Optimizing AI Workflows: Key Procedures
Human First, AI Assist ▶
The human focuses on high-level tasks and design, instructing AI for high-level decision-making or cross-domain tasks.
Examples: App architecture, core feature design, key tech stack decisions, complex problem-solving.
Arno's approach for high-level design, leveraging personal context engineering.
AI First, Human Assist (Sync) ▶
The human designs the task and instructs AI to perform it synchronously, with the human revising and reviewing the AI's work in real-time.
Examples: Module coding, writing technical docs, code reviews, DevOps tasks.
Uses dedicated tools likeCursororGithub Copilotfor real-time assistance.
AI First, Human Assist (Async) ▶
The human designs the task and instructs AI to perform it asynchronously using an `Agentic` approach. The human reviews and revises later.
Examples: Market research, writing unit tests, code refactoring, batch data processing.
Leverages tools likeGoogle Deep ResearchorJulesthat can work independently.
Practical AI Application Scenarios
Daily QA
Tools: Gemini Pro 2.5, OpenAI O3Pro
For public knowledge, time-sensitive info, and personal context queries.
Coding & Programming
Tools: Cursor (w/ Claude 4/o3)
For vibe-coding, code generation, reviews, and full-lifecycle development.
Research & Learning
Workflow: Deep Research → Gemini → Artifact → Notebook ML
A powerful cycle for deep knowledge discovery and synthesis.
Content Creation
Workflow: Cursor + Github + Markdown
Perfect for short, medium, and long-form writing tasks.
LLM Models Comparison (2025)
O3-Pro
Complex QA & Long-Context Logic
Fast Speed
Gemini Pro 2.5
General tasks with search context
Fast Speed, but can be expensive
Claude 4
Excellent for Coding
Medium Speed
AI Development Tools for Software Engineering
Other AI Tools
Legend
Free for now
Research
Recommended
Favorite
Valuable but Costly