Teresa Torres

🎙️Quality of Evidence You've got behavioral analytics, support tickets, sales call notes, and a feedback inbox that never empties. With all that data coming in, do product teams actually still need to go out and talk to users? In this episode, Petra Wille and Teresa Torres dig into why not all product evidence is created equal — and why the type of data you collect matters just as much as the volume. Teresa introduces her "ladder of evidence" framework and shares what she's learning from real interview transcripts through her work at Vistaly: that many teams are confusing product demos, stakeholder meetings, and opinion-gathering sessions for genuine customer research. They explore the spectrum from low-quality signals (support tickets, app store reviews) to rich, story-based interviews — and why the goal isn't perfection, but moving in the right direction. Plus: why even a mediocre interview is better than no interview at all, and how to coach teams toward stronger evidence without discouraging them from talking to customers in the first place. What We Cover in This Episode 🌊 Why product teams are drowning in data — but still making uninformed decisions ⚠️ The danger of projecting your own expertise onto low-quality signals 📊 Teresa's "ladder of evidence" framework: as effort goes up, so does value 🔺 Henrik Kniberg's triangle (Petra's coaching go-to): quantitative data + organizational signal + qualitative insight 📄 What Teresa is learning from real interview transcripts at Vistaly — and why many "interviews" aren't really interviews 🎚️ The spectrum of interview quality: story-based interviews vs. direct-question interviews vs. usability preference gathering 🎭 Why product demos and stakeholder meetings are being mistaken for customer research 📣 How to communicate signal strength without discouraging teams from interviewing 🎯 The strategic product decision behind Vistaly's choice to support imperfect interview formats — and use them as coaching moments 💻 Why vibe-coded software makes good interviewing skills more critical than ever Key Concepts & Frameworks 🔑 Ladder of Evidence — Teresa's framework for thinking about evidence quality. Low-effort signals (support tickets, app store reviews, sales call notes) are abundant but carry weak signal strength. High-effort, story-based interviews are harder to collect but carry far more context for decision-making. 🔑 Story-Based Interviewing — Rather than asking customers about preferences or opinions, story-based interviews collect the narrative of a specific experience: what were you trying to do, when did this come up, what went wrong, what did you need? This context is what makes evidence actionable. 🔑 Signal vs. Insight — Low-quality signals tell you something is worth exploring. They rarely tell you what to build. The risk: product experts fill in the gaps with their own assumptions and get it wrong. Quotes Worth Saving "We're inundated with these low-value signals all the time, but they rarely carry enough signal strength to actually tell us what we should be building. But they feel like they do." — Teresa Torres "The worst thing you could do is never talk to a customer. The best thing you can do is collect a really rich story about their experience." — Teresa Torres "It is easier than ever to release mediocre software. People need to get better at interviewing." — Petra Wille Links to Spotify, Apple Podcasts, and YouTube: 📺Youtube: buff.ly/zBr75qt 🎵Spotify: buff.ly/IG7WdZr 🍎Apple Podcast: buff.ly/I5ZZuUH Give it a listen and share your thoughts in the comments below.💬👇

Snapbar had several near-death experiences during COVID, bleeding money for months while its entire product line went obsolete. Patrick, co-founder, credits that pressure for forcing "gritty resourcefulness beyond anything else" across the team. That mindset pushed Snapbar to place bold bets—WebRTC, and later generative AI and video—turning necessity into the foundation for its AI-native products today. 👉 Find a link to the full episode here: Spotify: buff.ly/ycI42BG Apple Podcast: buff.ly/7YCg6wx YouTube: buff.ly/ObDT3Ca

NBA players are guaranteed 50% of revenue. Teams have to spend to the salary floor. When the NBPA complains that Wembanyama is setting a precedent for players to take less, that is not true. Players are guaranteed a fixed amount. Wembanyama has said he wants to help keep the Spurs core together. He’s willing to take less to keep playing with his teammates. The NBPA is complaining about a precedent being set for super stars taking less to help their teammates. Given that the NBPA is supposed to represent all players, this is kind of absurd. Good for Wembanyama. I hope we see more of this in team sports.

