Thanos Diacakis, Software Delivery Coach & Fractional CTO
- Childhood curiosity with technology’s “buttons and knobs”
- Problem-solving as creative satisfaction
Podcast
Overview
Ever wonder how technology leaders adapt between nimble startups and tech giants, or what it truly takes to ship software that matters? In this insightful TBCY episode (S7 E062), Ashutosh Garg sits down with Thanos Diacakis—software delivery coach, fractional CTO, patent-holder, and independent consultant—to unpack 25+ years of lessons from the front lines of engineering and startup life.
Below you’ll find key topics explored in the episode, organized by timestamp, complete with guiding questions to help you navigate the most relevant insights for your journey.
00:00:36- What sparked Thanos’s passion for software, and what fuels it after two decades?
- Childhood curiosity with technology’s “buttons and knobs”
- Problem-solving as creative satisfaction
- Evolving from coding to debugging teams and processes
00:02:12- How are startups and tech giants different, and what mindset shifts are needed?
- Resource constraints vs. abundance
- Wearing multiple hats in startups vs. specialization at scale
- Scaling via speed in startups; scaling via collaboration in big companies
- The challenge of consensus-building in larger organizations
00:03:54- What are the most common engineering bottlenecks in early-stage startups?
- Tech debt: shortcuts, their dangers, and the balance between speed vs. future pain
- The concept of good versus company-killing shortcuts
- The importance of returning to pay down technical debt
00:05:38- How can founders recognize when engineering processes are holding them back?
- Focusing on output (shipping value), not just input (featuring checklists)
- Diagnosing bottlenecks across process, culture, and technology
- The essential step-back: defining success, then aligning processes
00:07:27- What framework does Thanos use to align tech strategy with business outcomes?
- Bridging business and engineering expectations
- Key mental models: beyond simply adding engineers
- Four-stage process:
- Iteration: speed to deploy (idea to production in days)
- Quality: Observability and confidence in live systems
- Complexity: Managing growth (procedural, architectural, and cultural)
- Planning: True predictability emerges only after mastering the prior stages
00:12:51- What qualities matter when scaling engineering teams now (including AI impacts)?
- Generalist engineers over language-specific specialists
- Emphasis on learning, adaptability, and problem-solving
- Customer-first mentality, not tech-for-tech’s-sake
- Openness to AI as an accelerating tool—but balanced with understanding its limitations
00:15:48- When should startups invest in infrastructure versus performance optimizations?
- Infrastructure investment pays off early—multiplies dev productivity
- Performance comes after product-market fit; avoid premature optimization
- Lessons from Twitter: reliability can lag if your product gets traction
00:17:31- How should startups approach the classic build vs. buy dilemma?
- “Buy” first, unless the last 10–20% of features are business-critical
- Conduct real tests before committing to in-house builds
RESOURCES:
Learn more about Thanos Diacakis: Linkedin
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- Childhood curiosity with technology’s “buttons and knobs”
- Problem-solving as creative satisfaction
- Evolving from coding to debugging teams and processes
