Machine Learning and LLM Systems Design Interview Foundations
Audit machine learning systems design and LLM engineering interview patterns—from 3D parallelisms and PagedAttention serving to RAG and agent evaluation.
Fodev JEO
LATEST INVESTIGATIONS
Source audits & field notes
Hugging Face Agents 2.0 and AgentGen: Automated Planning and Code-Action Frameworks
A source audit analyzing Hugging Face Agents 2.0 and AgentGen, evaluating code-based action primitives, bidirectional planning evolution, and GAIA benchmarks.
Essential Statistical Foundations for LLM Evaluation and AI Systems
Examine essential statistical foundations—from bootstrap confidence intervals and Simpson’s paradox to Bayesian inference—vital for rigorous AI evaluation.
Database Scaling Architecture: From Read Replicas to Consistent Sharding
An architectural audit of database scaling strategies, evaluating indexing, WAL replication, consistent hashing sharding, and storage-compute separation.
Open-Source Data Engineering Architecture for Modern AI Systems
Audit open-source data engineering architectures—from Apache Iceberg lakehouses and Arrow engines to real-time vector ETL pipelines for AI workflows.
Architecting Production ML Inference: From Microservices to vLLM Clusters
A source audit of production ML serving architectures, comparing classical batch and REST microservices with continuous batching and disaggregated LLM serving.
Agentic Design Patterns: Reflection, Tool Use, Planning, and Multi-Agent Systems
A source audit analyzing foundational agentic design patterns, evaluating reflection loops, tool integration, iterative planning, and multi-agent systems.
Federated Learning Architecture: FedAvg, SecAgg, and Differential Privacy
An architectural audit of federated learning, detailing FedAvg decentralized optimization, non-IID client drift, Secure Aggregation, and Federated LoRA fine-tuning.
Agentic RAG with LlamaIndex Workflows: Event-Driven Retrieval and Qdrant Indexing
A source audit of LlamaIndex Workflows, evaluating event-driven agentic orchestration, Qdrant vector indexing, and self-correcting retrieval pipelines.
LazyLLM Framework: Low-Code Multi-Agent Pipelines and Component Abstractions
A source audit analyzing LazyLLM, evaluating low-code multi-agent orchestration, dataflow pipeline abstractions, grid-search tuning, and deployment runtimes.
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