Issue #725, 16th October 2026

The UI Is Not the Product: When Agents Do the Work, What's the Shape of the Product That Remains?
6 minutes read.

Great take by John Moriarty on where design effort goes once agents do the work: the approval moment: "The surface we author will get thinner, but the end-user experience will be richer. One question might pull from multiple sources, collapsing what used to be three separate tasks into one answer. [...] As Agents absorb more of the interface, what’s left to design gets smaller and more consequential. Fewer screens, each carrying more weight. The way you get those right is by deeply understanding the system underneath, because that’s what now drives the experience."

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Best Resume I've Seen So Far
1 minutes read.

My humble effort to help you start the weekend with a smile.

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Predictability vs Outcomes
4 minutes read.

I love this framing by Marty Cagan: "The real trade-off is the relative priority of outcomes versus predictability." Read it again. Outputs make it easier to predict when something is ready, not its value. Don't confuse features with dates to impact if you didn't verify the value first. If you can't verify it beforehand, at least focus on learning as much as you can by observing how users interact with the new feature and talking with them about it.

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Zapier's CEO Will Grade Your AI PM Skills Live (Video)
74 minutes read.

Great watch for anyone leading product teams. Wade Foster on the transformational level: "If a factory produces a defective part, what does the worker do? Does he try to fix each part individually? No, not really. He just rejects a batch, checks the equipment and asks himself: Why did the machine produce these defective parts? [...] And that's what the work is actually shifting to." -- This is the playbook for agentic engineering orgs too: the leader's job moves from reviewing every output to auditing the system that produced it, and deciding where the human checkpoints sit.

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Building a Second-Wave AI Business
7 minutes read.

Most businesses want to grow more than they want to optimize what they have. It's harder to predict what will drive higher revenue growth than to automate well-defined areas, so we're tempted to focus there. Seth is a master at narrowing down incentives and strategy to help you seek growth opportunities (relevant to everyone, and maybe more so for a bootstrapper who's aiming to get "default to live" urgently). If you map out your investment, how much of your time is invested in growth? How much is in optimization? Do you think the ratios serve the business or serve predictability?

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What It Takes to Run Inbound Sales With AI (Video)
13 minutes read.

This is the playbook for any agentic product: start with the prompt to learn, then pull the deterministic parts into code so you can debug which rule fired instead of reading what the model said. Jeanne Grosser (COO at Vercel) shares how they built a machine that yields 90% of Vercel's inbound leads for roughly $ 1,000 per year.

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15 Leadership Interview Questions and Answers to Find Great Leaders.
8 minutes read.

"Don't judge decisions solely by their outcomes. Explore the reasoning behind them and whether the candidate would make the same choice again." -- Stephanie Bowker shares this in a list of leadership interview questions. I agree, and it matters even more with AI in the loop: when agents make the first draft of every decision, the reasoning and tradeoffs a leader can articulate become the scarce skill to hire for.

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How to Effectively Debug When You No Longer Understand the Code
30 minutes read.

This is the most comprehensive view I've read on debugging with agents, from three real production bugs to a full framework and what it means for your software factory. Cindy Sridharan puts the shift well: "What ought to replace [the mental model of the code] isn't a better mental model of the code [...] but a better methodology for reliably and repeatably reasoning about outcomes. [...] The engineers who thrive in an agent-native world will be the ones who are good at designing such verification loops, whether or not they understand the code it verifies." -- I agree, and it's the same playbook for agentic products: the value moves from knowing the code to designing the checks that keep an agent honest.

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@paulg: If customers won't buy your stuff, try asking what they would buy. This sounds obvious, but surprisingly few founders do it, and it often yields valuable insights.

@shreyas: Something that’s changed rapidly in the past year: Product Managers who’ve historically viewed their role as “my main job is to execute” are finding it way harder to get hired at top companies than those who’ve viewed their role as “my main job is to make the product successful”