138 Waymo rides. A deleted Instagram account. A UI tweak that prints hundreds of millions. We catch up after a few months and discuss the moments that stuck with us from season two.

We revisit the most useful clips and stories: what big tech culture looks like from the inside, why your phone does not need to “listen” to know what you want, and how experimentation and A/B testing quietly shape everything from social media feeds to shopping behaviour. We also dig into the fastest-moving force of the past year: artificial intelligence. Using insights from OpenAI’s research on ChatGPT usage, we unpack the “asking, doing, expressing” framework and why it matters for large language models, prompt habits, and even emotional behaviour around LLMs.

From Microsoft’s Tay and the hard lessons of public failure, to eBay search misreading “iPad” as “iPod”, to John Lilly’s view on AI making prototyping radically easier, the common thread is learning speed. Jobs are not flipping overnight, but tasks are accelerating, software engineering is changing weekly, and the barrier between idea and product is collapsing.

Season three is on the way. Subscribe wherever you get your podcasts, leave us a review, and share this with a friend who is AI-curious. What topic do you most want us to tackle next?

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A season finale should feel like a recap, but ours turns into a snapshot of how fast tech is reshaping real work and daily life. Hugh’s back on the ground in Los Angeles in a new Paramount role, taking robotaxi rides like it’s normal, while Hannah steps into a Product Director job at Ocado tackling last mile logistics and the delivery experience. We talk honestly about what we’ve learned after 18 months of making Tech Overflow and why the “curious minds” approach works best when we keep it practical.

Waymo becomes our unexpected lens on product design, autonomy, and human behaviour. What happens when there’s no driver and no social friction? Why do other drivers treat a self-driving car differently? And what does “polite” software feel like as a passenger when the rest of the city learns it can always cut in?

Then we go deep on AI agents, vibe coding, and the gap between “LLMs make building easy” and actually shipping something useful. Hannah tries to build an agent to book Pilates classes and discovers that the hard part is not motivation, it’s foundations: terminals, tooling, and knowing how to break the problem into steps. From there we unpack AI at work, including token usage as a blunt adoption metric, the meaning of tokens, and why most organisations are still learning how to use AI as a co-pilot rather than an autopilot. Listener Q&A covers LLM tiers like Claude Haiku, Sonnet and Opus, local models you can run on your own machine, plus data privacy, enterprise terms, and API retention. We also answer a classic question clearly: how contactless payments and Apple Pay work, end to end.

Subscribe for season three, share this with a friend who’s trying to “use AI properly”, and leave a review if Tech Overflow helped you make sense of modern technology. What should we build or explain next?

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Entrepreneurs get the glory, investors get the spreadsheets, and John Lilly says that’s exactly how it should be. John (former VC, now angel and board member, and newly involved with Gigascale Capital) joins us to demystify venture capital for curious builders: how funds are raised from limited partners, why returns follow a power law, and what investors actually do between writing a cheque and a company becoming real. Along the way, we unpack his simple working framework: see, win, decide, then help build.

The best moments are the stories. John explains how a small real-world signal helped him chase Instagram, how “winning the right to invest” can mean recruiting a key hire, and why a fast acquisition can still feel like a mixed outcome when you believe the company could be worth far more. He also shares the long arc of Figma, including an early “no”, a year of breakfasts, and the traits he looks for in founders: grit, follow-through, and the ability to learn without defensiveness.

Then we widen the lens to the present shockwave: AI and large language models. John talks about prototyping at speed, what it means for startup formation, and why the next constraints may be compute, chips, cooling, and power rather than ideas. Finally, we touch climate tech and hard tech investing, where energy, materials, supply chains, and data centre demand collide.

Like, Subscribe, and Follow the Tech Overflow Podcast by visiting this link: https://linktr.ee/Techoverflowpodcast