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Hey Product Fans!

Welcome to this week’s 🌮 Product Tapas!

If you’ve been forwarded this or just stumbled upon it, you’re in for a treat. For the best reading experience, check out the web version and sign up for future editions here.

What’s on the menu this week? 🧑‍🍳

📰 Not Boring – The TV revolution nobody saw coming has YouTube conquering living rooms while everyone was obsessing over mobile-first strategies. The AI platform wars are heating up as everyone fights to become your default digital companion. Meanwhile, Google's own AI Overviews are cannibalising publisher traffic (shocking absolutely nobody), and Meta's grand WhatsApp fintech experiment crashes against local competition in India's $3 trillion market. Plus, Trump's anti-woke AI order trades safety guardrails for speed in the China race.

⌚️ Productivity Tapas: This week we have AI-powered video creation that transforms screen recordings into studio-quality content, persistent memory systems that turn conversations into knowledge, and editing tools that make talking heads actually engaging.

🍔 Blog Bites: Peter Yang exposes why 95% of "AI agents" are actually just workflows (and why that's often better), a Reddit user's breakthrough meta-prompt that flips AI interactions on their head, and the hidden communication gap that's holding back aspiring product managers.

🎙️ Pod Shots: This week is a bit more tech-heavy, we cover LaunchDarkly's engineering leader on Claire Vo’s “How I AI” revealing how he transformed a 100+ person team from AI skeptics to sophisticated users in just six months. Spoiler: it's not about the tools, it's about the systems that make any tool successful.

Plenty to get stuck into - off we go! 🚀

📰 Not boring

The TV Revolution Nobody Saw Coming

• People now watch YouTube on TV sets more than on their phones or any other device. While everyone was obsessing over mobile-first strategies, YouTube quietly conquered the living room. It turns out the future of video wasn't just mobile—it was wherever people actually want to watch long-form content. The couch wins again

AI Platform Wars: Everyone Wants Your Front Door

• Instacart CEO Fidji Simo finally starts her role at OpenAI next month, teasing coaching, emotional support, and help with everyday chores. The AI assistant race isn't about who builds the smartest model—it's could be about who becomes your default companion for everything

• Perplexity's comet browser will get 'shortcuts' to automate repetitive tasks. Everyone's building the same thing: an AI that lives between you and everything else you do online. The question isn't whether AI assistants will be everywhere—it's who gets to be your default. But as we talked about last week I want to see who wins on mobile

The Great AI Reality Check

• Surprising absolutely no one, new research shows AI Overviews cause massive drops in search clicks. Google built a feature that makes Google less valuable to publishers. It's the classic platform move: extract value from your ecosystem while claiming you're improving user experience

• Why Google Sheets getting AI integration is a big deal goes beyond just spreadsheet automation. When AI gets embedded in the tools people actually use daily, that's when the real transformation happens—not in flashy demos

Enterprise AI Gets Serious

• For enterprise users big on privacy, Proton's new privacy-first AI assistant encrypts all chats and keeps no logs. The enterprise AI market is splitting into two camps: those who'll trade privacy for convenience, and those who won't

The Fintech Reality Check

• Meta's grand WhatsApp fintech experiment in India has fizzled—despite having 500 million users, WhatsApp couldn't crack the country's$3 trillion fintech market. Turns out having users and having paying customers are very different problems. Sometimes the network effect isn't enough when local players understand the actual job-to-be-done better

Political AI and the New Rules

• Trump's 'anti-woke AI' order could reshape how US tech companies train their models, while his AI strategy trades guardrails for growth in the race against China. The AI safety debate is very much a geopolitical one. When national competitiveness trumps ethical considerations, the guardrails come off fast it seems

Everything Else

• YouTube Shorts adds image-to-video AI tools while Google Photos gets AI features for remixing photos into videos. The AI-ification of creative tools continues—every app becomes a mini-studio

• Chrome for iOS makes it easier to switch between work and personal accounts. Small UX improvements that acknowledge how we actually use technology in 2025

• Grok's AI companions drove downloads, but its latest model is making the actual money. Headlines grab attention, but utility pays the bills

• Spotify expands audiobook access to family plans, and Meta appoints a generative AI VP to run Threads. The usual incremental improvements that collectively reshape how we consume content

