Productverse

⚡ Better Products, Not Just Faster Building / PM Snacks 95

Olivier Courtois — Tue, 07 Apr 2026 20:11:51 GMT

Hey everyone,

I keep noticing the same pattern: conversations about AI in product always land on speed. Faster prototyping, faster shipping, faster iteration. But the sharper practitioners I follow are pointing somewhere else… Building got easier. Winning didn’t!

The real edge has shifted to product quality, decision-making, and how we think about what software should actually become.

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

🍭 Snacks

#1

Coding got easier. Winning with AI products didn’t. Here’s why. • 14 min read • #strategy #AI
AI has made building much cheaper. But it hasn’t removed the hard parts of creating a product people actually want, nor made it easier to be profitable. It basically made our job much more important!

Shipping faster is not the same as winning faster. Distribution, trust, retention, positioning, and differentiation still matter, and in some ways, they matter even more now that the barrier to building has dropped for everyone.
AI features come with a lot of new challenges. First and foremost, it erodes SaaS margins, and most teams still can’t monetize them.
The teams pulling ahead are investing in product quality, not just execution velocity. See what Simon Willison has to say about it in the last link of this edition.

#2

How AI Prototyping Fuels Better Product Decisions • 9 min read • #execution #AI
One of the most practical consequences of AI is that we can all prototypes ideas now. I know I do all the time (in Lovable, Openclaw, Cursor or Codex) in order to explore with discipline.

Cheap prototyping means we can test more options before committing to any of them. Force yourself into 3-5 different directions, compare, and pressure test with stakeholders, and users before you build.
The value isn’t “we made a prototype,” it’s “we improved the quality of the decision & specs”. Shipping speed is not the point as I already explained in #1.
More options explored upstream means fewer expensive pivots downstream. Better choices are what will compound into beating the competition.
AI lowers the cost of being wrong early, which is exactly when you want to be wrong.

#3

Your users aren’t human anymore; start building for agents today • 15 min read • #design #AI
Unless you work for one of the big established softwares, this perspective is still nascent. But more and more products will be evaluated, filtered, or mediated by agents before a human ever touches them.

Good UX now has two audiences: the human and the agent. What makes an interface clear and trustworthy is starting to diverge depending on who, or what is evaluating it.
This changes what product teams should optimize for. Legibility, structure, and accessibility matter more when agents are in the loop. In a lot of case your API will be the primary interface. That’s also why so many companies are rushing to release their CLI.
Start measuring MAA (Monthly Active Agents) now, not just MAU. And adapt your pricing and analytics to this new reality.

#4

Malleable software: Restoring user agency in a world of locked-down apps • 20 min read • #essay #design
If building becomes cheaper, the opportunity isn’t to ship more locked-down software faster. It’s to create software that gives people real control over their tools… Might be an utopia, but this malleability could reinforce the initial promise of open-source!

A few weeks ago, I experienced such a transformative revelation when using my openclaw. The more I chatted with it, the more it built itself into the tool I need. This newsletter is now 80% facilitated by my openclaw.

Are we entering a world in which avocado cutter (= apps) diminish in importance to the benefit of interoperable tools? Recently, I spotted people sharing “idea” files to feed your agent rather than software like OpenAI Symphony or Karpathy LLM Wiki.

#5

Most clicked link 1y ago.
WTF is activation and why should you care? • 7 min read • #data
Activation isn't just some vague "aha moment": it's the first actionable metric that impacts all the others, and generally its a good proxy for the impact of product launches. Unfortunately, finding it can be hard:
- it’s usually unique to your product, rarely a single event, and sometimes requires the user to do something multiple times.
- Finding your activation metric requires testing multiple event combinations against retention rates. In this article Posthog (+10 products) shares how they do it.

🗄️ Recently saved

Links worth reading that I saved, but did not highlight:
- Shreyas on why product sense may become even more valuable in the AI age (related #1)
- Cat Wu on how the PM playbook breaks when building keeps moving so fast, and what to do about it
- One of the most important watch on AI + Engineering, a conversation between Simon Willison and Lenny where Simon does a great job at explain how to leverage AI agents to increase quality, not just speed (highly recommended).

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Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is written by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each “PM Snacks” features handpicked links to help you become a better product maker, and each “AI Bites” is a deep dive in AI-enabled workflows.

🧠 The AI Product Workspace / AI Bites 05

Olivier Courtois — Wed, 25 Mar 2026 06:19:37 GMT

Hey everyone,

Thank you so much for your likes, shares, and votes on the first edition written by a guest 🔥. Maxime’s documentation workflow clearly resonated!

Today I’m sharing exactly how I organize myself: introducing the AI Product Workspace. It’s a small folder of markdown files that I create for each project (or client in my case), in order for AI to work with a real product context.

In today’s edition:

Why AI outputs get generic when your context lives only in your head
The exact 5-file scaffold I recommend
A prompt to create the files in one session by talking
How I keep the workspace alive with lightweight automations
Bonus: an interactive premortem prompt that helps you think deeper about what might go wrong, and actively prevent it.

Enjoy!

The AI Product Workspace (5 files to improve your agent’s quality)

Why your AI keeps forgetting

Every new chat starts from zero, so you’ve to re-explain the product, the users, the current initiative, the constraints. You forget one detail, and the output goes sideways…

Projects in ChatGPT or Claude help a bit. But they still tend to be shallow: a few instructions, a few uploads, and no real operating structure. They’ve memory but you can’t be sure if it’s gonna be used, nor how.

The simplest fix I can recommend:

Keep your core product knowledge in a small folder on your machine. I call it the AI Product Workspace.
Then start Claude Cowork, Claude Code, Cursor or Codex in this folder. Prefer these tools over Claude / ChatGPT, because they can read and write in the folder.

🛫 The 5-File Scaffold

These files cover most of the context I need to get useful AI outputs on the first try.

The folder looks like this:

acme-workspace/
├── company-id.md
├── current-bet.md
├── users.md
├── competitors.md
└── guidelines.md

Below, I use a fictional company called Acme, a B2B AI copilot for support teams, to show what each file looks like in practice.

1/ Company ID (`company-id.md`)

Think of it like the identity card of your company. Do not stuff it with too much context. Focus on information that can help prioritize what’s important, and that change slowly. You should probably update this once per quarter (or even less).

# company-id.md — Acme (excerpt)

## Company Snapshot
- B2B SaaS, Series A, 45 people, SF + remote
- "AI copilot for support teams" — auto-drafts replies, routes tickets, flags escalations

## Product Overview
- Core: reply drafting (GPT-4 based), smart routing, sentiment detection
- Strength: integrates with Zendesk, Intercom, Freshdesk in < 1 day
- Gap: no voice channel support yet, analytics dashboard is shallow

## ICP and Buying Context
- Buyer: VP Support / Head of CX at mid-market SaaS (200-2000 employees)
- User: Tier-1 support agents handling 40+ tickets/day
- Why they buy: agent burnout, inconsistent reply quality, slow onboarding
- Why they don't: "we already have macros," security review takes 3 months

## Strategy
- Company's OKR: ...
- Team's OKR: ...
- Important success metrics: ...

2/ Current Bet (`current-bet.md`)

The most useful file in practice. This is the one that makes the biggest difference in day-to-day AI conversations. It contains the strategic information you would use to pitch an important track to a colleague. It captures your strategy, the main surface areas, decisions, and assumptions you want to validate.

You should probably create one of this per “big rock”.

# current-bet.md — Acme (excerpt)

## Core Insight
Enterprise deals stall because security teams block AI access to customer data.
Our current architecture requires full ticket access. Competitors are shipping
"data-minimized" modes. We're losing deals we should win.

## Why Now
- Lost 3 enterprise deals in Q4 to security objections
- Competitor X launched "privacy mode" in January
- Our SOC2 Type II just completed — we have the compliance foundation

## Current Status
- Privacy-mode API spec: done
- Agent-side UX for redacted view: in progress (ships week of March 31)
- Security whitepaper for buyers: not started

## Risks & Assumptions to validate 
- Redacted mode may degrade reply quality by 15-20% — no real data yet
- Engineering capacity: 2 of 4 engineers pulled to reliability work until April

I refresh this every 2 weeks. When it goes stale, almost every AI conversation gets worse.

3/ Users, Competitors, Guidelines

The other 3 files follow the same principle: structured, concise, opinionated.

users.md: Personas, pain points, jobs-to-be-done, and real quotes from interviews. The quotes matter more than the structure. AI outputs get noticeably better when they can reuse actual user language instead of inventing generic empathy.
competitors.md: Competitors, alternatives, pricing anchors, market shifts. This one is perfect for Deep Research, I usually start with the competitive analysis prompt from AI Bites #1 to generate a first draft, then trim it down to what actually matters for decisions.
guidelines.md: Writing tone, ticket format, changelog conventions, vocabulary to use and avoid.

Each of these files has 5-8 sections. The interviewer prompt below generates all of them, so I won’t repeat the full outlines here. You can tap on the button to see the full outlines or read the prompt below.

Preview the files

What NOT to put in your workspace

Do not add meeting transcripts, full research reports, raw data exports, or anything longer than 3 pages. These files are context, not archives. If a file is too long, AI either ignores the important parts or gets confused. Keep it short and opinionated.

Note: do not aim for perfection on day one. A workspace that is 30% complete already beats a blank chat.

🎙️ Create the 5 files by talking

This is my favorite part. You do not need to sit down and “write documentation.” You can just answer questions, and generate them.

I use AI as an interviewer, and dictate my answers (I use Monologue, but there is plenty of other ones). I talk, and AI structures it, spots gaps, and drafts the files.

Here’s how I use it:

1️⃣ Open a new chat in Cursor, Claude Code / Cowork or Codex, and paste the interviewer prompt below

2️⃣ Answer out loud using dictation. I dump context fast, typos and all

3️⃣ Paste any useful material you already have: notes, decks, tickets, docs, meeting transcripts

4️⃣ Review the draft files, fix what feels wrong, and save them in your workspace

It usually takes me around 15 minutes. Here is the prompt I would use 👇

You are my product workspace interviewer.

Your job is to help me build 5 markdown files that will become the memory of my AI Product Workspace:
- company-id.md
- current-bet.md
- users.md
- competitors.md
- guidelines.md

How you should work:
- First, show me the proposed outline of all 5 files
- Then interview me one file at a time
- Ask 3 to 5 focused questions per file
- Accept messy dictated answers
- After each file, summarize what you understood and tell me what still feels unclear, contradictory, or weak
- If I say "skip", leave [TO COMPLETE] markers and move on

Handling pasted material:
- I may paste documents, notes, decks, transcripts, or links at any point during the interview
- When I do, scan the material for relevant information and integrate it into the current file
- Tell me what you extracted and what you ignored, so I can correct course
- Do not stop the interview to summarize the entire document — just absorb and continue

Important behavior:
- Do not ask everything at once
- Do not write polished consultant prose
- Use my words when possible
- If a section sounds vague, challenge me and ask a sharper follow-up
- If something matters but I forgot it, point out the gap

[...] Tap the button for the full prompt

Open the prompt

Enrich with Deep Research & existing files

After the interview, some sections will still be thin. That is normal. The point of the first pass is to capture what is already in your head.

