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The behaviors that get you promoted
The post How to Climb the Hidden Career Ladder of Data Science appeared first on Towards Data Science.
In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error.
Today, for Day 7 of the Machine Learning "Advent Calendar", we switch to classification. With just one numerical feature and two classes, we explore how a Decision Tree Classifier decides where to cut the data, using impurity measures like Gini and Entropy.
Even without doing the math, we can visually guess possible split points. But which one is best? And do impurity measures really make a difference? Let us build the first split step by step in Excel and see what happens.
The post The Machine Learning “Advent Calendar” Day 7: Decision Tree Classifier appeared first on Towards Data Science.
Understanding AI in 2026 — from machine learning to generative models
The post Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI — Clearly Explained appeared first on Towards Data Science.
For powering next-generation AI models in 2026, Bright Data’s Web Scraper API delivers on all fronts: dynamic site support, anti-bot automation, structured output, and global reach.
Remember this Quora comment (which also became a meme)?(Source: Quora)In the pre-large language model (LLM) Stack Overflow era, the challenge was discerning which code snippets to adopt and adapt effectively. Now, while generating code has become trivially easy, the more profound challenge lies in reliably identifying and integrating high-quality, enterprise-grade code into production environments.This article will examine the practical pitfalls and limitations observed when engineers use modern coding agents for real enterprise work, addressing the more complex issues around integration, scalability, accessibility, evolving security practices, data privacy and maintainability in live operational settings. We hope to balance out the hype and provide a more technically-grounded view of the capabilities of AI coding agents. Limited domain understanding and service limits
AI agents struggle significantly with designing scalable systems due to the sheer explosion of choices and a critical lack of enterprise-specific context. To describe the problem in broad strokes, large enterprise codebases and monorepos are often too vast for agents to directly learn from, and crucial knowledge can be frequently fragmented across internal documentation and individual expertise.More specifically, many popular coding agents encounter service limits that hinder their effectiveness in large-scale environments. Indexing features may fail or degrade in quality for repositories exceeding 2,500 files, or due to memory constraints. Furthermore, files larger than 500 KB are often excluded from indexing/search, which impacts established products with decades-old, larger code files (although newer projects may admittedly face this less frequently).For complex tasks involving extensive file contexts or refactoring, developers are expected to provide the relevant files and while also explicitly defining the refactoring procedure and the surrounding build/command sequences to validate the implementation without introducing feature regressions.Lack of hardware context and usage
AI agents have demonstrated a critical lack of awareness regarding OS machine, command-line and environment installations (conda/venv). This deficiency can lead to frustrating experiences, such as the agent attempting to execute Linux commands on PowerShell, which can consistently result in ‘unrecognized command’ errors. Furthermore, agents frequently exhibit inconsistent ‘wait tolerance’ on reading command outputs, prematurely declaring an inability to read results (and moving ahead to either retry/skip) before a command has even finished, especially on slower machines.This isn't merely about nitpicking features; rather, the devil is in these practical details. These experience gaps manifest as real points of friction and necessitate constant human vigilance to monitor the agent’s activity in real-time. Otherwise, the agent might ignore initial tool call information and either stop prematurely, or proceed with a half-baked solution requiring undoing some/all changes, re-triggering prompts and wasting tokens. Submitting a prompt on a Friday evening and expecting the code updates to be done when checking on Monday morning is not guaranteed.Hallucinations over repeated actions
Working with AI coding agents often presents a longstanding challenge of hallucinations, or incorrect or incomplete pieces of information (such as small code snippets) within a larger set of changesexpected to be fixed by a developer with trivial-to-low effort. However, what becomes particularly problematic is when incorrect behavior is repeated within a single thread, forcing users to either start a new thread and re-provide all context, or intervene manually to “unblock” the agent.For instance, during a Python Function code setup, an agent tasked with implementing complex production-readiness changes encountered a file (see below) containing special characters (parentheses, period, star). These characters are very common in computer science to denote software versions.