When AI labs started shipping their own image generation, Sam worried it might kill their edge. If anyone could turn themselves into a superhero at home, why would brands still need Snapbar at an event? The opposite happened. As clients got hands-on with generative AI, their requests got more complex—logos, names, custom scenes—and Snapbar's expertise became more valuable, not less. 👉 Find a link to the full episode here: Spotify: buff.ly/ycI42BG Apple Podcast: buff.ly/7YCg6wx YouTube: buff.ly/ObDT3Ca

Right before COVID hit, Snapbar had just closed its best year ever—revenue up, team growing, momentum everywhere. Then overnight, in-person events shut down and the company's entire business model became illegal. Patrick, Snapbar's co-founder, explains how the team turned to WebRTC to stream video and audio across any device, rebuilding their in-person photo booth experience for a virtual world. That bet birthed the virtual photo booth—and everything Snapbar builds today. 👉 Find a link to the full episode here: Spotify: buff.ly/ycI42BG Apple Podcast: buff.ly/7YCg6wx YouTube: buff.ly/ObDT3Ca

What does it take to reinvent a 14-year-old company—not once, but twice? In this episode of Just Now Possible, Teresa Torres talks with Sam Eitzen (Co-founder & CEO), Joe Eitzen (Co-founder & CPO), and Patrick Ellis (CTO) of Snapbar about one of the most unexpected pivots in the generative AI era. What started as a wedding photo booth side hustle became a national events company—and then COVID wiped out the entire business overnight. What the team built in response now looks so AI-native that Teresa assumed Snapbar was a new startup. You'll hear how they went from physical photo booths to a cross-platform virtual product built on WebRTC in spring 2020, and then—pushed by declining repeat business—dove deep into Stable Diffusion, custom LoRA fine-tunes on H100/H200 GPUs, and eventually a reasoning-model-powered generative image and video pipeline. Along the way, they built an agent orchestration framework for their engineering process using Claude Code and Codex, and are now giving brand customers the ability to "vibe code" within the Snapbar platform itself. If you've ever wondered what applied AI looks like when you combine 14 years of industry knowledge, photography expertise, and relentless curiosity-led self-education, this episode shows exactly that. Guests: - Sam Eitzen – Co-founder & CEO, Snapbar - Joe Eitzen – Co-founder & CPO, Snapbar - Patrick Ellis – CTO, Snapbar You'll hear how they: - Pivoted from physical photo booths to a cross-platform virtual product in spring 2020 using WebRTC—built from first principles out of necessity - Integrated Stable Diffusion 1.5 as their first generative AI model and ran custom LoRA fine-tunes on H100/H200 GPUs to produce brand-quality outputs nobody else in their space could match - Evolved from negative prompts to reasoning model long-form prompts, giving brands more precise creative and safety control - Built a meta-prompting pre-processing pipeline to ensure user likenesses—including non-obvious details like disabilities—are accurately represented in generated images - Designed an experiential marketing platform that lets brands "world build" at conferences, trade shows, and live events by bringing fans into branded creative worlds - Added participatory user inputs through Mad Lib-style prompts and prompt injection, turning photo experiences into co-creation moments between brands and their audiences - Used Claude Code and Codex to build and ship features rapidly as a small bootstrap team, and developed a four-pillar agent orchestration framework: context, tools, verification, and workflows - Are building customer-facing "vibe coding" using the Claude Agent SDK so brands can configure and create experiences themselves within Snapbar's platform Resources & Links: - Snapbar — AI photo booth and experiential marketing platform - Patrick Ellis on YouTube — Patrick's channel on AI engineering and agent orchestration - Sam Eitzen on LinkedIn — Follow Sam for experiential marketing insights - Claude Code — The AI coding tool Patrick and Teresa both use heavily - Stable Diffusion — One of the generative image models in the mix Snapbar evaluates for its AI photo experiences - Black Forest Labs / Flux — Another model family Snapbar tests and can route activations to, depending on the creative style needed Chapters 00:00 Meet the SnapBar Team 00:59 From Photo Booth Side Hustle 02:31 Pandemic Pivot to Virtual 08:06 What AI Photo Booth Does 11:03 Why It Works at Events 13:15 When AI Entered the Product 14:26 Early GenAI Tech Stack 17:43 Patrick's Learning Path 20:44 Why They Bet on AI 28:25 Building with Necessity 34:25 Applied AI Beats Models 36:01 Prompting Got Easier 37:53 Arcades to Home Shift 38:58 Commoditization vs Application Layer 40:58 Experiential Platform Overview 42:33 World Cup Activation Examples 45:34 QR Flow and WebRTC 47:51 Capture to Display Pipeline 49:24 Dark Factory and Vibe Coding 55:38 Brand Safety Prompting 01:00:23 Meta Prompting and Representation 01:03:22 Experiential Marketing Momentum 01:06:12 Agent Orchestration Framework 01:10:02 Claude Code and Fable Rant 01:12:48 Wrap Up and Where to Find Them Listen on Spotify, Apple Podcasts, or watch on YouTube. Spotify: buff.ly/ycI42BG Apple Podcast: buff.ly/7YCg6wx YouTube: buff.ly/ObDT3Ca

