Knowledge Base
Infrastructure scaling, technical co-founder decisions, AI engineering, fundraising due diligence, and the CTO transition — documented from 5 exits, 20 years, and 50+ founder advisory sessions.
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Scaling Infrastructure · 8 min read
The exact approach used at Nextdoor during COVID surge: 200% capacity increase in 8 weeks, zero degradation. Applicable to any seed-to-Series A team hitting a growth wall.
Founder Frameworks · 6 min read
The 30-60-90 framework from QConSF 2025. Week-by-week priorities for technical founders from idea to first institutional check — without burning the team.
AI Engineering · 5 min read
Not every problem needs an LLM. A decision framework for founders choosing between ML investment and shipping features that actually close deals.
Fundraising · 7 min read
Architecture, team structure, hiring plans, and the pitch — the four areas where technical founders lose deals they should win. What to fix before the term sheet.
Engineering Leadership · 9 min read
The IC-to-executive gap kills companies. What changes, what stays the same, and the specific mistakes that cause first-time CTOs to stall at 8–12 engineers.
Handouts and templates from Glo's QCon San Francisco 2025 talk. Practical frameworks for platform engineers transitioning to startup founders.
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Handouts and templates from Glo's QCon San Francisco 2025 talk. Practical frameworks for platform engineers transitioning to startup founders.
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Frameworks for technical founders from pre-seed through Series A. From the first 90 days to technical due diligence and exit preparation.
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When to add AI to your product — and when it will cost you customers. Practical guides for founders building AI features that close deals.
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Conference talks, technical deep-dives, and presentations on distributed systems and AI infrastructure.
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Long-form writing on engineering, leadership, and building systems.
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Curated books, papers, and articles that shape my thinking on engineering, leadership, and systems design.
Reflections on engineering culture, leadership principles, and the intersection of technology and humanity.
Frameworks, templates, and hard-won lessons from 20+ years building, scaling, and exiting startups. Covers fundraising, team building, product-market fit, and navigating the chaos of early-stage growth.
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Practical guides for building production AI/ML systems. From infrastructure and model deployment to cost optimization and reliability. Real-world patterns from scaling ML platforms at high-growth companies.
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