Structure-guided NER optimization for enterprise GraphRAG systems
The post Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs appeared first on Towards Data Science.
AI Workflows for Sales Teams: Prospect Research, Lead Qualification, and CRM Updates on Autopilot Using LangGraph
Sales teams spend hours every day on tasks that should never see a human. Research a prospect, score them against their fit, and put it all into a CRM. These are repeatable, rule based processes AI workflows driven by multi-agent systems can do all three, with speed and consistency that no human tea...
Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient
This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them...
RAG Is Burning Money — I Built a Cost Control Layer to Fix It
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without s...
25 Most Influential AI Pioneers to Meet at DataHack Summit 2026
The strongest AI voices are not just people with impressive job titles. They are researchers pushing the technical boundaries of AI. Founders building AI communities. Practitioners turning models into products. Even leaders, helping businesses understand what this technology can actually do. This be...
Claude Opus 4.8: A Smarter Model in the Right Direction
The AI industry has matured to the point where raw intelligence is no longer the only thing that matters. A year ago, every model release was a race to publish bigger benchmark numbers. More parameters, features and everything in between. Today, the conversation is shifting. Developers care about ...
The ‘Entry-Level’ Gatekeeper: Auditing Job Descriptions with Textstat
This article shows how to use free, open-source tools like Python and its Textstat library to build a script that automates the process of capturing "gatekeeping language" in job descriptions before publishing them.
Five Questions About Chronos-2, the Time Series Foundation Model
Part 1: A practitioner's walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting.
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EmoNet: Speaker-Aware Transformers for Emotion Recognition — and What I’d Build Differently in 2026
A retrospective on my MS thesis, the leaderboard it placed on, and the LLM shift that has reshaped the field since.
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The Infrastructure Behind Making Local LLM Agents Actually Useful
Lessons from building a fast, reliable scientific agent with local open-weight models, vLLM, and long-context infrastructure
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DiffuJudge-AV: A Diffusion-Inspired Framework for Calibrated AV Video Evaluation
A diffusion-inspired framework for stress-testing and denoising LLM-as-a-Judge pipelines, applied to safety-critical driving video.
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Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings
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Most AI Agents Fail in Production Because They’re Built Backwards
Good models don't save bad architecture, and most teams learn that the hard way.
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PySpark Optimization: 12 Proven Techniques to Speed Up Your Spark Jobs
Modern data pipelines handle massive volumes of structured and unstructured data every day. As datasets grow, poorly optimized Spark jobs become slower, more expensive, and harder to scale. Common issues include long execution times, excessive shuffling, memory bottlenecks, and inefficient joins. Ef...
The Statistics of Token Selection: Logits, Temperature, and Top-P Walkthrough
When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.
Visual Debugging Tools for Machine Learning Workflows
In this article, we cover three topics: what to visualize during training, the tools that provide those visualizations, and the methods to capture model computations directly using hooks and breakpoints.
How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents
The post Stop Using LLMs Like Giant Problem Solvers appeared first on Towards Data Science.
The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment
How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment.
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