Coding Chats Episode 81 - Ben Howdle talks to John Crickett about being a first time CTO in a start-up. Ben, author of The First CTO, shares what changes when you move from building technology to leading a technical team: the pressure, the trade-offs, and the realization that writing code is only part of the job.Drawing on startup experience and lessons learned the hard way, Ben discusses how to balance quality against runway, manage people, and communicate technical decisions in terms that founders and other non-technical stakeholders can understand. He also explores why resourcefulness, resilience, and comfort with ambiguity matter so much—and why CTOs often spend more time selling ideas than they expect.Whether you’re stepping into your first CTO role or leading a technical team at an early-stage company, this conversation offers candid insights into the realities of technical leadership.

Chapters

00:45 The Loneliness of the CTO

16:57 Essential Skills for a First-Time CTO

22:53 Understanding the Hierarchy: Who is the Boss?

30:14 The Importance of Advisory Network

38:06 Transitioning to People Management

44:27 Understanding the 40% Rule

52:54 Exploring Related Content

Ben's Links:

https://www.linkedin.com/in/ben-howdle/

John's Links:

John's LinkedIn: https://www.linkedin.com/in/johncrickett/

John’s YouTube: https://www.youtube.com/@johncrickett

John's Twitter: https://x.com/johncrickett

John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social

Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.

Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.

Takeaways

Taking on the CTO title without experience can be the thing that forces the growth you wouldn't have sought otherwise.

At a startup, runway isn't just a finance metric — it should be the lens every technical decision gets filtered through.

The tension between building things well and building things fast enough to survive is one of the defining challenges of being a first-time CTO.

The most chaotic, uncertain period of your career might also be the one where you're most alive.

A first-time CTO needs resourcefulness, comfort with ownership, and resilience more than any specific technical skill.

When you have no one to bounce ideas off internally, build an informal council from your network — it can double as a recruiting pipeline.

The quicker you can identify your own blind spots and weaknesses, the quicker you can plug the gaps.

Early-career engineers often drown non-technical stakeholders in jargon to mask imposter syndrome — the real skill is meeting people where they are and speaking to business outcomes.

Coding Chats Episode 80 - start-up advisor Alexander Berkovich shares his expertise on building successful start-ups, hiring strategies, CTO roles, and the importance of communication between technical and business teams. Discover practical tips for navigating the challenges of early-stage companies and how to align technical excellence with business goals.

Chapters

00:00 Introduction to Start-up Advising

02:09 The Day-to-Day of a Start-up Advisor

05:39 Hiring Challenges in Start-ups

07:39 Defining the Role of a CTO

10:36 Common Mistakes in CTO Hiring

12:59 Bridging the Gap: Technical and Business Communication

16:40 Utilizing Client Feedback for Product Improvement

20:06 Transitioning from Proof of Concept to Product

24:01 Exploring Computer Vision in AI

24:06 Balancing Technical Excellence and Business Focus

24:09 Exploring Related Content

Alex's Links:

Alex's LinkedIn: https://www.linkedin.com/in/alexander-berkovich-startup-advisor/

John's Links:

John's LinkedIn: https://www.linkedin.com/in/johncrickett/

John’s YouTube: https://www.youtube.com/@johncrickett

John's Twitter: https://x.com/johncrickett

John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social

Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.

Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.

Takeaways

A CTO's value is in leadership and strategy, not just how much they can code.AI has fundamentally changed the hiring process and what makes a good candidate.

Document every corner you cut in a POC — it will catch up with you later.

Engineers should seek direct client feedback to understand the real impact of their work.

Communication is the most underrated skill in any startup team.

A POC and a product are very different things — don't let one accidentally become the other.

Startups offer breadth of experience that large enterprises simply can't match.

Hiring for the right mindset matters more than hiring for pure technical skill.

Small technical decisions can ripple out and affect cost, timelines, and the whole product.

The best teams stay connected to the end goal, not just the task in front of them.

Coding Chats Episode 79 - Richmond Alake, Director of AI Developer Experience at Oracle, joins John to discuss agent memory — how AI agents store, retrieve, and adapt to information. He argues that developers building memory on flat files are naively reinventing the database, and that once you factor in concurrency, security, and scalability, a proper database is inevitable. The conversation covers the full memory stack and how Oracle's AI database keeps embeddings and data together without shipping sensitive information to external providers.

The pair also explore why memory is the most universally relatable concept in AI, the history of how neuroscience shaped LLMs, and the problem of Catastrophic Forgetting that still haunts models today. A sharp AGI debate lands on a sobering point: an LLM is just a function — tokens in, tokens out — and most AI engineers are unknowingly rediscovering solutions that database engineers spent decades building.

Chapters

00:00 — What Is Agent Memory and How Does It Work?

05:00 — File System vs Database: Which Should You Use for Agent Memory?

09:00 — Why Building on Files Means You'll Reinvent the Database

13:00 — How Oracle Is Meeting AI Developers Where They Are

15:00 — Why Memory Is the Most Universal Concept in AI

21:00 — From Computer Vision to LLMs: How Richmond Found His Path

24:00 — Catastrophic Forgetting: The Problem That Hasn't Gone Away

26:00 — Is AGI Real? Why the Goalposts Keep Moving

33:00 — Handling PII, Data Sovereignty, and Access Control in AI Apps

42:00 — The Rise of Memory Engineering: AI's Most Underrated Discipline

Richmond's Links:

LinkedIn: https://www.linkedin.com/in/richmondalake/

X: https://x.com/richmondalake

John's Links:

John's LinkedIn: https://www.linkedin.com/in/johncrickett/

John’s YouTube: https://www.youtube.com/@johncrickett

John's Twitter: https://x.com/johncrickett

John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social

Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.

Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.

Takeaways:

File systems are fine for prototyping, but the moment you hit production scale you're just slowly reinventing the database.

File systems are fine for prototyping, but the moment you hit production scale you're just slowly reinventing the database.

Agent memory isn't a new concept — it's data management, and database engineers have been solving it for decades.

Memory is the single most relatable entry point for explaining AI to anyone, technical or not.

Catastrophic Forgetting isn't a solved problem — it plagued RNNs and still quietly haunts LLMs today.

An LLM is ultimately just a function: tokens in, tokens out — which should temper any claims about sentience or AGI.

The definition of AGI keeps shifting to match whatever AI can't do yet, making the whole debate almost meaningless.

Most AI engineers have less than ten years of experience and are unknowingly rediscovering solutions that search and database engineers spent decades building.

"Vector search is all you need" is one of the most dangerous oversimplifications in AI engineering right now.

Memory engineering — the crossover between data engineering, search optimisation, and agent design — is an emerging discipline that doesn't have a name yet but absolutely should.

The real moat in AI products isn't the LLM itself, it's everything built around it — the harness, the memory, the retrieval pipeline.