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Notes on software, mathematics, and machine learning.
Google and Kaggle Gen AI study notes — the opening foundations
An opening-reading notebook, not a completed five-day course: distinguish recurrence, attention, autoregressive generation, prompting, retrieval and fine-tuning.
Apache Airflow - open source orchestration engine
Design retry-safe Airflow tasks without mistaking DAG ordering for a database transaction. A source-bounded lesson with worked reasoning, failure analysis and explicit runtime limitations.
Applied LLMs — origins, evidence and applications
A readable route through LLM foundations, attention, retrieval, prompting, audio, clinical NLP, statistical learning and deployment responsibilities.
Variant contracts: bounded budgets, interval cuts and subset routes
Extend the restored problem-family series with bounded knapsack, two resource budgets, interval reconstruction and Held–Karp routes, using independent tiny oracles.
Data Infrastructure for AI and Experimentation at Scale
Trace events, time-valid features, recommendation decisions and experiment outcomes through a source-reviewed data platform with executable SQL and statistical fixtures.
Data Platform & Orchestration
A data platform is a chain of independently testable contracts. A source-bounded lesson with worked reasoning, failure analysis and explicit runtime limitations.
Deep Learning and LLMs: an inspectable learning laboratory
A broad deep-learning and LLM progression with a verified NumPy spine, advanced framework and systems lessons, source evidence and explicit execution boundaries.
Recommendation quality starts before ranking
An executable retrieve-score-rerank pipeline that exposes candidate recall and diversity trade-offs.
Designing for Scale · Requirements to Architecture
Convert prompts into testable requirements, capacity constraints and failure contracts through four worked designs and a traceable review method.
Capacity estimates that survive an overload question
Derive traffic, storage, bandwidth, working set and backlog with explicit units, sensitivity analysis and exact retained laboratory programs.
Designing for Scale · The Storage Decision Tree
Storage selection has several independent axes. A source-bounded lesson with worked reasoning, failure analysis and explicit runtime limitations.
Designing for Scale · CAP and the Consistency Spectrum
CAP is a history argument, not a database label. A source-bounded lesson with worked reasoning, failure analysis and explicit runtime limitations.
Learning paths
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