(Image created manually with boilerplate code. Source: Microsoft Learn and Editing Application Host File (host.json) in Azure Portal)The agent incorrectly flagged this as an unsafe or harmful value, halting the entire generation process. This misidentification of an adversarial attack recurred 4 to 5 times despite various prompts attempting to restart or continue the modification. This version format is in-fact boilerplate, present in a Python HTTP-trigger code template. The only successful workaround involved instructing the agent to not read the file, and instead request it to simply provide the desired configuration and assure it that the developer will manually add it to that file, confirm and ask it to continue with remaining code changes.The inability to exit a repeatedly faulty agent output loop within the same thread highlights a practical limitation that significantly wastes development time. In essence, developers tend to now spend time on debugging/refining AI-generated code rather than Stack Overflow code snippets or their own.Lack of enterprise-grade coding practices
Security best practices: Coding agents often default to less secure authentication methods like key-based authentication (client secrets) rather than modern identity-based solutions (such as Entra ID or federated credentials). This oversight can introduce significant vulnerabilities and increase maintenance overhead, as key management and rotation are complex tasks increasingly restricted in enterprise environments.Outdated SDKs and reinventing the wheel: Agents may not consistently leverage the latest SDK methods, instead generating more verbose and harder-to-maintain implementations. Piggybacking on the Azure Function example, agents have outputted code using the pre-existing v1 SDK for read/write operations, rather than the much cleaner and more maintainable v2 SDK code. Developers must research the latest best practices online to have a mental map of dependencies and expected implementation that ensures long-term maintainability and reduces upcoming tech migration efforts.Limited intent recognition and repetitive code: Even for smaller-scoped, modular tasks (which are typically encouraged to minimize hallucinations or debugging downtime) like extending an existing function definition, agents may follow the instruction literally and produce logic that turns out to be near-repetitive, without anticipating the upcoming or unarticulated needs of the developer. That is, in these modular tasks the agent may not automatically identify and refactor similar logic into shared functions or improve class definitions, leading to tech debt and harder-to-manage codebases especially with vibe coding or lazy developers.Simply put, those viral YouTube reels showcasing rapid zero-to-one app development from a single-sentence prompt simply fail to capture the nuanced challenges of production-grade software, where security, scalability, maintainability and future-resistant design architectures are paramount.Confirmation bias alignmentConfirmation bias is a significant concern, as LLMs frequently affirm user premises even when the user expresses doubt and asks the agent to refine their understanding or suggest alternate ideas. This tendency, where models align with what they perceive the user wants to hear, leads to reduced overall output quality, especially for more objective/technical tasks like coding.There is ample literature to suggest that if a model begins by outputting a claim like “You are absolutely right!”, the rest of the output tokens tend to justify this claim.Constant need to babysitDespite the allure of autonomous coding, the reality of AI agents in enterprise development often demands constant human vigilance. Instances like an agent attempting to execute Linux commands on PowerShell, false-positive safety flags or introduce inaccuracies due to domain-specific reasons highlight critical gaps; developers simply cannot step away. Rather, they must constantly monitor the reasoning process and understand multi-file code additions to avoid wasting time with subpar responses.The worst possible experience with agents is a developer accepting multi-file code updates riddled with bugs, then evaporating time in debugging due to how ‘beautiful’ the code seemingly looks. This can even give rise to the sunk cost fallacy of hoping the code will work after just a few fixes, especially when the updates are across multiple files in a complex/unfamiliar codebase with connections to multiple independent services.It's akin to collaborating with a 10-year old prodigy who has memorized ample knowledge and even addresses every piece of user intent, but prioritizes showing-off that knowledge ove solving the actual problem, and lacks the foresight required for success in real-world use cases.This "babysitting" requirement, coupled with the frustrating recurrence of hallucinations, means that time spent debugging AI-generated code can eclipse the time savings anticipated with agent usage. Needless to say, developers in large companies need to be very intentional and strategic in navigating modern agentic tools and use-cases.ConclusionThere is no doubt that AI coding agents have been nothing short of revolutionary, accelerating prototyping, automating boilerplate coding and transforming how developers build. The real challenge now isn’t generating code, it’s knowing what to ship, how to secure it and where to scale it. Smart teams are learning to filter the hype, use agents strategically and double down on engineering judgment. As GitHub CEO Thomas Dohmke recently observed: The most advanced developers have “moved from writing code to architecting and verifying the implementation work that is carried out by AI agents.” In the agentic era, success belongs not to those who can prompt code, but those who can engineer systems that last.Rahul Raja is a staff software engineer at LinkedIn.Advitya Gemawat is a machine learning (ML) engineer at Microsoft.Editors note: The opinions expressed in this article are the authors' personal opinions and do not reflect the opinions of their employers.
AI saves life, live AI avatar, Nano-Banana prompting guide, AI memory, and more...
The V-JEPA system uses ordinary videos to understand the physics of the real world.
How I keep up with papers with a mix of manual and AI-assisted reading
The post Reading Research Papers in the Age of LLMs appeared first on Towards Data Science.