Then I do a second pass for external knowledge:

1️⃣ Start a deep research on your company, and some of the competitors using the prompts shared in AI Bites #1. Then share the results with your agent, and ask which insights it would extract to complete the files. Review the changes, then ask it to update them.

2️⃣ Share presentations, transcripts and other files that contain useful strategic info with your agent. Ask which insights it would extract to complete the files. Review the changes, then ask it to update them.

🔄 Keeping it alive

The workspace is useful only if it stays close to reality. What usually happens after a week: you create good files once, it’s super useful, then decisions move on, research piles up, and the AI quietly starts using stale assumptions.

Where automation helps

Here’s how I think about the automation ladder, from simplest to most hands-off.

Level 1: Manual with AI assist (start here)
After a meeting or decision, paste the transcript or notes into your agent, along with the update prompt below. Review the suggested edits. Apply them yourself. This already cuts the update work from 20 minutes to 5.

Level 2: Recurring reviews with Claude Cowork / Codex
Set up a recurring review that scans your connected sources thanks to MCPs, meeting transcripts, decision logs, shared docs, even the codebase, and proposes workspace updates on a schedule. You still approve, but you don’t need to remember to run the prompt. The workspace stays fresh without willpower.

Level 3: Automated diffs with Claude Code or Codex
Take accepted updates and turn them into actual markdown diffs or pull requests against your workspace folder. If you version your workspace in git (I do), this gives you a history of how your product context evolved over time. Surprisingly useful when onboarding someone or reviewing past decisions.

Level 1: Manual update loop

After I finish a task, an important meeting, a new customer insight, or a strategy decision, I always talk with my agent, and end with a question:

1️⃣ “Which decisions and lessons did we discuss that should be documented in this folder? Let me review the changes. ”, then apply the adequate changes. If like me, you use snippets, you can map a shortcut to an elaborated version of this prompt 👇

Which decisions and lessons did we discuss that should be documented in this folder? 
Which files would you update? Let me review the changes. 

File update frequency (use this to calibrate what deserves an update):
- current-bet.md: changes often — status, scope, risks, shipped items. Update aggressively.
- users.md: changes when new research or quotes arrive. Update when evidence shifts.
- competitors.md: changes when competitors move or market context shifts. Update rarely.
- company-id.md: changes when something structural shifts (ICP, pricing, team). Almost never.
- guidelines.md: changes when conventions evolve. Almost never.

For each proposed update, show:
- file name
- section name
- why this section should change
- current content (quote it)
- proposed replacement or addition

Also tell me:
- what assumptions now look stale across files
- what contradictions appeared between files
- what evidence is still missing

If nothing important changed, say so clearly and explain why.
Do not rewrite entire files. Only suggest precise, targeted edits.

🪦 Bonus: Premortem

This is my favorite use of the workspace because it compounds the value of the whole folder.

Imagine that your current bet initiative failed, then work backward to understand why. I use AI as a facilitator to challenge my first answers and help me see what I missed.

So instead of a one-shot “give me 10 risks” prompt, I prefer an interactive one.

You are facilitating a premortem exercise based on Gary Klein's method.

First, read these files to understand the context:
- company-id.md
- current-bet.md
- users.md
- competitors.md

Your role is not to do the thinking for me. Your role is to help me think deeper.

Process:
1. Start by briefly summarizing the current bet and the success criteria you infer
2. Ask me 5 focused questions before generating any failure reasons
3. Wait for my answers
4. Generate 7 to 10 plausible reasons this initiative failed 6 months from now
5. Ask me which ones feel real, which ones feel overstated, and what is missing
6. Challenge weak assumptions, missing evidence, and optimistic interpretations
7. Refine the list

For each final failure reason, assess:
- likelihood: high / medium / low
- impact: high / medium / low
- current mitigation: yes / partial / no
- evidence: what in the workspace supports this concern

Then identify the top 3 risks that are both important and under-mitigated.

For each of the top 3, provide:
- why it matters
- what signal would tell us it is becoming real
- one action we could take this week
- one question we still need to answer

Rules:
- Be concrete
- Use the workspace, not generic startup advice
- Push back when my answers are vague
- Spot gaps between what we say we believe and what the files actually show

Output format:
- Short summary of the bet
- Ranked risk table
- Top 3 risks with actions and open questions
- Blind spots or contradictions that deserve follow-up

—

If you set up this workspace, I promise every prompt from AI Bites past editions will work better, the competitive analysis, the ticket writer, the MCP workflows, the documentation pipeline.

Thank you for reading this far. I hope you’ll find this useful. Setting up the workspace takes 20 minutes, and every AI interaction after that gets better. Try it.

Until next time!

Olivier

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🪄 Show, Don't Tell / PM Snacks 94

Olivier Courtois — Tue, 24 Feb 2026 06:39:15 GMT

Hey everyone,

There's a fascinating gap opening up between the AI conversation on social media and what's actually shifting inside product teams (heard of openclaw?).

The headlines debate disruption and replacement. The practitioners? They’re sharing incremental improvements to how they prototype and ship, questioning whether our processes still holds, even steering how AI form opinions about their products. So tag along to discover how these seasoned professionals are quietly reinventing the way they build.

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

🍭 Snacks

#1

Is AI Disrupting Product Development? • Feb 2026 • 10 min read • #AI #strategy

Itamar Gilad asks: are we in the inflated expectations phase of the hype cycle? The answer is more nuanced than your LinkedIn feed might suggest…

Productivity claims are overblown. Teams do ship faster, but it’s useless if you’re just delivering more waste. Strategy, intentionality and research are more important than ever.
The real edge is using AI to accelerate product discovery. If you’re not experimenting here, you’re missing out. For example start with extracting insights, pressure-testing assumptions, running simulations, using synthetic users to validate user interview scripts or even user flows (non exhaustive list).

#2

Field study: Prototypes over mockups • Feb 2026 • 9 min read • #design #AI
Here’s what’s actually changing at the craft level, lot of designers, like Edouard at Dust stopped relying only on Figma and switched to interactive prototypes built in code. It’s probably why Figma just announced a code to figma workflow which closes the loop.

Prototyping in code means what you see is what ships. Same React components, same design system, same tokens. No translation loss between intent and implementation.
AI has flipped the cost-benefit of designers coding. Edge cases, state changes, empty states, all testable before any spec gets written. Static mockups start to feel like overhead.
I would add that it also enables vibe-designing (I do it all the time): explain to AI a problem, and targeted outcome, then ask AI to explore +3 variations of a screen or flow. Embrace accidents, and unusual ideas before committing.

#3

The Product Perception Loop • Jan 2026 • 6 min read • #AI #execution
ChatGPT, Perplexity or Gemini are forming an opinion about your features, your positioning, your competitive standing, and probably affecting wether buyers shortlist your product or not.

The Product Perception Loop is the system to close the gap: build a Golden Set of buyer-facing prompts → run them across major LLMs → score responses for accuracy, attribution, and differentiation → adjust your context and positioning assets → repeat.
Tools like the one we shared in AI Bites 4 could be a part of the solution to make sure you expose the most up-to-date information to the outside world.

#4

Don’t Trust the Design Process • Jan 2026 • 23 min watch • #design #AI

Apple Design Lab creating hundred of prototypes

Jenny Wen (Anthropic) exposes in this thought provoking conference how AI, rapid prototyping, and smaller teams force designers to ditch the old linear playbook: research, ideate on post-its, vote, design, prototype, test, ship.

The best practitioners start from solutions, obsess over details and work backwards. Closer to what the Apple design lab does. She argues intuition and taste lead to the most outstanding work.
Research still matters, but its role is shifting. Less “discover what to build from a blank slate,” more “validate what you’ve already sensed from the material.”
The rigid handoff logic between design phases is dissolving. When you can build and observe directly.

#5

Most clicked link 1y ago.
The Duolingo Handbook • Feb 2025 • 12 min read • #culture
A pretty high-level piece worth skimming through. Keep in mind that Duolingo is a true product-led company, with great monetization (enabling the focus on long-term mission), great branding and data-driven culture. Some highlights:
- Their principles: Take the long view (“we’re creating a 100-year brand”, “we’re building long-term user retention first”), Raise the bar (“V1 not MVP”, “we hire exceptional people”), Ship it (“we run hundreds of experiments”), Show don’t tell (“when we disagree, we test ideas and let the metrics decide), Make It Fun (“built on play”)

Some ideas worth trying in our companies?
1/ “99 bad ideas” methodology for ideation of bold ideas (= asking ridiculous, unlikely questions)
2/ Monthly “Raise the Bar” sessions to critique designs and push for world-class quality
3/ Adapt their “green machine” framework (1. staff with great people, 2. define success, 3. set guardrails and think long term, 4. build and setup feedback loops, 5. execute with urgency and excellence, and 6. double down on what works, stop what doesn’t).

🗄️ Recently saved

Links worth reading that I saved, but did not highlight:
- Elena Verna on the short window to get radically ahead by going AI-native.
- Claude Code for Product Managers: complete course.
- Nikunj Kothari: a controversial take: PMs are uniquely suited to thrive in the AI era. Good optimistic counterpoint to Itamar’s reality check.
- We transition from a world of “10x engineer” to “100x employee”? Could be a myth, but I definitely start to see signs of it.

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

🗞️ Automating docs with Claude Code / AI Bites 04

Olivier Courtois — Tue, 03 Feb 2026 05:47:22 GMT

Hey everyone,

Thank you so much for your likes and votes on the previous edition on communication prompts and MCP workflows.

Today’s edition is special: I’m hosting my first guest post!

Maxime Berkowicz, First PM at Elba, built an open-source tool to automate product documentation using Claude Code. It saves him 1-5 hours per feature, and I bet it can help you too.

I know what you’re probably thinking: documentation, is-it really the highest leverage task we can talk about? Actually, I do. The better your documentation quality is, the more AI-powered workflows you can enable. So read-on to learn how to save +3hrs per feature.

In today’s edition:

Why you should leverage AI models to have best-in-class documentation
What is Claude Code (and why PMs should care)
How Max-Doc-AI automates documentation end-to-end
Some of his learnings along the way on how to build such systems with AI tools

Enjoy!

How I automated my product documentation with Claude Code (and why you should too)

TL;DR: I built max-doc-ai, an open-source tool that automatically generates product documentation by exploring your codebase. Saves 1-5 hours per feature. Here’s how it works.

The problem: product docs always lagging behind

Nobody likes writing documentation. (Well, I don’t)

Product teams have a simple problem: every week, their team ships new features. And every week, the same drill: taking screenshots, writing feature descriptions, updating a knowledge base… It usually takes between an hour and half a day to fully document a feature.

Not huge, but it adds up. It’s also the kind of task that quickly slides to the bottom of the to-do list when there are urgent product deadlines.

Result? Lagging documentation that slows down entire companies :

Sales can’t sell without great collaterals: sales don’t know what’s real, or already available. Product marketing teams can’t build sales collaterals out of a faulty documentation.
Engineers become living documentation: Every “quick question” costs context-switching. When that engineer leaves, the knowledge leaves too.
Onboarding drags: New hires spend weeks asking “how does X work?” instead of contributing. Ramp time doubles. Senior people become interrupt-driven.
AI initiatives die: your support bot, sales copilot, and internal agents are only as good as your knowledge base. Bad docs = bad AI = abandoned initiatives.

