By the end, you'll have a Streamlit app with three tabs, one for news articles, one for YouTube videos, and one for social media and forum discussions. We'll use "serpapi" as the search query, but you can swap the brand or product name. 

The trouble starts once that workbook becomes part of a repeatable, audited reporting process — a regulatory filing, an IFRS report, a periodic compliance submission. At that point, a shared Excel file isn't enough on its own. What's actually needed is version control, validation, an audit trail, a review step, and a reliable way to connect the spreadsheet's logic to the systems downstream.

Probably you will start creating models and service modules. You will create some classes to represent the entities described in the scenarios, like Student, Course, and Subject. You will add conditionals and loops in the entity classes to control the business logic and restrict paths in the code:

A single flamegraph tells you where your application spends its time. But the questions that matter most in practice are comparative:

• Standalone JAR – download the latest microscope.jar from the GitHub releases page and start it with java -jar microscope.jar (Java 25 or newer).
• Docker – skip the setup entirely with docker run -it --network host petrbouda/microscope.
• Sample recordings – if you want to explore the tool before profiling your own application, the petrbouda/microscope-examples image ships with sample recordings preloaded (docker run -it --network host petrbouda/microscope-examples).

In this article, we'll use Jeffrey Microscope to analyze JFR flamegraphs and walk through how they help you find where your application actually spends its time.

A Native Mac Build From the iOS Pipeline

PR #5053 adds a Mac Native target that takes the existing project through the same build as the iPhone builder and the ParparVM pipeline that produces an iOS app. In this case, it emits a native Mac variant of it.

Irrespective of whether certain Java 25 language improvements are still available as preview features or not, this article briefly outlines a few. The main purpose is to first make the developers aware that Java is continuously refined and evolved by its API contributors and secondly, to raise the curiosity and interest of exploring these enhancements in detail.

So we did the work. We evaluated the resources data engineers actually use, judged on five things that matter: relevance to the DE interview loop, depth of practice, realism of the questions, feedback quality, and price. Below is the ranked list.

I have heard a version of this for fifteen years. The app ships. The customers are happy. The code is unfashionable. And somebody wants to tear it down and rebuild it on a stack that looks better on a resume.

This project demonstrates the implementation of a real-time face recognition application using Python, OpenCV, Dlib, and the Face Recognition library. The application captures video input, detects human faces, generates facial feature encodings, and compares them against a database of known individuals. Once a match is identified, the system displays the corresponding name on the video frame.

Columnar storage stores data in a column-wise format rather than the traditional row-wise storage, offering significant performance benefits for read-heavy operations such as reporting and analytics.

Despite this, API design has long faced a notable gap.

Together, feature flags and observability unlock powerful insights by correlating code changes with real-time system behavior. This combination reduces time-to-diagnosis and builds greater confidence when rolling out new features.

LangChain's SQL integration fixes this, translating plain English questions like "Which product category had the highest revenue last year' into valid SQL, executing it, and returning a human-readable answer.

For Python developers unfamiliar with industrial protocols, OPC UA PubSub (IEC 62541-14) is a standard that decouples data producers from consumers by allowing devices to broadcast telemetry via stateless middleware like UDP Multicast. For industrial engineers new to Python concurrency, asyncio is a standard library that uses an event loop to handle thousands of simultaneous network operations concurrently without the heavy overhead of traditional threading.

In these scenarios, simply using the application's identity is often insufficient. The downstream service may need to know which user initiated the request and enforce authorization based on that user's permissions. This is where the OAuth 2.0 On-Behalf-Of (OBO) flow becomes invaluable.

These features come from PR #5133 (WebSockets) and PR #5141 plus PR #5099 (the typed clients).

A Spark query written by a human and a Spark query executed by the engine are often very different things. The gap between them — the optimization — is what separates a job that runs in 3 minutes from one that runs in 3 hours on identical hardware.

Databricks compounds Spark's native Catalyst optimizer with two additional layers:

This article explores whether dependency injection (DI) can exist in Rust without sacrificing the language’s core philosophy of zero-cost abstractions.

We will approach the question from three angles:

But before we dig into the new features, there's one number I'm particularly proud of…