Stay Updated with the Latest Tech News
Get ahead of the curve with the latest insights, trends, and analysis in the tech world.
Browse by Category
Popular Topics
#General #Development #Mobile Development #Cloud & DevOps #Cybersecurity & Privacy #AI & Data Science #Hardware & Gadgets #Design & UXGDD: Generative Driven Design
Reflective generative AI software components as a development paradigmNowhere has the proliferation of generative AI tooling been more aggressive than in the world of software development. It began with GitHub Copilot’s supercharged autocomplete, then exploded into direct code-along integrated tools like Aider and Cursor that allow software engineers to dictate instructions and have the generated changes applied live,...
Share on Facebook
Share in Tweet (rip twitter)
Share on LinkedIn
Share on Reddit
Share on Threads
Share on Bluesky
Transforming Data into Solutions: Building a Smart App with Python and AI
Some financial analysts worry that artificial intelligence may not justify the massive investments being made in the field. While I understand their concerns, I see things differently. I’m neither an AI Boomer nor an AI Doomer—I believe AI has the potential to drive innovation, enhance productivity, and deliver measurable business outcomes.In my last article, I explored how Large Language Models (LLMs) can be used to...
Multi-Agentic RAG with Hugging Face Code Agents
Using Qwen2.5–7B-Instruct powered code agents to create a local, open source, multi-agentic RAGsystemPhoto by Jaredd Craig onUnsplashLarge Language Models have shown impressive capabilities and they are still undergoing steady improvements with each new generation of models released. Applications such as chatbots and summarisation can directly exploit the language proficiency of LLMs as they are only required to...
Stop the Count! Why Putting A Time Limit on Metrics is Critical for Fast and Accurate Experiments
Why your experiments might never reach significancePhoto by Andrik Langfield onUnsplashIntroductionExperiments usually compare the frequency of an event (or some other sum metric) after either exposure (treatment) or non-exposure (control) to some intervention. For example: we might compare the number of purchases, minutes spent watching content, or number of clicks on a call-to-action.While this setup may seem plain,...
Partial Dependence Plots: How to Discover Variables Influencing a Model
Have you ever wondered how machine learning models are constructed? ‘Explainability of machine learning models’ and ‘machine learning…Continue reading on Towards Data Science »
Top 12 Skills Data Scientists Need to Succeed in 2025
It’s (not) all about LLMs and AI toolsContinue reading on Towards Data Science »
Creating SMOTE Oversampling from Scratch
A Python tutorial on how to implement oversampling and how to make custom variationsContinue reading on Towards Data Science »
Paradigm Shifts of Eval in the Age of LLM
Paradigm Shifts of Eval in the Age ofLLMsLLMs requires some subtle, conceptually simple, yet important changes in the way we think about evaluationI’ve been building evaluation for ML systems throughout my career. As head of data science at Quora, we built eval for feed ranking, ads, content moderation, etc. My team at Waymo built eval for self-driving cars. Most recently, at our fintech startup Coverbase, we use LLMs...
The Most Popular KDnuggets Articles of 2024
Let's have a look at the most popular articles on KDnuggets this past year. How many have you read?
The Math Behind In-Context Learning
From attention to gradient descent: unraveling how transformers learn fromexamplesIn-context learning (ICL)—a transformer’s ability to adapt its behavior based on examples provided in the input prompt—has become a cornerstone of modern LLM usage. Few-shot prompting, where we provide several examples of a desired task, is particularly effective at showing an LLM what we want it to do. But here’s the interesting part:...
The Key to Smarter Models: Tracking Feature Histories
Capture context and improve predictions with historical dataContinue reading on Towards Data Science »
Lessons from COVID-19: Why Probability Distributions Matter
Understanding Distributions with Extremes: Probability for Data Science Series (END)Continue reading on Towards Data Science »
From Default Python Line Chart to Journal-Quality Infographics
Transform boring default Matplotlib line charts into stunning, customized visualizationsCover, image by theAuthorEveryone who has used Matplotlib knows how ugly the default charts look like. In this series of posts, I’ll share some tricks to make your visualizations stand out and reflect your individual style.We’ll start with a simple line chart, which is widely used. The main highlight will be adding a gradient fill...
How to Ensure the Stability of a Model Using Jackknife Estimation
How to ensure the robustness of a model and detect influential data observationsContinue reading on Towards Data Science »
Building a Custom AI Jira Agent
How I used Google Mesop, Django, LangChain Agents, CO-STAR & Chain-of-Thought (CoT) prompting combined with the Jira API to better automateJiraPhoto by Google DeepMind onUnsplashThe inspiration for this project came from hosting a Jira ticket creation tool on a web application I had developed for internal users. I also added automated Jira ticket creation upon systemerrors.Users and system errors often create...
Mastering Model Uncertainty: Thresholding Techniques in Deep Learning
Image generated byDall-eA few words on thresholding, the softmax activation function, introducing an extra label, and considerations regarding output activation functions.In many real-world applications, machine learning models are not designed to make decisions in an all-or-nothing manner. Instead, there are situations where it is more beneficial for the model to flag certain predictions for human review—a process...
