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Unlocking Faster Insights: How Cloudera and Cohere can deliver Smarter Document Analysis

Cloudera

Document analysis is crucial for efficiently extracting insights from large volumes of text. For example, cancer researchers can use document analysis to quickly understand the key findings of thousands of research papers on a certain type of cancer, helping them identify trends and knowledge gaps needed to set new research priorities.

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10 MongoDB Mini Projects Ideas for Beginners with Source Code

ProjectPro

MongoDB stores data in collections of JSON documents in a human-readable format. It is also compatible with IDEs like Studio3T, JetBrains (DataGrip), and VS Code. MongoDB’s scale-out architecture allows you to shard data to handle fast querying and documentation of massive datasets. Link to the source code.

MongoDB 66
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Code Review on Printed Paper: an Excerpt from the Twitoons Comic Book

The Pragmatic Engineer

Today’s newsletter closes with a full chapter from this book, visualizing when Elon Musk demanded all Twitter software engineers print out their code on paper (!!) and report for code review. Code review on printed paper: an excerpt from the Twitoons book A year ago, the end of October 2022 was a very turbulent time at Twitter.

Coding 231
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Introducing Agent Bricks: Auto-Optimized Agents Using Your Data

databricks

With building conversational agents over documents, for example, we measured quality average across several Q&A benchmarks. Figure 1 Figure 2 For document understanding, Agent Bricks builds higher quality and lower cost systems, compared to prompt optimized proprietary LLMs (Figure 2). Agent Bricks is now available in beta.

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Building a Custom PDF Parser with PyPDF and LangChain

KDnuggets

It will be used to extract the text from PDF files LangChain: A framework to build context-aware applications with language models (we’ll use it to process and chain document tasks). Tools Required(requirements.txt) The necessary libraries required are: PyPDF : A pure Python library to read and write PDF files.

Building 107
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An educational side project

The Pragmatic Engineer

for the simulation engine Go on the backend PostgreSQL for the data layer React and TypeScript on the frontend Prometheus and Grafana for monitoring and observability And if you were wondering how all of this was built, Juraj documented his process in an incredible, 34-part blog series. Documenting the steps. You can read this here.

Education 364
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Automating GitHub Workflows with Claude 4

KDnuggets

By Abid Ali Awan , KDnuggets Assistant Editor on June 13, 2025 in Programming Image by Author Claude Opus 4 is Anthropics most advanced and powerful AI model to date, setting a new benchmark for coding, reasoning, and long-running tasks. Copy the authentication code generated by the console and paste it into the Claude Code terminal.