I’m excited to share that I’ve developed a Sindhi-language chatbot dedicated to the life and wisdom of Shah Abdul Latif Bhittai — one of the greatest Sufi poets and thinkers of Sindh. 🚀 Key highlights of the project: 🌐 The chatbot communicates entirely in Sindhi, a language often considered difficult for digital integration. 🤖 It answers from knowledge base + feeded data of Shah Abdul Latif Bhittai’s life and works. 🎨 Built a user-friendly UI with Streamlit, making interaction smooth and engaging. This wasn’t just a technical challenge but also a cultural responsibility — preserving and promoting the Sindhi language and heritage through AI.

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Hello Everyone, As AI and language models become an integral part of modern applications, understanding their internal processes and optimizing their behavior has become more crucial than ever. To help developers, engineers, and data scientists gain deeper insight into their language models, we're excited to introduce LangSmith—a powerful framework for tracing, debugging, and analyzing the behavior of language models (LLMs). 🌐 LangSmith provides a comprehensive suite of tools and components designed to seamlessly integrate with your language model applications, enabling traceability at every step of the model's lifecycle. Whether you're developing chatbots, recommendation engines, or complex NLP systems, LangSmith ensures that you can monitor and optimize your model’s performance with ease. 🔍 Key Features of LangSmith: 1. 🔎 Traces: In-Depth Visibility of Model DecisionsLangSmith enables you to trace the execution of your language model at every level. By recording and visualizing the internal states, decisions, and outputs, it allows you to track how your model processes inputs and generates outputs. This insight can help identify inefficiencies, biases, or errors within the model and guide future improvements. 2. 🔧 Modular and Flexible ComponentsLangSmith is built with flexibility in mind. Its modular design lets you select and implement the components most relevant to your use case. Whether it’s logging, error tracking, performance optimization, or custom trace generation, LangSmith provides the building blocks to suit your needs. 3. ⚙️ Seamless IntegrationLangSmith is designed for quick integration into your existing projects. With easy-to-use APIs and minimal setup, you can incorporate LangSmith’s tracing and debugging capabilities into any language model workflow, reducing overhead while boosting your ability to optimize and debug. 4. 🛠️ Advanced Debugging and Performance MonitoringLangSmith’s debugging tools provide granular control over model behavior, making it easier to identify potential issues early in the development process. Whether it's tracking latency, pinpointing bottlenecks, or detecting incorrect model outputs, LangSmith empowers you to enhance your model’s reliability and efficiency. 5. 📊 Actionable Insights for OptimizationWith LangSmith, it’s not just about monitoring your model’s behavior—it’s about acting on the insights. The tool gives you data-driven feedback to refine your model, optimize its outputs, and ensure that it meets performance expectations across various use cases. 6. 📈 Comprehensive Visualization and ReportingLangSmith provides intuitive dashboards that visually represent trace data, allowing for easy analysis of model behavior. The interactive reports allow you to drill down into specific areas of interest, enabling more informed decisions on model refinement and deployment.

👉This workflow automatically takes an image, analyzes it, and generates a professional ad-ready version using AI. Let’s walk through how it works. 📌 Step 1 – Form Trigger “We start with a simple form trigger. Here, I can upload an image and give it a short description. This kicks off the entire workflow.” 📌 Step 2 – Upload to Google Drive “Next, the workflow uploads that image to Google Drive. This makes the file accessible for the next AI steps.” 📌 Step 3 – AI Image Analysis “Then, the image is analyzed using OpenAI’s vision model. It checks whether the picture is mainly a product, a character, or both, and describes the visual details in structured YAML format.” 📌 Step 4 – Prompt Generation “Based on that analysis, another AI agent creates a short and natural image prompt. This prompt is carefully designed for ad use, making sure the product looks authentic, the text stays accurate, and the style feels realistic—like a genuine ad photo.” 📌 Step 5 – Structured Parsing “To keep things consistent, the prompt is passed through a structured parser. This ensures the final text is clean and ready to use.” 📌 Step 6 – Image Generation via OpenRouter “Now comes the fun part! The workflow sends both the original image and the AI-generated prompt to the OpenRouter API, which calls Google’s Gemini 2.5 Flash model. This generates a brand new ad-style image.” 📌 Step 7 – File Conversion & Save “The generated image data is processed, converted into a file, and stored. So at the end, I get a ready-to-use ad image that matches my original product shot, but looks polished for marketing.” 👉drop “ad-ready” in the comments to get the file