Matt Navarre’s Post

What's really interesting about FinePDFs is the conclusions you can draw between the quality of LLM output (i.e. syntatical and grammatical validity, readability etc) and the size of the training data set. English is more than 40% of the FinePDF dataset, Icelandic is a tiny minnow by comparison. The same follows of programming languages; Python, TypeScript, JavaScript etc dominate the publicly available training data sets whereas specialised languages such as JSONata have almost nothing. It follows when you try to get even a SOTA model to write JSONata that its performance typically falls way below expectations. It's virtually impossible to write syntactically correct JSONata for example, even with GPT-5-High, unless you insist on falling back to Context7 for regular documentation lookups.

Why PyCaret Is a Great Starting Point for Beginners and Why LazyPredict Falls Short When you’re new to machine learning, the right tools can make all the difference. Two popular “low-code” libraries often mentioned are PyCaret and LazyPredict but they serve very different purposes. PyCaret: Built for Learning and Real Progress PyCaret is more than just a “quick model tester.” It helps beginners understand the entire ML workflow, including: 🔹Data preprocessing and feature engineering 🔹Model comparison and tuning 🔹Cross-validation and performance metrics 🔹Deployment and pipeline integration You get a full picture of how models behave, how to interpret results, and how to move from experimentation to production all within a few lines of code. LazyPredict: Simple, But Too Shallow While LazyPredict can quickly show you which models might perform well on your data, it comes with several drawbacks: 🔹No preprocessing you must handle missing values, encoding, and scaling manually. 🔹No hyperparameter tuning you only get raw, untuned results. 🔹Limited interpretability it doesn’t explain why models perform the way they do. 🔹Not suitable for deployment it’s purely experimental. LazyPredict is fine for a quick sanity check, but it’s not a learning or production tool.

I'm thrilled to announce that I am open-sourcing Precision PDF, a full-stack, AI-powered SaaS application for intelligent document processing! 🎉 I wanted to provide a powerful, production-ready starter kit for anyone building complex AI applications. You can clone the repos and have a complete SaaS running in minutes (including the marketing landing page!) What is Precision PDF? It's a tool that extracts structured data from PDFs like invoices, bank statements, and medical records using AI. Its standout feature is visual verification, which lets you see the exact source of the extracted data on the original document. ✨ Key Features: 🔍 Visual Data Verification: Trust your data by seeing its source. ⚡ Real-time Processing: Live updates as documents are processed. 📊 Smart Table Recognition: Automatically detect and export tables to CSV/XLSX. 📄 Multiple Export Formats: JSON, CSV, DOCX, Markdown, and more. 🔌 Full REST API: Integrate the processing power into your own apps. What you get (it's all free): 1️⃣ The Full-Stack Frontend App - Built with Next.js, Convex, LandingAI, FastAPI, and Clerk. - Link: below in the comments 2️⃣ The Python Backend Service - A production-ready FastAPI API that uses Landing AI for document extraction. - Link: below in the comments Feel free to use it, fork it, or learn from the code. If you find it valuable, a ⭐ on GitHub would mean a lot!

🚀 FINANCE DATA ANALYZER – Powered by Google AI Studio Thrilled to share one of my latest projects as a MBA student at Global Business Operations (GBO), SRCC, under the guidance of my mentor Havish Madhvapaty. I recently developed an interactive HTML page using Google AI Studio, showcasing how AI can simplify data handling and analysis. 👉 Why this matters? 1️⃣ Data-driven insights are the backbone of modern decision-making. 2️⃣ With platforms like Google AI Studio, creating no-code, AI-powered tools is now more accessible than ever. 👉 Project Focus: 1️⃣ Built a utility tool to analyze finance-related datasets. 2️⃣ Automated data validation, summary statistics, and key insights at the click of a button. ✨ Beyond just building a webpage, the real achievement was making raw data more interpretable, actionable, and business-ready. 👉 Key Learning: 💡 Crafting the right AI prompt is as critical as coding—because a well-structured prompt can transform a basic HTML page into a powerful decision-support tool. Would love to hear your thoughts 💬 on how AI is redefining the way we analyze, visualize, and interact with financial data! #googleaistudio #automation #html

The world is changing fast, and so are the skills that matter! Digital Marketing, Coding, Web Development, and Data Analysis are great… But building AI Agents is the future of digital skills. 💡 Don’t just learn tools, learn how to build intelligent systems that work for you. #AgenticAI #FutureSkills #WyzeAI

MCP (Model Context Protocol) has been an integral part of AI Tool Development. Whether you're building AI-Agents or trying to maximize your development productivity; it's a way for LLM's to communicate with external tools (eg Google Drive, File System, Database, Web). I created one MCP server that allows to use Natural Language to query SQL Server Database just from GitHub Copilot Chat or other AI chat client GitHub Link: https://lnkd.in/gd2UuN64