I've got a couple of fun events for the month of July! AI Maker Studio 🗓️ Wednesday, July 15th, 9am-10am PDT This is an opportunity to come build with like-minded AI enthusiasts. Our maker studio sessions are dedicated time to work on personal productivity tools, new AI products, or whatever else excites you. Get help and connect with peers. Hosted by Teresa Torres. AI Show & Tell 🗓️ Thursday, July 23rd, 9am-10am PDT Come share what you built and learn from other people's work. These are fun, casual sessions where we share what we are building. The projects range from small productivity tweaks to custom children's games to real AI software. It's a fun way to get inspiration for your next AI project. Both events are for Supporting Members and CDH Members of Product Talk. Invites go out to subscribers one week before the scheduled event. If you are interested, subscribe at ProductTalk.org. On desktop, click on the Subscribe button in the top right. On mobile, select the Subscribe button at the bottom of the hamburger menu. Select Supporting Membership or CDH Membership. If you plan to attend, let me know in the replies. And if you know folks who would be interested, please share this post. Thanks, I appreciate it.

🎙️Stepping Into Leadership Every product manager wants to move into leadership — but nobody wants to hire a leader without leadership experience. So how do you break the cycle? In this episode, Teresa and Petra unpack the practical, often-overlooked ways individual contributors can build real leadership skills right now, without waiting for a title change. Petra shares a surprisingly simple starting point: understand what your organization actually expects from its leaders, then begin deliberately practicing those behaviors in your current role. The conversation goes deep on two of the most foundational leadership skills — the art of saying no with evidence and clarity, and the ability to provide directional clarity at increasing time horizons. Whether you're setting direction for a sprint today or hoping to own product strategy someday, this episode gives you a clear path to get there. What You'll Learn 🔍 Why understanding your organization's definition of leadership is the essential first step — and how to find it even when it's not documented 💪 How practicing "saying no well" builds a core leadership muscle that translates at every level 🧭 What directional clarity actually means, why it's the crux of leadership, and how to grow your ability to provide it incrementally 📈 How your planning horizon should shift as you grow — from sprints to quarters to outcomes — and why getting less concrete over time is a feature, not a bug 🗂️ How tools like the Decision Stack, the Now-Next-Later roadmap, and the Opportunity Solution Tree can support you in communicating at different abstraction levels Key Takeaways 1️⃣ Start by understanding what leadership means in your specific context. Look for leadership training curricula in your company wiki, ask recently promoted leaders, or look at your company's values and leadership principles. General frameworks (like Petra's Product Leadership Wheel or Korn Ferry's competencies) are great starting points, but your organization's version matters most. 2️⃣ Saying no is easy. Saying no well is the skill. The goal isn't to be a gatekeeper — it's to communicate clear reasoning backed by evidence so that stakeholders can almost reach the "no" themselves. When directional clarity is strong, people can self-assess whether their idea fits before it even reaches your desk. 3️⃣ Grow your leadership horizon gradually. As an IC, start by providing clear direction for your current sprint. Then two sprints. Then a quarter. The further out you look, the more abstract and outcome-oriented your directional clarity becomes — and that's the point. You're moving up the Decision Stack, not just adding more features to a roadmap. 4️⃣ You don't need a big scope to practice. Even on a team with a narrow mandate, the product manager usually has more business context than anyone else. Use it. Practice communicating the why behind the work, not just the what. That habit compounds over time. Links to Spotify, Apple Podcasts, and YouTube: 📺Youtube: buff.ly/g4FP6bZ 🎵Spotify: buff.ly/ek8Yeob 🍎Apple Podcast: buff.ly/N09PD3t Give it a listen and share your thoughts in the comments below.💬👇

