How to Optimize Research Workflows with Tech

If you're a researcher who spends hours manually copying and pasting data from PDFs and web sources into spreadsheets before you can even begin analysis, this one's for you. Traditional research workflows are painfully manual: Find a document → download it → extract text → copy relevant data → paste into spreadsheet → clean and organize → finally start analyzing. Sound familiar? 😅 Last weekend, I created a streamlined system that automates this process: ✅ Step 1: Built a Hypermode agent that scrapes and performs OCR/text extraction from a given URL ✅ Step 2: Agent identifies entities and relationships ✅ Step 3: Agent creates structured database tables with a proper schema based on discovered relationships ✅ Step 4: Connected Anthropic's Claude Desktop to the database via MotherDuck's DuckDB MCP Server for querying and visualization What's so special? I gave the agent a URL and it was able to process multiple linked PDFs from that page about Iranian sanctions (my first test use-case for a project that John Doyle and a few others are working on). No manual downloads and no file uploads. The agent identified key entities, mapped their relationships, and populated a queryable database. Then using Claude Desktop, connected to that database through the MCP server, I was able to ask questions about the data, generate force graphs, and create infographics or dashboards that can be shared. For anyone drowning in manual research processes, this combination of automated data extraction + Claude's analytical capabilities through MCP servers isn't just a productivity boost...it's a fundamental shift in how people of all technical backgrounds can approach data-intensive research. What research workflows are you still doing manually that could benefit from this kind of automation? My next test use-case? Contracts, of course! 📄 #DataVisualization #CTI #OSINT #LegalTech #MCP

Turning AI Anxiety into Advantage: A Practical Guide 🎯 The AI revolution isn't abstract—it's already transforming how we work. Here's your concrete roadmap to mastering AI integration: 1️⃣ Build Your AI Testing Lab Create a personal sandbox environment where you can safely experiment. Start with: • Setting up ChatGPT plugins for your specific workflow • Testing GitHub Copilot if you're in development • Using Claude for complex analysis and writing tasks 2️⃣ Map Your AI Leverage Points Audit your weekly schedule and identify: • Tasks that take >2 hours but could be automated • Repetitive processes that drain your creativity • High-value work that could be enhanced with AI assistance 3️⃣ Master AI-Human Collaboration Learn the art of prompt engineering: • Write structured prompts that generate usable outputs • Break complex problems into AI-solvable components • Develop systems to verify AI-generated work efficiently 4️⃣ Create AI-Enhanced Workflows Build processes that combine AI tools: • Use AI for initial research, human insight for synthesis • Implement AI-powered quality checks in your deliverables • Design feedback loops where AI learns from your corrections 5️⃣ Measure and Optimize Impact Track concrete metrics: • Time saved per task • Quality improvements in outputs • New capabilities unlocked 🔍 Reality Check: The goal isn't to use AI everywhere—it's to identify where AI multiplication creates the highest value in your specific role. 📈 Next Step: Choose one process you'll enhance with AI this week. Start small, measure results, and iterate based on real outcomes. #AIStrategy #WorkflowOptimization #ProductivityTech #AITools #ProfessionalGrowth #USAII  United States Artificial Intelligence Institute

How Mechanical and Materials Engineers Can Start Using AI in Their Work Artificial Intelligence is no longer limited to computer science, it’s becoming an essential tool across disciplines, including engineering and academic research. For mechanical engineers, materials scientists, and educators, here are some practical ways to begin integrating AI into your workflow: 1. Automated Literature Reviews Tools like Elicit, Connected Papers, and ResearchRabbit use AI to identify relevant studies, suggest related work, and even generate summaries; saving hours of manual searching. 2. Data Analysis and Visualization AI-integrated platforms (e.g., PandasAI, ChatGPT Code Interpreter) can help analyze experimental data such as stress-strain curves, thermal profiles, or SEM image results. This can be particularly useful for high-throughput testing or large datasets. 3. Assistance with Simulations For those working with FEA or thermodynamic modeling (e.g., using COMSOL, ANSYS, or CALPHAD), AI tools can help debug code, suggest boundary conditions, or optimize parameters more efficiently. 4. AI in Teaching and Assessment Educators can use AI to generate quizzes, explain complex topics in simpler terms, and even provide feedback on written assignments. It can also support personalized learning pathways for students. 5. AI for Research Planning GPT-based tools can assist with writing research proposals, identifying potential research gaps, and even outlining experimental plans. 6. Exploring AI-Driven Design Algorithms like genetic algorithms, reinforcement learning, or neural networks can be trained to assist in materials discovery, structural optimization, or predictive modeling. Getting Started: • Choose one task from your current workflow (e.g., paper summary, data cleaning, teaching content creation). • Use a trusted AI tool to assist and not replace the process. • Evaluate and refine your use of the tool based on outcomes. AI is not a replacement for engineering knowledge; it’s a powerful extension of it. If you’re already using AI in your work, what tools have been most helpful to you? #AIinEngineering #MechanicalEngineering #MaterialsScience #AcademicResearch #EdTech #CALPHAD #FEA #PhDLife

📒 NLP for Data Analysts & Data Engineers – Beyond Expensive AI 🖋 Day 3: Let’s get straight to it! Today, we’re focusing on two powerful NLP techniques—Text Summarization and Text Matching. These methods can streamline your workflows and extract valuable insights—without relying on LLMs or large transformers! 📑 Text Summarization: Why waste hours sifting through lengthy documents? Text Summarization allows you to quickly extract key insights from extensive texts. 🔑 Methods to Use: Leverage LexRank or TextRank for effective extractive summarization that highlights the most important sentences. 📈 For Data Analysts: Summarize lengthy reports to focus on essential information, making it easier to present findings. 👨🔬 For Data Engineers: Automate summarization to enhance document processing efficiency, reducing manual review time. 📑 Text Matching: Need to connect relevant data? Text Matching identifies the best matches between different text sources, making your data more accessible. 🔑 Methods to Use: Utilize Cosine Similarity or Jaccard Similarity to measure the similarity between texts—an essential skill for data professionals. 📈 For Data Analysts: Match research papers to relevant articles or connect product specifications to related documents. 👨🔬 For Data Engineers: Implement these techniques to swiftly deliver accurate information to users based on their queries. Use Case: Consider a research team tasked with analyzing a large volume of scientific articles. By using Text Summarization, they can extract the main findings from lengthy papers, enabling them to quickly grasp essential information without needing to read each document in full. At the same time, Text Matching can help identify articles with similar themes or methodologies, streamlining the literature review process. This combination allows the team to efficiently navigate unstructured data, making it easier to draw insights and connections without the complexity and resource demands of LLMs. Takeaway: Both techniques are practical, cost-effective solutions that can enhance your data workflows. By mastering these methods, you can unlock powerful insights without relying on expensive solutions. What challenges do you face with summarization or matching? Let’s discuss! #NLP #LargeLanguageModels #LargeLanguageModel #DataAnalyst #DataEngineer #MachineLearning