Promotional banner for Galaxy Community Conference 2026 featuring an illustrated scene of Clermont-Ferrand with a volcano, cathedral spires, colorful buildings, and a hot air balloon. Text reads: “Galaxy Community Conference 2026. Clermont-Ferrand, France. June 22–24, 2026. Followed by CoFest: June 25–26, 2026. Early bird registration closes February 28th!”

⏳ Reminder: Early bird registration for GCC2026 closes February 28, 2026!

Join us in Clermont-Ferrand, France, June 22–24, followed by CoFest June 25–26.

Learn more and register now: galaxyproject.org/events/gcc20...

I’m reading an interesting paper while drinking a cup of hot cocoa made with organic cocoa powder from France. 🇫🇷

Reboot your system. When the computer starts up, you should see the Linux 6.18.0 option in the GRUB menu.

8- Check your CPU cores “nproc”
9- Compile the kernel (replace 4 with your CPU core count) using “make -j4”. It will take 1-2 hours depending on your hardware.
10- Once compilation finishes, install the modules and the kernel image: make modules_install && make install

7- Run these commands to prevent certification errors on Ubuntu/Debian:
scripts/config --disable CONFIG_SYSTEM_TRUSTED_KEYS
scripts/config --disable CONFIG_SYSTEM_REVOCATION_KEYS

6- Copy your current kernel configuration to ensure your hardware settings are preserved: “cp /boot/config-$(uname -r) .config”. To use your current linux kernel’s configurations, use this command “make olddefconfig”

5- You may need elevated permissions for the next steps so run “sudo su” command to prevent further errors.

3- Enter the directory usr/src via “cd /usr/src” and extract it using “sudo tar -xf linux-6.18.tar.xz”
4- Enter directory that contains the kernel source code via “cd linux-6.18”

1- Download the kernel from kernel.org
2- Move it to /usr/src using “sudo mv linux-6.18.tar.xz /usr/src”

Prerequisites: First, install the necessary build tools: “sudo apt update && sudo apt install build-essential libncurses-dev bison flex libssl-dev libelf-dev”

Linux Kernel 6.18, the latest stable version of the Linux kernel, has been released on Sunday, November 30, 2025. I’m compiling it on my Ubuntu machine to try it. Here is how you can compile and install it step by step.

I’ve implemented a multi-modal model that integrates histopathological images and gene expression profiles to classify breast cancer subtypes. The model leverages a Graph Mamba encoder for omics data and a Mamba-based image encoder built on a pretrained ConvNeXt model. github.com/ayyucedemirb...

Saved my Linux kernel configuration for future use via
“cp .config kernel-6.6-$(date +%Y%m%d).config”

github.com/ayyucedemirb...

Compiling the Linux kernel, pretraining my image feature extractor using contrastive learning (on a remote GPU via AnyDesk), and working on my paper.

Happy Star Trek day! Live long and prosper. 🖖🏻 #StarTrek #StarTrekDay

Training a multimodal model for 200 epochs. This should be done in about a week. Wish me luck. 🤞🏻

Instead of treating molecular features as nodes, I made patients the nodes. Now similar patients share information through graph connections, learning from each other's outcomes. Check out the implementation: github.com/ayyucedemirb...

I’ve implemented suffix arrays and suffix trees from scratch to find repeats, perform fast pattern matching, and generate the Burrows-Wheeler Transform. It also creates visualizations of the data structures. Check out the code on GitHub: github.com/ayyucedemirb...

Local alignment with Smith-Waterman algorithm

github.com/ayyucedemirb...

Global alignment with Needleman-Wunsch algorithm

github.com/ayyucedemirb...

This is how you can create a longest common subsequence graph using dynamic programming.

github.com/ayyucedemirb...

A GNN project to classify BRCA patient survival using multi-omics data (RNA-seq, CNV, mutations, proteins).

github.com/ayyucedemirb...

Now you can link your ORCID account to your GitHub account.

Predicting cancer subtypes using multi-omics data with Graph Convolutional Networks. I know the results are not good enough but I’m working on it. 🧬
GitHub repo: github.com/ayyucedemirb...

The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs

arxiv.org/pdf/2408.13296

You can use the method below to compute BLEU‑1 through BLEU‑4, METEOR, ROUGE‑1/2/L, and BERTScore metrics on your validation set at the end of each epoch.

If you are getting a warning that says, "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set padding_side='left' when initializing the tokenizer." you should add this to your code: processor.tokenizer.padding_side = 'left'.

A long time ago in a galaxy far, far away…
May the 4th be with you. ✨⚔️

Wow, that’s some amazing progress. An epoch takes an hour and the validation loss reduces by only 0.00022.

There has been a recurring issue with Kaggle notebooks for the past few days, the session freezes while training models. You don't even know which epoch this will happen in, and sometimes hours of training are lost because of this freeze.