Research

Notes, papers, and the ideas worth keeping.

This page is the written trail behind the work: what I read, what I learned, and what I want to remember.

Searchable notes

Store articles, observations, and summaries in one place.

Reading list

Books, courses, and papers can all live here.

Thought log

A place for the ideas you want to build on later.

Research notes

From the backend
Local AI Deployment: Ollama vs API Costs

Running models locally with Ollama + DeepSeek eliminates per-token API costs. Trade-off: need decent hardware (16GB+ RAM for 7B models). Groq provides fast inference via cloud but with rate limits. Best hybrid: local for development/experimentation, Groq for demo/production with caching.

AIdeploymentlocal-inferenceollama
ERC-721 vs ERC-1155 — When to Use Which

ERC-721: Each token is unique (1:1 NFTs, land deeds, identity). ERC-1155: Multi-token standard — can represent both fungible and non-fungible tokens in a single contract. Used ERC-1155 in Land Trust for batch transfers and gas efficiency. ERC-721 for unique property deeds.

web3ethereumNFTsolidity
Transformer Architecture — Key Takeaways

The transformer architecture replaces recurrence with self-attention, enabling parallelization. Key concepts: multi-head attention, positional encoding, layer normalization. The 'Attention Is All You Need' paper introduced this in 2017, and it's now the backbone of GPT, BERT, T5, and every modern LLM.

transformersattentionNLP

Learning stack

Build a Large Language Model (From Scratch) — Sebastian Raschka
Book
Attention Is All You Need (Vaswani et al., 2017)
Research Paper
A Survey of Large Language Models
Research Paper
Voronoi Approach for Sensor Node Coverage
Research Paper
Computer Networking: A Top-Down Approach — Jim Kurose
Book
Hands-On Machine Learning — O'Reilly (Aurélien Géron)
Book
Everyday Ethics for AI — IBM
IBM

Use this page for

  • Books I have read
  • Papers I have reviewed
  • Ideas worth revisiting
  • Topics I want to study next