▸ Tag · #ai
AI.
Applied AI engineering: shipping model-backed features that hold up in production, rather than demos that work once on a clean input.
-
AIMachine learning vs generative AI
Machine Learning predicts; Generative AI creates. The discriminative-vs-generative split decides your architecture, infra, and monthly cloud bill.
Read post →
-
ShopifyHow agentic commerce changes building on Shopify
Agentic commerce is reshaping how you build Shopify. The shift from human-centric themes to agent-ready infrastructure: Catalog, UCP, and MCP.
Read post →
-
AI7 mistakes wrecking your production RAG stack
Naive chunking, no reranker, embedding drift, latency blowups — the structural mistakes that wreck a production RAG stack, and the fixes that ship.
Read post →
-
AITrust is not a QA strategy: test AI code too
AI code is 95% syntactically clean and fails security tests 45% of the time. Half of agentic PRs ship no tests. The verification pipeline I run instead.
Read post →
-
ShopifyBuilding secure agentic commerce on Shopify in 2026
How agentic commerce on Shopify stays safe: UCP negotiation, an MCP governance layer, scoped credentials, signed requests, and human approval at checkout.
Read post →
-
ArchitectureMCP vs A2A vs ACP: the agent protocol stack
MCP connects agents to tools, A2A connects agents to each other, and ACP has folded into A2A. How the two-layer agent protocol stack fits together in 2026.
Read post →
-
AIVibe coding vs agentic engineering: which ships?
Vibe coding is for prototypes, agentic engineering is for production. The daily split I use to ship AI-written code without hidden technical debt.
Read post →
-
AIAI vs machine learning: an engineering deep dive
AI is the umbrella, machine learning is the engine. See how the distinction shapes your RAG pipelines, agentic systems, and Laravel or Shopify architecture.
Read post →
-
AIVector search vs graph search for RAG
Compare vector search and graph search for RAG. When to use embeddings via pgvector vs relationship-based knowledge graphs — and why GraphRAG often wins.
Read post →
-
AIPrompt engineering vs context engineering
The shift from instruction design to data infrastructure — how context engineering uses RAG and MCP to build robust, accurate AI systems.
Read post →
-
AIML engineer vs AI engineer
ML engineer vs AI engineer: who trains the model, who orchestrates the system, their diverging toolsets, and which role your AI roadmap actually needs.
Read post →
-
CareerData engineer vs data scientist: roles, tools, and overlap
Data engineer vs data scientist: who builds the pipelines, who builds the models, where the tools overlap, and which role your AI project needs first.
Read post →
-
ArchitectureAPI vs MCP: connecting AI systems
The core differences between traditional APIs and the Model Context Protocol (MCP) — and when to use each to build scalable, agentic AI systems.
Read post →
-
ShopifyShopify UCP quick-start: make your store agent-ready
AI agents are the new buyers, and they can't see your store without a UCP manifest. A quick-start to making your Shopify store agent-ready.
Read post →
-
ArchitectureCaching for speed: Redis and semantic layers in RAG
Stop paying for the same LLM call twice. Two-tier caching with Redis keys and RedisVL semantic lookups slashes RAG latency and trims your LLM API bill.
Read post →
-
ArchitectureSmart auto-scaling for modern AI apps
CPU auto-scaling fails GPU workloads. Why queue depth, KV-cache pressure, and TTFT beat CPU as triggers, plus the KEDA patterns to scale AI in time.
Read post →
-
ArchitectureGPU-aware load balancing for AI inference
Round-robin breaks when LLM requests span 50 to 50,000 tokens. GPU-aware load balancing with prefill/decode disaggregation and the metrics that cut P99.
Read post →
-
ArchitectureCircuit breakers: stopping vector DB failures
A slow vector DB kills SaaS faster than a dead one. The circuit-breaker pattern for AI infra — states, fallback tiers, and Laravel-friendly wiring.
Read post →
-
ArchitectureMessage queues for heavy-duty document processing
Stop running embeddings in the request cycle. Build a document pipeline on message queues with staged workers, retries, dead-letter queues, and autoscaling.
Read post →
-
ArchitectureAPI gateway: the front door of your AI stack
Stop exposing LLM providers to your frontend. The API gateway pattern for AI apps: tenant isolation, model aliases, rate limiting, and streaming-safe timeouts.
Read post →
-
AIWhy your RAG implementation is failing in production
Vector-only retrieval is the silent killer of production RAG. Hybrid search, BM25, rank fusion, re-rankers, and evals — the fixes that make it reliable.
Read post →
-
ArchitectureAI vs traditional development: which fits?
AI vs traditional development: when AI-assisted speed pays off, when traditional engineering is non-negotiable, and the hybrid workflow I favor.
Read post →
-
AIVibe coding: why projects need more than just logic
Taste, intent, and feel are the new senior-engineer superpowers in the Cursor and Claude era — and how to keep the codebase from becoming a ball of mud.
Read post →
- Career
Hello, world. Yes, another developer blog.
Why this developer blog exists, what I'll write about — Laravel, AI, Shopify — and why neobrutalism is the right call for an engineer's site in 2026.
Read post →