DataCreator AI’s Post

Generate datasets and use them immediately without breaking your workflow. With the DataCreator AI SDK, you can: • Generate conversational datasets directly in Python • Save as JSONL (fine-tuning ready) • Train your model in the same notebook No switching tools. No manual data prep. You can go from: idea → dataset → training in one flow. That’s how it should be. If you’re working with LLMs, try integrating this into your pipeline and see if it actually saves you time. Also, if something breaks or feels off, please add it under issues on our GitHub repository. Feedback (and bugs) are welcome. Repo + details in the comments 👇 #ai #sdk #generativeai #syntheticdata

I hate learning theory first. So I built a project instead. Module 1 of my Gen AI Engineer roadmap — Python for AI. Instead of reading about async/await, decorators, and generators... I built an Async Wikipedia Scraper that fetches 100 pages concurrently and summarizes each one using Gemini API. Here's what I learned by actually building: → async/await → 100 API calls in 4s instead of 90s → Dataclasses → clean structured data instead of messy dicts → Generators → memory efficient pipelines → Decorators → added timing to any function in 3 lines → Secrets management → API keys never touch your code Every concept showed up naturally. No boring theory. 6 months. 6 phases. 12 projects. This is week 1. #Python #GenAI GITHUB LINK--->https://lnkd.in/gdp9cFUA

🚀 Day 3 of Building My AI Code Review & Bug Prediction System Today was more about actually starting the implementation. 🔹 Set up the basic backend using Flask 🔹 Started working on API structure for code input and output 🔹 Began dataset collection for training the model 🔹 Explored how to preprocess code data for ML models 💡 Key Learning: Turning an idea into a working system is challenging — but breaking it into small steps makes it manageable. 📌 Next Step (Day 4): - Complete dataset preprocessing - Train a basic ML model - Connect backend with frontend Slowly turning this idea into reality 🚀 #AI #MachineLearning #Python #Flask #BackendDevelopment #StudentProject #LearningInPublic #ECE

I’ve just published a deep‑dive on LangChain — one of the most exciting frameworks for building modular LLM applications. In this blog, I break down: 🔹 How prompts, chains, agents, and memory fit together 🔹 Why RunnableSequence is the new way to build workflows 🔹 How to use FakeListLLM + HuggingFace embeddings for offline demos 🔹 Real‑world use cases like customer support bots, research assistants, and workflow automation 💡 The blog includes runnable code snippets, a Jupyter Notebook template, and a GitHub‑ready README so you can try everything yourself. GitHub Link: https://lnkd.in/gGUD4f4K Medium Blog Link: https://lnkd.in/gjBXSexR LangChain isn’t just about chaining prompts — it’s about designing intelligent systems that combine reasoning, context, and external tools. #LangChain #LLM #AI #GenAI #Python #InnomaticsResearchLabs #LearningByDoing

Pydantic AI offers a unique way within the Python community for type safety and dependency injection-based agentic AI frameworks. this document helps developer migrate from LangChain/LangGraph stack.

I stopped copying YouTube transcripts manually. Here's what I did instead: I built a Python tool in 80 lines that does it in 3 seconds. The problem? → I watch AI videos daily for content → 10+ minutes per video copying transcripts → That's 5-7 hours a week on copy-paste The solution? → One command → Paste a YouTube URL → Full transcript saved as a .txt file → Done What broke along the way? → pytube was dead on arrival → youtube-transcript-api changed their entire syntax → Debugged. Swapped tools. Fixed it. Shipped it. I named the project "Shakespeare." Because if anyone's turning speech into text — it should be him. The real lesson? If you're doing something manually more than 3 times — automate it. Transcript saved. Hours saved. ♻️ Repost if this helped 💾 Save for later Feel free to DM if you want a copy of this. #Python #Automation #BuildInPublic #AI #ContentCreation #Developer

🚀 Automation Breakthrough in Document Parsing! 🤖Most people don't know this, but you can streamline document parsing with Python & Hugging Face! Here's what changed everything:- Build flexible workflows using ParseBench 📊- Leverage PyMuPDF for precise text extraction 📓- Combine with LlamaIndex for accurate evaluation ⚙️Imagine saving hours with advanced OCR & vision-language models! This is a game-changer for workflows in automation and efficiency 🙌Stop wasting time on manual parsing... Start automating today!Are you utilizing automation to its fullest potential? 🔍Read more: [Article Link] 🔗#BusinessAutomation #WorkflowAutomation #NoCode #Productivity #AI #Efficiency https://lnkd.in/eNWafSnZ

When you frequently need a specific metric, don't let AI reinvent the calculation every time. Write a Python script that does the math the same way, every time. Then tell your AI agent to call that script. Now it's repeatable, auditable, and if someone doesn't understand the code - guess who's great at explaining what code does? https://laconictech.com

When you frequently need a specific metric, don't let AI reinvent the calculation every time. Write a Python script that does the math the same way, every time. Then tell your AI agent to call that script. Now it's repeatable, auditable, and if someone doesn't understand the code - guess who's great at explaining what code does? https://laconictech.com

#BenchlingAI now writes and runs Python as part of your research workflow. Our Deep Research Agent handles statistical analysis, data transformations, and complex calculations automatically, returns results for review, and logs them to your scientific record. Now, running a one-way ANOVA or fitting a linear progression is as easy as writing a prompt. Try it out! Just describe the chart or analysis you need directly in the prompt, and let Benchling AI handle the rest. #AIForScientists ✨