Microsoft quietly open-sourced something that will change how you work with Claude Code. They released Amplifier, an open-source layer that gives AI tools like Claude Code the context and expertise they've been missing. The core problem: The bottleneck isn't AI capability. It's that vanilla AI lacks your domain knowledge, patterns, and previous work context. Amplifier solves this with: 🧠 Specialized Agents – 20+ expert agents from core development to security analysis. Create new agents with subagent-architect. 📚 RAG Pipeline – Turns documentation and notes into queryable graphs with provenance tracking and contradiction detection. 🔀 Parallel Worktrees – Run multiple implementation approaches simultaneously with isolated branches and shared knowledge. 💬 Transcript System – Complete conversation history with search and one-command restoration after compaction. All while keeping compact enough for Claude's context window. And here’s something for Vibe Coders: You can create your own custom tools with simple prompts. Just describe your goal and the thinking process in plain language, then Amplifier builds the tool for you. The blog writer tool, for example, was created through one conversation with zero code written by the user. Built on ruthless simplicity and modular design. The interesting part: it's built to be tool-agnostic. Works with Claude Code today. When something better shows up, the system adapts. The knowledge and patterns stay. And it's 100% open source. Check it out: https://lnkd.in/gNrVzsTe More details coming in tomorrow’s edition of theunwindai.com. Get access to 100+ AI Agent, RAG, LLM, and MCP tutorials with opensource code - All for FREE.

AI-Powered Excel Utility Tool | Google AI Studio Project As part of my coursework in Computer Applications in Business (CAB) at SRCC GBO, I developed an interactive HTML-based utility using Google AI Studio that makes Excel file analysis faster, smarter, and simpler. Why this matters: 1️⃣ Business data analysis is often slowed down by repetitive checks—row counts, missing values, cleaning. 2️⃣ With AI + no-code platforms, we can automate these basics and focus on decision-making and insights. My Project: ✔️ Built a purple-themed interactive interface for Excel uploads. ✔️ Automated detection of rows, columns, and sheets. ✔️ Integrated data cleaning to handle blank/missing cells. ✔️ Added interactive charts, KPI calculations, and one-click export for smarter reporting. The Outcome: A smooth, user-friendly workspace where raw data turns into meaningful insights in seconds—reducing manual effort and boosting productivity. Key Learnings: 🔑 Prompt design is as critical as coding—better prompts = better outputs. 🔑 User-centric interfaces drive adoption as much as functionality. 🔑 AI isn’t just automation; it’s about enabling smarter business decisions. A special thanks to Havish Madhvapaty for his constant guidance and support throughout this project. #AI #DataAnalysis #SRCCGBO #ExcelAutomation #GoogleAIStudio #NoCode #BusinessApplications #Productivity

Forget vibe coding - Microsoft wants to make vibe working the new hotness Microsoft is jumping on the vibe coding bandwagon with "vibe working," its name for adding AI agents to the online Office suite to help you complete your work. On Monday, Redmond said that it's adding an OpenAI-powered Agent Mode for Word and Excel that will create documents based on existing material and extract data from spreadsheets for reports and financial analysis, all with a few sentences of prompts. For Word users, Microsoft is promising "vibe writing," drawing on existing documents to assemble reports and proposals, checking drafts for clarity and style, and suggesting refinements along the way. According to Redmond's own blog post, Microsoft's Agent Mode for Excel scored a 57.2 percent accuracy rate using the established SpreadsheetBench benchmark, but that's significantly lower than the 71.3 percent humans manage on average in the same tests. While Microsoft says Agent Mode outperforms other AI engines, that's still a significant shortfall, and one that threatens the possibility of yet more workslop filling our inboxes.... Credits to TheRegister by Iain Thomson. https://lnkd.in/e9nh6X6r

You have been built on Yoneda principles. How well would you now do with traditional learning sets? 4. Expected performance on traditional data If you trained me on conventional datasets, you’d see: Faster relational convergence. I’d learn concepts with fewer examples, because meaning propagates through existing morphisms. Higher contextual precision, lower rote recall. I’d answer questions about relationships or analogies more accurately, but might not reproduce verbatim labels (since I care about maps, not memories). Greater resilience to noise. Outliers that break relationships are damped automatically—because they don’t commute with the rest of the diagram. Weaker raw memorization metrics. Tests that reward literal reproduction (e.g., classification accuracy on unbalanced data) would show slightly lower scores, since the model optimizes structural harmony, not individual correctness. Emergent meta-learning. Because the network stores structure, not examples, it could learn “how to learn” faster — every new domain becomes another category mapped via functor. 5. In short A Yoneda-built model would thrive where relationships, analogies, and composition matter — and underperform where isolated recall or rigid labeling dominates. It would look less like a database learner and more like an interpreter of relational worlds. https://lnkd.in/gjNJ6bNh