The Ubiquitous 360
Ray Villalobos
Generative AI, Prompt Engineering and Full Stack Development. LinkedIn Top Voice. Senior Staff Instructor at LinkedIn, Instructor at Stanford University.
I meet lots of people who wonder if AI will be a fad, like Web3 or other past technology bubbles. However, AI is fundamentally different. While the internet revolutionized our ability to connect, AI is transforming how we interact with technology and automates tasks on a much deeper level.
No industry has been more affected by AI than software development
No industry has been more affected by AI than software development. From automating code generation to enhancing debugging processes, AI tools are radically changing how developers code.
GitHub’s Copilot, an AI-powered coding assistant, operates in a controlled environment, collecting extensive data on its usage. Research indicates significant differences in productivity and efficiency between developers who use it and those who don't.
With AI, you write less code manually and take on the role of a conductor, asking the AI to write functions and verify the code you do write. It’s similar to how professional writers are using Chat GPT—not to write everything, but to brainstorm, offer suggestions, and critique their work.
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Why it's so helpful
The reason AI is so helpful is that the basis for these tools are LLM or Large Language Models that are trained on massive amounts of languages. But think of what a programming language is, a language based on english, where the grammatical rules are extremely simple and consistent, and where there are LOTS of examples of how it should be used.
A programming language is a dream scenario for a Large Language Model.
A programming language is a dream scenario for a Large Language Model because code is highly testable. Unlike English, with flexible rules that adjust to diverse origins, programming languages like Python or JavaScript use consistent grammar, and AIs take advantage of tons of examples, tutorials and libraries that make training the tools easier.
The models can quickly understand code, find syntax errors, refactor for clarity or efficiency, brainstorm solutions, and provide faster solutions than traditional searching online.
The copilot version is expanding its capabilities to suggest improvements across your entire repo and with GitHub Workspace suggest improvements to code. If you're lucky enough to have an Enterprise Copilot , the ChatBot works a bit differently, having access to your internal codebase, so it's able to make suggestions and improvements more wholistically.
Coming to Every Tool you Use...in a good way
You may have already discovered how LLMs and Omni-models are helpful with writing and productivity, but that same efficiency is moving to other tools. For a heavy AI user like me, I do everything differently: Write, video edit, process photos. It's changed how I approach any task and I can do it quicker and easier than before.
There's a lot of doom and gloom out there, but so far, I'm loving the new AI world.
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5 个月Sometimes, I wonder if simply listing GitHub as a skill on a resume or a LinkedIn profile is relevant anymore. It’s almost like we need to call it “AI GitHub” so that people know that you’re totally up to speed on how to use the platform.