Pydantic’s Post

Generating a DC Solver for Linear Resistive Circuits with AI In our latest video: https://lnkd.in/eHig7Xx2 , we demonstrate how to use the ChatGPT AI to generate a general Python program, which you can then run directly within the TINA simulation software. By studying the Python code, you can learn about the principles behind how TINA works for linear DC circuits. Generating the Python Code with ChatGPT Start chatGPT (chatgpt.com) and enter the following request: “Generate a DC solver from netlist using Python. Include one test example with a voltage source of VS=100V, R1=20, R2=30.” The AI will immediately respond with a structured explanation, followed by the generated Python code. The generated code is typically quite general, allowing you to solve any DC circuit using the specified netlist syntax. Important Note: Keep in mind that ChatGPT and most other AI systems may generate slightly different Python code or result formats each time they are used. Integration into the TINA Environment Once the code is generated, we need to import it into the TINA simulation software for execution. Running the Code: -Use the “Copy code” icon to copy the generated Python code to your clipboard. -Start TINA. -Open the built-in Python compiler: Select Python Shell from the Tools menu. -Paste the Python code into the compiler’s editing area. -Press the “Run” icon. The Python program will execute and display the nodal voltages, including the source current. Comparison with TINA Simulation To check the validity and accuracy, draw the circuit using the Schematic Editor of TINA and run a DC analysis. Enter the schematic equivalent of the example circuit, then press the DC button. Results Match: The values calculated by TINA (V2 = 60 V and I= -2A) match exactly the results obtained from the AI-generated Python program. By studying the Python code, you can learn about the principles behind how TINA works for linear DC circuits.

Exploring how LangChain bridges data, reasoning, and APIs — a short experiment that shows how AI orchestration can power real-world apps beyond prompts. https://lnkd.in/dTAsyAry

The AI industry's dirty secret: "reasoning" models are just clever workarounds for stagnant foundations. • OpenAI's o1 "reasoning model" generates Python code and executes it in a sandbox, rather than calculating internally. • Agentic AI's autonomy is achieved through chained tool calls, not true intelligence. • The entire industry is betting on continued model improvement, but what we're getting is increasingly elaborate plumbing. Takeaway: The AI industry's progress is being driven by engineering workarounds, not true model improvements. #aihype #modelstagnation #engineeringworkarounds

🚀 Composable AI & Structured Outputs in TypeScript AI agents can’t think clearly without a structured output. feeding them raw or unvalidated data leads to errors and unpredictable results. Python #developers often use #pydantic for runtime validation and type coercion. in the typescript world, zod is your go-to library. it lets you: • validate runtime data ✅ • coerce types automatically 🔄 • handle nested objects, enums, and arrays 🏗️ Using #Zod , your ai always gets exactly what it expects, making your pipelines more reliable and maintainable. structured data isn’t optional. https://lnkd.in/gyhk3GYN #ai #composableai #typescript #zod #datavalidation #structuredata #aiengineering #pydantic #webdev #softwareengineering #python #zod #typescript #graph

Just published a new article on LangChain - the framework that turns large language models into context-aware, data-connected AI systems. 🚀 In it, I explain the core building blocks (chains, memory, agents, retrievers) and walk through a hands-on project: building a SQL database query bot with LangChain + OpenAI. If you’ve ever wanted your LLM to think, remember, and access real data, this guide is for you. 👉 Read here: https://lnkd.in/gJKUQ5f5 #LangChain #AI #LLM #Python #OpenAI #RAG #MachineLearning

Chatbot project — built and deployed on Hugging Face Spaces using Gradio! This bot is designed to handle real-time user queries with contextual understanding and a smooth conversational interface. Every build like this brings me one step closer to mastering Agentic AI Systems and intelligent workflow automation. Tech stack: Python • Gradio • Hugging Face • NLP Live Demo: https://lnkd.in/gnj4h8fd #AIEngineering #AgenticAI #ChatbotDevelopment #HuggingFace #Gradio #Automation #MachineLearning

💡 “Writing code is no longer just about syntax — it’s about guiding intelligence.” In web development, the shift is underway: front-end + back-end + AI-assist. From my experience working on deep learning projects, Python alone isn’t enough — you must connect data, logic, and design to create something intelligent and useful. 🔹 Master Python fundamentals: data structures, algorithms, clean code. 🔹 Integrate AI APIs into web projects — turn data into interaction. 🔹 Focus on real-world outcomes, not just technical perfection. The most valuable developer today is not “just a coder” — it’s an AI-enabled engineer who merges creativity with computation. Are you preparing for that evolution? #WebDevelopment #AIIntegration #Python #FullStack #TechJourney 𝗦𝗼𝘂𝗿𝗰𝗲: https://lnkd.in/ebSiJs2g

A good research tool does more than just scrape the web. It shows you its steps and sources. Tools like Perplexity use an agentic format, searching iteratively until the task is complete. You can even peek behind the scenes to see the Python code running and the data being analyzed. This transparency offers deep insights, like detailed competitor analysis and strategic market entry recommendations. #ResearchTools #AI #MarketAnalysis #CompetitiveIntelligence

Train your AI agents to automatically improve with this Python library 👀 Most agent frameworks require heavy prompt engineering and manual tuning to get good results. But with ART’s LangGraph integration, you can actually train your agents to improve themselves automatically. Here’s how it works: - You define tools for your agent to use (search, read, return answer, etc.). - You wrap the agent’s rollouts so every interaction is logged as a trajectory. - These trajectories are scored automatically (e.g., with ART’s RULER function). - The model is retrained on judged trajectories — improving tool usage and reasoning. - Training runs in scalable batches, letting agents learn across diverse scenarios. - Correctness and reward functions can be plugged in easily, without hand-crafting. Impact: - Agents get better at multi-step reasoning and tool usage without manual tuning. ♻️ Share it with anyone who is building AI agents :) I regularly share AI Agents and RAG projects on my newsletter 𝑨𝑰 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑾𝒊𝒕𝒉 𝑺𝒂𝒓𝒕𝒉𝒂𝒌: https://lnkd.in/gaJTcZBR Link to repo: https://lnkd.in/gzAaHTuP OpenPipe #AI #GenAI #AIAgents

Chatbot project — built and deployed on Hugging Face Spaces using Gradio! This bot is designed to handle real-time user queries with contextual understanding and a smooth conversational interface. Every build like this brings me one step closer to mastering Agentic AI Systems and intelligent workflow automation. Tech stack: Python • Gradio • Hugging Face • NLP Live Demo: https://lnkd.in/g2iEi63u #AIEngineering #AgenticAI #ChatbotDevelopment #HuggingFace #Gradio #Automation #MachineLearning