Notes on building AI that actually works.
RAG and semantic search, LLM integrations, agent pipelines and the backends underneath — practical posts written while shipping.
I build what I write about — RAG and semantic search, AI integrations and agent pipelines, on backends in Go and Python.
Free · no commitment · I read every request myselfTransforming Text into Vectors: OpenAI Embeddings in Python
Learn how to generate text embeddings with the OpenAI API in Python to power semantic search, recommendations, and more. Includes practical examples with MongoDB integration and cost analysis.
#openai#ai#python#mongodbRead the post
AI integrations
AI integrations
Infrastructure
InfrastructureThree topics. Also three things I build for clients.
Every topic has posts to read and a free quote if you want it built for your data and systems.
RAG & semantic search
Search and assistants that answer from your own documents — with sources, and a handoff when they’re not sure.
- Embeddings & vector search
- Hybrid keyword + meaning search
- Assistants with citations
AI integrations
LLMs wired into the systems you already run — tickets, CRM, catalog, inbox — with costs and quality you can see.
- Summaries, drafts, classification
- Translations and content generation
- APIs, webhooks, your own model
Agent pipelines
Multi-step agents as inspectable workflows — they plan, act and check, and a person approves anything that matters.
- Workflows you can open and trace
- Human approval steps
- Event-driven, retry-safe backends
One engineer. I write about what I build.
Software engineer working on RAG, AI integrations and agent pipelines on Go and Python backends. Author of the Redelay framework and the FlowDSL workflow engine. Remote, EU time zones.
Read something you need? I’ll quote it for free.
RAG, AI integrations, agents or the backend underneath — tell me what you have and what should change. I read every request myself.