ritesh.hft

My friend applied to 250 tech jobs in two years. No MIT. No Stanford. No PhD. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Andrej Karpathy's 4-hour course on "How to build a full LLM from scratch." Anthropic's own researcher gives up the exact playbook behind building LLMs like ChatGPT & Claude. I watched it last night. Halfway through, I realized I could break into an AI lab in months, not years. Bookmark this and read the article below. • 00:00 - LLMs as new OS • 40:58 - LLM tokenization architecture • 53:22 - LLM neural network internals • 1:04:20 - LLM pretraining • 3:21:37 - LLM RLHF & DeepSeek R1

this repo is f*cking insane 300+ real ML system design case studies from 80+ companies - Netflix, Uber, Airbnb, Stripe, DoorDash, LinkedIn, all in one place. helpful for quants, ml engineers and all developers. no theory. just how big tech actually ships ML. bookmark before this gets buried and then read article.

Neo4j VP of Product Innovation just showed how enterprise teams build GraphRAG pipelines that outperform basic RAG by 300%. 20-minutes. free. By Neo4j engineering leads. here's what they cover: • why basic vector databases hit an enterprise accuracy ceiling • 3-step KG pipeline - lexical structure, entity extraction, graph enrichment • graph algorithm integration - page rank, community detection, & clustering • 28.6% faster resolution metric benchmarked in production (LinkedIn study) watch full video then read article below.

Former $13B Guggenheim Quant Head just revealed why 99% of machine learning trading funds fail. 73-minutes. free. By Marcos Lopez de Prado. here's what he covers: • why solo "Sisyphus" quants fail vs factory-style task partitioning • fractional differentiation - preserving price memory while achieving stationarity • volume/dollar bars vs flawed chronological sampling • triple barrier labeling (profit-taking, stop-loss, time limit) + meta-labeling • backtest overfitting & why pure noise yields a fake Sharpe Ratio of 3.0 Bookmark & watch today. Then read the article below.

this repo is f*cking insane someone packaged holonic context graphs, graphRAG, and local LLM inferencing into a single self-hosted container harness. it builds 3D context graphs, extracts entity-relationship paths, and gives your AI agents full fact-level provenance with zero hallucinations. zero API keys needed. bookmark before this gets buried.

Jane Street pays $250K–$350K/year for new grad software engineers who pass interviews like this. 36-minutes. free. By Jane Street team. here's what they cover: • graph traversal approach to unit conversion (BFS vs DFS) • class abstractions for graphs, nodes, and directed edges • handling bi-directional graph conversion facts • 1 core mistake candidates make: talking constantly instead of syncing intent Bookmark & watch today. Then read the article below.