Étonnamment moins ronchon quand il ne trouve pas de cerise dans son plateau de fruits de mer, revoilà Bernard qui nous régale de sa savoureuse tartufferie sur les appellations animales.
🖍 @sandrine2loffre
Collaboration commerciale avec @laviefoods_fr
Hello all,
Après un peu plus de 7 mois d'attente, on continue la série d'article avec un nouvel opus ! Cette fois on parle des SAS, une façon d'écrire les tests d'acceptation centré sur les fonctionnalités !
Thx @GTechene !!
https://t.co/J9VEgic0Fl
Avec @GTechene, en début de semaine, nous avons commencé la publication d'une série d'article sur notre approche du #outsideInDiamond#TDD reprenant les idées de @tpierrain . En attendant le second opus qui devrait bientôt émerger, voici le premier :
https://t.co/KtIKaKUYBF
Choosing between C# and Python for your next project?
Consider F# instead:
* Power of C# - Runs on dotnet, access to full ecosystem of libraries
* Simplicity of Python
* Better types than both
Python vs F# - https://t.co/6B7kM9vl4M
#csharp#python#fsharp
Ecoutez bien Marine Tondelier face à Jean-Philippe Tanguy, député Front national !
C'était il y a quelques minutes lors de la soirée électorale sur France 2
#legislatives2024
Things Every Developer Should Know: Concurrency is 𝐍𝐎𝐓 parallelism.
In system design, it is important to understand the difference between concurrency and parallelism.
As Rob Pyke(one of the creators of GoLang) stated:“ Concurrency is about 𝐝𝐞𝐚𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 lots of things at once. Parallelism is about 𝐝𝐨𝐢𝐧𝐠 lots of things at once." This distinction emphasizes that concurrency is more about the 𝐝𝐞𝐬𝐢𝐠𝐧 of a program, while parallelism is about the 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧.
Concurrency is about dealing with multiple things at once. It involves structuring a program to handle multiple tasks simultaneously, where the tasks can start, run, and complete in overlapping time periods, but not necessarily at the same instant.
Concurrency is about the composition of independently executing processes and describes a program's ability to manage multiple tasks by making progress on them without necessarily completing one before it starts another.
Parallelism, on the other hand, refers to the simultaneous execution of multiple computations. It is the technique of running two or more tasks or computations at the same time, utilizing multiple processors or cores within a computer to perform several operations concurrently. Parallelism requires hardware with multiple processing units, and its primary goal is to increase the throughput and computational speed of a system.
In practical terms, concurrency enables a program to remain responsive to input, perform background tasks, and handle multiple operations in a seemingly simultaneous manner, even on a single-core processor. It's particularly useful in I/O-bound and high-latency operations where programs need to wait for external events, such as file, network, or user interactions.
Parallelism, with its ability to perform multiple operations at the same time, is crucial in CPU-bound tasks where computational speed and throughput are the bottlenecks. Applications that require heavy mathematical computations, data analysis, image processing, and real-time processing can significantly benefit from parallel execution.
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🇫🇷 C'est à nous tous de jouer maintenant. Écrivez un e-mail à votre gouvernement. Partagez l'appel à l'action. Appelez votre gouvernement pendant la journée. Ensemble, nous sommes la résistance. Arrêtez le #ChatControl!
https://t.co/nGzsX2eX8W
I publicly apologise to Romaric Philogène and Qovery for assuming that a formal event they hosted would not support diversity efforts in tech.
I am also sorry that they felt insulted by a subtweet that was addressed at people in general not making an effort to support diversity.
Avec @GTechene, en début de semaine, nous avons commencé la publication d'une série d'article sur notre approche du #outsideInDiamond#TDD reprenant les idées de @tpierrain . En attendant le second opus qui devrait bientôt émerger, voici le premier :
https://t.co/KtIKaKUYBF