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Software architecture, data systems, AI, reliability, and the decisions that come up in real engineering work.
The hard part of an integration is not the API. It is the inconsistent names, missing fields, and different versions of the same idea, and the patterns that keep a product adaptable instead of fragile.
data / architecture / integrations / software engineeringChoosing a database for analytics is a workload decision, not a popularity contest. Here is when Postgres and its extensions are enough, and the point where ClickHouse genuinely earns its place.
postgres / database / clickhouse / architectureMCP and LLM tooling make delivery, reporting, and testing more continuous. That changes what teams should optimize for.
ai / llms / software deliveryAdding an index speeds up reads. It also slows down writes, increases storage, and adds a maintenance burden that compounds over time.
postgres / database / architecture / software engineeringReal time sounds like an obvious improvement until you price it. The useful question is not how fast the data can arrive, it is how often anyone actually acts on it.
data / architecture / product engineering / software engineeringMost RAG systems fail at retrieval, not generation. The fix is usually better evidence, ranking, and product logic.
ai / llms / architecture / software engineeringZero-downtime deployment depends on overlapping versions, safe traffic shifts, and database changes that work before and after release.
devops / architecture / database / software engineeringUnit tests tell you when code breaks. Evals tell you if your LLM is still answering the right question. These are different problems, and treating them the same way is how AI products degrade silently.
ai / llms / software engineering / braintrustEvery retry, webhook, and payment system depends on idempotency. Most engineers treat it as an afterthought. Here is what happens when they do, and what it actually takes to build operations that are safe to repeat.
architecture / distributed systems / software engineeringMost teams reach for Kafka too early. Here is how to build a production event bus with Celery and Redis, the patterns that make it reliable, and the four conditions where Kafka actually earns its place.
architecture / kafka / software engineering / redisHow to give every pull request its own live URL using Docker Swarm, Traefik, and a wildcard DNS record, without running a Kubernetes cluster.
docker / devops / github-actionsETL has three letters. Most people can explain two of them. The third is where teams make decisions that shape who owns data, when it is trustworthy, and how hard it is to change.
data / architecture / engineering / software engineeringQueues move work forward. Streams preserve a history consumers can read from. That difference changes ownership, replay, and failure handling.
architecture / kafka / redis / software engineeringLLMs are not magic bullets. They are powerful translation engines for messy data, especially when you use them to summarize, structure, and move information between systems.
ai / llms / data / software engineeringPrompt engineering focuses on the question. Context engineering focuses on the world you hand the model before the question. That distinction changes how you design reliable LLM systems.
ai / llms / software engineering / platform engineeringAI can generate working software quickly, but AI scalability depends on architecture, constraints, and disciplined engineering after the proof of concept.
architecture / scalability / ai / software engineeringA migration that passes in staging can lock a busy production table for minutes. The difference is not the migration. It is the assumptions behind it.
postgres / database / architecture / software engineeringSearch sounds like one feature, but it is really a bundle of product decisions, customer expectations, matching strategies, ranking tradeoffs, and business context.
search / postgres / product engineering / software engineeringAdding more logs does not make a system observable. Observability is the ability to ask questions about a running system that nobody anticipated when the system was built.
observability / platform engineering / software engineeringAreas of expertise
Start with a subject, then follow the related articles through the decisions behind it.
Practical writing on software architecture, distributed systems, messaging, scalability, and the tradeoffs behind reliable system design.
View topicGuides to data pipelines, PostgreSQL, search, indexing, migrations, and the engineering decisions that make data systems trustworthy.
View topicEngineering-focused writing on LLM systems, context, evaluation, AI delivery, and using models as reliable parts of larger products.
View topicPractical platform engineering on observability, deployments, preview environments, delivery workflows, and operating software reliably.
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