Datanest

Saša shared “a smattering of hot takes” about some of the more widely used oracle networks in the space today– including Chainlink, UMA, Tellor, Nest and BAND

Saša Milić Offers Hot-Takes on Chainlink, UMA, Tellor, Nest, and Band Protocol

The significance of staring with a red test in test-driven development. Start with the red test to spot mistakes in a test

Why You Should Start With a Red Test in Test-Driven Development

How to achieve better type safety and code quality by improving types in TypeScript's standard library.

Unleashing the Power of TypeScript: Improving Standard Library Types

In this article, I’ll show you a simplified way to configure a critical open-source component, Zookeeper.

Setup Monitoring Using Apache Zookeeper and OpenTelemetry

From C++11, std::tuple is an incredible expansion to Modern C++, that offers a fixed-size col­lec­tion of het­ero­ge­neous values. Un­for­tu­nately, tu­ples can be somewhat dubious to manage in a conventional fash­ion. But, subsequently released C++ stan­dard in­tro­duced a few fea­tures & helpers that greatly re­duce the nec­es­sary boil­er­plate. So, in this article, I will explain the variadic template in C++ with the help of unsophisticated tuple 
implementation. And also walks you through a tricky part of tuple i.e. 
loop through tuple element. In spite of the fact that I have shrouded the variadic template in my prior article i.e. C++ Template: A Quick UpToDate Look. So, my focus here would be a blend of variadic template & tuple  implementation with more up to date C++ gauges.

Variadic Template in C++: Implementing Unsophisticated Tuple

I am a huge fan of combat sports, with boxing in particular being my favourite. As much as it may appear as a purely physical sport where your sole objective is to either outbox or knock your opponent out, it is far more strategic that one would expect and incorporates an element psychology. Like a chess game, each punch thrown has to be calculated, recklessly overextending yourself might leave you more vulnerable to a counter punch, while being overly passive and defensive might swing the momentum in your opponent’s favour and not get you enough points to win the fight. If you let self-doubt sink in or are intimidated by your opponent you have already lost the battle. On top of all this, you need to remain respectful of the sport and the life threatening dangers it presents. In the words of of Sugar Ray Leonard, 'you don't play boxing'.

How I Built an Interactive Dashboard Web App to Visualize Boxing Data

Today, I’m honored to be talking to another great kaggler from the ODS community: (kaggle: iglovikov) Competitions Grandmaster (Ranked #97), Discussions Expert (Ranked #30): Dr. Vladimir I. Iglovikov

Interview with Kaggle Grandmaster, Senior CV Engineer at Lyft: Dr. Vladimir I. Iglovikov

“Machine Learning, Artificial Intelligence, Deep Learning, Data Science, Neural Networks”

How to Understand Machine Learning with simple Code Examples

I play FIFA games occasionally but classify myself as a relatively strong player who wins more often than not against other casual players. I am not a huge soccer fan in general and do not try and play a strategic game. Instead I rely heavily on player sprint speed and making unpredictable runs and turns. I often combine these skills to find and make spaces in my opponents space and dribble my way to a victory - much to the frustration of my opponent. Given this backdrop, I decided to download the <a href="https://www.kaggle.com/karangadiya/fifa19" target="_blank">FIFA19 dataset from Kaggle</a> with the intention of predicting player sprint speed based on variables/features that I believed would best predict a player's sprint speed.

Using a multivariable linear regression model to predict the sprint speed of players in FIFA 19

Learn to make a simple machine learning website which adapts text colour 🖌 according to changeable background contrast.

How to make a simple Machine Learning Website from scratch

If you are a developer and want to integrate data manipulation or science into your product or starting your journey in data science, here are the Python libraries you need to know.

Fundamental Python Data Science Libraries: A Cheatsheet (Part 4/4)

I’ve done node/express apps for quite some time now. Many others I’ve talked to enjoy it’s simplicity as well, they might be using a different framework like Hapi, but at the core, they’re very similar. And one of the core concepts are middlewares. I rarely see them used to their full potential by beginners, so I wanted to share my opinion on why I think they are the most important core of any <a href="https://hackernoon.com/tagged/nodejs" target="_blank">nodejs</a> backend app.

Middleware: THE core of node.js backend apps

Docker 1.13 simplifies deployment of composed application to a <a href="https://hackernoon.com/tagged/swarm" target="_blank">swarm</a> (mode) cluster. And you can do it without creating a new <code class="markup--code markup--p-code">dab</code> (Distribution Application Bundle) file, but just using familiar and well-known <code class="markup--code markup--p-code">docker-compose.yml</code> syntax (with some additions) and <code class="markup--code markup--p-code">--compose-file</code> option.

