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This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI agents are not your “coworkers” Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool—one…
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context. The post How Far Can Classical NLP Go? Fro
Natural language processing is reshaping professional communication on online platforms, enabling more relevant and personalised networking interactions. As AI-driven systems increasingly comprehend and generate human language, these technological advances affect how users pursue and maintain profes
Indian businesses are racing towards AI-powered customer support, with nearly 78% expecting AI agents to handle interactions soon. However, a significant gap exists between these ambitions and operational readiness, particularly concerning data infrastructure and quality. While companies see early g
Modern businesses are in a constant, uphill battle against what to do with unstructured data: PDFs, contracts, scanned images, customer The post Template-based data extraction is dead. Here’s what comes next. appeared first on The New Stack.
Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large The post Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the page-alignment step everyone forgets The post Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section appear
I'm a junior SRE during the day. For the last few months I've spent nights and weekends building ResumeSnap, a tool that takes your experience plus a job posting and writes a resume tuned to that role in about a minute. Six people have paid for it so far. That's real money from strangers who found i
Overview Tokenization is the first step of almost every NLP pipeline. NLTK's sent_tokenize uses Punkt — an unsupervised ML model trained on abbreviation lists — to split sentences. word_tokenize then applies a regex with Penn Treebank conventions. Both are high-quality, widely used, and measurably s
