Feature Leakage in Machine Learning: The Silent Killer Destroying Your Model’s Real Performance
Author(s): Rohan Mistry Originally published on Towards AI. Understanding Data Leakage, Target Leakage, and Temporal Leakage — And How to Detect and Prevent Them Your machine learning model achieves 98% accuracy on validation data. Your team celebrates. You deploy to production. Source: …
Google Titans Crushes Transformers: Neural Memory for Infinite Context
Author(s): Divy Yadav Originally published on Towards AI. The powerful shift from the transformer to Titans Remember that time you walked into a room and completely forgot why you went there? That frustrating “brain fart” is your short-term memory failing you. By …
DeepSeek’s Engram: The Missing Primitive That Makes LLMs Stop Wasting Compute on Memory
Author(s): Gowtham Boyina Originally published on Towards AI. The Problem Nobody Noticed On January 13th, DeepSeek dropped a research paper that’s been making waves in the LLM community: “Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models.” …
I Built a Voice Assistant That Actually Understands What I Mean, Not What I Said
Author(s): Gowtham Boyina Originally published on Towards AI. Three months of building. $347 in API costs. A voice assistant that couldn’t tell the difference between “What’s ML?” and machine learning. Then I found Qdrant. Response times dropped from 12 seconds to under …
Build Advanced RAG with LangGraph
Author(s): tanta base Originally published on Towards AI. image by author We all know and love Retrieval-Augmented Generation (RAG). The simplest implementation of Retrieval-Augmented Generation (RAG) is a vector store with documents connected to a Large Language Model to generate a response …
The 7 Essential Types of LLM Benchmarking: A Complete Guide to Evaluating AI Language Models
Author(s): TANVEER MUSTAFA Originally published on Towards AI. The 7 Essential Types of LLM Benchmarking: A Complete Guide to Evaluating AI Language Models As Large Language Models (LLMs) become integral to business operations and everyday applications, understanding their true capabilities has never …
The Barnyard Reality Check: Why Applied AI Is Nothing Like a Web Service
Author(s): Vladimir Artus Originally published on Towards AI. Image generated with Midjourney. Introduction In the sterile vacuum of a Jupyter notebook, building AI feels like a clean, linear process. You collect data, you train a model, you wrap it in an API, …
From Model Releases to Model Reliability: AI’s 2026 Reality
Author(s): TANVEER MUSTAFA Originally published on Towards AI. From Model Releases to Model Reliability: AI’s 2026 Reality The artificial intelligence landscape stands at a critical inflection point. After years of breakneck innovation marked by increasingly powerful models, breakthrough architectures, and headline-grabbing demonstrations, …
The Anti-LLM: Yann LeCun’s $3.5 Billion Bet on World Models
Author(s): Mandar Karhade, MD. PhD. Originally published on Towards AI. Why the godfather of AI is leaving Silicon Valley to build a machine that thinks before it speaks Silicon Valley is currently in the grip of a fever dream, hypnotized by the …
Important LLM Papers for the Week From 12/01/2026 To 17/01/2026
Author(s): Youssef Hosni Originally published on Towards AI. Stay Updated with Recent Large Language Models Research Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers must stay informed about the latest …
No Libraries No Shortcuts: Reasoning LLMs from Scratch with PyTorch — Part 2
Author(s): Ashish Abraham Originally published on Towards AI. The no BS Guide to implementing reasoning models from scratch with SFT & RL In Part 1 of this series, we laid the groundwork for understanding how reasoning large language models (LLMs) can be …
DAX Measure Library Architecture: From Messy to Maintainable
Author(s): Gulab Chand Tejwani Originally published on Towards AI. How we stopped wasting $93,600 per year searching for measures we’d already built The Slack message appeared at 2:37 PM on a Tuesday. DAX Measure Library ArchitectureThe article discusses the author’s struggle to …
Unlocking the Magic of Adam: The Math Behind Deep Learning’s Favorite Optimizer
Author(s): Raaja Selvanaathan DATCHANAMOURTHY Originally published on Towards AI. Source: Author At the heart of every deep learning model lies a simple goal: minimizing error. We measure this error using something called a cost Function (or objective function). But knowing the error …
AI Agents in 2026: The Data Problem No One Mentions
Author(s): Ahmed M. Abdelfattah Originally published on Towards AI. Why vendors promise 3–5 employee productivity but Forrester finds 0% improvement and what your data infrastructure needs before deployment works Google Cloud claims AI agents deliver productivity equivalent to hiring 3–5 employees. Forrester’s …
AI’s Next Strategic Phase: From Lab Curiosity to Core Economy Driver
Author(s): Vivek Acharya Originally published on Towards AI. AI’s Next Strategic Phase: From Lab Curiosity to Core Economy Driver AI is undergoing a profound strategic shift. Not long ago, success in AI was measured by flashy model demos and incremental accuracy gains. …
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