Deploy Docker Compose (v3) to Swarm (mode) Cluster

MaGGIe introduces the IHIM50K image dataset and V-HIM60 video benchmark. These sets test model robustness against varying mask quality and instance overlap.

Video Instance Matting: V-HIM60 Dataset and Difficulty Levels

NewsUnfold users generated high-quality bias annotations, boosting dataset accuracy, expert agreement, and classifier performance in a detailed evaluation.

User Feedback Boosts Media-Bias Dataset Accuracy in NewsUnfold Evaluation

How researchers collected and cleaned 17K developer–ChatGPT conversations from GitHub to explore AI’s role in software development.

Building the DevGPT Dataset for Developer–ChatGPT Studies

PerSense-D is a new benchmark dataset for personalized dense image segmentation, advancing AI accuracy in crowded visual environments.

New Dataset PerSense-D Enables Model-Agnostic Dense Object Segmentation

Oversampling improved every model’s performance, with the neural network and AutoML achieving superior recall and F‑scores.



Model Design and Comparative Metrics for Bankruptcy Prediction in Imbalanced Datasets

How we built a dataset to compare human and LLM responses, aligning true/false reasoning tasks and testing performance head-to-head.

Notes on Building a Dataset for LLM True/False Reasoning

Mel-spectrograms feed a CNN-RNN; last layers retrain on tiny patient sets, then log-quant weights trim memory 4× for wearables.

Dataset, Features, Model, and Quantization Strategy for Respiratory Sound Classification

Benchmarks span MRPC→GQA; text splits follow prior work, images downsampled to a 7×7 grid, visual encoder is frozen for fair param counts.

Dataset Splits, Vision Encoder, and Hyper-PELT Implementation Details

Fine-tune RetinaNet (ResNet-50 FPN) on the BCCD dataset: convert COCO→TFRecords, tweak configs, train/eval with mAP, export SavedModel, and run inference.

Learn to Build, Train, and Export RetinaNet (ResNet-50) on a Custom Dataset

Exploring dataset imbalance and ColBERT-inspired re-ranking in medical image retrieval—balancing recall accuracy with real-world feasibility.

How Dataset Imbalances Shape Medical Image Retrieval Accuracy

ROVR Releases Open Dataset To Power The Future of Spatial AI, Robotics, And Autonomous Systems

Datanestis in the Vertical Business Intelligence Industry

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Datanest's stories on HackerNoon

Saša Milić Offers Hot-Takes on Chainlink, UMA, Tellor, Nest, and Band Protocol

Why You Should Start With a Red Test in Test-Driven Development

Unleashing the Power of TypeScript: Improving Standard Library Types

Setup Monitoring Using Apache Zookeeper and OpenTelemetry

Variadic Template in C++: Implementing Unsophisticated Tuple

How I Built an Interactive Dashboard Web App to Visualize Boxing Data

Interview with Kaggle Grandmaster, Senior CV Engineer at Lyft: Dr. Vladimir I. Iglovikov

How to Understand Machine Learning with simple Code Examples

Using a multivariable linear regression model to predict the sprint speed of players in FIFA 19

How to make a simple Machine Learning Website from scratch

Fundamental Python Data Science Libraries: A Cheatsheet (Part 4/4)

Middleware: THE core of node.js backend apps

Deploy Docker Compose (v3) to Swarm (mode) Cluster

Video Instance Matting: V-HIM60 Dataset and Difficulty Levels

User Feedback Boosts Media-Bias Dataset Accuracy in NewsUnfold Evaluation

Building the DevGPT Dataset for Developer–ChatGPT Studies

New Dataset PerSense-D Enables Model-Agnostic Dense Object Segmentation

Model Design and Comparative Metrics for Bankruptcy Prediction in Imbalanced Datasets

Notes on Building a Dataset for LLM True/False Reasoning

Dataset, Features, Model, and Quantization Strategy for Respiratory Sound Classification

Dataset Splits, Vision Encoder, and Hyper-PELT Implementation Details

Learn to Build, Train, and Export RetinaNet (ResNet-50) on a Custom Dataset

How Dataset Imbalances Shape Medical Image Retrieval Accuracy

ROVR Releases Open Dataset To Power The Future of Spatial AI, Robotics, And Autonomous Systems

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