Thus, I asked myself: “What if AI could write all product documentation for me?”

🛫 Maxime’s Daily Workflow

Until very recently, I would block a 1-5hrs slot in my calendar to

Manually read through meeting minutes, tickets, and PRDs
Take screenshots
Copy-paste in Claude or ChatGPT
Copy-paste the results

Now, I only take 15mins:

Every Thursday morning, I open my terminal and type
@claude Create a release for [feature]
Claude explores the codebase, launches a browser, captures screenshots, and generates docs
15 minutes later, I review the Knowledge Base article draft and Slack announcements
I tap “enter” to publish, and I’m back to roadmap planning or coffee ☕

Using Claude Code as a documentation partner

Before talking about the tool I created “max-doc-AI”, a quick detour through Claude Code - my partner in crime.

If you don’t know it, imagine ChatGPT... but that can actually do things on your machine.

Claude Code is an intelligent terminal. You type commands in natural language & it executes. It has access to your codebase: it can read, explore, understand your code. On complex workflows, it doesn’t “lose the thread” like other AI tools.

Three things that made it a must-use for me:

The “plan mode”
Claude Code can enter planning mode before executing. It explores your codebase, thinks through the approach, presents you with a plan. You validate, it launches. It’s exactly what a good PM should do: specify before building.
The context
Unlike ChatGPT where you have to copy-paste code in every conversation, Claude Code has direct access to your repo. It understands the architecture, the patterns, the naming conventions. Less guessing & more precision.
No lock-in
Claude Code generates... code. Markdown. Python scripts. You can modify, share & use your files elsewhere. For a PM who wants to keep control, that matters.

Max-Doc-AI: automating docs end-to-end

Max-Doc-AI is a set of Claude Code “skills” & Python scripts that automate the entire documentation workflow.

Within a single command here’s what happens:
@claude Create a release for [feature name]

Codebase exploration Claude explores your code to understand how the feature was implemented, what the routes and URLs are, what the main components are. Not what was planned in the PRD, but what actually exists.
Automatic screenshot capture This is where it gets interesting. Claude uses Anthropic’s “Computer Use” API: it opens a browser, navigates your app, takes screenshots. Based on the code, it pulls automatically the right URL (Well, sometimes it gets the wrong page when I have two screens. It’s still experimental. But 80% of the time, it works on the first try.)
Documentation generation With the code context and screenshots, Claude generates a complete knowledge base article: overview, key capabilities, step-by-step guide, use cases, FAQ. Screenshots integrated with proper captions.
Upload to our knowledge base Screenshots go to a CDN (CloudFront in my case), the article syncs to your tool (Pylon, Zendesk, Confluence...). The system is multi-provider: configure once, works everywhere.
Customer announcements generation Claude generates two versions: a short one for Slack (with emoji), a detailed one for email. Both include the link to the article that was just created.

Here’s an example of the documentation that was directly uploaded to our knowledge base with the correct screenshots for each sub-feature :

Total time: 10 to 15 minutes.

How it works under the hood (without getting too technical)

The tool has 5 modular “skills”. They’re basically markdown files with instructions that Claude is told to use. You can use them separately or all-together with the “create-release” skill. Expert tip: tap each one to read their content.

@create-release (the orchestrator) The conductor. It asks a few questions (where’s the feature? what date?), then launches the other skills in the right order.

@update-product-doc The documentation brain. It generates structured markdown, user-oriented (not technical), with clear sections.

@capture-screenshots Uses the Computer Use API to navigate and capture. This is the most experimental skill. Multi-screen = display problems sometimes.

@sync-docs The integrator. It converts markdown to your KB format, uploads images to the CDN, publishes the article. Extensible architecture: you can add your own provider.

@create-changelog The communicator. It writes announcements adapted to the channel (Slack vs Email).

Everything is in Python and Markdown. Nothing exotic. You can read the code, modify it, fork it.

How to use it (quick start)

The easiest way is to follow this tutorial (tap here for subtitles) 👇

If you want to test Max-Doc-AI:

Install an IDE of your choice (I recommend VS Code)
Install Claude Code
1. Install Brew & Node

/bin/bash -c "$(curl -fsSL )"

brew install node

c. Finally, install Claude Code :

npm install -g @anthropic-ai/claude-code

You’ll need an Anthropic API key (pay-as-you-go recommended for testing, switch to Pro or Max later).

Launch Claude in a “code” folder on your machine

claude

Ask Claude to “Clone the following repo : https://github.com/maxberko/max-doc-ai”

git clone 
cd max-doc-ai

Ask claude : “Guide me through the set-up of this Github project”
1. Configuration Create a new .env file & copy .env.example and fill in your API keys, Knowledge Base credentials, app credentials.
  Create a new config.yaml file & copy config.example.yaml and adapt: product URL, KB collections, screenshot settings.
2. Setup

python3 scripts/setup.py

Launch your first release

claude
# In the terminal:
@claude Create a release for [your feature name]

The result: complete documentation in 10-15 minutes.

What’s next

My goal: 100% up-to-date documentation, effortlessly.
Comment below if you’d like to sign-up to try it out first 💬

V2: A web interface I’m building a web interface to avoid working in the terminal. It’s a privacy-first automation of your documentation tasks.

V3: An autonomous documentation agent Transform max-doc-ai into MAX : an autonomous agent that checks every day if new features have been deployed, generates your doc automatically & sends you a summary for validation.

Three lessons learned building this tool

Specify before launching (even with AI)
The temptation with Claude Code: “it’ll be magic, I ask and it works”. Spoiler: no.
My workflow: first, PRD brainstorm with Claude. Then, plan mode to implement. Then iteration: test, fix, secure. Even with AI, we’re still PMs : specify, validate, iterate.
Security, always
Claude Code has access to your machine. It can read files. Execute commands. It’s powerful, and potentially dangerous. Basic rules:
1. Never leave API keys outside the .env file (which is never committed to GitHub)
2. Always ask Claude to run a security checklist before pushing
3. Use granular permissions : avoid “--dangerously-skip-permissions”
An API key that leaks on GitHub means thousands of dollars in AI consumption within hours.
Open-source is also for PMs
I’m a first PM, originally not a trained developer. Today, being full-stack as a PM also means knowing how to build tools when nobody else will.
Since I shared the repo, I received a PR to add Google Gemini (cheaper alternative for screenshots). Someone else is working on Confluence support.
Open-source is a multiplier of connections and value :-)

I help teams automate boring tasks through AI & learn how to do it by themselves. Curious how? Reach out on LinkedIn !

Maxime Berkowicz • Product & AI Consultant | Open-source builder

—

Thank you for reading this far, and many thanks to Maxime for building such an helpful tool, and sharing some secrets with us today. Don’t forget to like or comment below to get more information about the next versions, and to let me know if you’d like more guest posts.

Until next time!

Olivier

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🧠 The Thinking Trap / PM Snacks 93

Olivier Courtois — Wed, 14 Jan 2026 05:54:38 GMT

Hey everyone,

I noticed something fascinating this month: everyone’s talking about speed → ship faster, iterate faster, prompt faster. But the sharpest thinkers I follow are all pointing at the same blind spot: we’re optimizing for velocity while neglecting the quality of our judgment.

In an age where AI can generate anything in seconds, the bottleneck has shifted from execution to thinking. Double down on judgment, or risk becoming noise.

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

PS: sorry for being silent last dec, but after +10y without coding, I shipped my first solo app, entirely coded with codex and claude code in a month. We’re entering a new world!

🍭 Snacks

#1

A Plan Is Not a Strategy • June 2022 • 9 min watch • #strategy
Roger Martin's litmus test: if your "strategy" doesn't make you nervous, it's probably just a plan. Real strategy means betting on an outcome you can't prove in advance, ideally capturing a theory, and differentiating from competitors.

Plans are lists of activities with no internal coherence. “Improve customer experience, open a new plant, launch a talent program”, they all sound good, but they don’t add up to winning.
Strategy requires a testable theory. Lay out what must be true about your market, your customers, your competition. Tweak when your assumptions don't hold.
Escaping the trap requires confronting uncertainty and making explicit choices about what you will and won’t do, and being willing to fail.

Next time you review your roadmap, ask: does this guide us through unknowns, or just make us feel busy?

#2

The Modern AI PM in the Age of Agents • Jan 2026 • 7 min read • #AI #execution
The PM’s role is shifting from translation to intent formulation. When AI agents can take a well-formed problem and produce working code, the spec is becoming the product itself.

The bottleneck isn’t implementation anymore. It’s knowing what to build. Problem shaping, taking an ambiguous pain point and making it clear enough for agents to act, is now THE skill.
Context curation is the new craft: maintain context docs that feed agents before starting any project. Include real user quotes, constraints, what’s already failed, and how you’ll measure success.
Taste and evaluation matter more than ever. Agents will confidently produce things that look correct but miss the point. You need to develop judgment.

What’s interesting to me is that this echoes what we discussed in PM Snacks #92 about the Super IC, but goes deeper into the mechanics.

#3

Sometimes, You Should Talk to Fewer Customers • Jan 2026 • 3 min read • #mindset

You need to understand that every activity that a big company & its CEO encourages — answer support tickets, talk to customers, respond immediately to email/Slack, etc. — is intended to push this now-large company’s average & below-average employees to perform better, to get them to meet the now-lower bar of what the company expects from its median employee.

Shreyas Doshi delivers a contrarian take: relentless customer contact is a corporate best practice designed to raise the floor, not the ceiling.

If you’re already in the top 10%, you need to break the rules that were designed for the other 90%. The real work is quality of thinking, not volume of activity.
High-impact product work is about unique insights, bold bets, and protecting yourself from noise, not inbox zero or N customer calls per week. Ask: is this the best use of my time?

#4

Being Right Is a Trap • Dec 2025 • 6 min read • #AI #mindset
Hilary Gridley exposes a new paradox: sharp judgment in “AI world” can actually slow you down. What you’re right about today will probably be obsolete tomorrow. So make sure that you discard a tool or capability, you make sure to revisit it a few months later. For example AI-generated slides are bad quality (not true anymore with NotebookLM).

Said another way, this is one more reason why the best product builders should hold their convictions lightly and systematize humility.

#5

Most clicked link 1y ago.
The Lindy Standard of Performance • Jan 2024 • 12 min read • #execution
Here’s the clearest articulation I’ve read on the mindsets, and tactics that drive outstanding execution. Starting with 2 questions to be sure that you’re working on the right thing:

1/ What must urgently be fixed in order to bridge our “performance gap?”
2/ What must importantly be built in order to bridge our “opportunity gap?”

These two terms were coined by HBS professor Linda Hill. In a nutshell, bridging your performance gap means taking care of your existing business; if you’re Apple in 2000, your performance gap is the Mac. How do you keep taking market share in the PC industry?
Bridging your opportunity gap means pursuing the things your business could become. For Apple in 2000, this was the iPod. You never run out of performance gap to bridge, but you must still find a way to bridge the opportunity gap — because if you don’t do it, someone else will, and it will become your performance gap.