During the first days of this Machine Learning Advent Calendar, we explored models based on distances. Today, we switch to a completely different way of learning: Decision Trees.
With a simple one-feature dataset, we can see how a tree chooses its first split. The idea is always the same: if humans can guess the split visually, then we can rebuild the logic step by step in Excel.
By listing all possible split values and computing the MSE for each one, we identify the split that reduces the error the most. This gives us a clear intuition of how a Decision Tree grows, how it makes predictions, and why the first split is such a crucial step.
The post The Machine Learning “Advent Calendar” Day 6: Decision Tree Regressor appeared first on Towards Data Science.
Testing that your AI agent is performing as expected is not easy. Here are a few strategies we learned the hard way.
The post How We Are Testing Our Agents in Dev appeared first on Towards Data Science.
Gemini takes off, memory crisis, AI game backlash, cyberterror weapon, and more...
In this episode of Uncanny Valley, we bring you the news of the week, then dive into how some DOGE operatives are still at work in the federal government—despite reports claiming otherwise.
Here are Google’s latest AI updates from November 2025
Cursor is great at writing code but not as good when it comes to design
The post The Step-by-Step Process of Adding a New Feature to My IOS App with Cursor appeared first on Towards Data Science.
This article introduces the Gaussian Mixture Model as a natural extension of k-Means, by improving how distance is measured through variances and the Mahalanobis distance. Instead of assigning points to clusters with hard boundaries, GMM uses probabilities learned through the Expectation–Maximization algorithm – the general form of Lloyd’s method.
Using simple Excel formulas, we implement EM step by step in 1D and 2D, and we visualise how the Gaussian curves or ellipses move during training. The means shift, the variances adjust, and the shapes gradually settle around the true structure of the data.
GMM provides a richer, more flexible way to model clusters, and becomes intuitive once the process is made visible in a spreadsheet.
The post The Machine Learning “Advent Calendar” Day 5: GMM in Excel appeared first on Towards Data Science.
Google Workspace has released findings from our second survey that looks at how people aged 22-39 are using AI at work. Commissioned by Workspace in partnership with the…
Build with Gemini 3 Pro, the best model in the world for multimodal capabilities.
AI startups say the promise of turning dazzling models into useful products is harder than anyone expected. Three founders discuss what it takes.
What a simple puzzle game reveals about experimentation, product thinking, and data science
The post A Product Data Scientist’s Take on LinkedIn Games After 500 Days of Play appeared first on Towards Data Science.
Pixi makes python environment management simple, consistent, and portable.
A detailed walkthrough of the YOLOv1 architecture and its PyTorch implementation from scratch
The post YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World appeared first on Towards Data Science.
This article is for vibe coders and developers seeking private, fast, and affordable AI coding solutions.
Three years ago, ChatGPT was born. It amazed the world and ignited unprecedented investment and excitement in AI. Today, ChatGPT is still a toddler, but public sentiment around the AI boom has turned sharply negative. The shift began when OpenAI released GPT-5 this summer to mixed reviews, mostly from casual users who, unsurprisingly, judged the system by its surface flaws rather than its underlying capabilities.Since then, pundits and influencers have declared that AI progress is slowing, that scaling has “hit the wall,” and that the entire field is just another tech bubble inflated by blusterous hype. In fact, many influencers have latched onto the dismissive phrase “AI slop” to diminish the amazing images, documents, videos and code that frontier AI models generate on command.This perspective is not just wrong, it is dangerous.It makes me wonder, where were all these “experts” on irrational technology bubbles when electric scooter startups were touted as a transportation revolution and cartoon NFTs were being auctioned for millions? They were probably too busy buying worthless land in the metaverse or adding to their positions in GameStop. But when it comes to the AI boom, which is easily the most significant technological and economic transformation agent of the last 25 years, journalists and influencers can’t write the word “slop” enough times. Doth we protest too much? After all, by any objective measure AI is wildly more capable than the vast majority of computer scientists predicted only five years ago and it is still improving at a surprising pace. The impressive leap demonstrated by Gemini 3 is only the latest example. At the same time, McKinsey recently reported that 20% of organizations already derive tangible value from genAI. Also, a recent survey by Deloitte indicates that 85% of organizations boosted their AI investment in 2025, and 91% plan to increase again in 2026.This doesn’t fit the “bubble” narrative and the dismissive “slop” language. As a computer scientist and research engineer who began working with neural networks back in 1989 and tracked progress through cold winters and hot booms ever since, I find myself amazed almost every day by the rapidly increasing capabilities