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18 / JOURNAL CLUB / MACHINE LEARNING / ARXIV / LANGUAGE MODELS
Breaking Free from Context Limits: Recursive Language Models Explained

BREAKING FREE FROM CONTEXT LIMITS: RECURSIVE LANGUAGE MODELS EXPLAINED

"This entry is a summary of the paper "Recursive Language Models" by Zhang, Kraska & Khattab"
17 / DOCKER / PROTEIN FOLDING / BIOINFORMATICS
Boltz-1x: A Comprehensive Guide to Next-Generation Protein Structure Prediction Using Boltzmann-Inspired Deep Learning

BOLTZ-1X: A COMPREHENSIVE GUIDE TO NEXT-GENERATION PROTEIN STRUCTURE PREDICTION USING BOLTZMANN-INSPIRED DEEP LEARNING

This technical blog provides a complete tutorial for implementing Boltz-1x, a novel protein structure prediction model that combines Boltzmann-inspired architecture with modern deep learning, including Docker setup instructions and a practical demonstration predicting the GSK3A-FRAT1 protein complex with high accuracy (0.71 Å RMSD).
16 / CLOUD / GCP / MACHINE LEARNING
Scaling Your ML Training with Vertex AI Custom Jobs on GCP

SCALING YOUR ML TRAINING WITH VERTEX AI CUSTOM JOBS ON GCP

This blog is a step-by-step guide to scaling machine learning training with Vertex AI Custom Jobs on Google Cloud, covering Docker image creation, data upload, job submission, and GPU optimization for efficient cloud-based workflows.
15 / DOCKER / MACHINE LEARNING
Reproducibility in ML with Docker

REPRODUCIBILITY IN ML WITH DOCKER

Learn how to use Docker to ensure reproducibility in machine learning projects, from local development to production deployment.
14 / DOCKER / BIOINFORMATICS / GENOMICS
FastQC-RS: Quality Control for Omics Data

FASTQC-RS: QUALITY CONTROL FOR OMICS DATA

FastQC-RS is a modern, Rust-based tool for fast and efficient quality control of FASTQ files, delivering lightweight performance and detailed HTML reports—perfect for ensuring high-quality omics data in genomics and transcriptomics workflows. This guide walks you through Docker-based setup, usage, and key features.
13 / BIOINFORMATICS / DOCKER / GENOMICS
Speeding Up FASTQ Preprocessing with FastP

SPEEDING UP FASTQ PREPROCESSING WITH FASTP

FastP is an ultra-fast, all-in-one tool for trimming, filtering, and quality-checking FASTQ files, helping you quickly generate clean, high-quality datasets for genomics and transcriptomics projects. This guide walks you through installation, usage, and key features of FastP, making it an essential part of your NGS workflow.
12 / DOCKER / BIOINFORMATICS / GENOMICS
Dragen-GATK: High-Performance Variant Calling

DRAGEN-GATK: HIGH-PERFORMANCE VARIANT CALLING

Dragen-GATK combines Illumina’s hardware acceleration with GATK’s best-practice workflows to deliver ultra-fast, clinically robust germline variant calling. This guide walks you through Docker-based setup, sample analysis, and key parameters to optimize high-performance variant discovery in genomics projects.
11 / DOCKER / BIOINFORMATICS / GENOMICS
NanoPlot: Quality Control for Nanopore Sequencing Data

NANOPLOT: QUALITY CONTROL FOR NANOPORE SEQUENCING DATA

COMING SOON
10 / DOCKER / BIOINFORMATICS / GENOMICS
Funannotate: A Tool for Genome Annotation

FUNANNOTATE: A TOOL FOR GENOME ANNOTATION

COMING SOON
09 / DOCKER / PROTEIN FOLDING / BIOINFORMATICS
ESM2: Setting Up Evolutionary Scale Modeling with Docker

ESM2: SETTING UP EVOLUTIONARY SCALE MODELING WITH DOCKER

COMING SOON
08 / DOCKER / BIOINFORMATICS / GENOMICS
FastPLong: Quality Control for Long Read Sequencing Data

FASTPLONG: QUALITY CONTROL FOR LONG READ SEQUENCING DATA

COMING SOON
07 / DOCKER / BIOINFORMATICS / GENOMICS
FastQC: Quality Control for Omics Data

FASTQC: QUALITY CONTROL FOR OMICS DATA

COMING SOON
06 / DOCKER / WEBDEVELOPMENT / HTML
Web Development with FastHTML

WEB DEVELOPMENT WITH FASTHTML

COMING SOON
05 / DOCKER / BIOINFORMATICS / GENOMICS
Flye: Assembling Long Reads with Ease

FLYE: ASSEMBLING LONG READS WITH EASE

COMING SOON
04 / DOCKER / PROTEIN FOLDING / BIOINFORMATICS
RFDiffusion: Protein Design with Diffusion Models

RFDIFFUSION: PROTEIN DESIGN WITH DIFFUSION MODELS

COMING SOON
03 / MACHINE LEARNING / DEEP-LEARNING / STATE-SPACE-MODELS
Forecasting Bitcoin with Mamba State Space Models

FORECASTING BITCOIN WITH MAMBA STATE SPACE MODELS

An intuitive guide to forecasting minute-by-minute Bitcoin OHLCV data using the fast, memory-efficient Mamba State Space Model—from Docker setup and minimal preprocessing to PyTorch Lightning training and comparison against Transformer baselines.
02 / MACHINE LEARNING / DEEP-LEARNING / FLASHATTENTION
FlashAttention: Accelerating Deep Learning with Docker

FLASHATTENTION: ACCELERATING DEEP LEARNING WITH DOCKER

A concise, step-by-step demo of how to containerize FlashAttention and train a simple autoregressive Transformer on minimally preprocessed Bitcoin minute-by-minute data.
01 / JOURNAL CLUB / MACHINE LEARNING / NEURIPS / LANGUAGE MODELS
Small Batch Training for Language Models: Why Simple SGD Works

SMALL BATCH TRAINING FOR LANGUAGE MODELS: WHY SIMPLE SGD WORKS

"This journal club blog reviews the paper "Small Batch Training for Language Models: Why Simple SGD Works" by Marek et al."