Darshil Shah

Everyone’s building “AI tools.” But if your stack doesn’t include: - LangGraph for deterministic control - FastAPI for scalable endpoints - n8n for chaining ops and retries - FAISS or Qdrant for context recall Then you're not building tools. You're duct-taping APIs.

If you can't turn your AI idea into a working tool with an API and UI—you're not building, you're just prototyping.

If you're a founder spending six hours or more screening resumes… You're doing $10/hr work while building a million-dollar product. AI filters junk, runs interviews, and ranks top devs for you. So you only show up when it counts.

Founders are doing 10 jobs. Hiring shouldn’t be one of them. You're building, not reading 67 resumes. AI filters junk, runs async interviews, and ranks the top devs. so you only meet who matters. Smart founders delegate. Fast.

What if AI could: - auto-screen junk applications - run AI-Driven tech interviews - Hand you the top 5 pre-qualified candidates? Curious to know: Would this actually save you 10+ hrs per hire?

LLMs don’t “understand” your prompt—they predict the next token based on billions of patterns. Prompt engineering is system design, not magic.

If your AI app doesn’t have vector search, error handling, and task orchestration—it’s not production-ready. It’s a demo.

Why AI tools fail in the real world: - No frontend to trigger or configure them - No backend to scale or secure them - No automation to keep them running - No context of the actual pain point It’s not the AI that’s broken—it’s your system design.

You don’t need to become a world-class ML researcher. You need to solve boring problems with smart tools. Build systems that save time. Wrap them in a UI people understand. Scale them with code that doesn’t break. That’s what 99% of clients actually want.

Most AI devs overcomplicate things because they don’t understand the user—they just understand the model.

You don’t need 10 new tools. You need 1 full stack you actually finish: • LangChain for logic • n8n for automation • FastAPI for backend • MongoDB for data • Your favorite frontend Then ship. Again. And again.

The worst mistake AI devs make? They learn LangChain, build a chat bot, and stop. No backend. No dashboard. No data pipeline. That’s not a product. It’s a science fair project. To build real AI, treat your models like features—not the whole app.