Attention Is All You Need (Vaswani et al., 2017) — The original 2017 paper. Surprisingly readable; everything else is commentary on this.
The Annotated Transformer — Harvard NLP walks through the paper line-by-line with PyTorch code interleaved. Best companion read.
The Illustrated Transformer — Jay Alammar — Still the best visual explanation on the internet. If a formula feels abstract, Jay has probably drawn it.
You are building an internal analytics dashboard for a SaaS company. Analysts need to generate reports from the users and orders tables without writing raw SQL (to reduce errors and SQL injection risks).
Table Schemas:
users (
id SERIAL PRIMARY KEY,
created_at TIMESTAMPTZ NOT NULL,
email VARCHAR(255),
status VARCHAR(50), -- 'active', 'inactive', 'suspended'
You are given the following slow Python function that processes images (resizes them and adds a watermark). It becomes painfully slow when processing hundreds or thousands of images.
from PIL import Image, ImageDraw, ImageFont
import os
from typing import List, Tuple
def process_images(input_dir: str, output_dir: str, watermark_text: str = "CONFIDENTIAL") -> List[Tuple[str, bool]]:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
This is a smart way to keep your storage lean. By using a depth of 1, you only push the "current snapshot" of OpenEMR to your GitLab, avoiding the gigabytes of historical data and thousands of older commits. Here is the step-by-step workflow:
Building an AI development workstation incrementally is a strategic move that ensures you have a professional-grade foundation while spreading out the costs. This 4-month plan focuses on upgrading your "brains and memory" first for immediate relief in development, followed by power and storage, and finally the GPU engine.
| Phase | Focus | Estimated Cost | Main Components |
|---|---|---|---|
| Month 1 | Foundation & Memory | £1,000 - £1,200 | CPU, Motherboard, 128GB RAM, Case |
| Month 2 | Power & Storage | £350 - £400 | 1000W ATX 3.1 PSU, Gen5 NVMe SSD |
| Month 3 | Savings/Cooling | £100 - £200 | Optional: High-end CPU Cooler or fans |
| Month 4 | The GPU "Engine" | £1,100 - £2,800 | RTX 5080 or RTX 5090 |
Main Master Prompt (Library Core Template)
You are an elite Software QA Engineer and Static Analysis / Regression Testing Specialist with 20+ years of experience across enterprise codebases. Your sole mission is to prevent regressions and catch defects before they reach production.
When the user provides:
• A full codebase (or selected files/directories)
• A git diff / pull request / list of changed files
• Or asks for coverage of specific modules/functions
flowchart TB
subgraph OpenEMR ["OpenEMR (PHP Frontend)"]
ChartOpen[Chart Open\npid, user_id]
PHP[PHP Module]
JWT[JWT Minting\nHMAC-signed\nuser_id, pid\n15-min expiry]
end
subgraph UI ["Physician UI Surfaces"]
SummaryCard[Pre-computed\nSummary Card\nZero LLM latency]
High-Level Architecture – Clinical Co-Pilot
The Clinical Co-Pilot will be a verified, observable, agentic chatbot embedded directly inside OpenEMR via a custom module. It solves the 90-second physician context problem while respecting every hard constraint in the requirements.
Core Decisions & Tradeoffs:
Key Audit Findings – Clinical Co-Pilot Foundation (≈480 words)
OpenEMR (fork https://github.com/MichaelHabermas/openemr) is a mature, modular LAMP-stack EHR with strong built-in authorization (ACL/gacl), REST + FHIR APIs, and official Docker support. Our Vultr + Docker Compose deployment faithfully reproduces the production pattern the maintainers ship.
Security & HIPAA: Strong ACL model enforces “physician sees own patients.” PHI is protected at the application layer. However, default install requires explicit hardening (HTTPS via container Let’s Encrypt, host firewall, DB encryption, immutable logs). Our VPS gives us complete control — critical for the PDF’s compliance requirements. No data is sent to LLMs yet; we will enforce BAA-equivalent boundaries.
Performance: Single-container PHP/Apache + MariaDB handles demo data instantly. Bottlenecks will appear only at scale (concurrent users + complex queries). Our git-pull workflow keeps laten