Chirag Patel’s Post

From Visual "Vibe" to Functional Code in 60 Minutes Is it possible to take a web redesign from a concept to a live, data-driven Proof of Concept (PoC) in an hour? Spoiler: Yes, but it takes more than just "chatting" with an AI. It requires orchestration. I spent my part of my evening diving deep into a workflow that bridges the gap between Vibedesign and Vibecode, using dotCMS, Google AI (Stitch & Antigravity), and Python. The stack: Infrastructure: OrbStack + Docker for a zero-friction setup. Design: Google AI Stitch for visual brainstorming and redesign. Logic: Google Antigravity to generate VTL templates and containers. The Bridge: Custom Python scripts to transform raw LLM output into dotCMS-ready content via REST API. The "Aha!" Moment: AI agents are powerful, but they aren't mind readers. The breakthrough happened when I stopped giving descriptions and started providing context. By feeding the AI the actual dotCMS Java Beans, I "taught" it exactly how to structure the JSON payloads for bulk content ingestion. Key Takeaways: Context is King: Providing source code as a reference is 10x more effective than writing long prompts. Hybrid-Headless Agility: dotCMS is a beast for SMEs looking for enterprise power without the complexity. Speed to Value: We moved from screenshots to a manageable, structured CMS site in record time. I’ve documented the full technical "struggle"—including the Python transformation logic and the prompt engineering— #dotCMS #GenerativeAI #WebDevelopment #Python #DigitalTransformation #Productivity #SoftwareEngineering #googleStitch #googleAntigravity

🚀 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘃𝘀 𝗙𝗹𝗮𝘀𝗸 - 𝗠𝘆 𝗛𝗼𝗻𝗲𝘀𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 I've worked with Flask before, and recently I started learning FastAPI. At first, I thought both are kind of similar… but while practicing, I started noticing some real differences. Let me explain in simple words 🔹 𝗙𝗹𝗮𝘀𝗸 (𝗦𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀) Flask works in a step-by-step way. It means if one request is being processed, the next one has to wait. It uses WSGI (Werkzeug) and is very simple to start with. But the downside is, you have to handle many things manually like validation and API documentation. 🔹 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 (𝗔𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗼𝘂𝘀) FastAPI works in a smarter way. It can handle multiple requests at the same time without making others wait. It uses ASGI (built on Starlette) and supports async programming. It also provides built-in validation using Pydantic and automatically generates interactive API docs (Swagger UI). ⚙️ 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 Flask is commonly used with Gunicorn FastAPI is commonly used with Uvicorn (ASGI server) 💡 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 Synchronous → one task at a time (wait and then move) Asynchronous → handle multiple tasks together (no waiting) 💡 𝗠𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Flask is great for beginners and small projects. But FastAPI feels more modern, faster, and very useful for scalable apps, especially in AI/ML projects where performance matters. I am still learning FastAPI, but so far, it feels really powerful and clean to work with 🔥 𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝗽𝗿𝗲𝗳𝗲𝗿 - 𝗙𝗹𝗮𝘀𝗸 𝗼𝗿 𝗙𝗮𝘀𝘁𝗔𝗣𝗜? #FastAPI #Flask #Python #BackendDevelopment #WebDevelopment #APIs #MachineLearning #AI #SoftwareDevelopment #LearningJourney

Built an Advanced Personal Assistant from scratch. Here's what it actually does. Started with a blank Next.js project and a FastAPI skeleton. The result is Ava — an AI assistant that reasons, remembers, and acts across sessions. The stack: Next.js 16 · FastAPI · SQLite · Groq API · Python Groq handles inference at blazing speed. Everything else — memory, plugins, sessions, file operations — runs on your own machine. ● Agentic tool calling: The LLM doesn't just respond — it decides. Every message goes through an orchestration loop that determines whether to answer directly or invoke a tool. Weather, time zones, calculations, web search, GitHub stats, crypto prices — all fire as live tool calls with transparent execution blocks in the UI. ● Multi-model fallback cascade: If the primary model hits rate limits, the system silently falls back through a chain of models without breaking the conversation. The user never sees an error. ● Code execution: Ava writes Python, runs it in a sandboxed subprocess, reads the output, fixes errors, and iterates — all in a single turn. The full execution trace is visible inline . ● Persistent memory: After every conversation, a background extraction pass pulls facts, preferences, and events into a structured vault. Location, tech stack, habits — remembered across sessions without any manual tagging. ● Voice and Vision: Push-to-talk via MediaRecorder piped to Groq Whisper for transcription. Image upload routes to a vision model for analysis, OCR, and structured extraction. ● Dynamic plugin system: Install and uninstall tools at runtime. Register a custom skill by uploading a markdown file — the parser extracts the schema and makes it callable immediately, no backend changes required. ● Session archive: Every conversation is stored and browsable. Restore any past session back into the live chat with one click. The hardest parts were never the features themselves. They were the details — preventing tool call JSON from truncating mid-generation, stripping internal reasoning tokens before they reach the UI, making a free tier feel unlimited through intelligent model routing. The gap between a working demo and a reliable product is where most AI projects fall apart. This one doesn't. Happy to go deep on any part of the architecture in the comments. #llm #nextjs #fastapi #python #ai #groq #softwaredevelopment #webdevelopment

