The article describes a tool called CodeOwnership that measures how much of the code an AI writes can be attributed to the user. The author shares a personal experiment where they rated their confidence at 4 out of 5 and then spent 8 minutes trying to locate a planted bug, ultimately finding none. They provide a walkthrough of the tool’s usage, its results, and discuss its limitations.
The article argues that AI agents have surpassed human capacity in code production, prompting a shift in perspective from merely using AI to actively employing it. It highlights how this development has altered the author's view on AI’s role in software development. The piece suggests that embracing AI as a collaborator is now essential.
Five scikit-learn defaults that deserve a closer look before your next model reaches production The post Your AI Assistant Wrote the Code. Who Checked the Defaults? appeared first on Towards Data Scie...
One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code? The post AI Made Me 5x Faster. It Also Made Me 5x Wors...
The article discusses how engineers should view the lack of benefit from current AI tools as a finding rather than a failure. It encourages documenting these observations, sharing them with peers and managers, and actively seeking solutions. By explaining where AI falls short, engineers demonstrate the judgment expected of them.
The analytics career I signed up for five years ago doesn't exist anymore, and honestly, I am fine with that. The post How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI appeared first on Towards Data Science .
For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead.
An AI agent passed every metric in the eval harness I published, then the CFO killed it — its successful resolutions cost more than the humans it replaced. The one metric that predicts whether an agent survives production, and how to measure it without a rebuild.
A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science .
The article titled "Everyone Is Selling AI at You — Here’s How to Keep Your Judgement" discusses three strategies for navigating the current AI hype. It offers practical moves to help readers maintain critical thinking and discern genuine value from marketing claims. The piece was originally published on Towards Data Science.
The article titled "Your AI Bill Is a Toll Booth. Stop Paying Twice." discusses how users are unexpectedly paying more for AI services than anticipated, likening the experience to a toll booth where one pays twice. It highlights the unseen costs that can arise when using AI tools and urges readers to be vigilant about their expenses. The piece was first published on Towards Data Science.
You "vibe coded" the import. Understand Adam's optimization dynamics, why it fails spectacularly, and how to fix it.
The article discusses how an agent can continue to produce confident answers even when its tools fail, leading to a misleadingly positive dashboard display. It highlights the issue of agents generating responses from nothing after tool breakdowns, which can create a false sense of reliability. The piece serves as a cautionary note for developers deploying agentic AI systems.