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Creatives on Contra have earned over $150M and we are just getting started
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My core stack is less "generate pretty pictures" and more "build the pipeline that does the thinking":
n8n — the glue. Great for orchestrating multi-step agent workflows and connecting APIs without writing a service from scratch. Weakness: gets messy fast on complex branching logic, and debugging a broken node chain eats time.
RAG (usually with a vector DB + LangChain-style retrieval) — lets me ground LLM output in actual client data instead of hallucinated generalities. Strength: dramatically improves accuracy for domain-specific asks. Weakness: chunking/retrieval quality is finicky, and a bad embedding setup silently tanks results without throwing an error.
Claude/GPT for reasoning + code — my default for anything needing judgment: analysis writeups, SQL/Python generation, proposal drafting. Strength: fast iteration. Weakness: still needs a human check on numbers — confident-sounding ≠ correct.
Power BI / Tableau — where the AI output actually becomes something a client trusts. AI can shortcut the analysis, but the dashboard is what gets signed off on.
The honest lesson: the "AI" part is maybe 30% of the workflow. The rest is validation, data plumbing, and making the output legible to a non-technical stakeholder.
Silent failures are what I watch closest. I treat an answer as untrusted unless I can show exactly which source text the model read, because fluent output can hide a bad match.
A lot of people think building a successful software business means creating the next Facebook, TikTok, or Instagram.
It doesn't.
Sometimes, the bigger opportunity is hidden inside a small problem that a specific group of people experiences every single day.
Think about it.
A simple SaaS product solving a painful business problem could have:
100 customers paying $20/month = $2,000/month
500 customers paying $20/month = $10,000/month
1,000 customers paying $20/month = $20,000/month
You don't need millions of users to build something meaningful.
The real question is:
What problem are people already willing to pay to solve?
It could be:
Business reporting
Document processing
Inventory management
Workflow automation
Sales tracking
Payment management
Data analysis
Customer management
The best software idea isn't always the most exciting one.
Sometimes it's the boring problem that keeps costing businesses time and money.
Find the pain.
Build the solution.
Get your first users.
Listen to them.
Improve the product.
Then scale.
Don't chase millions of users before you've solved a problem for 10.
That first 10 can teach you more than 10,000 random users ever will.
What everyday problem do you think deserves a software solution?
Drop it in the comments. Someone reading this might build it.
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This project demonstrates a Retrieval-Augmented Generation (RAG) architecture using open-source tools. It solves the "knowledge cutoff" problem of LLMs by giving them access to live tools.
How it works:
Intent Classification: The user's input is first processed by a local Llama 3 instance, which acts as a semantic router. It analyzes the prompt to determine if external data is required.
Tool Execution: If a search intent is detected, the Python backend executes a query against the SerpApi (Google Shopping) endpoint. Data Structuring: The raw JSON response from the API is parsed, cleaned, and structured into standardized product objects.
UI Rendering: The structured data is dynamically rendered into interactive product cards using Streamlit's component system, allowing users to view images and click direct purchase links without leaving the chat interface.