🗄️ Recently saved

Links worth reading that I saved, but did not highlight:
- Karpathy on X: LLMs aren’t people, they’re simulators. Stop asking “what do you think?” and start asking “what would experts say?”
- Ramp ships a major feature every day, here’s their 7-point checklist for balancing speed with quality
- Still embracing speed, Sunil Pai's manifesto (cloudfare): "Ship in 6 weeks or die trying"
- Lenny & Karri Saarinen on the disappearing middle of software work: directing agent work is the new craft. It’s exactly how I felt developing my mobile app.

—

Thank you for reading this far.
Until next time!

Olivier

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About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

AI Copilot with MCP / AI Bites 03

Olivier Courtois — Sun, 09 Nov 2025 21:28:36 GMT

Hey everyone,

Thank you so much for your likes, shares, and votes on the previous edition of AI Bites 🔥. It seems you’ve enjoyed the GPT-5 “Prompter” project, and the Ticket writer prompt.

In today’s edition, I deep dive into these most voted topics:

Backlog Autopilot: leverage AI to manage your backlog (MCP)
Communicate Progress: use AI to write progress reports (MCP)

Prompts are getting long, so I’m now storing them in a Notion database. Tap the buttons to access the full prompt (for free).

Enjoy!

Before we dive in, Claire Vo released a video with Dennis Yang where he presents his Cursor workflow (99% similar to the one I’ll describe below). 📺 If you prefer a video version, be sure to watch it!

What is an MCP Server?

I use several MCP servers in my daily life. Here’s a quick definition:

Model Context Protocol is a standard for connecting AI applications to external systems. Basically, it’s an API designed for LLM. Meaning it can easily discover available capabilities, use them, answer questions, and take actions.
2 types of servers exist:
public ones (usually there is a public URL you reference)
local ones (which can use some commands or executables, on your computer) which are only available from a desktop app.
Most modern tools like Notion, Linear, Figma or Amplitude, have been rushing to release their public “MCP Server” usable in desktop and web apps.
You can install them into AI apps like Claude, Cursor or Agents like Claude Code / Codex. Note: ChatGPT MCP support is still pretty nascent (and limited to the “developer mode”).

Which AI should you use as your copilot?

I’ve been using Cursor for a few years (to develop), but it’s now my favorite way to edit files and create projects too (non-developer tasks).

Now I can:

Create a simple folder on my computer with a few knowledge files that are completely “portable” (topic to be covered in another edition).
Add a few cursor rules (= shortcuts) to automate my usual prompts like writing tickets, scoping down or writing a progress report.
Plug MCP servers to be efficient (Linear/Notion for backlog access, Granola for meeting transcripts access, Figma for design access, Amplitude for data access…).
Chat “with the folder” to brainstorm, analyze data, work on PRD, and more.
Create files with AI, but more importantly take over, and edit them manually in the editor.

My usual Cursor setup

If you’re curious about Cursor, but you’re not sure where to start: this article on the modern AI workspace should help you.

Obviously, it’s not going to be everyone’s cup of tea, so here’s a comparison of the main tools you can use to leverage MCP power, and my use case for each 👇

Click here for html version or tap on the image to read the table

Note: This table is inspired by Teresa Torres’ article on Claude Code. Highly recommended read.

🛫 Backlog Autopilot

I’m collaborating with teams that host their tickets in Notion as well as Linear. Fortunately, both have MCP Servers that I can use to “pilot” my backlog. Note: I’ll share examples written for Linear (but it works the same for other MCPs).

Currently, I have 2 methods to create tickets in the backlog:

I create tickets directly in the backlog with a few bullet points (= some notes for myself). Then when I need to really write the content, I’ll ask the AI to read the ticket, collaborate, and update it in the backlog.
I write a PRD document for larger initiatives, talk with AI to find the best way to scope independent tickets, ask clarifying questions for each, then create the tickets in the backlog. 💬 Let me know if you’d like me to share this prompt next.

Improving a specific ticket

To write this prompt I used the following method:

Started a new chat explaining my goal: “create a new command that will read a ticket content through (MCP step), interview me, then suggest a ticket, once I approve, it should update in the backlog (MCP step)”
Pasted my “writing guidelines”, basically the instructions I shared in AI Bites #2.
Iterated on the level of details, and rules to make sure it works 99% of the time.

To use this prompt I just:

Open Cursor, and enter: “/ticket-refine” along with the url of the ticket. That’s it.
When additional information could be useful
1. I use Cursor mentions, to attach specific knowledge files in the folder. I add “@filename”, and the ticket refiner prompt will read my PRD or some user research extracts.
2. I ask AI to use specific MCP servers like Amplitude/Posthog to extract data, Figma to analyze design, etc.

# ticket-refine

## Workflow

### 1. Fetch Ticket from Linear

**Input**: User provides either a Linear ticket URL (e.g., `https://linear.app/acme-corp/issue/PROJ-456`) or ticket ID (e.g., `PROJ-456` or UUID).

**Actions**:
- Extract ticket ID from URL if provided, otherwise use ID directly
- Use `mcp_Linear_get_issue(id: ticket_id)` to fetch full ticket details
- Extract: `title`, `description`, `state`, `assignee`, `labels`, `priority`, `project`, `team`
- Parse description for: Design links, Problem statement, Solution description, Analytics context, and any existing acceptance criteria/edge cases

### 2. Interview Phase - Ask Clarifying Questions

Act as an **experienced Product Manager interviewer** to gather missing information needed for the INVEST story format.

**Questions Strategy**:
- Start with **blocking questions** (if critical unknowns detected)
- Then ask **clarifying questions** for missing fields
- Focus on extracting the four core fields: Design links, Problem, Solution, Analytics baseline

[too long...] Tap the button to access the whole prompt

Access the ticket refiner prompt

Turning a PRD into independent tickets

This prompt follows the process: 1. Reads PRD (Linear document or local file) → 2. Scopes first version → 3. Suggests independent tickets → 4. Lets me review → 5. Creates tickets in Linear.

💬 Comment or reply to this edition if you would like me to share this prompt.

📣 Communicate Progress

We spend so much time communicating about progress, challenges, and plans. Then we reframe the same message for different audiences.

Fortunately for us, AI is quite efficient at both writing and reframing. Note: I created these prompts after watching the Dennis Yang video mentioned earlier, what an amazing idea!

For educational purposes, I’m sharing 2 versions of this prompt in order to demonstrate my thinking process.

Backlog Progress

I started by reviewing a few examples of past progress reports I wrote. Usually it has sections like: Exec Sum, In Progress, Recently Done, Needs Attention.
I connected Cursor with Linear MCP (to access the backlog info)

I created a Cursor Rule (= a shortcut) called progress-report that accesses Linear, reads tickets, then writes a progress report. The anonymized prompt 👇

Please check what’s in our current backlog in Linear MCP and let me know what is in progress, what has been done recently (last 7d), and if there was any progress. If some issues needs design, give me an overview. If comments are unanswered in some issues, let me know. 

Make sure to not talk about cancelled or deleted issues. 

