Tech sector says only carbon-emitting gas plants are reliable enough today to power the EU’s AI goals.
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Bienvenue dans le privacy paradox : bien que conscients de l’utilisation de leurs données, les Français ne s’engagent pas dans leur protection.
Trois tendances fortes ressortent de l’étude :
- Les Français sont 92% à se déclarer conscients et préoccupés par l’utilisation de leurs données confidentielles. Toutefois, seuls 35% déclarent renoncer à un service numérique (site ou application) pour protéger leurs données personnelles.
- 48% des sondés déclarent mettre en place des solutions pour protéger leurs données, comme la désactivation de la localisation (44%) ou encore la navigation privée (25%) ; privilégiant ainsi très souvent la simplicité et l’immédiateté vs la protection de leurs données.
- L’étude met en lumière le « privacy paradox », cette opposition entre la prise de conscience et la réalité des usages même si le souhait des Français d’être accompagnés témoigne d’un changement en cours. 86% des Français aimeraient ainsi être accompagnés pour mettre en place des solutions pour protéger leurs données : ils ont de fortes attentes pour mieux comprendre les enjeux et mettre en place les bonnes pratiques.
Chaine youtube qui parle du fonctionnement des algorithmes en programmation, et des technologies sous-jacentes.
What follows is a version of an email I sent our entire R&D team about an explicit goal and deliberate action we’ll take to become twice as productive through our embrace of AI.
Darragh Curran
Jun 13, 2025
Both sides are grappling with a real existential threat, and both sides feel like they are screaming into the void. There is a way to close the gap and get everyone pulling in the same direction.
La semaine dernière, j'ai écrit « Delete your backlog. » L'article a été bien reçu et beaucoup tourné, et une question est revenue plus souvent en MP presque toujours formulée de la même manière : « Oui, mais avec l'IA, alors ? »
Et c'est une vraie question. Parce qu'au moment exact où je plaidais pour supprimer, purger, faire de l'espace, une bonne partie des équipes Produit découvraient l'inverse : qu'il était devenu trivial de produire. De générer des epics, des features, des user stories, des critères d'acceptation, des variantes, en quelques secondes, presque gratuitement*.
A passing test suite can be 100% green while production silently ships the wrong total on every checkout. This is why unit tests lie — and what senior engineers test instead: real integration, property-based testing, observability, and contracts.
247 tests passing in 800 milliseconds tells you almost nothing about whether the system actually works. A green unit test only confirms that one bolt, in isolation, is fine — while the engine is on fire. The moment you mock the database and stub the API, you stop testing your application and start testing your imagination of how those things behave. This video breaks down the three things seniors trust instead of the green bar, with real Python and TypeScript examples
E-ink 60 fps 13 inch tactile display
The OOP Interview Question That Breaks Everyone ! | Object-Oriented Programming (OOP) Explained | Tech Interview Prep
Want to understand Object-Oriented Programming (OOP) the right way? This complete guide goes beyond the basic textbook definitions of Encapsulation, Abstraction, Inheritance, and Polymorphism, diving deep into how OOP is used in real-world backend systems and enterprise architecture.
If you're preparing for software engineering interviews, learning system design, or tired of writing spaghetti code — this video will give you a clear, practical understanding of OOP principles, composition over inheritance, and the architectural trade-offs you must know.
We break down complex topics like Objects vs Classes, the Diamond Problem, Entity-Component-Systems (ECS), and the 4 Pillars in a highly visual, easy-to-understand way.
