AI-Powered Software Development activity of the week - Implementation #2
Marko Jaanu
Head Of AI Powered Technology and Advisor at Siili Solutions, Board Member at Double Open
This article delves into the current trends and future directions of AI-assisted development, highlighting key advancements and their implications for developers and continues the first article about implementation phase released earlier.
Moving Beyond the Inner Loop
Current Scenario
Today's development process heavily involves humans in the "inner loop" where constant interaction with AI tools like GPT is necessary for writing and debugging code. This iterative process is often slow and cumbersome.
Future Vision
The next major evolution in AI-assisted development will shift humans to the "outer loop." In this scenario, AI will handle the repetitive, inner loop tasks, allowing developers to focus on overseeing the process. This transition will streamline workflows, reduce cognitive load, and enhance productivity.
Evolution of Core Technologies
Plateau Predictions
AI development is predicted to follow a sigmoid growth curve, suggesting a potential plateau. However, recent advancements from companies like OpenAI indicate ongoing progress, with the timing of this plateau remaining uncertain.
Reasons for Optimism
Despite concerns, significant investments and positive insider comments suggest a bright future. Models such as GPT-4o and Gemini 1.5 Pro, despite being smaller, demonstrate impressive performance. Key developments for developers include larger context windows, faster and cheaper models, open-source options, and self-hosting capabilities.
Claude 3.5 sonnet just came out and it is impressive as well.
Retrieval Augmented Generation (RAG)
Current State
Tools like ChatGPT are powerful but lack the wider context needed for solving complex real-life problems. Existing solutions such as CoPilot and JetBrains AI use vector search but are limited in handling code comprehensively.
Future Impact
Larger context windows will allow the inclusion of entire repositories, outperforming current vector-based RAG solutions. Although costly, practical implementations will involve AI building representations of code to enhance context.
IDE Integrations
Future integrations with IDE tools, Language Server Protocols (LSPs), and debuggers will significantly improve AI assistance. Upcoming tools like GitHub Copilot X promise to address current limitations, offering more seamless AI integration.
Agentic Workflows
领英推荐
Foundation of Agents
Agents are essentially LLM prompts equipped with various tools, functioning like objects with exposed methods to achieve specific tasks. These agents automate repetitive coding processes, producing clean, tested, and reviewed code.
Types of Agents
Intelligence as a Service
Integrating GenAI
GenAI can be envisioned as "intelligence as a service," integrating intelligent capabilities into applications effortlessly. This integration is becoming an essential tool in a developer’s toolkit.
Versatile Applications
GenAI’s potential spans across simple to complex tasks, serving as a critical component in enhancing application functionality and developer efficiency.
Autonomous Agents
Devin’s Journey
Devin, an autonomous coding agent, despite early criticisms, showcased promising ideas. Now under Microsoft's development, Devin aims to integrate deeply with tools like VSCode, enhancing the developer experience.
Future Integrations
Expect robust integrations in IDEs where autonomous agents assist with coding tasks. While fully autonomous agents for senior-level tasks are still distant, intermediate integrations will significantly aid development processes.
Github Copilot Workspace has now been introduced where developers can now brainstorm, plan, build, test, and run code in natural language. This new task-centric experience leverages different Copilot-powered agents from start to finish, while giving developers full control over every step of the process.
Conclusion
The future of AI-assisted development is filled with potential and exciting advancements. By transitioning developers to oversight roles, enhancing core technologies, and integrating intelligent agents, we are on the brink of a more efficient and innovative era in software development. Embracing these changes will not only streamline workflows but also pave the way for groundbreaking applications of AI in everyday coding practices.
Used GenerativeAI tools: OpenAI ChatGPT