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Awesome Curated: The Tools

Deep dives into the tools that pass our automated curation system's filter. Each post breaks down a specific tool: why it made the list, when to use it, when not to.

13 posts

  1. 01

    Docker for Novices: The Resource That 16 Lists Can't Be Wrong About

    A 2019 conference talk that showed up in 16 independent awesome lists. Still worth it in 2024? We dig into why Docker for Novices keeps earning its spot as a solid entry point for containers.

    Reflections · 4′

  2. 02

    Themis: Serious Cryptography Without Losing Your Mind

    Themis is the crypto library devs actually needed: AES, ECC, and forward secrecy wrapped in an API that won't make you quit the profession. It showed up in 7 independent awesome lists. That's not a coincidence.

    Experiments · 5′

  3. 03

    m2cgen: export your ML model without shipping Python to production

    m2cgen (PyPI, Python) converts trained scikit-learn models into standalone Java, Go, C#, Rust and 8+ other languages — no Python runtime, no API, no serialized binary. Here's how it works and when to skip it.

    Experiments · 5′

  4. 04

    TensorFlow: the ML elephant that's still standing

    TensorFlow isn't sexy in 2025, but it's still the serious infrastructure behind deployment at scale. Why it made the list and when you actually need it.

    Experiments · 5′

  5. 05

    PyTorch: the deep learning framework that won the war

    PyTorch showed up in 6 independent awesome lists and the reason is simple: it won. This isn't hype — it's infrastructure. Here's why it made our list and when it actually makes sense to use it.

    Experiments · 5′

  6. 06

    XGBoost: the gradient boosting that dominated Kaggle and survived the hype

    XGBoost isn't a trend — it's the algorithm that won hundreds of ML competitions on tabular data. Why it's still the mandatory reference in 2025 and when you should reach for it.

    Experiments · 5′

  7. 07

    Sniffnet: monitor your network without losing your mind to tcpdump

    Sniffnet is a cross-platform network traffic monitor written in Rust. Real UI, real-time charts, no security PhD required to understand what's actually going on.

    Experiments · 5′

  8. 08

    Netron: Open Any ML Model and See What's Actually Inside

    Netron lets you inspect the architecture of any ML model — no Jupyter, no code, no drama. ONNX, PyTorch, TensorFlow: open it and see everything.

    Experiments · 5′

  9. 09

    Node.js: the runtime that changed how we think about backend

    Node.js isn't just "JavaScript on the server." It's a paradigm shift in how we handle I/O. Thirty years in tech taught me to recognize when something genuinely moves the ground beneath your feet.

    Experiments · 5′

  10. 10

    Swiper: the touch slider that won't wreck your sprint

    Swiper has been the undisputed standard for touch carousels on the web for years. Zero dependencies, official wrappers for React, Vue and Angular, and transitions that feel genuinely native.

    Experiments · 5′

  11. 13

    That messy 4000-line notebook taught me to stop hating Jupyter

    Why Jupyter is still the de facto standard for exploring data, even though versioning it with git will make you want to smash your keyboard.

    Experiments · 5′

  12. 13

    Apache Spark won't melt down when your data doesn't fit in RAM anymore

    Apache Spark has been the de facto standard for processing data at scale for over a decade. We go over why it's still alive, how it works under the hood, and when you should NOT use it.

    Experiments · 5′

  13. 13

    gRPC shows up when REST starts breaking under its own weight

    gRPC solves inter-service communication with code generation and HTTP/2. It's fast, it's typed, and the learning curve is going to hurt a bit. Spoiler: it's worth it.

    Experiments · 5′