Build logs, implementation guides, and practical explanations from real software work.
18
published notes
4
topic paths
1
author archive
Content structure
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Frontend architecture, deployment, hosting, performance, and implementation guides.
Start here for frontend, hosting, deployment, monitoring, and practical website engineering.
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Deploying One Git Project to Both GitHub and Hugging Face Spaces
RAG, LLM workflows, vector search, embeddings, and applied machine learning systems.
Use this path for RAG, embeddings, vector search, LLM behavior, and applied ML systems.
Example note
Deploying One Git Project to Both GitHub and Hugging Face Spaces
Build logs, debugging notes, algorithms, and practical implementation details.
Shorter implementation notes, algorithms, debugging notes, and system explanations live here.
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Beyond the Black Box: Implementing KNN for Feature Similarity from Scratch
Implementation case studies that document the problem, setup, trade-offs, and result.
Build breakdowns that show the problem, setup, trade-offs, implementation, and result.
Example note
Deploying One Git Project to Both GitHub and Hugging Face Spaces
Featured implementation note
A practical email setup note on debugging Zoho Mail when MX records are configured but filters, auto-reply, or contact@ custom domain behavior do not trigger as expected.
Read note
Reading paths
Each article now connects back to topic pages and related technical notes, so the archive reads like a structured content site.
A practical Git deployment note on managing two remotes in one project: pushing source code to GitHub while also deploying the same app to Hugging Face Spaces.
Read noteA practical Git build log on fixing the fatal: current branch main has no upstream branch error after connecting a local project to a new remote repository.
Read noteA practical walkthrough of K-Nearest Neighbors built from scratch in Python, showing how feature similarity is calculated in multi-dimensional space.
Read noteA practical breakdown of how to reduce LLM hallucinations with a lightweight RAG pipeline using embeddings, FAISS, and top-k retrieval for technical documentation.
Read noteAuthor and method
I write about the systems I build, test, compare, or debug. The goal is to show the problem, implementation path, constraints, and what I learned from the result.