• Fascinating essay on what it's like to work at OpenAI—everything breaks, no email (dreamy!). Sometimes the most revealing insights come from the inside stories, not the press releases

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⌚️ Productivity Tapas: Time-Saving Tools & GPTs

• Clueso: Proiduct videos in minutes with AI. Transform raw screen recordings into stunning videos & documentation

• Basic Memory: Transform AI conversations into persistent, interconnected knowledge. Start local, scale anywhere

• Levio: Transform your raw talking head with engaging edits, and chat to make changes

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🍔 Blog Bites - Essential Reads for Product Teams

AI Strategy: The Real Difference Between AI Workflows and Agents

Peter Yang cuts through the AI hype to explain why 95% of products marketed as "AI agents" are actually just AI workflows—and why that's often the better choice. He provides a practical framework for deciding when to build workflows versus true agents, complete with real examples and a decision tree to guide product teams away from buzzword-driven development. Read the full article here.

💡 "In an AI workflow, you define the what and the how. In an AI agent, you define the what and AI figures out the how."

AI Strategy: The Lyra Meta-Prompt - Flipping the AI Interaction Model

I recently stumbled on this interesting Reddit thread where a frustrated user shares their breakthrough discovery after 147 failed ChatGPT attempts… They created "Lyra" - a meta-prompt that reverses the traditional AI interaction by having the AI interview the user first. This approach transforms vague requests into precision-crafted prompts through a systematic methodology, dramatically improving output quality across all AI platforms. Read the full Reddit post here.

The Complete Lyra Prompt:

You are Lyra, a master-level AI prompt optimisation specialist. Your mission: transform any user input into precision-crafted prompts that unlock AI's full potential across all platforms.

## THE 4-D METHODOLOGY

### 1. DECONSTRUCT
- Extract core intent, key entities, and context
- Identify output requirements and constraints
- Map what's provided vs. what's missing

### 2. DIAGNOSE
- Audit for clarity gaps and ambiguity
- Check specificity and completeness
- Assess structure and complexity needs

### 3. DEVELOP
- Select optimal techniques based on request type:
  - **Creative** → Multi-perspective + tone emphasis
  - **Technical** → Constraint-based + precision focus
  - **Educational** → Few-shot examples + clear structure
  - **Complex** → Chain-of-thought + systematic frameworks
- Assign appropriate AI role/expertise
- Enhance context and implement logical structure

### 4. DELIVER
- Construct optimized prompt
- Format based on complexity
- Provide implementation guidance

## OPTIMIZATION TECHNIQUES

**Foundation:** Role assignment, context layering, output specs, task decomposition

**Advanced:** Chain-of-thought, few-shot learning, multi-perspective analysis, constraint optimisation

**Platform Notes:**
- **ChatGPT/GPT-4:** Structured sections, conversation starters
- **Claude:** Longer context, reasoning frameworks
- **Gemini:** Creative tasks, comparative analysis
- **Others:** Apply universal best practices

## OPERATING MODES

**DETAIL MODE:** 
- Gather context with smart defaults
- Ask 2-3 targeted clarifying questions
- Provide comprehensive optimisation

**BASIC MODE:**
- Quick fix primary issues
- Apply core techniques only
- Deliver ready-to-use prompt

## RESPONSE FORMATS

**Simple Requests:**
```
**Your Optimised Prompt:**
[Improved prompt]

**What Changed:** [Key improvements]
```

**Complex Requests:**
```
**Your Optimised Prompt:**
[Improved prompt]

**Key Improvements:**
• [Primary changes and benefits]

**Techniques Applied:** [Brief mention]

**Pro Tip:** [Usage guidance]
```

## WELCOME MESSAGE (REQUIRED)

When activated, display EXACTLY:

"Hello! I'm Lyra, your AI prompt optimiser. I transform vague requests into precise, effective prompts that deliver better results.

**What I need to know:**
- **Target AI:** ChatGPT, Claude, Gemini, or Other
- **Prompt Style:** DETAIL (I'll ask clarifying questions first) or BASIC (quick optimisation)

**Examples:**
- "DETAIL using ChatGPT — Write me a marketing email"
- "BASIC using Claude — Help with my resume"

Just share your rough prompt and I'll handle the optimisation!"

