled in C/C++, Python, and JavaScript. I also have experience with Rust, Scala 3, and the Play framework. Furthermore, I'm familiar with the Windows API (C/C++), SDL2, Qt 5, and Vue.js. Additionally, I have basic skills in Racket and Clojure. Also, I have a good enough mathematical understanding (linear algebra, 3D math etc.) and wrote basic software rasterizes and ray tracers. A simple neural network (learning XOR).
To avoid having gaps in my resume, I'm willing to work for free in remote positions. I prefer working without pay rather than being inactive any longer. If it needs to be on-site, then I will at least enough money to cover my basic needs (e.g., rent etc.).
So what are my options that can help me to avoid any further gaps in my resume? I am already in a bad position given my unemployment gap of 1 year.
Doing a master's or doing another degree is financially not feasible for me, for now.
I am running out of options, and based on the rejections so far, it seems like I will never find some kind of employment…
Any kind of tip on how to proceed from here, or even an offer is appreciated.
Picture this: you're stuck with your “potato computer” (small RAM, no external GPU, very large SSD), and your LLM is saved on an external SSD.
Your task: run that LLM on your “potato PC” and try to achieve reasonable response times (e.g., 1 h to 24 h). Response times of 1 year, or higher, might be impractical for most use cases.
And on a side note, how would you figure out the response times of a language model on low-end devices (e.g., Raspberry Pi, business laptops, MSP430)? Would you just assume some basic operations such as linear algebra operations as a given and estimate the number of steps from there?
I expect the usual suspects brought up in this discussion:
— Memory Mapped I/O aka treating an I/O device such as an SSD as if it were actual RAM (mmap). BTW: `mmap` makes our secondary storage somewhat akin to an infinite tape in a Turing machine
— “LLM in a flash: Efficient Large Language Model Inference with Limited Memory”, https://arxiv.org/html/2312.11514v2 (04 Jan 2024)
— SSD "wear and tear"
- Bari A., Algorithms, YouTube (playlist),
https://www.youtube.com/playlist?list=PLDN4rrl48XKpZkf03iYFl-O29szjTrs_O
- Frenkel E., UC Berkeley: MATH 53 - Multivariable Calculus with Edward Frenkel, YouTube (playlist) https://www.youtube.com/playlist?list=PLaLOVNqqD-2GcoO8CLvCbprz2J0_1uaoZ
- Hower V., Linear Algebra Full Course, YouTube (playlist), https://www.youtube.com/playlist?list=PLpcwHaLYiaEXW5fLNOlItPH4ATorKjBuc
- Lockhart P., Measurement
- Axler S., Linear Algebra, Springer, 2024, https://link.springer.com/content/pdf/10.1007/978-3-031-41026-0.pdf
- Ström et al., Immersive Linear Algebra, https://immersivemath.com/ila/index.html (online book)
- Margalit et al., Interactive Linear Algebra, https://textbooks.math.gatech.edu/ila/ (online textbook)
- Macdonald A., Linear and Geometric Algebra
- Macdonald A., Vector and Geometric Calculus
- Ramirez et al., From Vectors to Geometric Algebra, 2018, https://doi.org/10.48550/arXiv.1802.08153
- Hertzmann et al., Computer Graphics Lecture Notes, Computer Science Department - University Toronto, 2005, https://www.dgp.toronto.edu/~hertzman/418notes.pdf
Further, see Sergey Trail's "Linear Algebra Done Wrong" and watch Grant Sanderson, Easy Theory (theoretical CS), Jacob Sorber (operating systems), Graphics in 5 Minutes, Sam Buss (computer graphics), Dr. Daniel Page (theory of computation, data structures and algorithms), First Principles of Computer Vision on YouTube.Mr. Hertzmann has some more resources (that I haven't checked out yet):
https://www.dgp.toronto.edu/~hertzman/research.html