Embeddings have become the backbone of many modern AI applications. From semantic search to retrieval-augmented generation (RAG) and intelligent recommendation systems, embedding models enable systems to understand the meaning behind text, code, or documents, not just the literal words. But generating embeddings comes with trade-offs. Using a hosted API for embedding generation often results in reduced data privacy, higher call costs, and time-consuming model regeneration. When your data is private or constantly evolving (think internal documentation, proprietary code, or customer support content), these limitations quickly become blockers. Instead of sending data to a remote service, you can easily run local…
Author: drweb
After I have understood the details for a certification test (part 1), including format and the rules for taking it and after I have taken time to learn how the test is structured and what’s covered (part 2), next is studying for the exam. First, let’s talk about focus. Study for too long a block and after a certain amount of time, it becomes less productive. That’s why I am a fan of the pomodoro technique. It presents a big enough block of time to make a dent in the material while ensuring a bit of rest between those blocks.…
I recently wrote about a logical diagram with Redgate Data Modeler. That was interesting, but creating all the objects is a pain. I decided to try creating a physical diagram from an existing database. This post looks at the experience.This is part of a series of posts on Redgate Data Modeler.Getting StartedAs with the logical model, I right click and choose New document.Then I get a list of options. I’ll choose the middle one, Physical Model. This is what I mostly need to work with an existing system. Since I don’t often move models from one platform to the other,…
Learn how to apply Joel Greenblatt’s legendary ‘Magic Formula’ investing strategy without paying for expensive software.I’ll break down the core math behind buying good companies at cheap prices (High Return on Capital + High Earnings Yield) and give you a step-by-step tutorial on setting up a custom scan in Finviz.♥️ Info: Are you AI curious but you still have to create real impactful projects? Join our official AI builder club on Skool (only $5): SHIP! – One Project Per MonthWatch to see exactly how to filter the market to find the best potential value stocks for your portfolio right now:…
This article explains how to use Spring Boot built-in API versioning feature to expose different versions of REST endpoints. This is one of the most interesting updates introduced with Spring Boot 4. API versioning can be implemented using Spring Web’s standard REST API capabilities. If you’re interested in this approach, check out my somewhat outdated article on the subject here. Interestingly, the Micronaut framework also provides built-in API versioning. You can read more about it in the framework’s documentation here. Source Code Feel free to use my source code if you’d like to try it out yourself. To do that,…
I just finished this video comparing which AI model is better in building a Monet clone image: ♥️ Info: Are you AI curious but you still have to create real impactful projects? Join our official AI builder club on Skool (only $5): SHIP! – One Project Per MonthThis is the first image generated by ChatGPT 5.1:This one nails the sunny summer vibe with those bright colors, but it’s honestly way too orange and saturated to pass for a real Monet. The generic ‘ART’ and ‘CAFFE’ signs look kind of fake, like something you’d find on a jigsaw puzzle rather than…
Claymont, Delaware, 1st December 2025, CyberNewsWire
Every tutorial you read shows a different way to import Statsmodels. One guide starts with import statsmodels.api as sm. Another uses from statsmodels.formula.api import ols. A third imports directly from submodules like from statsmodels.regression.linear_model import OLS. Which approach should you use?The confusion stems from a deliberate design choice. Statsmodels offers multiple import paths because different users need different things. Researchers writing academic papers want one workflow. Data scientists doing quick exploratory analysis want another. Understanding these three approaches will save you from blindly copying code that doesn’t match your actual needs.Statsmodel Beginner’s Learning PathUnderstanding the Three Import ApproachesApproach 1: statsmodels.api…
When you’re building regression models with Python’s statsmodels library, you’ll quickly encounter add_constant. This function determines whether your model fits y = mx + b or just y = mx, which fundamentally changes how your model interprets data.I’ll walk you through what add_constant does, why you need it, and how to use it correctly in your statistical modeling work.Statsmodel Beginner’s Learning PathWhat Does add_constant Actually Do?The add_constant function adds a column of ones to your data array. That’s it at a mechanical level. But what this column of ones accomplishes is mathematically significant.When you run a linear regression, you’re estimating…
I’ve been working with statistical models in Python for years, and one feature that transformed how I approach regression analysis is statsmodels’ R-style formula syntax. Coming from R, I appreciated having a familiar, readable way to specify models without manually constructing design matrices. Let me show you how this works and why it matters for your statistical modeling workflow.Statsmodel Beginner’s Learning PathWhat are R-style formulas in statsmodels?Statsmodels allows users to fit statistical models using R-style formulas since version 0.5.0, using the patsy package internally to convert formulas and data into matrices for model fitting. The formula syntax provides an intuitive,…