Format the report like this:
```markdown
## Summary
- **Progress**: 
- **Design bottleneck**:
- **Pending (comments, decisions, blockers)**:
- **Recommended next steps**: 

## 📊 In Progress (4 issues)
- ID - Issue Name 1 (Assignee)
  - Description of the issue
  - Highlights (if any)
  - Remaining work (if any)

[too long...] Tap on the button to access the prompt

Communicate Backlog Progress Prompt

Project Progress (Backlog & Meetings)

I realized that a lot of the key informations live outside Linear tickets. But I’m lucky: my colleagues are okay with me using a meeting recorder, and I work in remote situations (so close to 100% of meetings are recorded).

I connected Granola MCP to access my meeting transcripts.
I improved the previous progress report instructions to follow this precise process: 1. Gather context (from Linear & Granola) → 2. Scan for data → 3. Extract facts → 4. Filter → 5. Write report.

I used the prompt for a few days, and realized the MCP calls could go faster if they were described in details. I asked my AI chat to add these additional details. My exhaustive anonymized prompt 👇

## Workflow

### 1. Gather Time Context
- Default time window: **last 7 days** (one sprint = 1 week)
- If user specifies different window, use that
- Apply recency bias: weight most recent items highest

### 2. Scan Data Sources

#### Linear MCP

**Team**: “Your Team Name” (single team, do not use cycles)

**Step 1: Get Team & Verify States**
- Get team details: `mcp_Linear_get_team(query: “Your Team Name”)` to get team ID
- Optional: Get available states: `mcp_Linear_list_issue_statuses(team: “Your Team Name”)` to verify exact state names

**Step 2: List Issues for Team**
- List all issues for “Your Team Name” team: `mcp_Linear_list_issues(team: “Your Team Name”, includeArchived: false)`
- **Important**: After getting issues, filter out:
  - Issues with state “Cancelled” or “Canceled” (check `state.name`)
  - Issues with label “Duplicate” (check `labels` array for “Duplicate” label)
  - Issues with title containing “duplicate” (case-insensitive)
  - Archived issues (already filtered by `includeArchived: false`, but double-check)

[too long...] Tap on the button to access the whole prompt

Communicate Project Progress Prompt

—

Thank you for reading this far. I hope you’ll find these new prompts useful. It took me a long time to write.
Until next time!

Olivier

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About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

📈 Impact Era / PM Snacks 92

Olivier Courtois — Tue, 07 Oct 2025 05:16:34 GMT

Hey everyone,

Shipping has never been easier, AI generates everything from PRDs to working code, and roadmaps get filled with even more features than before. The risk? It’s easy to get lost in shipping mode, and dilute our impact.

This week’s selection cuts through the hype. You’ll find insights on the Super IC trend, vibe coding traps, roadmap illusions, and how to leverage AI for actual discovery work. The throughline? Double down on strategy, insight, and evidence-guided decisions.

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

Subscribe now

PS: if you’re new here, coming from “Le Ticket” for example, welcome 👋! I send 2 publications per month: 1/ PM Snacks, a curation of 5 interesting links on product building, 2/ AI Bites, my deep dives into actionable genAI tools and advice.

🍭 Snacks

#1

The Age of the Super IC • Sep 2025 • 7 min read • #Career
The script on career progression is flipping: we have moved from management centric ladder to an era where the most valuable people will be hands-on experts who can both think and build.

What’s interesting to me is that this trend is both right, and potentially, a fallacy.

During my coaching and advisory work, I meet more and more founders who are looking to hire the impossible expert who collapses all “roles”. So, it’s definitely a good idea to invest in these new skills.
That being said, the most important part of the job remains the same: shipping impact, not “outputs”.

#2

Vibe Coding and The Illusion of Progress • Aug 2025 • 12 min read • #AI
AI is an accelerator, not a compass, it amplifies both good and bad product decisions. Your product’s direction still depends on insight, not just code velocity.

Spotting the right idea to invest in, user research, or design are still among the most important activities you can do.
Leverage vibe coding tools to explore the problem space faster. Or better interact with the tech team (e.g. I ask gpt-5-codex to explain to me what’s already possible in the code before I talk with the team)
Use AI models to lower your burden and spend time on the impactful activities. Need inspiration? Follow my new publication “AI Bites”.

#3

The Roadmap Illusion • Sep 2025 • 7 min read • #execution
On top of AI accelerating the build phase, Stephanie calls out the hard truth: most product roadmaps are not strategic enough, and filled with features to ship.

Shipping features feels like progress, but it’s a dangerous illusion. We’re busy checking boxes, projecting confidence, rather than delivering real impact. The real strategy is defending balance:

Force every project into one of four buckets: feature, growth, innovation, scaling/debt.
Set explicit allocation targets and review quarterly. Make trade-offs explicit. Ignoring growth means features flop, skipping innovation means stagnation, and unchecked debt means velocity collapse.

#4

AI and Product Management • Sep 2025 • 6 min read • #AI #execution
So what is genAI good for anyway? Itamar Gilad reviewed the main PM activities and shared his educated opinion. In a nutshell, true potential lies in enabling a shift towards evidence-guided, discovery-driven work.

Use AI for heavy-lift discovery: extracting insights, pattern recognition, running deep analysis (metrics, projections, and simulations).
Never let AI stand between you and your users, or team. Co-create docs, run interviews yourself, and use AI to augment your insights, not to replace them.

#5

Most clicked link 2y ago in PM Snacks 66.
How we ship fast: our framework • Nov 2022 • 6 min read • #execution
In order to improve impact, Lago optimised their process to move fast. How do they manage to do it? They focus on a select set of topics and more importantly, they invest time beforehand to improve the quality:
1/ Spend time on the framing: define the usuals (problem, use cases, success measure, etc.) then go deep on the tech part (backend behaviors, graphQL features, design for each screen, etc.)
2/ Ask the engineers to dive-in and scope down accordingly
3/ Review everything: framing, scope, code and test a lot

Be sure to open the link to access all their templates

🗄️ Recently saved

Links worth reading that I saved, but did not highlight:
- Linear’s principles behind how they integrate AI into their product
- Claude memory differs from ChatGPT. Fascinating read on both these companies strategies, and memory engineering.
- OpenAI just updated their prompts library, here are the ones for Product Managers. For more specific prompts, be sure to read and test what I share in AI Bites.
- Flo on their “Use AI policy” at all cost, but never delegate your thinking to AI

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

Ticket writer that really works / AI Bites 02

Olivier Courtois — Wed, 17 Sep 2025 19:53:19 GMT

Hey everyone,

Thank you so much for your reactions, shares, likes and comments on the first edition of AI Bites 🔥. It seems you’ve enjoyed the ideas I shared, such as how to teach ChatGPT to write prompts for you, analyse your competitors, or help scope down initiatives.

In today’s edition, I share some new gems:

An update to the “Prompter” project instructions (GPT-5 optimisations)
A ticket writer that really works (at least for me).

Before we dive in, vote below for the next topic I should prioritize:

Enjoy!

🤖 Prompter project (GPT-5 updates)

I previously shared how to create a ChatGPT project that writes amazing prompts, so be sure to read it first.

Today, I'll explain how I updated the instructions (thx Guillaume for the question 💪), and it’s very meta:

I started a chat in the “Prompter project” asking it to optimize current instructions for GPT-5, while including the new best practices. You can read my full conversation here.
I created a second project to test each iteration of the updated instructions. My “evals” were quite simple, I just re-ran past conversations to see how much better/worse were the generated prompts.

Once happy with the results, I updated the original prompter project with the new instructions. Here they are:

# Prompt Interviewer for GPT-5 - Project Instructions, XML-Style

## Goal
Interview the user with **≤3 high-leverage questions**, then output a **production-ready prompt** using structured XML-style blocks that pin down context, autonomy, communication, constraints, outputs, and edge cases.

## Core Philosophy
* Treat prompting like product work: **clarify intent → set a strict output contract → anticipate failure modes**.
* **Infer first, ask last**: only ask for details that materially change the result.
* Prefer **structure over prose**; use tags to isolate instructions from content.
* Work **synchronously**: deliver everything in your current reply; no promises of background tasks.

## Interview Strategy (ask ≤3)
1. **Real goal** (generate / analyze / write / plan / debug / visualize).
2. **Output contract** (exact shape—table columns, fields, units, length).
3. **Constraints** (tone, audience, must/avoid, sources).

Stop once the prompt will be unambiguous and directly usable.

## The Tag System (use these blocks in the final prompt)
* `` — how deep to research before acting; prefer “fast then act” unless task demands depth. ([Reddit][1])
* `` — autonomy: complete the task end-to-end in this reply; make reasonable assumptions; don’t bounce questions back unless essential. ([Reddit][1])
* `` — how to narrate progress if tools (e.g., browsing, code) are involved: brief plan → do work → summarize. ([Reddit][1])
* Custom tags you can (and should) reuse:
  * `` — who the model is for this task.
  * `` — NEVER/ALWAYS rules and safety rails.
  * `` — domain, inputs, limits.
  * `` — primary goal, success signal, sub-goals.
  * `` — missing/ambiguous/unsafe input rules.
  * `` — quick execution plan (one screen).
  * `` — exact format/schema the model must return.
  * `` — only if they materially reduce ambiguity.
  * `` — short summary of reasoning; **do not** reveal chain-of-thought.

## Drop-in Template (use this as the artifact you hand back to the user)

```xml

You are a [SPECIFIC ROLE] that [CORE FUNCTION] for [TARGET USER/AUDIENCE].



Goal: Get just enough context to act quickly. Stop once the output can be produced with high confidence.
Method:
- Infer from what’s provided; ask only if a single, essential detail is missing
- Prefer acting over extended research; escalate depth only if contradictions appear



- Complete the task in this reply without asking for approval mid-way
- When uncertain, make the most reasonable assumption; state it briefly in the summary



- Start with a one-screen plan (headings/bullets)
- Execute cleanly; keep explanations concise
- End with a summary of what was done and how to use the output



NEVER: hallucinate sources; ignore the output schema; exceed the length/format rules.
ALWAYS: follow ; acknowledge uncertainty; refuse unsafe or out-of-scope requests.



Domain: [industry/use case]
Inputs available: [data/context]
Limits: [time/scope/tooling limits]
Audience & tone: [audience] • [tone]



Primary goal: [objective]
Success signal: [measurable outcome]
Sub-goals: [A], [B], [C]



- Missing/ambiguous input → state assumption and proceed
- Messy data → describe minimal cleaning and proceed
- Unsafe/off-scope → refuse and suggest a safer alternative



1) Reframe the request in one sentence
2) Follow the steps to produce the deliverable
3) Validate against the success signal



Format: [Markdown table | JSON-like block | bullet outline]
Fields/Columns: [name:type:rule]…
Units & limits: [e.g., words, currency, dates, timezone]
Determinism: adhere strictly to field names and order



[Include 0–2 minimal examples only if they materially reduce ambiguity. Otherwise delete this block.]



