Engineering Notes

Practical guides for AI, automation, and product engineering.

Field notes from systems I build: agents, n8n workflows, developer tools, micro-SaaS, and production architecture.

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BuildBuilding an AI Agent That Writes to Notion: Schema Design & API Logic

Avnish Yadav – February 21, 2026 · 7 MIN READ

Building an AI Agent That Writes to Notion: Schema Design & API Logic

Stop pasting LLM outputs manually. Learn how to architect a Notion database schema and build a Python-based agent that autonomously writes, formats, and updates status fields via the Notion API.

RetrospectivesWeek 2 Retro: Escaping the Hype Cycle & Systemizing the Build

Avnish Yadav – February 21, 2026 · 7 MIN READ

Week 2 Retro: Escaping the Hype Cycle & Systemizing the Build

The 'launch energy' has faded. Week 2 is where the real engineering begins. Here is the breakdown of the systems built, the retention data analyzed, and the automation workflows implemented to survive the grind.

EngineeringBuilding an Automated AI Daily Brief: My Morning Workflow

Avnish Yadav – February 21, 2026 · 7 MIN READ

Building an Automated AI Daily Brief: My Morning Workflow

Stop checking five different apps to start your day. Here’s how I built a Python-based system that fetches tasks, prioritizes them with GPT-4, and emails me a strategic briefing every morning at 7 AM. We cover API integration, prompt engineering for planning, and robust scheduling.

TutorialsBuilding an AI Inbox Triage Agent with n8n and OpenAI

Avnish Yadav – February 21, 2026 · 8 MIN READ

Building an AI Inbox Triage Agent with n8n and OpenAI

Stop using your inbox as a to-do list. Learn how to build an automated workflow in n8n that reads emails, classifies them using LLMs, summarizes context, and pushes actionable tasks to your project management tool.

EngineeringSystematizing Creativity: Building an Automated Content Repurposing Pipeline

Avnish Yadav – February 21, 2026 · 8 MIN READ

Systematizing Creativity: Building an Automated Content Repurposing Pipeline

Stop writing the same post three times. Here is a technical breakdown of how to build a content operations pipeline that transforms raw Notion inputs into platform-specific JSON payloads using AI agents.

EngineeringWeekly Retro #01: The Reality of Shipping AI Agents & Micro-SaaS

Avnish Yadav – February 21, 2026 · 7 MIN READ

Weekly Retro #01: The Reality of Shipping AI Agents & Micro-SaaS

A raw look at Week 1 of building in public. From shipping intelligent RAG agents to battling serverless timeouts, here is the breakdown of what worked, what broke, and the metrics behind the code.

AI DevelopmentFrom Chatbot to Agent: The Engineer's Guide to LLM Memory Systems

Avnish Yadav – February 21, 2026 · 7 MIN READ

From Chatbot to Agent: The Engineer's Guide to LLM Memory Systems

Stateless LLMs are just goldfish. To build true agents, you need persistence. We break down memory architectures, implementation patterns, and the hidden pitfalls of context poisoning.

EngineeringLangChain vs. LlamaIndex vs. Vanilla: The Builder's Stack Selection Matrix

Avnish Yadav – February 21, 2026 · 7 MIN READ

LangChain vs. LlamaIndex vs. Vanilla: The Builder's Stack Selection Matrix

Stop agonizing over the perfect LLM framework. A pragmatic guide comparing LangChain, LlamaIndex, and Vanilla implementations with decision charts and folder structures for shipping production AI apps.

TutorialsBuilding a Deep Research Agent: An End-to-End Walkthrough

Avnish Yadav – February 21, 2026 · 7 MIN READ

Building a Deep Research Agent: An End-to-End Walkthrough

LLMs are terrible researchers out of the box. In this technical walkthrough, I break down how to build an autonomous research agent that searches the web, verifies sources, and generates structured briefs without hallucinating.

AutomationEngineering a Personal Prompt System: Library, Versioning, and Automated Tests

Avnish Yadav – February 21, 2026 · 7 MIN READ

Engineering a Personal Prompt System: Library, Versioning, and Automated Tests

Stop guessing with your prompts. Learn how to build a centralized prompt library, version control your system instructions, and write a simple Python test harness to evaluate output quality scientifically.