Principles

Reusable engineering rules and heuristics from experience.

These are foundational, evergreen concepts that transcend specific languages or frameworks. They are useful for applying established heuristics to break ties on ambiguous engineering choices; providing a shared vocabulary and justification for requested changes during code reviews; and solidifying the underlying rules of distributed systems, resilience, and software architecture.

Algorithms & Performance — algorithms , performance
Principles for clear algorithms and effective performance engineering; utilizing strict micro-benchmarking, careful memory allocation discipline, avoiding cache misses, and deterministic random generators.
Compiler Design — compiler
Guidelines for robust compiler design and implementation; covering clear Intermediate Representation (IR) boundaries, enforcing deterministic semantics, generating actionable error reports, and optimizing for incremental compilation.
Data Pipelines — data-pipelines , etl , streaming
Architectural principles for reliable batch and streaming data pipelines; focusing on strict time semantics, exactly-once processing, optimal partitioning, observability, and reproducible states.
Extensibility & Plugin Architecture — extensibility
Guidelines for architecting modular systems with secure plugin boundaries, stable API contracts, and robust cross-language FFI bindings.
Intervals & Constraints — analytics , data-pipelines , streaming
A framework for balancing Latency (system Completeness) against Verification (data Integrity) by effectively choosing between Speculative execution and Pessimistic consensus intervals.
Media Analysis — feature-extraction , media
Best practices for resilient media feature extraction pipelines; ensuring stable representation schemas, choosing between streaming and batch modes, enforcing metadata preservation, and performance engineering.
Migration & Deduplication — deduplication
Strategic practices for safe, idempotent, and efficient large-scale data migrations; incorporating reliable deduplication heuristics, checksum-based integrity validation, and seamless fallbacks.
ML Experiments — ml
Guidance for reproducible, resource-aware machine learning experiments; leveraging lightweight MLOps primitives, strict environment versioning, seed management, and rigorous, deterministic model evaluation protocols.
Model Serving & Inference — ml , monitoring
Principles for highly available production ML inference; utilizing immutable model registries, dynamic request micro-batching, safe canary rollouts, graceful fallback degradation, and efficient GPU memory management.
Monitoring & Observability — monitoring
Best practices for establishing robust observability using RED/USE metrics, contextual structured logging, distributed tracing, actionable alerting, and SLO-driven reliability engineering.
Networking & Services — networking
Operational guidance for resilient networked services; detailing robust API contracts, exponential retries with jitter, sliding-window rate limiting, strict timeout budgets, connection pooling, and mutual TLS.
Privacy & Agents — privacy
Privacy-first design rules for autonomous AI agents; establishing local-first execution defaults, strict data minimization and redaction, explicit user consent flows, and transparent, auditable action logs.
Retrieval & RAG — retrieval
Operational principles for robust search retrieval and RAG pipelines; focusing on hybrid lexical-semantic retrieval techniques, long-term embedding model stability, automated ranking evaluation, and privacy-aware indexing.
Service Resilience — distributed-systems , networking , queues
Design patterns for reliable microservice behavior under load; implementing strict request idempotency, non-blocking async I/O, robust circuit breakers, durable background queues, and observability.
SQL vs. NoSQL — database
A decision-making framework for choosing between SQL (relational, strict ACID) and NoSQL (distributed, flexible schemas) databases, evaluating data structures, horizontal scalability needs, and tunable consistency requirements.
Web App — extensibility
Guiding principles for production-ready web apps using 12-factor methodologies; encompassing stateless scaling, durable media handling, robust background processing, CI/CD, and deep observability.