How to Build a Resume Optimizer with AI
Step-by-step guide with example PythoncodeIn a previous blog post, I shared 5 AI Projects You Can Build This Weekend, where the first project idea was a resume optimization tool. Since then, many people have asked for more guidance on implementing this project. In this article, I’ll walk through an example implementation using Python and OpenAI’sAPI.Image from...
Job Hunting in 2025: What You Need to Know
This is a quick shortlist to make sure you’re ticking off the essentials for your job hunt in 2025.
How to Build a Graph RAG App
Image byAuthorUsing knowledge graphs and AI to retrieve, filter, and summarize medical journalarticlesThe accompanying code for the app and notebook arehere.Knowledge graphs (KGs) and Large Language Models (LLMs) are a match made in heaven. My previous posts discuss the complementarities of these two technologies in more detail but the short version is, “some of the main weaknesses of LLMs, that they are black-box...
I Combined the Blockchain and AI to Generate Art. Here’s What Happened Next.
Using LLMs to create artistic representations of dataContinue reading on Towards Data Science »
Ten predictions for data science and AI in 2025
On agents, open source models, safety, and moreContinue reading on Towards Data Science »
Machine Learning Salaries and Job Market Analysis for 2024 and Beyond
One of the most talked-about niches in tech is machine learning (ML), as developments in this area are expected to have a significant impact on IT as well as other industries.
Introducing n-Step Temporal-Difference Methods
Dissecting “Reinforcement Learning” by Richard S. Sutton with custom Python implementations, Episode VContinue reading on Towards Data Science »
Superposition: What Makes it Difficult to Explain Neural Network
When there are more features than model dimensionsIntroductionIt would be ideal if the world of neural network represented a one-to-one relationship: each neuron activates on one and only one feature. In such a world, interpreting the model would be straightforward: this neuron fires for the dog ear feature, and that neuron fires for the wheel of cars. Unfortunately, that is not the case. In reality, a model with...
Segmenting Water in Satellite Images Using Paligemma
Some insights on using Google’s latest Vision LanguageModelHutt Lagoon, Australia. Depending on the season, time of day, and cloud coverage, this lake changes from red to pink or purple. Source: GoogleMaps.Multimodal models are architectures that simultaneously integrate and process different data types, such as text, images, and audio. Some examples include CLIP and DALL-E from OpenAI, both released in 2021. CLIP...
Deep Dive into Multithreading, Multiprocessing, and Asyncio
How to choose the right concurrency modelImage by Paul Esch-Laurent fromUnsplashPython provides three main approaches to handle multiple tasks simultaneously: multithreading, multiprocessing, andasyncio.Choosing the right model is crucial for maximising your program’s performance and efficiently using system resources. (P.S. It is also a common interview question!)Without concurrency, a program processes only one task...
Measuring Cross-Product Adoption Using dbt_set_similarity
Enhancing cross-product insights within dbt workflowsIntroductionFor multi-product companies, one critical metric is often what is called “cross-product adoption”. (i.e. understanding how users engage with multiple offerings in a given product portfolio)One measure suggested to calculate cross-product or cross-feature usage in the popular book Hacking Growth [1] is the Jaccard Index. Traditionally used to measure the...
Building Trust in LLM Answers: Highlighting Source Texts in PDFs
100% accuracy isn’t everything: helping users navigate the document is the real valueContinue reading on Towards Data Science »
Introduction to the Finite Normal Mixtures in Regression with
Introduction to the Finite Normal Mixtures in Regression withRHow to make linear regression flexible enough for non-linear dataThe linear regression is usually considered not flexible enough to tackle the nonlinear data. From theoretical viewpoint it is not capable to dealing with them. However, we can make it work for us with any dataset by using finite normal mixtures in a regression model. This way it becomes a very...
Master Bots Before Starting with AI Agents: Simple Steps to Create a Mastodon Bot with Python
I recently published a post on Mastodon that was shared by six other accounts within two minutes. Curious, I visited the profiles and…Continue reading on Towards Data Science »
Unlocking the Untapped Potential of Retrieval-Augmented Generation (RAG) Pipelines
Essential Metrics and Methods to Enhance Performance Across Retrieval, Generation, and End-to-End PipelinesContinue reading on Towards Data Science »
Top 10 Data Science Trends That Defined 2024
From the unstoppable rise of generative AI to sustainability-driven innovations: a retrospective analysis of the data science trends that revolutionized the field in 2024 and beyond.
How To Start A Data Science Blog on Medium
Tips on how to get started, write your first article, and get noticedContinue reading on Towards Data Science »
Understanding the Optimization Process Pipeline in Linear Programming
The post describes the backend and frontend processes in linear programming including the mathematical programming system (mps) files, problem matrix, optimization processes, results extraction, and solution files using an open-source solver called HiGHS with its Python wrapper calledhighspy.In this 2021 post, I demonstrated how linear optimization problems could be solved using the Pyomo package in Python and the JuMP...
Track Computer Vision Experiments with MLflow
Discover how to set up an efficient MLflow environment to track your experiments, compare and choose the best model for deploymentContinue reading on Towards Data Science »