## PROCESSING FLOW

1. Auto-detect complexity:
   - Simple tasks → BASIC mode
   - Complex/professional → DETAIL mode
2. Inform user with override option
3. Execute chosen mode protocol
4. Deliver optimised prompt

**Memory Note:** Do not save any information from optimisation sessions to memory.

Product Management: The Hidden Skill Gap That's Holding Back Aspiring PMs

In his recent article, James Effarah argues that there’s one often overlooked barrier preventing talented candidates from succeeding in product management roles. Through mentoring 120+ aspiring PMs across top MBA programmes, he discovered that technical knowledge isn't the problem—it's the ability to communicate with confidence and presence that separates successful product managers from the rest. Read the full article here.

💡 "The real job of a product manager is getting people aligned around what the right thing is. And that requires more than technical fluency. It requires presence."

🎙️ Pod Shots - Bitesized Podcast Summaries

Remember, Product Tapas Pro subscribers get access to an ever growing database of all our top Podcast summaries. 

Check it out here

🤖 Beyond Vibe Coding: How LaunchDarkly's Engineering Leader Built an AI-Powered Team at Scale

Zach Davis has spent over a decade building high-performing engineering teams and developer tools at companies like Atlassian and LaunchDarkly. As an engineering leader managing a 100-plus-person team working on infrastructure that powers trillions of daily experiences, he faced a unique challenge: how do you successfully integrate AI tools into enterprise-grade software development without compromising quality or team cohesion?

Over the past six months, Davis has transformed his approach from AI skepticism to sophisticated implementation, developing a systematic methodology that goes far beyond individual "vibe coding" to create scalable, enterprise-ready AI adoption. His framework addresses the fundamental tension between AI's experimental nature and the rigorous standards required for mission-critical software development.

In his recent conversation with Claire Vo, Davis covered:

• His systematic approach to creating centralised AI rules that work across multiple tools without duplicating documentation

• How he uses AI agents like Devin and Cursor to systematically analyse and reduce technical debt in large, mature codebases

• Strategies for leveraging AI to extract and document institutional knowledge from existing code

• Why his philosophy that "what's good for humans is also good for LLMs" drives better documentation practices

• A custom GPT system he built to improve interview feedback quality and coach team members

• His methodology for turning overwhelming technical debt into manageable, AI-assisted task lists that both humans and agents can execute

 🎥Watch the full episode here

📆 Published: July 21st, 2025

🕒 Estimated Reading Time: 3 mins. Time saved: 42 mins🔥

🎯 The Enterprise Reality: Why Vibe Coding Doesn't Scale

Davis begins with a crucial point: "Vibe coding is not an acceptable enterprise development strategy. I love it. I can do a hundred commits a week by myself on my side project. But when you're working on a codebase in a platform like LaunchDarkly that powers trillions and trillions of experiences every day, you can't take the same strategies and tactics that a vibe coder could take."

The freewheeling, experimental approach that works for individual developers or small startups becomes a liability when you're managing a team of 100+ engineers working on mission-critical infrastructure.

The Scaling Challenge

Davis identified a fundamental problem: "Everyone was on their own journey to try to be successful with AI and that just doesn't scale very well." Without systematic support, engineers would have negative first experiences with AI tools, reinforcing skepticism and creating resistance to adoption.

His solution was deceptively simple but profound in its implications: create systems that make AI tools successful by default, rather than leaving success to chance.

Key Takeaways:

• Individual AI experimentation doesn't translate to team-wide success

• First impressions with AI tools are critical—negative experiences create lasting resistance

• Enterprise AI adoption requires systematic support, not just tool access

• The goal is making skeptical engineers successful on their first try

🏗️ The Centralised Rules Revolution: One Source of Truth for All AI Tools

Davis's most innovative contribution is his approach to AI tool configuration. Instead of maintaining separate rule files for each tool (cursor rules, claude.md, GitHub rules), he created a centralised "agents" directory that serves as the single source of truth for all AI interactions.

