7 min read
How I Launch a SaaS Product in 30 Days: My Full Process From Idea to Live
The exact validation and execution system I use to launch SaaS products in 30 days, from interview-led demand validation to production rollout.
Category
Building and Operating AI Products
10 posts in this category. Use these guides to move from experiments to production-grade outcomes.
7 min read
Compound AI Systems: Why the Future of AI Products Isn't a Single Model
The most capable AI products in 2026 aren't single model calls — they're compound systems. Multiple models, retrievers, validators, and tools working together. Here's the architecture pattern shaping the next generation of AI products.
7 min read
AI Observability: Monitoring Models in Production Like a Pro
How to monitor AI systems in production with practical observability patterns for drift, latency, and business impact.
7 min read
How to Choose Between Open-Source and Proprietary AI Models
A founder-friendly decision framework for balancing cost, control, quality, and compliance in model selection.
7 min read
Designing Data Strategies for AI Products (Without a Data Team)
A lean data strategy framework for founders who need reliable AI outcomes without hiring a full data team.
7 min read
Evaluating AI Model Quality: Metrics Non-Researchers Should Use
A practical guide to evaluating AI quality with metrics and workflows that non-research teams can run every week.
7 min read
From MVP to AI-Native Product: A Founder’s Roadmap
A practical roadmap to move from an MVP with AI features to a truly AI-native product with durable defensibility.
7 min read
Multimodal AI: Combining Text, Image, and Sensor Data in Real Products
How to design multimodal AI systems that combine text, image, and sensor data for robust real-world product behavior.
7 min read
Using AI to Validate Startup Ideas Before You Build
How to use AI assistants for faster idea validation while preserving the human interviews that matter most.
7 min read
Vector Databases and RAG: When You Actually Need Them
A practical decision guide for when RAG and vector databases are the right architecture choice—and when they are not.