of frontier AI models. When I talk with other professionals in the field, I hear similar sentiments. If anything, the rate of AI advancement leaves many experts feeling overwhelmed and frankly somewhat scared. The dangers of AI denialSo why is the public buying into the narrative that AI is faltering, that the output is “slop,” and that the AI boom lacks authentic use cases? Personally, I believe it’s because we’ve fallen into a collective state of AI denial, latching onto the narratives we want to hear in the face of strong evidence to the contrary. Denial is the first stage of grief and thus a reasonable reaction to the very disturbing prospect that we humans may soon lose cognitive supremacy here on planet earth. In other words, the overblown AI bubble narrative is a societal defense mechanism. Believe me, I get it. I’ve been warning about the destabilizing risks and demoralizing impact of superintelligence for well over a decade, and I too feel AI is getting too smart too fast. The fact is, we are rapidly headed towards a future where widely available AI systems will be able to outperform most humans in most cognitive tasks, solving problems faster, more accurately and yes, more creatively than any individual can. I emphasize “creativity” because AI denialists often insist that certain human qualities (particularly creativity and emotional intelligence) will always be out of reach of AI systems. Unfortunately, there is little evidence supporting this perspective.On the creativity front, today’s AI models can generate content faster and with more variation than any individual human. Critics argue that true creativity requires inner motivation. I resonate with that argument but find it circular — we're defining creativity based on how we experience it rather than the quality, originality or usefulness of the output. Also, we just don’t know if AI systems will develop internal drives or a sense of agency. Either way, if AI can produce original work that rivals most human professionals, the impact on creative jobs will still be quite devastating.The AI manipulation problemOur human edge around emotional intelligence is even more precarious. It’s likely that AI will soon be able to read our emotions faster and more accurately than any human, tracking subtle cues in our micro-expressions, vocal patterns, posture, gaze and even breathing. And as we integrate AI assistants into our phones, glasses and other wearable devices, these systems will monitor our emotional reactions throughout our day, building predictive models of our behaviors. Without strict regulation, which is increasingly unlikely, these predictive models could be used to target us with individually optimized influence that maximizes persuasion.This is called the AI manipulation problem and it suggests that emotional intelligence may not give humanity an advantage. In fact, it could be a significant weakness, fostering an asymmetric dynamic where AI systems can read us with superhuman accuracy, while we can’t read AI at all. When you talk with photorealistic AI agents (and you will) you’ll see a smiling façade designed to appear warm, empathic and trustworthy. It will look and feel human, but that’s just an illusion, and it could easily sway your perspectives. After all, our emotional reactions to faces are visceral reflexes shaped by millions of years of evolution on a planet where every interactive human face we encountered was actually human. Soon, that will no longer be true.We are rapidly heading toward a world where many of the faces we encounter will belong to AI agents hiding behind digital facades. In fact, these “virtual spokespeople” could easily have appearances that are designed for each of us based on our prior reactions – whatever gets us to best let down our guard. And yet many insist that AI is just another tech cycle.This is wishful thinking. The massive investment pouring into AI isn’t driven by hype — it’s driven by the expectation that AI will permeate every aspect of daily life, embodied as intelligent actors we engage throughout our day. These systems will assist us, teach us and influence us. They will reshape our lives, and it will happen faster than most people think.To be clear, we are not witnessing an AI bubble filling with empty gas. We are watching a new planet form, a molten world rapidly taking shape, and it will solidify into a new AI-powered society. Denial will not stop this. It will only make us less prepared for the risks.Louis Rosenberg is an early pioneer of augmented reality and a longtime AI researcher.
How to upgrade and optimize legacy AI/ML models
The post On the Challenge of Converting TensorFlow Models to PyTorch appeared first on Towards Data Science.
Reddit is considered one of the most human spaces left on the internet, but mods and users are overwhelmed with slop posts in the most popular subreddits.
An AI image generator startup’s database was left accessible to the open internet, revealing more than 1 million images and videos, including photos of real people who had been “nudified.”
The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.
In January 2024, the phone rang in homes all around New Hampshire. On the other end was Joe Biden’s voice, urging Democrats to “save your vote” by skipping the primary. It sounded authentic, but it wasn’t. The call was a fake, generated by artificial intelligence. Today, the technology behind that hoax looks quaint. Tools like…
Court orders handover, Claude's Soul, gorilla warning, Apple's app awards, and more...