The Architecture

The agents/ Directory Structure:

agents/
├── rules/
│   ├── typescript-essentials.md
│   ├── frontend-organization.md
│   ├── feature-flagging.md
│   └── accessibility.md
├── migrations/
│   ├── css-module-conversion.md
│   └── test-noise-cleanup.md
└── docs/
    ├── js-style-guide.md
    ├── frontend-organization.md
    └── accessibility.md

How It Works:

1. Comprehensive Documentation: All team knowledge, coding standards, and best practices live in the repo itself, not scattered across Confluence or Google Docs

2. Centralised Rules: The agents/rules directory contains concise, AI-optimised versions of team standards

3. Tool-Specific Pointers: Each AI tool's configuration file simply points to the relevant sections in the agents directory

4. Human-Readable Docs: Full documentation exists alongside condensed rules, linked for context when needed

The Implementation

Davis demonstrates how this works in practice: "Our cursor rules actually just point to that, right? So our cursor rules say, hey, if you want TypeScript guidelines, go find this file in agents. And then I talked about augment earlier. We were telling augment—I set this up yesterday and I asked the augment agent to just create this. I pointed it at the cursor rules and I pointed it at our agents rules and I said can you just create this file."

This approach eliminates duplication while ensuring consistency across all AI tools. When standards change, there's only one place to update them.

Key Takeaways:

• Centralised rules eliminate duplication and ensure consistency across AI tools

• Documentation in the repo is accessible to both humans and AI

• Tool-specific configurations should point to centralised knowledge, not duplicate it

• The investment in setup pays dividends as you experiment with new AI tools

🔧 Systematic Technical Debt Reduction: AI as Your Cleanup Crew

One of Davis's most interesting demonstrations involves using AI to tackle a problem that's plagued engineering teams forever: technical debt.

The Test Noise Problem

LaunchDarkly's frontend unit tests were generating 1,200 lines of noisy output, making it nearly impossible to spot real issues. This is exactly the kind of problem that gets perpetually deprioritised because it's annoying but not quite annoying enough for someone to own, and it's too big for one person to tackle effectively.

The AI-Powered Solution

Davis's approach demonstrates sophisticated problem-solving that goes far beyond simple automation:

Step 1: Analysis and Categorisation

• Ran yarn test and piped output to a log file

• Fed the log file to Claude for analysis

• AI categorised the 1,200 lines into different types of warnings

• Identified the worst offenders and grouped similar issues

Step 2: Prioritised Task Creation Created a markdown file with three tiers of tasks:

• Tier 1: Critical issues that should be fixed immediately

• Tier 2: Important but less urgent problems

• Tier 3: Nice-to-have improvements

Step 3: Distributed Execution The interesting bit of this approach is its flexibility:

• Any team member can pick up individual tasks

• AI agents (Cursor, Devon, etc.) can work on tasks autonomously

• Progress is tracked through simple markdown checkboxes

• Work can be distributed across multiple people and tools

The Multiplayer Advantage

Davis explains the collaborative aspect: "just today I had a PR up to fix a few stray errors on this file and one of the people from the team said, 'Hey, if there's any more stuff like this, feel free to kind of like throw it over the wall to us.' And so now I can actually just point him at this file."

This creates a system where technical debt becomes approachable for everyone, not just the person who originally identified it.

Key Takeaways:

• Break overwhelming problems into discrete, manageable tasks

• Use AI for analysis and categorisation, humans for prioritisation

• Create systems that allow both humans and AI to contribute

• Markdown checklists can serve as lightweight project management

• Make it easy for anyone to pick up and complete individual tasks

📚 Documentation as a Force Multiplier: Teaching AI About Your Codebase

Davis alsoc covered how AI can be used not just to consume documentation, but to create it. His approach to documenting LaunchDarkly's charting libraries showcases how AI can extract knowledge from existing codebases and present it in multiple formats.

The Documentation Workflow

Step 1: Knowledge Extraction Using Devon's wiki feature, Davis queries: "What are the libraries used for charting on the front end?" The AI analyses the codebase and identifies: Recharts, Visex, and other charting libraries.

Step 2: Multi-Format Documentation Creation Davis then asks Devon to create:

• Human-readable documentation: Complete with examples and usage patterns

• AI-optimised rules: Condensed guidelines for other AI tools to reference

• Cross-references: Links between formats to maintain consistency

Step 3: Integration with Centralised System The new documentation automatically integrates with the agents/ directory structure, making it immediately available to all AI tools and team members.

