AI integrations · September 15, 202602
How AI Agents Work: Tools, Memory, and Planning
A practical breakdown of how AI agents plan multi-step tasks, call tools, and retain context - and how that differs from a single-shot AI assistant.
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Practical notes on MVP development, AI integrations, and improving existing products.
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6 notesAI integrations · September 13, 202603
Building a Production RAG Pipeline: A Practical Guide
A step-by-step guide to building a retrieval-augmented generation pipeline that survives production: chunking, hybrid retrieval, reranking, evaluation, and the mistakes that sink most RAG systems.
Read MoreAI integrations · September 13, 202604
What Is RAG (Retrieval-Augmented Generation)? How It Works
A clear, practical explanation of retrieval-augmented generation: how it grounds LLM answers in real data, the core pipeline, and when it's the right architecture choice.
Read MoreSeptember 12, 202605
Using AI Coding Assistants Without Losing Code Quality
Practical habits for using AI coding assistants without style drift, duplicated logic, or review fatigue - context setup, review discipline, and scoping requests well.
Read MoreSeptember 12, 202606
How AI Coding Assistants Actually Work
How AI coding assistants really work: context windows, retrieval, why they hallucinate, and how agentic tools plan and execute multi-file changes.
Read MoreSeptember 11, 202607
AI Workflow Automation: From Manual Tasks to Intelligent Pipelines
Build AI-powered workflows that automate complex, multi-step processes. Learn how to combine LLMs, prompt engineering, and workflow automation to handle tasks that previously required manual work-and maintain quality at scale.
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