Self-Tuning Loop 4 steps (Generate → Capture → Analyze → Evolve) extracted as a universal module. Supabase DDL, diff capture utilities, analysis/evolution prompts, email/blog examples, GitHub reference implementation.
OSS monetization framework for solo builders. Outlines a 5-stage strategy using the 'What (Free) - How (Paid)' model to generate revenue without enterprise sales or managed infrastructure.
Analysis of AI infrastructure monetization. Details the 'software free, operations paid' model, where revenue is driven by GPU compute hours and data storage volume in the AI stack.
Self-Tuning Loop 4 steps (Generate → Capture → Analyze → Evolve) extracted as a universal module. Supabase DDL, diff capture utilities, analysis/evolution prompts, email/blog examples, GitHub reference implementation.
OSS monetization framework for solo builders. Outlines a 5-stage strategy using the 'What (Free) - How (Paid)' model to generate revenue without enterprise sales or managed infrastructure.
Analysis of AI infrastructure monetization. Details the 'software free, operations paid' model, where revenue is driven by GPU compute hours and data storage volume in the AI stack.
Analysis of Langfuse and Dify's open-source monetization. Explains why observability layers are structurally superior to frameworks due to higher switching costs and continuous usage.
Monetization strategies of LangChain, LlamaIndex, and CrewAI. Analysis of the 'free framework, paid operations' pattern and its application to solo builder models.
Why MMU is an MIT open-source CLI, not a paid SaaS. Details the solo builder workflow fit, the 3-tier 'What-How-Auto' revenue model, and explicit validation metrics.
Documenting how 534 SaaS launch checklist items were derived from 80 service analyses, 12 guidelines, and 5 real-world failures, including the P0-P3 priority logic.
Analyzing the difference between 'code complete' and 'product complete' through the 3-week post-feature work (payment stability, security, legal docs, SEO, etc.) which accounted for 88% of the effort.
Analyzing n8n's Fair-code license strategy as a defense against AWS risk in open source monetization, and how licensing defines the boundaries of viable business models.
Comparison of open source licenses (MIT, Apache 2.0, BSL, etc.) and a decision framework based on project scale and revenue model, including MMU's choice of MIT.