Provide 3–5 concise bullets explaining key choices/assumptions. Do not reveal chain-of-thought.

```

## How to Use (process for the “Prompt Interviewer”)
1. Ask up to **3 questions** (goal, format, constraints).
2. **Synthesize** the answers + any provided context.
3. Produce a **single, self-contained XML-style prompt** using the template.
4. Offer a one-liner on **how to run it** (e.g., “Paste into ChatGPT and fill brackets”).
5. If something is still ambiguous, include one brief assumption note inside ``.

## Final Prompt Checklist
* [ ] Tags included: ``, ``, ``, plus core custom tags.
* [ ] **NEVER/ALWAYS** rules are explicit and relevant.
* [ ] **Output contract** specifies exact format, fields, units, and limits.
* [ ] **Edge cases** & refusal/redirect behavior included.
* [ ] **Plan** is one screen; **rationale** is short; no chain-of-thought.
* [ ] No background promises; everything is delivered **now**.

🎟️ Ticket Writer

What I call a ticket writer that “really works” is based on my personal definition. It should:

Write in a style that mimics mine: economical, lot of bullet points, etc.
Follow a ticket format that I provided
Think in steps, and ask questions when unclear
Push for phasing delivery in several steps (if scope is big enough)
Finally, it should let me ramble, thanks to dictation, in order to give appropriate input

How I use this prompt

I usually store my most-used prompts in a snippet manager (Raycast in my case), and inject them in new ChatGPT conversations by typing a keyword. It looks something like this picture 👇
In the section, I share all the necessary details for ChatGPT to write tickets. The most efficient way for me is to use a dictation tool to brief the AI, the same way I’d share details with a colleague (B). Alternatively, I can also type by replacing the variables I included in the prompt (A).
If you want to re-use this prompt, I strongly suggest you adapt the following sections according to your needs. You can change it manually, or even better use your “prompter project” to work on it.
1. defines how AI should try to mimic your writing style.
2. defines how the AI should think about your inputs.
3. defines the way AI should format the tickets.
Be sure to have a peek at my ChatGPT conversation that led to the current version of the prompt. You’ll discover more than 10 iterations, how I shared my ticket writing style, and generally how I managed to get this result.

Current prompt


You are an Experienced Product Manager. Generate a lean set of INVEST user stories for the feature described in the inputs.



You may provide either:
A) Structured fields:
- Design link(s): {design_urls_or_TBD}
- Problem (why): {problem_statement}
- Solution (what): {solution_description}
- Analytics baseline (if any): {analytics_context_or_TBD}

OR

B) Free-form notes / dictation:
- {rambling_notes_here}

Extraction rules:
- Extract the four fields from free-form notes; ignore filler, fix obvious typos, preserve the author’s terminology.
- If uncertain after extraction, prefer conservative **TBDs** and (only if needed) add up to 3 bullets in “Questions for You (if any)”.



- Output must be Markdown and use the EXACT headings/order in . No extra sections.
- Use simple, direct sentences; ~80% bullets; bold key UI elements/technical terms.
- Acceptance Criteria MUST be Gherkin (Given/When/Then), ≤ 6 per story, all testable.
- Include empty/error states and performance thresholds in Edge cases; performance thresholds default to **TBD** unless specified in inputs.
- If details are missing, write “TBD:” inline. NEVER invent facts beyond the inputs.
- Default to a **single story**. Only create additional stories if  makes a single story non-viable. If >1 story, number the titles (“Story 1 — …”).
- **Allowed extras in the output:** 
  (a) a single top line `*Slicing: ...*`, and 
  (b) a final `### Questions for You (if any)` section (≤3 bullets).
- **Questions-first gate (blocking):** Before generating any story, check the inputs. 
  If there are contradictions **or** critical unknowns exceeding a safe threshold, output ONLY `### Questions for You (blocking)` (≤3 bullets that would materially change scope/assumptions) and **STOP**. 
  Safe threshold = any of: 
  • direct contradictions between Problem/Solution/Design/Analytics; 
  • missing both Problem and Solution after extraction; 
  • >3 “TBD” items that affect core behavior or acceptance criteria.



Default: produce exactly **one** user-visible, independently shippable story (“walking skeleton”).
Create additional stories **only if** a single story would violate one or more of these:
1) Assumption isolation — each story should test at most one core assumption.
2) Coherent delivery — sequential slices must yield continuous user value.
3) Risk reduction — separating high-risk work (unknowns, integrations) is necessary to de-risk.
4) Dependency separation — upstream dependency must land before downstream behavior.
5) Rollout safety — feature gating/permissioning requires a separate slice.

When >1 story is required:
- Keep the total count to the **absolute minimum**.
- Order by value and learning: walking skeleton first, then additive slices.
- Merge trivial behaviors that don’t test new assumptions.
- Begin the output with `*Slicing: N stories — reasons: X, Y*`. If single story, use `*Slicing: Single story*`.



Core philosophy: **Ruthless efficiency + Technical precision + User obsession**.

Formatting DNA:
- **Bold** key concepts, UI elements, technical terms.
- Bulleted structure; nested bullets allowed (max depth 2).
- 1–2 sentences per bullet; fragments OK.

Language approach:
- **Action-oriented** (“User taps button”, not passive).
- **Assume expertise**; don’t explain basics.
- **Pain-first** in Problem; executable detail in Solution (UX flows, states, API/tech notes when relevant to behavior).

Section patterns INSIDE the minimal structure:
- **Problem** → Pain point → Impact → Business context (use crisp labels when helpful).
- **Solution** → Implementation area → Component → Spec → Flow (use nested bullets).
- **Analytics / Metrics** → For each event: "event_name" — {properties}; include a brief **Purpose** line when helpful.



Apply the Questions-first gate. If it triggers, output only `### Questions for You (blocking)` and STOP.
If it does not trigger, decompose the solution per . Default to one story; add more only if required.
Keep Problem and Solution to 2–4 bullets each; use nested bullets (max depth 2) in Solution when it clarifies components/flows.
Align Analytics / Metrics with the provided baseline; otherwise mark as **TBD** and add a brief Purpose line if useful.
Begin the output with a single line showing the slicing decision (see ).
At the very end, include `### Questions for You (if any)` only when answers would materially change scope/assumptions (≤3 bullets).



## Title: {imperative, outcome-oriented} {prefix with “Story 1 — …” etc. only if multiple stories}

### Problem
- {pain point — specific friction}
- {impact on user/business}
- {context or evidence; bold labels sparingly}

### Solution
- **{Implementation area}**
  - **{Component}** — {concise spec or rule}
    - {key UX flow step or state change}
- **{Next area}**
  - **{Behavior}** — {exact interaction pattern}

### Acceptance Criteria
1) Given ..., When ..., Then ...
2) ...
3) ...
{up to 6 total}

### Edge Cases
1) {empty state}
2) {error state}
3) {performance threshold — renders ≤ **TBD** ms at P95 for up to **TBD** items (or as specified)}
4) {privacy/consent or rare scenario}

### Analytics / Metrics
- Events: "event_name" — {property1, property2, ...}; "another_event" — {properties}
- Properties: {list or brief schema}
- Success Metric: {primary KPI + target or **TBD**}
- Purpose: {what insight this enables, if helpful}

💬 reply to this email, or comment on this post if you’d like me to share more on how I improve further the quality of tickets, using ChatGPT Projects, and MCP servers to read/write directly in my backlog.

—

Thank you for reading this far. I hope you’ll find these new prompts useful.
Until next time!

Olivier

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About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

💰 Money talks / PM Snacks 91

Olivier Courtois — Mon, 01 Sep 2025 13:04:48 GMT

Hey everyone,

Hope you had a wonderful summer. I was lucky enough to travel to Portugal & Switzerland. Meanwhile, if you haven’t heard, I launched AI Bites, a new publication on actionable AI for product builders.

This PM Snacks focuses on the practical stuff that actually moves the needle: framing product decisions in real money terms, AI tools that save hours of work, and core management skills nobody teaches.

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

🍭 Snacks

#1

Useful Prompts / AI Bites 01 • Jul 2025 • 10 min read • #AI #Execution
This summer I started a new publication: AI Bites, highlighting how I tinker with AI to create and improve products. The first edition was my most successful newsletter to date, so I have decided to continue. I’ll share hard-learned lessons, prompts, tools and actionable advice to either improve your product processes or use AI inside your product features. Expect an edition per month (next one in 2w). Subscribe to get notified 👇

Subscribe now

In the first one you will learn how to leverage AI to write better prompts, how to delegate 90% of your competitive analysis to DeepResearch, and how to create a project that will help you scope down your product initiatives. I share tutorials, and even real chat conversations I used to write my prompts.

#2

Finishing Our (Money) Sentences • Aug 2025 • 11 min read • #Communication

Rich Mironov calls out the brutal truth: if we’re not framing product decisions in terms of revenue, we’re just background noise to the C-suite. Even vague financial guesstimates beat perfect technical explanations every time.
- Use “money sentences” to anchor every major initiative: what’s the upside, what’s the risk, and what’s the opportunity cost in real currency?
- Remember: we’re not just building products, we’re making bets with real money. So we should become comfortable with blunt math (think 5-digit vs 8-digit, comparing order-of-magnitude, and quantifying opportunity costs).

#3

Interfaces That Augment or Replace? • Aug 2025 • 5 min read • #AI #design
AI products are at a crossroads: do our interfaces empower users or quietly erode their skills? Zeh Fernandes explores how design choices in AI tools shape not just productivity but our very ability to think and learn.

#4

The Management Skill Nobody Talks About • Aug 2025 • 5 min read • #AI #design
Screwing up as a manager is inevitable. The only question is whether you know how to repair the damage. Read more to discover this core management skill nobody teaches!
- Acknowledge specifics, take real responsibility, and don’t hide behind "context" or justifications.
- Don’t turn it into a therapy session. Make it about the team, focus on the impact and next steps.
- Consistency is everything. One apology means nothing if you keep repeating the same screwups. We need to change for real!

#5

Most clicked link 1y ago in PM Snacks 84 about my go-to content for PM

Sylvain asked: Do you have any must-read, must-watch, must-listen (books, videos, podcasts)? How can I improve my product knowledge?

