Conversation Checkpoint: Framework Development & Productivity Workflow

Context Summary

Developer with 6+ years Python experience, specializing in prompt engineering and AI collaboration frameworks. Has built sophisticated meta-cognitive frameworks (IPEV for execution tasks, A-HIRD for debugging) that structure human-AI collaboration. Uses primarily free/open-source tools: Gemini CLI (primary), Cline+Claude (expensive tasks), VS Code with dual monitors.

Current Framework Portfolio

• IPEV Loop: Intent-Plan-Execute-Verify framework for reliable execution tasks
• A-HIRD Framework: Anticipate-Hypothesis-Investigate-Reflect-Decide for debugging/testing
• Prompt Factories: Generate complete mission prompts with minimal questions (2-3 max)
• Three-Party System: Developer → LLM Factory → Agentic Code Editor workflow
• Post-mortem Analysis: Uses powerful LLMs to analyze session logs and extract High Impact/Low Effort improvements

Developer Profile Key Points

• Philosophy: Practical solutions over theoretical complexity, automation-first mindset
• Preferences: Python-centric, HTMX/AlpineJS frontend, minimal JavaScript
• Anti-patterns: Dislikes bureaucratic processes, poor UI/UX, unnecessary complexity
• AI Integration: 2.5+ years experience, seeks structured workflows beyond autocompletion

Trajectory Discussion Outcomes

Three Initial Milestone Options Presented:

1. AI-First Development Environment (automation layer)
2. Framework Evolution Engine (adaptive learning systems)
3. Developer Productivity Platform (broader tooling ecosystem)

Developer Feedback & Insights:

Option 1 Reaction: - Rejected automated VS Code watching ("Not that fast... don't see high impact") - Rejected auto-prompt generation from git commits ("Meh!") - ACCEPTED: Key binding/hotkey system for instant prompt access - Key insight: "I waste time digging through directories in Windows, which creates a lot of friction and context switching"

Option 2 Reaction: - Already implementing via post-mortem analysis with powerful LLMs - Uses JSON session logs + well-designed personas for performance evaluation - A-HIRD framework itself evolved this way (added "Anticipate" component through post-mortem analysis)

Option 3 Reaction: - Rejected web tool concept - ACCEPTED/EVOLVED: Framework selector concept - LLM that understands framework portfolio, developer profile, and problem statements to recommend appropriate framework or identify new framework opportunities

Identified Real Friction Points

Primary Pain Point: Directory Digging

Current waste: Multiple times daily - browsing directories to find prompts, snippets, templates Impact: Context switching, mental energy drain Specific example: Proofreading prompts workflow requires: 1. Open browser → ChatGPT website
2. Navigate to proofreading chat 3. Discover chat session too long, start new one 4. Copy/paste/wait for response 5. Context switching back to original work

Proofreading prompt in use:

SYSTEM PROMPT:
Your ONLY role is to proofread and polish prompts that will be sent to OTHER language models...
[Complete prompt provided - focuses on grammar/spelling/clarity only, no execution]

Secondary Consideration: Framework Selection

Occasional uncertainty: "Is this an A-HIRD problem or IPEV problem?" creates decision friction.

Immediate Next Steps Identified

High-Priority Solution: Simple Desktop Prompt Processor

Problem: Browser-dependent proofreading workflow wastes 2-3 minutes per use Solution: Desktop executable with two text boxes - paste draft → click "Polish" → get result Technical approach: Python + tkinter, uses existing Gemini CLI, ~50 lines of code Benefit: 15 seconds instead of 2-3 minutes, zero habit change required

Implementation Status: Added to TODO list for detailed discussion

Key Developer Preferences Confirmed

• High Impact / Low Effort / Low Friction solutions only
• Skeptical of automation that adds complexity without clear value
• Values solutions that work with existing tools rather than replacing them
• Prefers incremental improvements to existing workflows over new system adoption
• Recognizes workflow adoption takes time and habit formation

Framework Evolution Philosophy

Uses sophisticated post-mortem analysis rather than automatic pattern detection. Employs powerful LLMs with large context windows and well-designed personas to evaluate agentic performance and generate actionable improvement recommendations.

Next Session Objectives

1. Detailed technical discussion of Desktop Prompt Processor implementation
2. Explore framework selector concept in more depth
3. Identify other high-impact, low-friction productivity improvements
4. Consider broader trajectory planning based on refined understanding of preferences

Developer's Current Mindset

"What we want to do is launch a new workflow, and adopting a new workflow takes habit and time. It won't happen overnight. I need to go through things, consider potential conflicts of interest, and see what works and what doesn't."

Focus on small, specific friction points that cause daily annoyance rather than large system changes.