Everyone asks which Python backend to learn. I've used all three in production. Here's the answer no one gives you. 🔷 FLASK Pros: Total freedom, minimal, very learnable Best for: Learning, tiny internal tools Honest truth: You'll outgrow it fast. And that's fine. 🐍 DJANGO Pros: Full-featured, brilliant ORM, auth built in Best for: Full web products, SaaS, content platforms Honest truth: Heavyweight for APIs. Brilliant for what it's designed for. ⚡ FASTAPI Pros: Async native, blazing fast, auto-generates docs, Pydantic validation Best for: AI/ML APIs, microservices, anything real-time Honest truth: If you're building AI systems in 2026 and you're not using FastAPI, you're making your life harder. My verdict: → AI/ML/microservices: FastAPI. Every time. No debate. → Full web product: Django. → Just learning? Start Flask for one project so you understand the fundamentals. Then move to FastAPI and never look back. Stop debating. Pick one. Build something this week. Which are you currently using — and what are you building? Drop it below. I'll tell you if you've made the right call. #python #fastapi #django #flask #webdevelopment

I wanted to understand how AI fits into a real web app — not just call an API in a notebook, but actually build something end-to-end. So I built an AI Personal Assistant over the past few days. It's straightforward: ask it a question, get an answer. Paste in an email, get a clean 2–3 sentence summary. Nothing groundbreaking — but building it taught me more than I expected. A few things I learned: ✅ How to structure Flask routes that talk to an LLM ✅ How to handle async requests on the frontend without a framework ✅ How to manage API keys properly using dotenv ✅ How different temperature settings affect model output The email summariser ended up being the more interesting piece — there's a real use case there, and I'm planning to extend it with Gmail API integration so it can fetch and process emails directly. Stack: Python · Flask · OpenAI API · Vanilla JS GitHub link in the comments. Always open to feedback 👇 #Python #OpenAI #Flask #AI #BuildInPublic #WebDev

Built a Vector Database + RAG System from Scratch (No Libraries) I recently developed a fully functional AI system using Python that implements core search algorithms and integrates with local LLMs. What I built: • HNSW (Hierarchical Navigable Small World) • KD-Tree (Exact Search) • Brute Force (Baseline) • Custom Vector Database Engine AI Features: • RAG (Retrieval-Augmented Generation) pipeline • Local LLM integration using Ollama • Semantic search with embeddings • Interactive visualization dashboard Tech Stack: Python • FastAPI • Vector Search • LLM • Ollama This project helped me deeply understand how modern AI systems like ChatGPT retrieve and process information. 📌 GitHub: https://lnkd.in/gChm7mjD Would love feedback and suggestions. #AI #MachineLearning #Python #RAG #LLM #OpenSource #StudentDeveloper

Last weekend, I officially merged my first open-source PR into Headroom, a context optimization layer for LLM apps. Here's the backstory: I connected with Tejas Chopra a couple weeks ago and learned about how Headroom works under the hood. The core idea is compelling. Most tool outputs in AI agent workflows are massively redundant. A database query might return 1,000 rows when the LLM only needs the summary stats and the one row that errored. Headroom compresses that context by 50-90% using statistical analysis, not summarization. No extra LLM calls, no hallucination risk. It works as a proxy, a Python library, or a drop-in integration for frameworks like LangChain, LiteLLM, and Agno. As I explored the architecture with Tejas, I noticed an opportunity that matched my experience with LangChain and LangGraph: there was no built-in way to compress tool outputs between cycles in a LangGraph agent. LangGraph builds AI agents as state graphs where messages accumulate across cycles. After a few tool calls, the message history balloons with raw JSON that the model doesn't fully need. This is a known pain point (LangGraph issues #3717, #11405, #2140). So I built a compress_tool_messages hook, a graph node that sits between the tools node and the agent node. It scans ToolMessages, compresses the large ones with SmartCrusher, and passes the trimmed state back to the LLM. Small outputs and error messages are preserved untouched. If you're building LLM-powered apps and spending more on tokens than you'd like, or hitting context limits on agent workflows, Headroom is worth checking out. It's open source, runs locally, and you can get started with a single function call or by pointing your existing client at the proxy. PR #68: https://lnkd.in/gpWfWxZy Repo: https://lnkd.in/gmx9WYUY