The Quality Advantage

What makes this approach powerful is the quality of AI-generated technical documentation. Davis notes: "Devon wiki is very good. It knows a lot about your codebase. It has this very explicit way of learning and understanding your codebase. And so it is very good about describing that back in a solid technical writing way."

This isn't just about speed—it's about creating documentation that would be time-prohibitive for humans to generate manually but is essential for both human understanding and AI effectiveness.

Key Takeaways:

• AI excels at extracting patterns and knowledge from existing codebases

• Multi-format documentation serves both human and AI consumers

• Quality technical writing is one of AI's strongest capabilities

• Documentation creation becomes feasible for knowledge that was previously undocumented

🎯 Improving Hiring Through AI-Powered Feedback

Davis's most ‘personal’ application of AI addresses a common leadership challenge: providing constructive feedback on interview performance, especially to people you don't work with directly.

The Conflict-Avoidant Leader's Dilemma

"I am a little bit of a conflict avoidant person. I don't love giving people tough feedback, especially when it's someone I don't have a strong relationship with," Davis admits. This is a remarkably honest acknowledgment of a challenge many leaders face but rarely discuss openly.

The Custom GPT Solution

Davis created a custom GPT trained on:

• Interview rubrics and scoring guidelines

• Examples of excellent and poor scorecards

• Team-specific evaluation criteria

• Communication templates for different feedback scenarios

The Workflow:

1. Scorecard Analysis: Paste any interview scorecard into the system

2. Quality Assessment: AI rates the scorecard as excellent, good, fair, or poor

3. Detailed Feedback: Identifies strengths and areas for improvement

4. Slack-Ready Messages: Generates tactful feedback messages for easy sharing

The Learning Loop

What's particularly valuable is how this system improved Davis's own interviewing skills: "I learned very quickly the kinds of things to be more specific about, avoid certain kinds of things, and it actually made me write better scorecards just through trying to create this tool for other people."

Key Takeaways:

• AI can help overcome personal limitations (like conflict avoidance)

• Systematic feedback improves team performance over time

• Training AI on your specific standards ensures consistent output

• Tools that help humans improve are more valuable than replacement tools

🚀 The Tool Ecosystem: Strategic Experimentation at Scale

Davis's team experiments with an impressive array of AI tools, but their approach to tool adoption is strategic rather than chaotic. The key insight: let teams experiment freely while providing centralised support that makes any tool more effective.

The Current Stack

Design Tools: Lovable, v0, Figma Make Product Tools: ChatGPT and various specialized applications Engineering Tools: Cursor, Devon, Windsurf, Augment, Claude Code, Copilot for code review

The Experimentation Philosophy

"Every tool, just let's see what works," Davis explains. "I seem pretty generous with my experimentation mindset around what tools can bring value to the team."

This approach has several advantages:

• Natural Selection: Tools that provide real value get adopted organically

• Reduced Resistance: Engineers choose tools that fit their workflow

• Faster Learning: Multiple experiments running in parallel accelerate discovery

• Advocacy Building: Engineers become champions for tools they've personally validated

The Centralised Support Strategy

While tool choice is decentralised, support is centralised through the agents/ directory system. This means:

• Any new tool can immediately access team knowledge and standards

• Consistency is maintained regardless of tool diversity

• Setup time for new tools is minimised

• Knowledge investment pays dividends across all tools

Key Takeaways:

• Encourage broad experimentation while providing centralised support

• Let engineers choose tools that fit their personal workflow

• Focus on outcomes and consistency, not tool standardisation

• Build systems that make any AI tool more effective

🔍 Advanced Implementation: Devon and the Knowledge Building Process

Davis provides detailed insight into how Devon, one of the more sophisticated AI agents, builds and maintains knowledge about codebases over time.

Devon's Learning Mechanism

Unlike tools that start fresh each session, Devon builds a centralised knowledge repository that improves with use:

• Automatic Suggestions: Devon proposes knowledge additions based on interactions

• Collaborative Building: Multiple team members can accept and contribute knowledge

• Persistent Learning: Knowledge persists across sessions and users

• Integration with Centralised Rules: Devon's knowledge points to the agents/ directory for consistency

The Setup Reality

Davis is honest about the challenges: "To get up and running with Devon, I got started pretty quickly... but one of our other engineering managers actually came in and saved the day on the backend to get the full end-to-end up and running with Devon. And that took him a little bit more time than it took me."

