The reading order
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1.
The Pre-Series-A AI Startup Hiring Plan: Who to Hire, in What Order, and Why Most Get It Wrong
The full sequence from founding engineer to fifteen, with the comp framework and the mis-leveled hires to avoid.
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2.
From One Engineer to Fifteen: What Co-Founding Taught Me About Engineering Leadership
What changes at each scale boundary. The shift from delivery to leverage to org design.
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3.
How to Manage a 4-Person Engineering Team Without Becoming a Manager
The rituals that work at this size — 1:1 cadence, planning loop, decision-making — and the failure modes when you outgrow them.
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4.
How I'd Hire a Staff Engineer at an AI Startup
The interview loop, the failure modes, the senior-vs-staff calibration that produces correctly-leveled hires.
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5.
The 'Smallest Possible Slice' Heuristic for Shipping Complex Features
The leverage move that defines senior IC work. Smaller than MVP. Earns the next slice.
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6.
The Perfect Hire Is Killing Your Team
The whiteboard interview rewards memorization and pedigree — the weakest predictors of real performance. Hire for trajectory instead.
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7.
We're About to Stop Making Senior Engineers
The path to senior ran through the grunt work AI now does in one prompt. How to grow architects when the ladder's bottom rungs are gone.
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8.
A Professional Owns the Whole Outcome
Professionalism isn't process or titles. It's owning the whole outcome — the cost, the failure, the 3am page, the wrong call.
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9.
Prove the Return or Don't Spend the Time
Time is the one budget you can't refill. Every tool and hour has to show a demonstrable return, or it's a tax on attention.
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10.
How I Triage a New Codebase in 90 Minutes
The 7-step protocol for a useful POV on an unfamiliar codebase in 90 minutes — readme, deps, git log, tests, hot files, auth, incidents.
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11.
How We Cut $350K From Cloud Spend in 6 Months (And What I'd Do Differently)
A real GCP-to-Azure migration: lift-and-shift first, optimize after. The $350K saved, the $50K mistake, and when not to.
Other topic guides
- AI engineering What production AI engineering actually looks like in 2026 — the autonomy ladder for agents, the workflow shift, telemetry, and team sizing.
- Security for startups A guided reading order for startup security — what to read first on SOC 2 as a revenue tool, vCISO hiring, and securing AI-native products.
- Elixir and the BEAM for AI systems Why language choice matters for AI systems — BEAM concurrency for agents, what Go frameworks cost you, and why Elixir is the language AI writes best.