𝗘𝘃𝗲𝗿 𝘄𝗼𝗻𝗱𝗲𝗿𝗲𝗱 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗿𝗮𝘄 𝗔𝗜 𝗽𝗼𝘄𝗲𝗿 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗹𝗲𝗲𝗸, 𝗺𝗼𝗱𝗲𝗿𝗻 𝗨𝗜?  𝗜 𝗷𝘂𝘀𝘁 𝗳𝗶𝗻𝗶𝘀𝗵𝗲𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝗹𝗹𝘆 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 𝗜𝗺𝗮𝗴𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿 𝗔𝗽𝗽 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵, 𝗮𝗻𝗱 𝗜 𝗿𝗲𝗰𝗼𝗿𝗱𝗲𝗱 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘁𝗼 𝘀𝗵𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂 𝗮𝗹𝗹! As developers, we often get caught up in the backend logic. But I truly believe a powerful AI tool is only as good as the user experience it provides. So, I set out to bridge the gap between a robust Python backend and a premium, dark-themed frontend. Here is the engine under the hood : Backend: Python & Flask (Handling asynchronous API logic smoothly). AI Engine: OpenAI API (Leveraging DALL-E for high-fidelity outputs). Frontend: HTML5 & Advanced CSS3 (Featuring custom Animated Gradient Borders for that premium feel). The Biggest Challenge? Handling OpenAI's response states while keeping the CSS animations smooth and responsive across devices without killing performance. I’ve documented the entire build process and the complete data flow in my latest video tutorial. Whether you want to see how the Flask API connects or how to build the animated UI, I've broken it all down. Watch the full step-by-step breakdown here: What do you think is the hardest part of building full-stack AI apps right now: handling the API logic, or getting the UI/UX just right? Let’s debate in the comments! #Python #Flask #OpenAI #WebDevelopment #FullStack #SoftwareEngineering #GenerativeAI #UIDesign #Programming #TechCommunity #BuildInPublic

🚀 Just shipped: RAG Chat — a Document Q&A chatbot that actually knows when to say "I don't know." Most LLM-powered apps hallucinate freely. I built one that can't — it only answers from documents you upload, and explicitly refuses if the answer isn't there. Here's what's under the hood: 🧠 Agentic RAG pipeline QueryRewriter → CacheAgent → RetrieverAgent → AnswerAgent — each a modular, swappable agent. Adding a new step is literally one Python file. ⚡ Semantic Q&A cache Repeated or similar questions skip the LLM entirely (cosine similarity threshold: 0.92). Real-world chatbots ask the same things constantly — this makes it fast and cheap. 🗂️ 3-Layer memory architecture • STM — Redis, last 20 messages, 24h TTL • MTM — Rolling summaries in DashVector • LTM — Document chunks + facts, ANN + SQL fusion retrieval 📄 4 chunking strategies Recursive, Fixed Size, Sentence-based, Paragraph-based — pick the one that fits your document type. 🛡️ Anti-hallucination by design 7 hard rules in the system prompt. If the context doesn't have the answer, the assistant says so. Stack: FastAPI · Next.js 14 · DashVector · Qwen (qwen-plus) · Redis · PostgreSQL · SQLAlchemy 2.0 👇 Full source on GitHub (link below) — contributions welcome! #RAG #LLM #AIEngineering #GenerativeAI #Python #NextJS #OpenSource

AI is shifting how we build backends. We’re no longer just serving CRUD apps; we’re architecting for high-throughput LLM chains and streaming inference. Last week on a client project, we had to decide between Django and FastAPI for a new AI-first feature. The choice wasn’t about "which is better," but about where the complexity lives. If you need a robust Admin panel, complex auth, and a massive ecosystem of plugins ready to go, Django is still king. It handles the heavy lifting so your team can ship stable features fast. But when you’re building an inference engine where every millisecond counts, FastAPI’s async-first architecture is the winner. Its Type hinting makes integrating with Pydantic and LLM SDKs feel seamless. For us, if the backend is the heart of the product, we choose Django. If the backend is the pipe for AI models, we choose FastAPI. Stop chasing the "best" framework. Pick the one that matches your data lifecycle. Which one are you reaching for in 2024? #Python #Django #FastAPI #SoftwareEngineering #AI