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Building AI systems that solve real problems — not just toy demos.
Latest project: a phishing URL detector powered by Random Forest, trained on 58,000+ URLs, hitting 95.8% accuracy on the test set.
Correctly flagged 5,905 out of 6,133 URLs — the kind of precision that matters when security is on the line.
Part of a growing portfolio of AI/ML projects, including real-time object detection and NLP chatbots. I believe in shipping working models, not just writing about theory.
If you need a Python/AI developer who delivers fast and communicates clearly — let's build something together.
#pythondeveloper #aidevelopment #AIEngineer #MachineLearning
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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AI can help founders build products faster than ever. But building is only half the journey.
In this episode, I share a real engineering lesson about why products that look "finished" often aren't ready for production and what actually happens between a working prototype and a successful launch.
Building is no longer the bottleneck. Launching is.
#FounderEngineeringJournal #AgenticAI #AISaaS #ProductEngineering #StartupFounder #AIEngineer #MVP #Launch #ProductDevelopment #BuildInPublic
founderinsightsaisaasstartupmvpAI Engineer Full Stack DevelopmentSoftware DevelopmentGoogle GeminiLovableRork
Absolutely; AI can accelerate development, but strong launch planning, validation, and execution are what turn a prototype into a successful product.
Hype, hype and hype... It's the first page of the notebook that I write in tells me.
I was building automated workflows around Signalwatch, and it got me thinking about all the hype.
Hype around just one piece of the puzzle, LLMs. They just predict the next word while generating... No magic.The better they are trained, the better the output. Super helpful in a lot of contexts...
Tool calling. Helpful, but if LangChain and RAG + LLMs, for example, are not included where appropriate, ta ta, bye bye.
Automation. Someone bringing me a glass of water, for example, isn't something to be automated or waste resources or tokens on...
This brings me back to structural understanding, especially python + machine learning basics where you train a model based on a small piece of dataset you download from hugging face, for example... a lot becomes clear here
