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Like so many LLM-based workflows before it, vibe coding has attracted strong opposition and sharp criticism not since it offers no value, but resulting from unrealistic, hype-based expectations.
The thought of leveraging powerful AI tools to experiment with app-building, generate quick-and-dirty prototypes, and iterate quickly seems noncontroversial. The issues often begin when human practitioners take whatever output the model produced and assume it’s robust and error-free.
To assist us sort through the great, bad, and ambiguous features of vibe coding, we turn to our experts. The lineup we prepared for you this week offers nuanced and pragmatic takes on how AI code assistants work, and when and methods to use them.
The Unbearable Lightness of Coding
“The quantity of technical doubt weighs heavily on my shoulders, rather more than I’m used to.” In her powerful, brutally honest “confessions of a vibe coder,” Elena Jolkver takes an unflinching have a look at what it means to be a developer within the age of Cursor, Claude Code, et al. She also argues that the trail forward entails acknowledging each vibe coding’s speed and productivity advantages its (many) potential pitfalls.
The right way to Run Claude Code for Free with Local and Cloud Models from Ollama
If you happen to’re already sold on the promise of AI-assisted coding but are concerned about its nontrivial costs, you shouldn’t miss Thomas Reid’s recent tutorial.
How Cursor Actually Indexes Your Codebase
Interested by the inner workings of probably the most popular vibe-coding tools? Kenneth Leung presents an in depth have a look at the Cursor RAG pipeline that ensures coding agents are efficient at indexing and retrieval.
This Week’s Most-Read Stories
In case you missed them, listed below are three articles that resonated with a large audience up to now week.
Going Beyond the Context Window: Recursive Language Models in Motion, by Mariya Mansurova
Explore a practical approach to analysing massive datasets with LLMs.
Causal ML for the Aspiring Data Scientist, by Ross Lauterbach
An accessible introduction to causal inference and ML.
Optimizing Vector Search: Why You Should Flatten Structured Data, by Oleg Tereshin
An evaluation of how flattening structured data can boost precision and recall by as much as 20%.
Other Really useful Reads
Python skills, MLOps, and LLM evaluation are only just a few of the topics we’re highlighting with this week’s number of top-notch stories.
Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026, by Yassin Zehar
Creating an Etch A Sketch App Using Python and Turtle, by Mahnoor Javed
Machine Learning in Production? What This Really Means, by Sabrine Bendimerad
Evaluating Multi-Step LLM-Generated Content: Why Customer Journeys Require Structural Metrics, by Diana Schneider
Google Trends is Misleading You: The right way to Do Machine Learning with Google Trends Data, by Leigh Collier
Meet Our Recent Authors
We hope you are taking the time to explore excellent work from TDS contributors who recently joined our community:
- Luke Stuckey checked out how neural networks approach the query of musical similarity within the context of suggestion apps.
- Aneesh Patil walked us through a geospatial-data project geared toward estimating neighborhood-level pedestrian risk.
- Tom Narock argues that one of the best approach to tackle data science’s “identity crisis” is by reframing it as an engineering practice.
We love publishing articles from recent authors, so should you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
