Data Core // Library

Intelligence Archives

Classified reading materials, acquired operational knowledge, and the manuals that dictate combat strategies.

Reading List

Long-form intelligence briefs and classified texts.

Tracked Training

Combat courses, VR simulations, and tactical drills.

Field Notes

Scribbled takeaways and essential intel drops.

Training Catalog

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

Decrypted Notes

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.

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.

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.