▸ Category · AI
Notes on AI.
20 posts in this category.
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AIMachine learning vs generative AI
Machine Learning predicts; Generative AI creates. The discriminative-vs-generative split decides your architecture, infra, and monthly cloud bill.
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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.
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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.
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AIAI coding is like an addiction (in the best way)
A level-by-level map of the AI coding workflow: gateway prompts, codebase RAG, then multi-agent orchestration over MCP — and why manual coding now drags.
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AIStop reading every line of AI-generated code
Line-by-line review breaks on agent-sized diffs. How I moved to specs, property tests, and a multi-agent review pipeline — and the 3% I still read.
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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.
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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.
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AITraining vs inference: scaling AI systems
How training and inference differ in compute, cost, and hardware, and how to architect each phase so your AI app stays fast and affordable at scale.
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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.
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AIRAG vs fine-tuning: choosing your AI architecture
RAG vs fine-tuning for production LLMs — knowledge vs behavior, latency, cost, privacy, and the hybrid approach. How to pick the right AI architecture.
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AIRAG architectures: traditional, agentic, corrective
Compare traditional, agentic, and corrective RAG architectures, with the latency, cost, and accuracy trade-offs that decide which fits your AI app.
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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.
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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.
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AILLM vs AI agent: from prompts to action
The architectural shift from LLMs to autonomous AI agents. How memory, tool-use, and planning turn a stateless model into a system that takes action.
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AIContext window vs memory: building AI that remembers
Stop context-stuffing your LLM prompts. The difference between the context window and persistent memory — and how RAG + pgvector scale AI agents affordably.
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AIClaude MCP: connecting my dev tools to LLMs
The Model Context Protocol is a USB-C port for LLMs: one MCP server, any host, no MxN integration tax. The architecture, the servers I run, and why it is safe.
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AIMCP tool-use: building context-aware agents
Build context-aware agents with MCP. How tools, resources, and prompts let one server talk to any client — and kill brittle one-off integrations for good.
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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.
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AIPicking the right RAG stack: vector databases for AI
pgvector, Pinecone, Weaviate, Qdrant — a 2026 field guide to picking the right vector store for your AI app, with hybrid search and scaling tips.
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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.
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