Building an AI App That Solves a Real Problem: My Journey & Lessons Learned
From Idea Overload to Focused Execution | 5 Free Open Source tools
For years, I’ve been building apps, learning the latest tech, and chasing ideas I thought people might want. But here’s the hard truth: just building cool tech isn’t enough. The real game-changer? Solving one specific problem with one powerful solution.
That’s where my AI journey took a turn. Instead of throwing features at the wall and hoping something sticks, I shifted my focus to creating a single, streamlined AI tool that delivers value. No fluff, no distractions – just one problem, one tool, one solution.
Tech Stack & Learning Process
To make this a reality, I dived deep into Next.js, TypeScript, and AI development – not just as a builder but as a learner refining my process. I started using:
•IDE Cursor & Lovable Dev – Tools designed to optimize coding speed and workflow.
•Hugging Face Models – Experimenting with models like bert-base-uncased and others to ensure the AI understands and processes data efficiently.
•Next.js & TypeScript – To create a scalable, fast, and developer-friendly frontend.
Each step came with challenges – debugging errors like KeyError(‘bert-base-uncased’), fine-tuning model integrations, and ensuring a seamless user experience. But every challenge brought a new level of expertise and clarity on how to make this tool as effective as possible.
Giving Users Free Access First
Rather than charging users upfront, I decided to offer the tool for free – letting people test it, provide feedback, and validate its effectiveness before monetization. Real users, real insights, real improvement.
This approach isn’t just about generosity; it’s smart business. The best products grow through word of mouth, engagement, and trust. If it works, people will spread the word.
Join the Journey: Like, Comment, Subscribe
I’m documenting everything – my coding process, AI model selection, debugging struggles, and successes – on YouTube. If you’re an aspiring AI developer or entrepreneur, this is your chance to learn alongside me.
➡️ Like, comment, and subscribe to follow the journey.
➡️ Share this with developers and AI enthusiasts who are looking for practical, real-world AI applications.
➡️ Try out the tool for free and let me know what you think!
Resources & Links
🔗 👉 Connect with me on LinkedIn:
👉 Explore my GitHub projects:
🔗 https://github.com/coinvest518/
👉 Follow me on Twitter/X:
🔗. https://x.com/omniai_ai?s=21
🔗 Contact: https://linktr.ee/omniai
AI COURSES & EBOOKS FREE
https://theleap.co/creator/coinvest
Here are five beginner-friendly resources for learning and building RAG systems with open-source LLMs:
1. Build Your First RAG System with Free Open-Source LLM
Tutorial on setting up embeddings, Faiss, and LangChain to create a custom chatbot.
Link: https://data-heroes-2.kit.com/rag-beginners
2. 5 Beginner-Friendly Projects to Learn LLMs & RAG
Includes step-by-step guides for building Q&A chatbots, summarizers, and RAG systems.
Link: https://machinelearningmastery.com/5-beginner-friendly-projects-learn-llms-rag/
3. Building RAG from Scratch (Open-source only!)
A tutorial using Sentence Transformers, Postgres, and Llama 2 for building a RAG pipeline.
Link: https://docs.llamaindex.ai/en/stable/examples/low_level/oss_ingestion_retrieval/
4. Step-by-Step Guide: Building a RAG Model with Open-Source LLM
Demonstrates Python implementation using Llama 2 and FAISS.
5. Top 5 Beginner-Friendly Open Source Libraries for RAG
Features libraries like LLMWare for easy document ingestion, embedding, and retrieval.
Link: https://dev.to/llmware/top-5-beginner-friendly-open-source-libraries-for-rag-1mhb
This is just the beginning. One problem, one solution, massive impact. Let’s build something that truly matters.
🚀 #AI #NextJS #MachineLearning #StartupJourney #IndieHacker #DevTools