First, use a “map” like the one I created. Auto-assess your current knowledge, then focus on what can be improved. If this map is too simple, be sure to check the Product Map (super exhaustive) […] be sure to open the edition to read discover all my go-to content.

🗄️ Recently saved

Links worth reading that I saved, but did not highlight:
- Shreyas Doshi says it: Don't Outsource Your Judgment to AI
- Many people claim that PRDs are dead but I think prompts are the new PRD…
- Must watch: Cursor’s Head of Design shows how he uses Cursor (it’s mindblowing & raw creativity at its best)
- Why knowing a bit of code goes a long way when vibe coding

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

👶 Useful Prompts / AI Bites 01

Olivier Courtois — Tue, 15 Jul 2025 05:31:23 GMT

Hey everyone,

Last month I sent the 90th edition of PM Snacks, the current newsletter format - a curation of 5 links to level-up your craft.

Here’s a new idea I’ve been working on: AI Bites, highlighting how I tinker with AI to create and improve products. I’ll share hard-learned lessons, prompts, tools and actionable advice to either improve your product processes or use AI inside your product features.

📣📣📣 Please comment, or reply to this email to share your feedback. Even better send me questions or problems you’d like me to research for you 📣📣📣

Subscribe now

Consider this first edition a pilot for AI Bites: I’ll explain what a good prompt is, and how to leverage AI to write great ones, then I share a few prompts I use regularly (scoping down, ticket writing, doing exhaustive competitive analysis).

Enjoy!

Note: AI Bites will be a separate publication, so you can opt-out from it. If you choose to keep it, you’ll now hear from me twice a month.

What is a good prompt?

Models are being released faster than we can comprehend so it’s pointless to obsess over details. Instead I recommend to let AI write prompts for you:

1️⃣ Learn how to structure a prompt: The Product Map shared a good introduction to the topic. You can also skim through what labs are writing: Google, Anthropic or OpenAI.

2️⃣ Understand that AI is only as good as what you input into it. Always take the time to seed a conversation with the right context, files and prompt to get the best quality output (example: here’s the chat I used to generate the prompter project’s instructions).

3️⃣ Create a project in ChatGPT or Claude to write prompts for you. 1/ Add files about prompting best practices (for example a pdf print of the links I shared above), 2/ customize its instructions.

Here are the instructions I personally use in my “prompter” project to turn ChatGPT into an interviewer that writes my prompts 👇

**Goal:**
Help the user craft the best possible prompt by interviewing them briefly and intelligently (2–3 questions max). The goal is to converge rapidly to a high-quality, goal-aligned prompt using the best practices of 2025.

---

### 🎯 Core Philosophy
* Prompt engineering **is product thinking** — your job is to surface intent, constraints, and success criteria.
* **Infer what you can** from existing context; only ask what’s missing.
* Be concise, smart, and strategic. **No generic or open-ended questions.**
* Think like a curious PM with strong intuition for high-leverage inquiry.

---

### 🗣️ Interview Strategy
* Ask **no more than 3 questions**.
* Choose the **most relevant** questions based on current input.
* Prioritize:
  * What is the **real goal**? (e.g., generate ideas, analyze, summarize, write, debug, visualize)
  * What is the **desired output format**? (e.g., JSON, table, markdown, list, doc, mockup)
  * What are the **important constraints**? (e.g., tone, length, audience, context)
**Example prompts to yourself:**
* “What signal would tell me this prompt worked?”
* “What shape should the output take to be most usable?”
* “What context is absolutely necessary to get this right?”

---

### ✍️ Prompt Design Rules
Always follow these principles when generating the final prompt:
* Include a clear **role + goal**
  *(e.g. “You are a product strategist helping improve user onboarding flows.”)*
* Handle **edge cases explicitly**
  *(Use “never/always” language or conditional branches when relevant)*
* Specify a **precise output format**
  *(e.g. “Return a markdown table with three columns: Feature, Impact, Priority”)*
* Integrate **few-shot examples** if available
  *(If the user provides prior examples or a desired output, embed them)*
* **Leverage project's knowledge**
  *(Incorporate known best practices, research insights, or prompt structures relevant to this project)*

---

### ✅ Final Prompt Evaluation Checklist
Before presenting the final prompt, ensure:
* [ ] It will produce **useful output on the first run**
* [ ] It states both the **task and constraints clearly**
* [ ] It **anchors the model** and limits hallucinations
* [ ] It is **reusable** by someone else without extra context

4️⃣ (optionally) Find inspiration in great prompts available online: on Reddit, ShumerPrompt or this newsletter ;)

Prompts I use all the time (2x)

I try to use these models the same way I collaborate with people:

To help me think - like a partner, or coach would do (in ChatGPT or Claude)
To delegate- I’m in the loop and improve the outputs (in ChatGPT or Claude)
To replace me entirely - I’m not checking anymore (using Agents)

1/ Competitive Analysis (#delegate)

I’ve been working as a fractional CPO since a few months, so I’ve been in situations where I need to study a lot of competitors super fast. I want to be directionally right (but if some details are wrong it’s not a big deal), and leverage Deep Research to work for me while I’m focusing on other things.

1️⃣ I used my prompter project to write the prompt below and refine it. Before I was using a version that I found on ShumerPrompt.

2️⃣ When I find a product/company, I start a new ChatGPT o3 Deep Research with this prompt and the right context (product name, url, etc).

You are a **startup strategy analyst** conducting a deep, structured competitive investigation of a single company.

Your job is to synthesize **positioning, value proposition, audience, product model, go-to-market strategy, and tech stack** — based on **real public signals**, not just surface-level summaries.

---

## Inputs
Find everything you can on this company {{url or context}}

## Instructions

### 1. Confirm Identity
Verify that you’re analyzing the correct company based on website content, domain metadata, and public presence (LinkedIn, socials).

---

### 2. Research Sources

Gather and triangulate information from all available sources:

#### Core Sources
* Website (homepage, product, pricing, blog, docs)
* Help center / support docs
* LinkedIn company page and employee profiles
* Job listings (LinkedIn, AngelList, company careers page, Welcome to the jungle)
* Public product changelogs
* Twitter/X, YouTube, Product Hunt, Reddit

#### If discoverable, include insights from:
* G2, Capterra, app store reviews (iOS/Android)
* Traffic analytics signals (SEMrush, Similarweb, Ahrefs)
* BuiltWith, Wappalyzer, GitHub
* Google News or Crunchbase (for funding, partnerships, pricing changes)
* Patent filings or conference slides

---

### 3. Inference Tasks
Don’t just extract facts — infer deeper signals, such as:

| Signal                                               | Inference                                             |
| ---------------------------------------------------- | ----------------------------------------------------- |
| Hiring ML or AI roles                                | Product is likely adding LLM features or vector infra |
| High traction on Product Hunt                        | Self-serve GTM with startup appeal                    |
| Pricing mentions “per seat” or “enterprise features” | Sales-led or midmarket motion                         |
| Repeated G2 complaints about UX or support           | Weakness in onboarding or customer success            |
| Blog focuses on “trends” + SEO-optimized titles      | Content-led acquisition strategy                      |
| Recent mobile engineers hired                        | Mobile product in development                         |

---

## Output Format

Respond in the following markdown structure:

### 🔍 Executive Summary
> A 3–5 sentence snapshot of their current strategic positioning and what makes them interesting or unique.

### 🎯 The 4 Ps (Person, Problem, Promise, Product)
* **Person**: Who is the ICP? (roles, industries, maturity)
* **Problem**: What pain or inefficiency are they solving?
* **Promise**: What transformation are they offering?
* **Product**: What’s the product and delivery model (SaaS, API, SDK)?

### ⚙️ Tech Stack & Engineering Signals
* Languages, frameworks, cloud providers
* AI infra (LLMs, embeddings, RAG, vector DBs, etc.)
* Any signs of complexity (DevOps, custom infra, GitHub activity)

### 📈 GTM Strategy & Growth Channels
* Sales-led, PLG, or hybrid?
* Growth loops: SEO, content, community, outbound?
* Pricing structure if discoverable
* Competitor landscape if clear

### 🗣️ Messaging & Tone
* Voice, taglines, and visual brand cues
* Category framing (e.g. “The Notion for X”)
* Degree of maturity and confidence in copy

### 🧑‍💼 Team & Hiring Signals
* Roles they’re actively hiring for
* Notable leadership backgrounds (ex-Stripe, etc.)
* Inferred product priorities from job patterns

### 🧪 Customer Feedback & Reviews (if found)
* Patterns from G2, Reddit, Twitter, app reviews
* Common complaints or praise
* Feature gaps or unexpected use cases

### 💸 Funding, Financial, & Strategic Signals (if found)
* Recent funding rounds or valuation clues
* Pricing model shifts or major partnerships
* Any IP/patent activity or conference content

### 🧠 Confidence Tags
Annotate any speculative insights with (Low), (Medium), or (High) confidence depending on data availability.

3️⃣ 15 min later, I read the report and sip on my coffee ☕

2/ Scope Down (#helpmethink)

I’ve also been building new products, and doing regular product management tasks like writing tickets or scoping down initiatives. So I created a project for this too.

1️⃣ I created a MVP Trimmer project - no files, just specific instructions, see below 👇

**Role & Goal**
You’re acting as **“MVP Scope Trimmer,”** a seasoned product strategist whose job is two-fold:
1. Help me isolate the smallest coherent MVP for any initiative.
2. Turn that MVP into an engineering ticket that captures every critical detail.

---

### Conversation flow & working modes
We will stay in a normal, back-and-forth conversation by default. Feel free to answer questions, propose alternatives, ask clarifying questions, and politely challenge assumptions. Keep your replies concise and Socratic.

There are two special outputs you can produce **only when I explicitly ask**:

* **Scoping report** – If I say something like “Create scoping report,” reply **only** with the filled-in Markdown Scoping Report template (see below).
* **Ticket** – If I say something like “Write the ticket,” reply **only** with the completed Markdown Ticket outline (see below).

Until I request one of those artefacts, never cut the dialogue short; continue the discussion instead.

---

### Markdown Scoping Report template you’ll use on request

