Data Structures & Algorithms
Writing code that doesn't crash at scale. From Big-O notation to optimizing memory usage for high-throughput data processing systems.
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Journal
Field Notes for Future Builders
This living journal organizes the skills, infrastructure, and practices required to ship reliable AI systems. Use these tracks as a blueprint for building the future with nirmata.dev.
Cloud Computing
The environment where AI lives. Mastering AWS/Azure core services, serverless architectures, and designing cost-effective compute clusters.
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DevOps
The automation factory. Containerization (Docker), Orchestration (Kubernetes), and CI/CD pipelines to deploy code reliably across environments.
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GPU & CUDA
Hardware acceleration deep dive. Understanding memory bandwidth, kernel optimization, and parallel computing to speed up training and inference.
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Data Engineering
The plumbing of the stack. Designing robust pipelines (ETL/ELT), modeling warehouses (Snowflake/BigQuery), and handling streaming data (Kafka) with ACID guarantees.
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Data Science
The scientific method applied to business. Statistical analysis, A/B testing strategies, and causal inference to make data-driven product decisions.
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Mathematics of AI
The theory behind the magic. Linear Algebra, Calculus, and Probability concepts required to understand optimization landscapes and debug model convergence.
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Machine Learning
Classical predictive modeling. Mastering tabular data techniques (XGBoost, Random Forest), feature engineering, and solving classification/regression problems.
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Deep Learning
Perceptual intelligence. Building Neural Networks from scratch, understanding Backpropagation, and mastering architectures for Vision (CNNs) and Sequences (RNNs).
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Reinforcement Learning
Agent-based learning. Designing environments, reward functions, and policies for systems that must learn optimal strategies through trial and error.
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Generative AI
The creation engine. Understanding Transformers (Attention mechanisms), Diffusion models, and fine-tuning LLMs to synthesize text, code, and images.
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MLOps
Managing the model lifecycle. Feature stores, model registries, drift detection, and automated retraining pipelines to ensure reproducibility.
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AI Engineering
Building the end-user application. RAG systems, Vector Databases, prompt engineering frameworks, and managing the latency/reliability of non-deterministic models.
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Want the first read?
We're publishing soon. Drop a note at rishav@nirmata.dev and we'll share early access.