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Most of the issues practitioners encountered when LLMs first burst onto the scene have turn out to be more manageable up to now couple of years. Poor reasoning and limited context-window size come to mind.
Today, models’ raw power isn’t a blocker. What stays a pain point, nonetheless, is our ability to extract meaningful outputs out of LLMs in a cost- and time-effective way.
Previous Variable editions have devoted numerous space to prompt engineering, which stays a necessary tool for anyone working with LLMs. This week, though, we’re turning the highlight on newer approaches that aim to push our AI-powered workflows to the subsequent level. Let’s dive in.
Beyond Prompting: The Power of Context Engineering
To learn how one can create self-improving LLM workflows and structured playbooks, don’t miss Mariya Mansurova‘s comprehensive guide. It traces the history of context engineering, unpacks the emerging role of agents, and bridges the theory-to-practice gap with a whole, hands-on example.
Understanding Vibe Proving
“After Vibe Coding,” argues Jacopo Tagliabue, “we appear to have entered the (very area of interest, but much cooler) era of Vibe Proving.” Learn all concerning the promise of sturdy LLM reasoning that follows a verifiable, step-by-step logic.
Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Automobile Example
As a substitute of leaving prompts entirely behind, Vincent Koc’s deep dive shows how one can leverage agents to offer prompting a considerable performance boost.
This Week’s Most-Read Stories
In case you missed them, listed here are the three articles that resonated essentially the most with our readers up to now week.
The Great Data Closure: Why Databricks and Snowflake Are Hitting Their Ceiling, by Hugo Lu
Acquisitions, enterprise, and an increasingly competitive landscape all point to a market ceiling.
Easy methods to Maximize Claude Code Effectiveness, by Eivind Kjosbakken
Learn how one can get essentially the most out of agentic coding.
Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels, by Ryan Pégoud
Why your final LLM layer is OOMing and how one can fix it with a custom Triton kernel.
Other Advisable Reads
From data poisoning to topic modeling, we’ve chosen a few of our favourite recent articles, covering a big selection of topics, concepts, and tools.
- Do You Smell That? Hidden Technical Debt in AI Development, by Erika Gomes-Gonçalves
- Data Poisoning in Machine Learning: Why and How People Manipulate Training Data, by Stephanie Kirmer
- From RGB to Lab: Addressing Color Artifacts in AI Image Compositing, by Eric Chung
- Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries, by Petr Koráb, Martin Feldkircher, and Márton Kardos
- Why Human-Centered Data Analytics Matters More Than Ever, by Rashi Desai
Meet Our Latest Authors
We hope you’re taking the time to explore excellent work from TDS contributors who recently joined our community:
- Gary Zavaleta checked out the built-in limitations of self-service analytics.
- Leigh Collier devoted her debut TDS article to the risks of using Google Trends in machine learning projects.
- Dan Yeaw walked us through the advantages of sharded indexing patterns for package management.
The previous few months have produced strong results for participants in our Creator Payment Program, so if you happen to’re enthusiastic about sending us an article, now’s nearly as good a time as any!