🔥 What you'll learn in this Object-Oriented Programming tutorial:
00:00 — The Interview Trap (Why memorizing the 4 pillars isn't enough)
01:27 — Objects, Classes & Constructors explained visually
03:52 — Encapsulation (Protecting state & data hiding)
05:37 — Abstraction (Managing complexity & interfaces)
06:55 — Inheritance (Code reuse & the "Is-A" relationship)
08:56 — Polymorphism (Method overriding & overloading)
10:45 — Composition over Inheritance (The Entity-Component-System pattern)
13:49 — OOP Trade-offs & Common Developer Misconceptions
16:10 — The Complete Picture & Final Summary
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💡 Keywords covered (for learning & interviews):
Object-Oriented Programming, OOP Tutorial, 4 Pillars of OOP, Encapsulation, Abstraction, Inheritance, Polymorphism, Composition vs Inheritance, System Design Interview, Software Engineering, Backend Development, Java OOP, TypeScript OOP, Clean Code, Programming Basics
🎯 This video is perfect for:
Backend developers (Java, Python, TypeScript, C#)
Full stack developers wanting to write cleaner code
Students preparing for tech interviews & system design rounds
Anyone struggling to bridge the gap between OOP theory and real-world application
⚠️ By the end, you’ll understand:
How OOP principles work in real enterprise systems
When to use Inheritance vs Composition (and why Composition usually wins)
Common architectural mistakes junior developers make
How to answer advanced OOP questions in technical interviews
📈 Search-friendly topics covered:
OOP concepts in Java/TypeScript
java oop
4 Pillars of Object-Oriented Programming
Composition over inheritance explained
Software engineering interview preparation
How to write clean object-oriented code
Object-oriented programming for beginners
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Why the Best Codebases Barely Use Inheritance Anymore ? | Composition vs Inheritance Explained
Stop struggling with inheritance vs composition. What actually happens to your codebase when requirements change?
In this video, we break down the fundamental concepts of software architecture and explore why relying on deep class hierarchies often leads to fragile code. We contrast this with the modular approach of composition, treating your components like LEGOs.
Understanding these coding design patterns is essential if you want to write clean code that scales. Whether you are learning object oriented programming or preparing for system design interviews, this tutorial gives you a concrete strategy to avoid common pitfalls.
We simplify concepts like class hierarchies, code reuse, abstract methods, interface contracts, loose coupling, and even touch on Dependency Injection using a practical, real-world architecture example. Subscribe for weekly software development tips and let us know in the comments: do you prefer composition or inheritance in your current projects?
🔥 What you'll learn in this Object-Oriented tutorial:
00:00 — Intro
00:28 — Member Shoutouts
00:43 — The Code Reuse Problem
01:05 — What is Inheritance?
02:12 — Where Inheritance Breaks
04:24 — What is Composition?
06:08 — What is an Interface?
07:35 — What is Dependency Injection?
08:11 — When to use Inheritance
09:15 — Summary & Takeaways
💡 Keywords covered (for learning & interviews):
Composition over Inheritance Object-Oriented Programming OOP Concepts Software Architecture System Design Interfaces Dependency Injection Loose Coupling Code Reuse Clean Code Refactoring
🎯 This video is perfect for:
Backend and frontend developers (Java, C#, TypeScript, Python, C++)
Software engineers trying to write cleaner, more maintainable code
Students learning Object-Oriented Programming and Design Patterns
Anyone struggling with deep, messy class hierarchies
⚠️ By the end, you'll understand:
Why traditional inheritance makes code fragile
How composition solves the fragile base class problem
Why interfaces are crucial for abstraction
How to decouple your code using dependency injection
When inheritance is actually the right tool for the job
Install and configure your own second brain: a private, personal, and robust assistant inside Claude that forgets nothing, helps you connect the dots, and makes you far more effective on your day-to-day tasks (semantic search via RAG included).
Ten takeaways from the Acceleration Whiplash report
Two years of telemetry. 22,000 developers. More than 4,000 teams.
The AI Engineering Report 2026 is not a survey of how developers feel about AI. It is a measurement of what AI is actually producing across the full software development lifecycle, tracking metric change between periods of lowest and highest AI adoption within each organization.
What it found has a name: the Acceleration Whiplash. AI has flooded a system built around human-paced development and human-quality code with output it was never designed to absorb.
Throughput is up. So are bugs, incidents, and the hidden costs accumulating at every stage downstream.
This report examines seven areas where that tension is visible: adoption, throughput, context switching, code complexity, pre-merge quality, workflow efficiency, and production quality. Here are ten takeaways from the data.