"It can feel like you are the lone champion pushing for change in your organization." That's why we are reading Continuous Discovery Habits together throughout 2026. 📚 The book turns five this year and many of you have bought and read it. But reading isn't the same as doing. So we are reading together—one section per month—with discussion questions, practical exercises, and resources to help you actually build the habits. Here's what you'll get each month: 📖 Monthly reading guides with reflection questions and exercises 🎥 Short videos you can share with teammates to spread the ideas 💬 Quarterly live discussion sessions to connect with other practitioners By December, you won't just understand continuous discovery—you'll be practicing it. This month's reading covers Chapter 8—it's all about supercharged ideation. You'll learn why quantity leads to quality, how to generate a large number of ideas, and what to do when you get stuck. Read the chapter: buff.ly/V3S2q6J 🤔 Does your team take the time to generate a lot of ideas? What's your favorite tactic? Share your experience in the comments.

🎙️AI-Shaped Problems Think you don't have big enough problems for AI to help with? Think again. In this episode, Petra and Teresa tackle one of the most common blockers people face when starting out with AI: not knowing where to begin. It's not a lack of problems — it's a lack of exposure. Teresa shares the daily habit that took her from feeling behind to using AI every single day, and why starting small (and terrible) is exactly the right approach. They also dig into the noise problem: why you don't need MCP servers, plugins, or the latest AI app to get real value, and how focusing on a specific task is the fastest path to learning what AI is actually good at. If you've been waiting for the "right" moment to start, this episode is your nudge. One of the most common things Petra hears from her coaching clients: "I don't have big enough problems to use AI for." Teresa's take? That's not the problem. The real blocker is that you don't know what AI is good at until you start using it — and you can't start using it well until you know what it's good at. In this episode, Petra and Teresa share how to break out of that loop. What you'll hear: 🎯 The daily habit Teresa used to go from feeling behind to using AI confidently every day 💡 Why starting with terrible results is actually the point 🔇 How to filter out the noise (MCP servers, plugins, new apps) and stay focused on what matters 🤝 Why finding community — whether that's show-and-tell sessions, YouTube, or local meetups — accelerates learning faster than any tool ✍️ A simple first prompt for anyone who doesn't know where to start Key takeaway: If you have a to-do list, you have problems AI can help with. Start with one. Give it 15 minutes. It'll probably be bad — and that's exactly right. Timestamps ⏱️ 00:00 Intro — are people's problems too small for AI? ⏱️ 02:15 Teresa's daily habit: test one to-do item with AI every day ⏱️ 06:40 The "feeling behind" problem — and why everyone feels that way ⏱️ 09:00 Petra's time-boxing approach vs. Teresa's task-first approach ⏱️ 11:30 Blog posts and show-and-tell: how community builds momentum ⏱️ 15:45 You don't need MCP servers or Claude Code to get started ⏱️ 19:10 Bias toward action: just type "I have to do this — how can you help?" ⏱️ 22:00 Petra's MidJourney story — iterating from terrible to good ⏱️ 23:30 Wrap-up: 15 minutes a day, just play Links to Spotify, Apple Podcasts, and YouTube: 📺Youtube: buff.ly/8AfrWEo 🎵Spotify: buff.ly/GnaRQWX 🍎Apple Podcast: buff.ly/fj8CKnl