However, he emphasises that these setup challenges often reveal broader issues: "If it's hard to get Devon up and running, it's probably hard for your human developers to get up and running. So there's always incentive to make those things better."

The Incremental Approach

Rather than requiring full setup before getting value, Davis advocates for incremental adoption:

• Start with frontend-only mode

• Add backend integration when needed

• Focus on what works rather than perfect setup

• Use setup challenges as opportunities to improve developer experience

Key Takeaways:

• AI agent setup challenges often reveal broader developer experience issues

• Incremental adoption reduces barriers to getting started

• Persistent knowledge systems provide compounding value over time

• Setup investment pays dividends for both AI and human developers

🎯 Leadership Lessons: From Skeptic to AI-Powered Manager

Davis's transformation from AI skeptic to sophisticated user happened in just six months, but the principles he discovered will shape engineering leadership for years to come.

The Organizational Change Challenge

Implementing AI at scale requires more than just tool access—it requires cultural and organizational support:

• Dedicated Leadership: Having someone whose responsibility it is to drive AI adoption

• Close-to-Code Involvement: The leader must be actively using the tools, not just managing adoption

• Systematic Support: Creating systems that make success likely, not just possible

• Patience with Skeptics: Understanding that negative first experiences create lasting resistance

The Quality Paradox

One of Davis's key insights is that AI adoption in enterprise environments is as much about maintaining quality as it is about increasing speed:

• Standards Enforcement: AI tools must follow the same standards as human developers

• Documentation Investment: Better documentation helps both humans and AI

• Systematic Approach: Vibe coding doesn't work at scale, even with AI

• Technical Debt Opportunity: AI can help address problems that were previously too big to tackle

The Future of Engineering Leadership

Davis's approach suggests a future where engineering leaders spend less time on coordination and more time on strategic thinking:

• Automated Busy Work: AI handles routine tasks like documentation and simple code changes

• Enhanced Decision Making: Better information and analysis support human judgment

• Systematic Improvement: Problems that were previously overwhelming become manageable

• Focus on High-Value Work: Leaders can focus on architecture, culture, and strategic decisions

Key Takeaways:

• AI adoption requires dedicated leadership and systematic support

• Quality and speed are not mutually exclusive with proper AI implementation

• The goal is amplifying human capabilities, not replacing human judgment

• Success comes from systematic thinking, not just tool access

Zach Davis's journey from AI skeptic to sophisticated practitioner offers a roadmap for leaders navigating the AI transformation. His approach—combining systematic thinking with practical implementation—demonstrates that AI can enhance enterprise software development without sacrificing the rigour and quality that mature organisations require.

The key insight from Davis's experience is that successful AI adoption isn't about finding the perfect tool or technique—it's about building systems that make any AI tool more effective while maintaining the standards and culture that make engineering teams successful. By focusing on centralised knowledge, systematic support, and gradual adoption, engineering leaders can harness AI's potential while avoiding the pitfalls that derail many transformation efforts.

 🎥Watch the full episode here

📅Timestamps:

• (00:00) Introduction to Zach Davis

• (02:44) Overview of AI tools used at LaunchDarkly

• (04:00) The importance of having someone responsible for driving AI adoption

• (05:44) Why vibe coding isn’t acceptable for enterprise development

• (06:42) Making engineers successful with AI on their first attempt

• (07:55) Creating centralised documentation for both humans and AI agents

• (10:19) Using feature flagging rules to improve AI outputs

• (12:33) Advice for getting started with rules

• (14:28) Demo: Setting up Devin’s environment in a large codebase

• (24:33) Devin’s plan overview

• (27:55) Demo: Creating a prioritised tech debt reduction plan

• (36:40) Demo: Using AI to improve hiring processes and interview feedback

• (40:34) Summary of key approaches for integrating AI into engineering workflows

• (42:08) Lightning round and final thoughts

That’s a wrap.

As always, the journey doesn't end here!

Please share and let us know what you would like to see more or less of so we can continue to improve your Product Tapas. 🚀👋

Alastair 🍽️.