What if we could bring that same philosophy to the way we build software? In his latest post, Patrick Debois dives deep into a second AI-native pattern—one that flips our traditional dev mindset. Instead of obsessing over how things are built, we zero in on what we want built and let AI handle the rest. 🧠🤖 Here’s what’s inside: - Programming in 𝐧𝐚𝐭𝐮𝐫𝐚𝐥 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 instead of just syntax - Building and sharing 𝐩𝐫𝐨𝐦𝐩𝐭 𝐥𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 like modern standard libs - Turning 𝐭𝐞𝐬𝐭𝐬 𝐢𝐧𝐭𝐨 𝐞𝐱𝐞𝐜𝐮𝐭𝐚𝐛𝐥𝐞 𝐬𝐩𝐞𝐜𝐬 to align AI with intent - Prioritizing 𝐠𝐨𝐚𝐥𝐬 𝐨𝐯𝐞𝐫 𝐠𝐫𝐚𝐧𝐮𝐥𝐚𝐫 𝐥𝐨𝐠𝐢𝐜 for faster iteration and innovation This isn’t just theory, it’s a practical shift in how we design, collaborate, and ship software in the age of LLMs. 💬 Got thoughts or experiments of your own? Share them below. #AINative #PromptLibraries #DevPatterns #AIxDev #LLMEngineering #SpecDrivenDev #Tessl
Read the full piece: http://ainativedev.co/flw
Intent > execution — applies far beyond engineering. Really inspiring shift in mindset.
The future programming language is English!
This reminds me of micro managing people with very precise instructions vs keeping your eye on the bigger picture !
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Senior Advisor | ex-IBM Distinguished Engineer | Artificial Intelligence | Blockchain and Digital Assets
3wThis reminds me a lot of Prolog—maybe the original “intent-driven” language. Prolog was all about describing what you want and letting the engine figure out how to get there. What is described here feels like a modern, probabilistic version of that same philosophy—albeit expressed in natural language instead of logical predicates. Prompt libraries mirror predicate libraries, tests-as-executable-specs are native to logic programming, and goal-oriented execution is pretty much how Prolog (and other constraint-logic programming lanagues) work.