Technology FAQ

Build a Python script that renders Markdown to email-safe HTML, use Cloudflare Workers KV for subscriber storage, and send via Resend API. The email contains no tracking pixels and no click tracking. The HTML goes straight into the email body with only preview thumbnails hosted externally.

Zero with free tiers. Cloudflare Workers has a generous free tier, Cloudflare R2 offers 10GB free storage, and Resend provides 3,000 emails per month free. The Python script runs locally or on any server you control.

Use Python libraries like markdown2 and Beautiful Soup to render Markdown, fetch OpenGraph metadata for links, and generate table-based HTML (required for email client compatibility). Inline all CSS and optimize images for email (240px width for retina displays).

Cloudflare Workers KV provides key-value storage ideal for subscriber lists. POST to a /api/subscribe endpoint, validate the email address (RFC-compliant), apply rate limiting and honeypot spam protection, and store the email with timestamp in KV.

Yes. In this test, Cursor connected via SSH to an Azure VM, installed dependencies, configured Apache virtual hosts, set up MySQL, and handled SSL certificates. It made sensible decisions about file permissions and security settings without manual intervention.

In this YOURLS deployment test, Cursor completed the entire build and deployment in about 15 minutes, including server configuration, database setup, and SSL certificates. The same process previously took at least an hour of manual work and troubleshooting.

Yes. When asked to create a custom YOURLS plugin to add date prefixes to short URLs, Cursor built it on the first try. The URL shortener is now live and working at pdub.click.

A gradient vector points in the direction of steepest ascent of a function at a given point. In a 2D input space, gradient vectors live in the x-y plane and indicate the direction in which the function value increases most rapidly. Their magnitude tells you how steep that increase is. This geometric intuition is the foundation of why gradient descent works: by moving opposite to the gradient, you move toward lower function values.

Surface plots show function values as a 3D colored surface while simultaneously displaying gradient vectors in the input plane below. This makes it easier to see the relationship between the terrain of the function and the direction of steepest ascent. Contour plots flatten the picture and can obscure how steep gradients correspond to tightly packed level curves.

In higher dimensions, the gradient remains a vector in the input space that points toward steepest ascent. While you can no longer visualize the full surface, the same principle holds: each component of the gradient is the partial derivative with respect to that input variable. This is exactly how neural network training works, where gradients are computed across thousands or millions of parameters simultaneously.

PyTorch uses its autograd engine to perform automatic differentiation. When you define a function using PyTorch tensors with requires_grad=True, the framework builds a computational graph and applies the chain rule to compute gradients via backpropagation. These gradient values can then be extracted and plotted as vector fields over the input domain.

For this project, 409 annotated playing cards across 117 images achieved 99.5% mAP@50 after iterating on data quality. The initial smaller dataset of roughly half that size produced decent results at 80.5% mAP, but doubling the annotations and fixing bounding polygon errors was what pushed accuracy to near-perfect levels.

In testing, OCR proved unreliable for this use case. While the Claude Vision API achieved 99.9% accuracy as a secondary verification layer, it was too slow for real-time use. EasyOCR, running locally, could identify card numbers when it detected them but failed to recognize roughly half the cards entirely, making it unsuitable for consistent card recognition.

The difference is substantial. Roboflow's hosted API took around 4 seconds per inference, while running the same YOLOv11 model locally on a laptop achieved inference times under 0.1 seconds per image (approximately 45.5ms for inference alone). This 40x speed improvement made real-time card detection practical.

Transfer learning means starting from a model pre-trained on millions of general images rather than training from scratch. For card detection with YOLOv11, this approach lets the model apply visual patterns it already understands (edges, shapes, textures) to the specific task of identifying playing cards, requiring far less training data and time than building a model from zero.

Yes. This project feeds detected card values from the YOLOv11 model directly into a Monte Carlo simulation that calculates real-time blackjack odds. The system captures the browser window, identifies all visible cards, and runs thousands of simulated hands to display hit/stand probabilities on screen.

Machine learning models like XGBoost can predict certain aspects of postprandial glucose response, particularly the amplitude (how high blood sugar rises after eating). Using features such as meal macronutrients, individual characteristics, and CGM-derived metrics, the model achieved an R-squared of 0.46 for amplitude prediction. However, predicting the timing and duration of the glucose response proved far more difficult, suggesting that meal composition alone provides limited information about when and how long blood sugar stays elevated.

Fitting each postprandial glucose response to a normalized Gaussian function simplifies the prediction problem from modeling an entire glucose curve to predicting just three parameters: amplitude (how high glucose rises), time-to-peak (when it peaks), and curve width (how long the response lasts). This approximation works well for most glucose responses in non-diabetic individuals, though some curves fit better than others due to variation between individuals and meals.

Sample size is one of the most critical factors in glucose prediction accuracy. A model trained on 112 standardized meals from 19 non-diabetic subjects achieved moderate amplitude prediction (R-squared of 0.46). In comparison, the EPFL Food and You study with over 1,000 participants achieved a correlation of 0.71. Studies that reach R-squared values above 0.7 typically require datasets with more than 1,000 participants, showing that individual glycemic prediction at scale demands large and diverse training data.

In XGBoost-based glucose prediction, 27 engineered features were used across multiple categories: meal composition (carbohydrates, protein, fat, and their interaction terms), participant characteristics (age, BMI), and CGM statistical features calculated over 24-hour and 4-hour windows, including time-in-range and glucose variability metrics. While macronutrients are primary drivers, pre-meal glucose state and individual metabolic characteristics provide additional predictive signal for amplitude prediction.