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Articole: 19
  1. Your AI Agent Forgets Everything. Lore Fixes That.

    You’re building something with Claude Code.

  2. Functors and Monads: Wrapping Your Data (Not Your Head) Around Them

    Why these words sound scarier than they are If you’ve ever Googled “what is a monad”, you’ve probably landed on a Stack Overflow answer that says something like “a monad is just a monoid in the category of endofunctors” and immediately closed the tab.

  3. Currying and Partial Application: Shaping Intent

    Why this topic is confusing Currying and partial application are concepts that almost every Python developer has seen, but very few feel confident using.

  4. Choosing Functional Python (Without Forcing It)

    Why this matters Python gives you multiple ways to solve the same problem.

  5. Spot Optimizer ≠ Spot Fleet: decide smart, then launch

    Picking EC2 Spot instances shouldn’t feel like rolling dice.

  6. How Not to Open a Pull Request – And How to Do It Right

    Picture this: You’re maintaining an open-source project, and you receive a pull request that promises to revolutionize your codebase.

  7. Never Push a Work Commit with Your Personal Email Again

    As developers, it’s common to wear multiple hats — working on company projects during the day and tinkering with open-source or personal side-projects at night.

  8. From Bottlenecks to Balance: Dynamic Skew Join Fixes in Spark

    When working with large datasets in Spark, joins are a common operation.

  9. 4TB RAM, Yet an OOM Error? Debugging a Spark Memory Mystery

    Everything seemed right—ample resources, a well-sized cluster, and yet, the Spark job kept failing with an out-of-memory error.

  10. Deep Dive into Spark Jobs and Stages

    When working with large-scale data processing using Apache Spark, understanding how jobs and stages work is crucial to optimizing performance.

  11. Balancing the RUM Conjecture: Navigating Database Trade-Offs

    When designing databases, there’s a constant balancing act among three main factors: Read times Update cost Memory/storage overhead The RUM Conjecture suggests that optimizing any two of these factors will negatively impact the third.

  12. The CAP Theorem: Balancing the Big Three in Distributed Databases

    The CAP theorem, also known as Brewer’s theorem (named after computer scientist Eric Brewer), defines a fundamental trade-off in distributed systems: any distributed data store can provide only two out of three guarantees at any time: C: Consistency A: Av…

  13. Fine-Tuning Shuffle Partitions in Apache Spark for Maximum Efficiency

    Apache Spark’s shuffle partitions play a critical role in data processing, especially during operations like joins and aggregations.

  14. Handling Large Broadcast Joins in Apache Spark

    In Apache Spark, efficient data processing often relies on the use of broadcast joins.

  15. Symptoms of Bad Code

    1.

  16. Docker - the right way

    Docker is a software framework for building, running, and managing containers on servers and the cloud.

  17. GitOps - the easy way

    What is GitOps?

  18. Finger Detection and Tracking using OpenCV and Python

    TL;DR.

  19. What is Google Summer of Code? How to prepare for it?

    We will talk about Google Summer of Code but before that let’s talk about what Open Source Development is.