# AI for autocomplete Published articles for AI for autocomplete. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AI Programming Paradigms: A Timeline DevFeed: [AI Programming Paradigms: A Timeline]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2025-07-08T00:00:00Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Artificial Intelligence](), [agentic-coding](), [AI-assisted coding](), [Programming](), [cursor](), [ide](), [GitHub Copilot](), [Large Language Model](), [GitHub]() Tags: [agentic](), [agentic-coding](), [agents](), [ai](), [ai-for-autocomplete](), [autocomplete](), [code](), [code-completion](), [coding](), [context](), [copilot](), [cursor](), [llm](), [programming](), [visual-studio]() ### AI overview The article outlines a progression of AI programming paradigms: super autocomplete, conversational coding, agentic coding, and possible multi-agent workflows. It describes how each approach changes the way developers interact with code and development tools. ### Source excerpt A developer podcast host recently said they only use AI for autocomplete. This shocked me. That's two generations behind today's state of the art. This is how the field is evolving: I spoke about this topic in episode #301 of Fragmented. It was recorded more recently so reflects my more current thoughts on the topic. Download directly AI Programming Paradigms at a Glance Super autocomplete: AI predicts code, not just keywords. Conversational Coding: Chat with your IDE, direct the AI, iterate together. Agentic Coding: AI acts independently - runs commands, checks work, iterates. Simul Agentic? : Multiple sub-agents, parallel workflows, working together. I. Super autocomplete # This used to be one of the biggest selling points of certain IDEs like JetBrains & Visual Studio.1 The first wave of AI changed this. IDEs now predict entire classes and logic blocks, not just keywords. They excel because they feed surrounding code context to the LLM for relevant suggestions. GitHub deserves credit for kicking off the revolution with their Copilot offering. Here's an early YouTube video of mine from June 20222 showing it in action. Autocomplete keeps advancing. Cursor (today's most popular AI IDE) has "Tab". JetBrains, the pre-AI autocomplete champion, is building Mellum (an LLM built for code completion). This paradigm is alive and well, but it's become table stakes. Most developers use it, but it's far from the frontier. II - Conversational Coding # When ChatGPT took the world by storm, a new paradigm emerged. Conversational coding, where you chat with the AI and pair program together. Unlike the autocomplete era, where you trust the AI's suggestions, here you direct the AI, give it context, and nudge it toward better solutions. This is arguably what most devs use today and envision when they hear "AI programming". It feels magical, productivity jumps are real, and it's hard to think of every going backwards. Cursor leads this charge: chat with your IDE and have it make code ## Ep. 1: Enhancing Your Go Projects with Generative AI: Exploring LLMs DevFeed: [Ep. 1: Enhancing Your Go Projects with Generative AI: Exploring LLMs]() Original publisher: [Read original article]() Published: 2024-06-20T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Go Language](), [Large Language Model](), [Generative AI](), [AI-assisted coding](), [llama](), [Code]() Tags: [ai-driven-text-based-tasks](), [ai-enhanced-go-workflows](), [ai-for-autocomplete](), [ai-for-code-translation](), [ai-for-customer-service-automation](), [ai-for-document-summarization](), [ai-for-interactive-applications](), [ai-for-sql-query-generation](), [ai-generated-code-completions](), [ai-in-customer-support](), [ai-in-programming](), [code-generation](), [code-generation-with-ai](), [codium-ai-integration](), [daniel-s-ai-tutorial-series](), [enhancing-coding-efficiency-with-ai](), [generate](), [generative-ai](), [generative-ai-for-developers](), [generative-ai-in-go-projects](), [github-copilot-alternatives](), [go](), [go-programming-with-ai](), [go-projects](), [harnessing-ai-in-go-programming](), [integrating-llms-in-go](), [intro-to-generative-ai-series](), [large-language-models-llms](), [llama-3-model](), [llms](), [llms-architecture-and-functionality](), [llms-in-code-editors](), [practical-applications-of-llms](), [predictive-text-ai-models](), [text-generation-with-ai](), [understanding-llms]() ### AI overview The first episode of an introductory series explains how large language models such as Llama 3 work and how developers can integrate them into Go projects. It covers text generation, autocomplete, code generation, customer service automation, SQL generation, code translation, and document summarization. ### Source excerpt Introduction: Welcome to the first episode of our "Intro to Generative A.I" series! In this episode, Daniel dives into the intriguing world of large language models (LLMs), providing a comprehensive understanding of how these powerful tools work and their practical applications. Gain insights into the architecture and functionality of large language models like Llama 3, and how they process and generate language-based responses. Learn how these models can be integrated into Go projects, enhancing capabilities like autocomplete, code generation, and more through practical examples and demonstrations.