André Lindenberg’s Post

Posted a tiny Python demo: atlas-kvd-demo. It models a bounded state update with: K = carried state  V = outside pressure  Δ = local event nudge Small repo, but the idea matters to me: systems should drift, react, and settle instead of snapping all over the place. https://lnkd.in/e8WFcFPv Not a giant framework.  Not “AI solves everything.”  Just one legible state law, in code. #Python #GitHub #Simulation #StateMachines #AI #IndieDev #BuildInPublic

Standard LLM chains are great for straight lines, but real-world problems are messy. They need loops, second guesses, and human check-ins. LangGraph solves that problem. It’s the "pro mode" for LangChain. I just put together a quick post on how to get started with this framework with a simple code example. Here’s the TL;DR: ✅ Define State Early: Use TypedDict to give your agent a reliable memory. ✅ Conditional Edges: Let the LLM decide when to pivot or retry. ✅ Persistence: Never lose progress again with built-in checkpointers. ✅ Human-in-the-Loop: Add "pause" buttons for high-stakes actions. ✅ Keep Nodes Pure: Small, focused functions make debugging a breeze. Check out the full breakdown below! 👇 https://lnkd.in/e_T-bCaG #AI #DataScience #Python #LangChain #LangGraph #MachineLearning #LLM

Built a GenAI application that converts YouTube videos into structured articles, downloadable PDFs, and responsive webpages. The system uses a multi-step pipeline: transcript extraction, content cleaning, article generation, and multi-format output creation. Tech stack: Python, Streamlit, LangChain, Groq (LLaMA 3.3), FPDF This project helped me understand how to design end-to-end GenAI workflows beyond simple summarization. 🔗 GitHub: https://lnkd.in/gygZUgGG #Innomaticsresearchlabs #GenerativeAI #Langchain #Python #Datascience

Built a RAG-based Real Estate Research Tool using LangChain, ChromaDB, Groq, Hugging Face Embeddings, and Streamlit : This project allows users to: ✅ Input article URLs ✅ Process and store content using vector embeddings ✅ Ask context-based questions ✅ Retrieve accurate answers from the provided sources While building this project, I also explored real-world challenges like: • Dynamic website scraping issues • Retrieval quality tuning • Context-based answering in RAG systems Tech Stack: Python | LangChain | ChromaDB | Groq | Streamlit | HuggingFace Embeddings Sharing a small demo below 👇 #GenerativeAI #RAG #LangChain #LLM #Python #AIProjects #Streamlit #GenAI

Built a voice controlled AI agent using Python, Gradio, and Groq APIs (Whisper + Llama 3.3). Implemented a 3-tier intent classifier (regex, keyword scoring, LLM fallback) that minimizes API calls. Supports 4 intents: create file, write code, summarize, and general chat. All file operations sandboxed to output/ directory. layering features: compound commands, human-in-the-loop confirmation, session memory, and graceful error handling.

Spent the last few weeks building a 10-part LangChain tutorial series. It’s finally complete. This series focuses on the internal mechanics of LangChain while building practical applications. The final 3 tutorials just dropped: → Conversational Memory (5 memory types, explained and compared clearly) → Agents & Custom Tools (ReAct agents, structured inputs, multi-tool routing) → Callbacks & Tracing (Custom handlers, cost tracking, LangSmith integration) Each tutorial follows this flow: Problem → Solution → Analogy → Code Architecture diagrams included in every module. 🔗 https://lnkd.in/epvYCJeY #LangChain #Python #AI #LLM #RAG #OpenSource #BuildInPublic #GenerativeAI

ClimateCare Bot! 💻 I’m excited to share a look at a mini-project I built to practice Conditional Statements (if-elif-else) in Python! In this project, I focused on: Logic Building: Creating specific instructions based on different temperature ranges. User Input: Making the program interactive and responsive. Practical Application: Solving a real-world scenario with clean code. Understanding how to control the flow of a program through conditions is a major milestone in my coding journey. Each small script is a step closer to mastering AI and Data Science! #PythonProgramming #CodingJourney #WomenInTech #LogicBuilding #VSCode #ClimateCareBot"

WirelessAI, built in collaboration with TSFwaves and DivergenceAI, brings AI-driven automation to wireless communication workflows. Instead of working directly in Python, users can interact with AI agents through DivergenceAI’s interface to set up and run CoreSM's workflows more easily.

I am happy about the progress on our collaboration with DivergenceAI and Gustavo Navarro. Now wireless system simulation and channel modeling with KPI extraction and post-processing becomes easier than ever with AI automation. #5G #6G #3GPP #Cellular #WIFI #MIMO #PhasedArray #HardwareDesign #LinkLevelSimulations #DigitalTwin #Synopsys #Ansys #TSFWaves