My Experience Using GPT 5.6 Sol to Build a Windows App

2026-07-19 · Source: AI on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

The author details their experience using GPT 5.6 Sol to develop a new Screen Recorder feature for their Windows app, WindowShot, which enhances screenshots with backgrounds and effects. Initially, building WindowShot's first version with GPT 5.5 required extensive micro-tasking and multiple prompts for tiny edits. However, with GPT 5.6 Sol, the development process dramatically improved. The model demonstrated an ability to anticipate logical next steps and implement subsequent parts of a feature without explicit prompting, significantly reducing the number of interactions and increasing progress per prompt. This led to higher code quality, better architectural fit, and handling of edge cases, transforming the AI from a code-writing assistant to a feature-building collaborator.

Key takeaway

For software engineers building complex application features, GPT 5.6 Sol offers a more integrated and anticipatory development experience than previous versions. You should consider leveraging advanced LLMs like GPT 5.6 Sol for feature-level development, trusting them to anticipate and integrate logical next steps, rather than micro-managing prompts. This approach can significantly accelerate your workflow and improve code quality.

Key insights

GPT 5.6 Sol significantly enhances developer productivity by anticipating next steps and integrating solutions during feature development.

Principles

• Advanced LLMs can anticipate logical next development steps.
• Contextual understanding improves AI-assisted code quality.

Method

Shift from micro-tasking prompts to feature-level requests, allowing the AI to anticipate and implement logical subsequent steps, thereby accelerating development and improving code integration.

In practice

• Use GPT 5.6 Sol for complex feature development.
• Trust the model to anticipate and fill code gaps.

Topics

• GPT 5.6 Sol
• AI-assisted Development
• Windows App Development
• Large Language Models
• Screen Recording
• WindowShot

Best for: Machine Learning Engineer, AI Engineer, Software Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.