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Veridian updates: The verification layer for AI agents — correctness enforced by the runtime, not by prompting. What's new: → Pause/resume that survives crashes → Zero-duplicate LLM calls via activity journal → Graph execution engine with verified edges → LangGraph + CrewAI adapters (drop-in reliability) → Multi-tenant isolation (budget, rate limit, data) → Operator plane: approval queue, DLQ triage, replay CLI → Plugin SDK with certification matrix → SLO engine + PII trace filtering Python 3.11+. MIT. https://lnkd.in/gSUhw97Q #AI #OpenSource #AIAgents #Python #LLM #DevTools

Day 3 of building AI agents on 8GB RAM. Today: My Meeting Summarizer now reads files. Drop a .txt, .docx, or .csv → AI extracts decisions, action items, next meeting. No copy-paste. No formatting. Just drag and drop. Built with: - Python - Ollama (local AI) - Qwen 1.5B - 50 lines of code Cost: $0 Runs on: My 8GB laptop Tomorrow: Adding web search so the AI can research topics mentioned in meetings. #AI #BuildInPublic #LocalAI #Python #Automation

Most people use keyboards every day — but very few think about how typing efficiency can actually be measured. I built a machine learning model to predict typing time between key pairs (bigrams) based on keyboard layout and key positions. By applying feature engineering and model optimization, I reduced prediction error (MAE) from 174.8 ms to 94.4 ms — a 45.9% improvement. This project helped me understand how data-driven approaches can be used to evaluate and improve real-world user interactions. Next, I’m working on turning this into a simple usable application. GitHub: https://lnkd.in/gbHiKdGu #MachineLearning #Python #AI

Building StockMind's inventory agent taught me that AI workflows aren't single decisions — they're chains. LangGraph lets you model that chain as a stateful graph: ↳ Each agent is an isolated, testable node ↳ One typed state object flows through every step ↳ Conditional edges skip what isn't needed ↳ Compiled once. Reused for every run. The result? A 4-agent pipeline that goes from stock drop → supplier selected → PO drafted → manager notified in under 15 seconds. And when is stock fine? The graph exists after the first node. Zero wasted LLM calls. LangGraph didn't just make the code cleaner — it made it debuggable, extensible, and production-ready. #LangGraph #AIAgents #LLM #Python #BuildInPublic #GenerativeAI #MachineLearning

Smarter LLM outputs don’t always require bigger models. Introducing Inferscale 0.1.1—a lightweight Python package that improves response quality using inference-time scaling techniques. Designed for developers who want better results without increasing compute costs, Inferscale focuses on practical gains you can integrate quickly into your workflows. If you're building AI products, experimenting with prompts, or optimizing pipelines, this is worth exploring. Check out the README: https://lnkd.in/giq8KJ5g Let’s make LLMs more efficient together. #AI #LLM #Python #GenAI #MachineLearning #OpenSource #AIDev

Excited to share my latest technical guide: Build Your First MCP Server As AI tools like Claude become increasingly embedded in real workflows, understanding how to extend their capabilities is becoming an essential skill for developers and engineers alike. I put together a comprehensive yet beginner-friendly guide on the Model Context Protocol (MCP) — the open standard that allows AI assistants to connect with external tools, APIs, and data sources. #AI #MCP #ModelContextProtocol #Claude #Anthropic #Python #DeveloperTools #ArtificialIntelligence #SoftwareEngineering #TechGuide

What can you automate? Recently I have been playing around with AI on a more personal level and looking at my professional level at what can be made more efficient. Couple of years ago I learnt python by creating services and scripts on my system to perform certain tasks, but today we can do so much more. I recently built a meeting summarizer, basically after a meeting the recordings are sent to an endpoint and with an LLM it generates a summary of the meeting with key insights and what to do next. Next is hosting to store my suite of tools What of you? What can you automate in your life?

Units Matter in AI. If you aren’t scaling your features, you’re basically telling your AI models that "cents" are more important than "dollars." Scaling ensures every feature gets a fair vote in the final prediction. I’ve put together a quick visual guide on why this happens and the two main paths to fix it: Normalization and Standardization. 🚀 Part 1: The Theory 🔜 Part 2: Python Implementation (Coming Soon!) Check out the visual breakdown below! 🎥 #DataAnalytics #DataScienceTips #MachineLearningEngineer #TechTips #PythonProgramming #DataVisualization #CareerInTech

Gen AI project #1 Started with the AI ingestion pipeline. - PDF and DOCX parsing - Text cleaning - Tokenization ( helper function for token counting ) - Chunking with RecursiveCharacterTextSplitter I thought these things were going to be hard but they weren't. Using Python for the AI side of things. Next up: SemanticChunker ,embeddings and ChromaDB.