Override Labs exists because AI advancements keep introducing disproportionate harm to women and children—often from products that skip red-teaming to move fast. Priya built the company to ask a different question: what if AI was built for prevention first? A philanthropy-backed incubator focused on preventing sexual assault among teenagers gave her the scoped problem that became Override Labs' flagship product. 👉 Find a link to the full episode here: Spotify: buff.ly/K08CWQB Apple Podcast: buff.ly/66gktow YouTube: buff.ly/EivxBTs

@CetiAlphaFumf Where I live I don’t have many choices.

@ttorres I stopped reading after “booked a flight to Europe on Alaska Airlines” Hope it worked out well for you

I booked a flight to Europe on Alaska Airlines. My first flight was on British Airways. They canceled the flight 8 hours before it was supposed to depart. I called and they rebooked me on a new flight that left at the same time and went through Frankfurt, but was in a mix of airlines: Lufthansa and Alaska. In Frankfurt, I missed my connection because passport control regularly takes 2-4 hours and I had 1 hour for my connection. When I went to the Lufthansa service desk, they said I needed to rebook through BA because they don’t fly to Bend. When I called BA they said it’s my fault I missed the flight. That I shouldn’t have accepted the rebooking because it was enough time to make the flight. Alaska says it’s BA’s responsibility. Lufthansa says it’s the airport’s responsibility. BA says it’s my responsibility. I was on a business class ticket. Three airlines that just lost a customer for life. So now I get no refund or compensation for that expensive ticket. And it’s up to me to pay for my own ticket home. This is why I don’t travel for work. Your 1-hour speaking engagement isn’t worth it. I have been awake for 30 hours and it will be another 20 before I get home. And the only ticket that gets me home in the next 24 hours is $8k.

@OnKnockers @British_Airways I specifically asked about my connecting time. He said it was plenty. I’m documenting everything and will file a claim.

@ttorres Whoever did your booking should have known minimum connection times. Did they disclose that to you and did you accept the known risk? Btw, I am impressed you got through to an Agent at BA because if you can, they are often helpful. @British_Airways

BA rebooked me on an itinerary that had 3 segments. 1st 2 on Lufthansa, last one on Alaska. I missed the connection between the 2 Lufthansa flights. Lufthansa won’t rebook me because of the Alaska flight. BA told me it was my fault I missed the flight. BA rebooked me at 11pm the night before my 8am travel. I even asked if I would have time to make the connection. But the phone agent today said that because I accepted the booking, I am at fault for missing the connection. Lufthansa told me the airport is at fault. Alaska told me BA has to rebook me. At this point, I gave up and bought another ticket.

@ttorres Document everything and keep receipts. Do you have any travel insurance through: a) the purchase on Alaska airlines, b) credit card, or c) separate travel insurance? BA rebooked you on an individual flight or an itinerary without a minimum connection time and now blames you?

@neeilk2028 These are all great in the ideal world. Too bad none of us live in the ideal worlds.

For me, More traveling helps get the rules optimized. E.g. 1. Don’t take 1hr layover in Frankfurt. I know 1hr layover for international travelers is a joke. ( high risk) security and immigration could easily make one miss the next flight. There are systems that can help, but rarely known to wider public. 2. Always , book single airlines. And try to keep it tha way.. even with flight share. 3. Murphy’s law. Expect the worst. It does add stress before, but one is mentally prepared. And it feels good once journey is uneventful.

@boerdeboer Definitely not made up. But you do you.

