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I spent a few wonderful years helping Algorithmia build innovative serverless ML hosting services - deep in the trenches with Python datascientists, churning through Jupyter notebooks, debugging memory issues and race conditions. Data pipelines were a nightmare of custom scripts and complex orchestration. Fast-forward to the present: I wanted to get back into datascience, so I spent a day experimenting with GitHub #Copilot and Daft to build an AI-powered image search engine. Things have changed, for the better: 🤖 AI pair-programming is fast, but needs guidance! In just a couple hours, I built days worth of code. GitHub Copilot and Anthropic Claude Sonnet 4 are a truly admirable combo... but while this accelerated initial code generation, it still took many well-guided iterations to arrive at a useful product. AI tools are powerful accelerators, but you must know how to guide them effectively. There's no substitute for real-world experience in the problem domain, or for coding in general. ⚡ Daft.ai brings multimodal magic, and SQL queries into nonrelational data. Unlike traditional dataframes that treat images as file paths, Daft natively processes them. For example, you can bulk-chain image operations like decode(), resize(), encode() ... And querying heterogenous and non-relational data is easy via their SQL dialect! 🔎 The result? Clean code focusing on business logic instead of infrastructure headaches, sculpted in just a few hours. It's like the datascience and software development world finally caught up to what we dreamed of back in the Algorithmia days. - Read the full writeup and try out the demo at https://lnkd.in/gKNN8aFA - Try GitHub Copilot for free at https://gh.io/copilot/free - Experience multimodal data processing and SQL queries at Daft.ai