All episodes
Conversations with the founders and engineers building with AI: what they use, what works, and what breaks.
How Michael Batko gets AI to do the busywork
November 12, 2026 · 58m
The outsized returns from AI come from revenue growth, not cost-cutting—yet 99% of businesses outside the tech bubble have barely started exploring it. Michael Batko, former Startmate CEO and now founder of Hourglass AI, joins Yaniv to discuss how he helps companies of 20-100 people with no engineering team build bespoke AI systems. The conversation covers the company 'AI brain' built from structured Markdown files, the skills-based collaboration model that prevents teams from rebuilding the same tools, and why behaviour change is the hardest part of any AI transformation. For engineers and founders, the episode is a practical look at what actually works when AI building is cheap but maintenance and adoption remain hard.
Death by small bugs: securing code in the Vulnpocalypse, with Patrick Collins and Simon Harloff
November 5, 2026 · 48m
The frontier models that refuse to help you hack your own software are, paradoxically, making you less safe—while open weights models like DeepSeek Flash V4.1 will comply, cost a tenth as much as GPT-6, and find more vulnerabilities in the process. Patrick Collins and Simon Harloff from Dam Secure join Yaniv to explain why agent-built code needs typed languages, why PR gating has flipped from taboo to essential, and how compliance frameworks like SOC 2 are lagging years behind actual threats. They name specific red teaming tools, model providers, and infrastructure choices, and lay out a three-stage security factory that slots into automated software pipelines without slowing human engineers down.
Where humans still matter when agents write the code
October 29, 2026 · 50m
The traditional GitHub-style pull request review is a waste of opportunity cost for most code changes in the agentic era. Yet simply removing it without a replacement invites complexity death spirals and unbounded product behaviour that agents cannot unwind. James McGill and Haziq Nordin join Yaniv to argue that engineers should stop reading diffs line-by-line and instead scan for architectural complexity, shift accountability to spec quality, and build new verification patchworks of end-to-end tests, fuzzing, and product review. The conversation is for any engineer or founder trying to extract real leverage from AI coding tools rather than bottlenecking them with cargo-cult process.
Why list prices mislead: OpenRouter Head of Insights on how to choose the right model
October 22, 2026 · 55m
Peter Walker, Head of Insights at OpenRouter and previously at Carta, sees how about 120,000 US organisations use AI models every day. He expected their usage to spread across models. Instead, the typical organisation now uses about 12 models a month, with one daily driver taking most of the tokens and specialists around it. He and Yaniv get into why, in his view, the same model can get opposite reviews depending on who serves it, how cache rates quietly set the bill for agent workloads, why writing quality pushed Yaniv from Claude loyalty to using OpenAI too, and whether open-weight models are falling further behind the frontier. For engineers and founders deciding which models to run in production, it is a map of what actually decides what you get and what you pay.
Cutting AI coding token costs, with Dave Slutzkin
October 15, 2026 · 59m
Dave Slutzkin, co-founder of Antiburn, joins Yaniv to break down what actually drives token costs in agentic development and how to bring them down. They cover how caching differs between Anthropic and OpenAI, when to reach for which model and thinking mode, and why the habit of compaction matters more than any other optimisation. If you're spending real money on AI-assisted development, you'll come away with concrete tactics for cutting token burn without cutting capability.