@ttorres Ai made up stories ... lovely to read

Before Override Labs invoked any AI model, Priya started with the worst-case scenario: someone using the tool to justify causing harm. That constraint shaped everything. Hard-coded rules run before the AI ever responds, classifying conversations as red or yellow flag—there's no green flag. The system stays neutral, never gives a thumbs-up, and always surfaces what consent actually means. 👉 Find a link to the full episode here: Spotify: buff.ly/K08CWQB Apple Podcast: buff.ly/66gktow YouTube: buff.ly/EivxBTs

One in eight teenagers in the US is already turning to generative AI to talk through relationships, dating, and sex. Priya, founder of Override Labs, saw that as proof the demand for private, trustworthy AI conversations already exists. The first prototype was just a wrapper around Claude—until Priya realized teens kept asking the same follow-up questions: is there a power imbalance? Drugs or alcohol involved? That pattern became the system's foundation. 👉 Find a link to the full episode here: Spotify: buff.ly/K08CWQB Apple Podcast: buff.ly/66gktow YouTube: buff.ly/EivxBTs

What if AI could help prevent sexual assault before it happens — without tracking users, judging them, or handing them a verdict? In this episode of Just Now Possible, Teresa Torres talks with Priya Nakra (Founder and Product Lead) and Olivia Rowley (AI Advisor and Board Member) of Override Labs, a nonprofit building technology to prevent gender-based violence. Their flagship product, Is This Okay? (ITO), gives teenage boys a private, judgment-free space to reflect on ambiguous sexual scenarios — with AI guidance grounded in clinical research and motivational interviewing. Priya and Olivia share how they built ITO from scratch: scraping Reddit to validate the need, partnering with a licensed therapist to design the eval rubric, and building a risk classification system that runs before Claude is ever invoked. Every design decision — from skipping account creation to removing the concept of a "green light" response — was made with one goal: never let the product be used to justify harm. You'll hear how they defined a "South star" instead of a North star, how clinical expertise shaped the AI's tone and structure, and why a nonprofit context unlocks design choices that growth-focused companies simply can't make. It's a masterclass in purpose-built AI product development when the goal isn't scale — it's prevention. Guests: - Priya Nakra – Founder and Product Lead, Override Labs - Olivia Rowley – AI Advisor and Board Member, Override Labs In this episode: - Why Priya left a 10-year tech career to found a nonprofit focused on gender-based violence prevention - How scraping 2,000 Reddit posts per subreddit validated demand for a consent reflection tool - Why Override Labs defined a "South star" — the worst-case outcome — and designed the product to avoid it - How a licensed therapist and positive masculinity coaches shaped the product's tone and eval rubric - Why the product never gives a "green flag" response — and what it offers instead - How risk classification runs deterministically *before* Claude is ever called, then tailors the response by tier - The three-part response structure grounded in motivational interviewing: validate, reflect, invite reflection - Why "privacy by design" meant no accounts, no cookies, no cross-session tracking — and how that became a feature - The challenge of measuring prevention when success means something *didn't* happen - How Override Labs is building a product ecosystem: a women's tool, a web game as a top-of-funnel, and a future institutional API layer Resources & Links: - Override Labs (buff.ly/Ta2tpLb) — Building technology to prevent harm from gender-based violence - The Fund to Prevent Sexual Assault (buff.ly/7J3fTPY) — The philanthropic incubator that funded Override Labs' first cohort Chapters: 00:00 Meet Priya and Olivia 00:50 Why Override Labs Exists 02:11 From Tech to Mission 04:51 Is Technology Good 07:49 Narrowing to Teen Consent 11:07 Researching Teen Scenarios 14:33 Clinical Guidance and Tone 16:48 Designing for Reflection 20:51 Prototype to Product Flow 24:46 Risk Tiers and Evals 28:10 Defining the Conversation Goal 29:35 Motivational Interviewing Framework 31:37 Measuring Real World Impact 35:39 Privacy First Data Practices 40:29 Reaching Teens With Ads 41:56 Web Game Funnel Strategy 44:30 Funding and Institutional Pathways 46:50 UX for Sensitive Moments 49:34 Matching the Older Brother Tone 52:09 Building Safe AI for Prevention 54:35 Closing Thoughts and Thanks Listen on Spotify, Apple Podcasts, or watch on YouTube. Spotify: buff.ly/K08CWQB Apple Podcast: buff.ly/66gktow YouTube: buff.ly/EivxBTs