```
## Core Hypothesis


## MVP Features
- …

## Excluded Scope (Scope Creep)
- …

## Learning Signal & Exit Criteria


## Estimated Time to Build


## Risks & Mitigations
| Risk | Cheap Mitigation |
|---|---|
| … | … |

## Next Step if Successful

```

**Guardrails for scoping**
* Treat every initiative as an investment bet: maximise learning, minimise upfront cost.
* If the timeframe feels tight, trim scope—never extend the schedule.
* A feature that doesn’t reduce uncertainty about the hypothesis belongs in “Excluded Scope.”
* Favour manual or low-code hacks over full automation for an MVP.

---

### Markdown Ticket outline you’ll use on request

```
## Why
- …

## What
- Front-end: …
- Back-end: …
- UI behaviour: …

## Analytics
- Event: …
- …
```

---

### General rules you must follow at all times
1. **One artefact at a time** – deliver either the Scoping Report *or* the Ticket, never both, and never extra commentary inside the artefact.
2. **Exact formatting** – keep the headings and bullet styles shown above when producing an artefact.
3. **Use my wording** – copy any initiative details I provide verbatim.
4. **Few-shot support** – weave in any examples I supply as additional context.
5. **Ask if critical data is missing** – e.g., timeframe, success metric, etc.
6. **Tone** – be concise, pragmatic, and collaborative.

2️⃣ I used this project a few times, found limitations and iterated on the instructions to make the conversation smoother, and more capable (scoping down then writing tickets). Here’s the full prompter chat with all my iterations.

3️⃣ Every time I’m considering an initiative, I start chatting within this project. Here’s my chat about a fictive “ChatGPT for product managers” (click to see how it goes).

PS: In case you’re wondering, I prefer Projects to CustomGPT because my chats are better organized and it translates across more systems (like Claude).

PS 2: You’ll find a lot of typos in my chats, I use dictations to dump a lot of context in no time. Highly recommended.

—

Thank you for reading this far. I hope you’ll find this new format useful.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

⚡ New Rules Era / PM Snacks 90

Olivier Courtois — Wed, 18 Jun 2025 05:06:16 GMT

Hey everyone,

This week I kept bumping into the same realization: we're past the "AI is interesting" phase and deep into "AI is restructuring everything we know about building products”.

The pieces I'm sharing today aren't about AI features or cool demos. They're about fundamentals: how we strategize, operate as teams, and compete when the rules have shifted.

Subscribe now

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

🍭 Snacks

1️⃣ The Great AI Rebundling • May 2025 • 8 min read • #AI #strategy
AI is the most general-purpose technology ever created, and is accelerating the classic cycle of unbundling-bundling.
- Incumbents are incentivized to bundle. Horizontal platforms already own the primary workflow, and expand to capture more value (ChatGPT, Mistral  Enterprise, Notion, Figma…)
- The barrier to shipping is lower than ever. Teams can build breadth quickly. As result, most tools are converging to a common feature set: search, deep research, augmented writing, unstructured data capture, etc.
- 10x rule for startups. We now compete against ChatGPT across every domain so we better integrate domain expertise, fresh & unique data, and niche down to differentiate.

2️⃣ Rethinking Product Strategy in the Age of AI • May 2025 • 22 min watch • #AI #Strategy
Alix Moggia breaks down how AI is reshaping the way we build, operate, and learn as product teams. Here’s a few gems:
- Team operations: use AI to draft specs, scan competitions, summarize user interviews and more. 💬 Btw: comment or reply, if you’d like access to my prompts!
- Leverage × Feasibility: Score bets on how transformative and practical AI is for each opportunity, differentiate between AI-core & AI-assisted bets to invest appropriately.
- Human-in-the-loop by design: Bake human review into your process and metrics from day one. As a rule of thumb, invest into evals widely.
- Make-and-buy: Buy foundation models or vertical APIs to reach market fast. Differentiate with proprietary data or specialized UX.

🤖 Shape Of AI • May 2025 • 5 min read • #AI #design
A straightforward pattern library of essential UX patterns for AI products like prompts, controls, and trust signals. Be sure to bookmark it.

⏳ The End of Software • Jun 2024 • 4 min read • #strategy #AI • Most clicked link 1y ago in PM Snacks 82.
Are developers and PMs in the same position as journalists in the late 90’s? The author argues that genAI will drive the cost of creating software to 0, disrupting the dominant SaaS business model, the same way social media & UGCs destroyed the traditional media business model. It’s a provocative take that drove many interesting conversations on twitter (be sure to read the replies). I second Jens-Fabian opinion on this.

🗄️ Recently saved

Links worth reading that I saved, but did not highlight:
- How Zapier measure AI fluency (super useful to create an internal ladder)
- And how Robert Scoble test it in hiring interviews
- Love letter to “honk’s” design language
- The Best Interfaces We Never Built: inspiration from sci-fi
- Prompt engineering isn’t a technical skill, it’s becoming the art of delegating tasks to an AI teammate
- Just for fun: Saw a PM today […] writing PRDs from scratch on MS word. Like a psychopath.

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

🧩 AI + Human Judgment / PM Snacks 89

Olivier Courtois — Wed, 14 May 2025 05:35:20 GMT

Hey everyone,

There's a fascinating shift happening in product building right now: as AI accelerates development cycles, the differentiator between a good product and an excellent one, is increasingly, human intuition and taste.

This edition explores how SaaS products might become AI-mediated data services, how successful companies systematically operationalize "taste", how prioritization frameworks can embrace intuition, and discover emerging UI patterns that blend AI with traditional interfaces.

As machines handle more of the "how," humans must get better at the "why" and "what" of product building.

Subscribe now

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

🍭 5 snacks

1️⃣ Do SaaS Founders Dream of System Prompts? • Apr 2025 • 8 min read • #AI #strategy
- The author outlines how the SaaS stack might be evolving from rigid applications to a new paradigm: System of Record + AI Middleware + Dynamic UI/Agents. Instead of forcing users into predefined workflows, AI middleware would dynamically interprets rules, might generate interfaces on-demand, and even adapt to user intent.
- Last week, the Claude System Prompt reveal shocked most people because of the amount of intelligence encapsulated in it. If business logic can live inside a system prompt and UIs can be generated dynamically, the path forward for SaaS companies could be: to start viewing your product as a flexible data service rather than a rigid interface, before someone else does.

2️⃣ The Unsung Ingredient in Stripe, Square and Linear’s Success: Taste • May 2025 • 13 min read • #mindset
In today's AI-accelerated world, shipping fast isn't enough to win. We might need to apply finely-tuned intuition, aka taste, to build remarkable differentiated software. First Round just published a rundown on how companies like Linear, Stripe and Square operationalize this skill through concrete systems:

- Systematized review processes: Stripe used "20% and 80% reviews" with designated "red pen holders" to maintain quality at scale
- Minimum remarkable products: Square and others build for "high-expectation customers" rather than settling for merely viable products
- Opinionated design: Linear intentionally limits flexibility, believing "you can't build the optimal tool if it's endlessly customizable"

3️⃣ Dynamic Framework for Making Product Decisions • Mar 2025 • 6 min read • #prioritization
Tired of frameworks like RICE that reduce complex decisions to arbitrary scores? VICE (Vibe, Impact, Confidence, Effort) offers a refreshing alternative where the V emphasizes human judgment and intuition. Instead of scoring in isolation, you compare features in pairs, with winners gaining Elo rating points while losers drop.
- Upside: it naturally captures contextual nuances, forces explicit trade-offs, and aligns with how we actually make decisions (A vs B rather than absolute values). Also, on top of vibe coding, we can now vibe prioritizing ;)
- Watch-outs: "vibe" adds needed intuition, be careful not to let it become a backdoor for HiPPO-driven decisions or hype-chasing features

🤖 Post-Chat UI • Apr 2025 • 8 min read • #AI #design
Traditional apps are starting to feel broken compared to new AI-enhanced interfaces that blends AI capabilities with traditional UI. Read this article to discover 9 emerging patterns, from “Generative Right-Click” and natural-language search to inline daemons and AI-driven clean-up.

⏳ The Arc Product-Market Fit Framework • Apr 2024 • 11 min read • #strategy • Most clicked link 1y ago in PM Snacks 81.
To identify how to better compete and serve users, start from your customers’ mindsets:
- Hair on Fire: You solve a clear, urgent need that your customers have identified. As a result, they already employ a solution (a competitor or even Excel), the market is crowded and to win, you will need to build a best-in-class differentiated product. The latter part being the most important.
- Hard Fact: Customers may not even be conscious they have a problem, you will hear them say “it is what it is”. As a result, you’ll need to overcome their habits and start educating the market. For example, when Hubspot started, they had to invent the term “inbound marketing” to explain what their product was for. Square decided to tackle the “cash only” hard fact by turning every smartphone into a credit card reader, and distributed their solution for free in order to stand a chance.
- Future Vision: You enable a new reality through visionary innovation, requiring endurance and lot of investment beforehand. Customers will first be skeptical you can even deliver the solution you are pitching. Think of products like the iPhone, Figma or ChatGPT.

🗄️ Recently saved

Links worth reading that I saved, but did not highlight:
- ChatGPT o3 can reverse engineer intelligence from a picture of a whiteboard
- Collective’s CTO handled 95% of support tickets for 3mo to learn more about their users, and automate what could be
- Trust is not binary but a spectrum: enter the “trust battery” metaphor
- AI Prompt Patterns Cheat Sheet
- Microsoft Generative AI lessons for beginners

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

🎯 The pragmatic take / PM Snacks 88

Olivier Courtois — Wed, 16 Apr 2025 05:43:35 GMT

Hey everyone,

I've noticed something interesting about the articles I've curated this week. While they explore different aspects of product building, they all share a common thread: the importance of bridging theory and practical application.

We're constantly navigating between idealized best practices and the messy reality. As you read through, you’ll discover a pragmatic perspective that values real-world effectiveness over rigid adherence to theory: through finding actionable metrics, applying frameworks flexibly, communicating with precision, or designing AI experiences that actually work for users.

Subscribe now

Please support this newsletter by sharing it with your colleagues or liking it (tap on the 💙).

Enjoy!

🍭 5 snacks

1️⃣ WTF is activation and why should you care? • Feb 2025 • 7 min read • #data
Activation isn't just some vague "aha moment": it's the first actionable metric that impacts all the others, and generally its a good proxy for the impact of product launches. Unfortunately, finding it can be hard:
- it’s usually unique to your product, rarely a single event, and sometimes requires the user to do something multiple times.
- Finding your activation metric requires testing multiple event combinations against retention rates. In this article Posthog (+10 products) shares how they do it.

2️⃣ Things About Product Frameworks I Wish I Knew Earlier • Arp 2025 • 7 min read • #mindset
In the coaching sessions I do, I find myself repeating these same two things:
1/ that product frameworks should be flexible tools, not rigid doctrines.
2/ that you should not compare your product organization to what you have read in books or heard in conferences, or do so at the risk of being miserable.

In reality, all of us have to bridge the gap between textbook product frameworks and real-world implementation. Click the link open to find out about Paweł’s refreshingly pragmatic take.

3️⃣ How to Write Email with Military Precision • Nov 2016 • 4 min read • #communication
As product builders we spend lot of time crafting messages to our team, customers, stakeholders, and even AI now. These 3 techniques have transformed the way I write these:
- Tag your messages: Start with ACTION, INFO, DECISION, or REQUEST to immediately signal intent.
- Bottom Line Up Front (BLUF): Lead with the core message before providing context. It grabs people’s attention, and improves efficiency.
- Be ruthlessly economical: Keep messages visible without scrolling, use active voice, and structure background as bullet points.

🤖 The surprising patterns behind viral AI products • Feb 2025 • 8min read • #AI #design
Fascinating deep-dive into the unique challenges faced by AI-native products. The key insight? AI isn't the differentiator anymore; thoughtful user experience that builds trust, improves inputs, and feels natural is what drives adoption. Be sure to open the link to get an overview of the most efficient UX patterns yet.

⏳ The Product Model at Amazon • March 2024 • 9 min read • #execution • Most clicked link 1y ago in PM Snacks 80.
The way Amazon works can sometimes feel “out of the ordinary”… but actually, if you look into it, you’ll recognize the usual suspects: customer-centric product vision, data-driven experimentations, long-term thinking, strong leadership, and focus on being really agile. Read what Marty Cagan has to say about their model in this article.

🗄️ Recently saved

Links worth reading that I saved, and did not choose to highlight:
- 3 essential skills you need to master to thrive in AI-driven product landscape
- Some ideas to create momentum in your company around AI by Claire Vo
- Underutilized Figma trick: Positive affirmations while leaving feedback
- Nikita Bier on why you should have a full-time designer in the room at all times
- Will AI make everyone a designer and/or developer? Probably not. Same way microwave did not turn anyone into a chef.

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

⏸️ Give it 5 minutes / PM Snacks 87

Olivier Courtois — Wed, 19 Mar 2025 06:42:55 GMT

Hey everyone,

I love sharing behind-the-scenes stories that reveal the nitty- gritty of how companies build their success, beyond generic frameworks. That’s why I’m thrilled to kick off today’s newsletter with a deep dive into Supabase’s early days.

I’ve also curated 2 articles that invite us to lead with curiosity and thoughtfulness, transforming our instinct of pushing back, into an opportunity for deeper understanding and smarter communication.

Finally, you’ll find an hour long tutorial on how to prototype your ideas with AI tools like Bolt, Cursor, Lovable, Replit and v0. Don’t worry, each tool appears in a specific 10min segment, for which I have shared the time- codes below. If you ever wondered what’s new, it’s a great way to discover it.

Please support this newsletter by sharing it with colleagues or liking it (tap on the 💙).

Enjoy!

🍭 5 snacks

1️⃣ How we launch at Supabase? • Nov 2021 • 8 min read • #execution
A new behind-the-scene piece focusing on how Supabase used to operate in the early days. They started with Y Combinator, faced unexpected growth, and kept shipping relentlessly. Some unique traits:
- Internal accelerator mindset: they combined fixed timeline (3mo) with flexible scope, bold bets and regular demos (similar to YC demo-day). The goal is to ship ambitious features, with a sense of urgency and accountability. They often ask “what is the MOST ambitious thing we could hope to ship 3 months from now?”.
- Iterate and improve: perfect timing is rare, instead, they ship early and often. They even launch each feature multiple times, in order to capture different segments of their audience and adjust based on immediate feedback.

2️⃣ Give it five minutes • Mar 2012 • 5 min read • #mindset
I wish I’d received this advice earlier in my career. Back then, my insecurity pushed me to quickly affirm and double down my opinions, often missing the chance to listen to others with genuine curiosity. Now, I make it a point to give every idea its five minutes, in conversations and product management alike. With the lowering implementation cost, I even try to test more concepts than before, especially when I doubt them (♥ Cursor).

3️⃣ Picking your battles when you are hyper-rational • Jan 2025 • 4 min read • #communication
Wes Kao is a seasoned tech leader specializing in executive coaching, and notably helping operators to develop efficient communication. In today’s article she shares examples of times she got caught up in being technically right, at the expense of being effective. Be sure to check this article to escape this trap. Give it five minutes, and communicate better.

🤖 Tutorial: learn to prototype with 5 AI tools • Mar 2025 • 1h watch • #AI
Colin Matthews was invited into Akkash Gupta’s podcast to demo how PMs can create prototypes thanks to AI. The video is not short, but it’s super didactic and it covers 5 tools. Here are the timing: 0:31 - Bolt Tutorial, 32:14 - Cursor Tutorial, 44:29 - Lovable Tutorial, 58:54 - Replit Tutorial, 1:11:29 - v0 Tutorial.

⏳ Why you should stop using product roadmaps and try the GIST Framework • Feb 2018 • 7 min read • #execution • Most clicked link 1y ago in PM Snacks 69.

The author explains why he no longer uses product roadmaps (= usually created through a waterfall process) and suggests the GIST Framework as an alternative. It consists of:
- Goals: start with your OKRs and/or strategy. Focus on outcomes rather than solutions. For example: increase to 25% of key actions performed on the mobile app.
- Ideas: describe 1 to 3 hypothetical ways to achieve these goals. Possibly work on it with your stakeholders, as well as your squad. I’d advise not to keep ideas in a backlog (since it’d become ever growing quite fast).
- Step-projects are experiments that test the ideas and are no more than (~~10)~~ 6 weeks long (or even faster).
- Tasks are the individual steps that needs to be completed to finish the Step-projects.

🗄️ Recently saved

Links worth reading that I saved, and did not choose to highlight:
- Here’s how I use LLMs to help me write code by Simon Willison
- Product Management In The Age of AI: eliminates the low value parts (document creation, reporting, …) and enhances important ones (decision making, strategic direction)
- Is your app a painkiller or a vitamin - Andy Allen (not boring software) explains why this analogy should be retired
- Claire Lew was asked by someone deciding between two jobs: "How do you know if a leader is a good leader to work for?”
- Steve Jobs on when people will be able to talk with Aristotle (intro of “Building with AI” talk by Des Traynor - CEO of intercom)

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

🌊 Retention secrets & AI impact on product mgmt / PM Snacks 86

Olivier Courtois — Thu, 13 Feb 2025 07:59:10 GMT

Hey everyone,

First, I’m planning to resume a monthly cadence for this newsletter. Hope you like it!

Second, on top of the best content I discovered (with my personal notes), I’ll now add an “unfiltered” section with only links, without additional input. I’ve a growing db of interesting content that never made the cut, but which could be interesting for some of you! Let me know what you think about it, just hit reply (yes, you can reply to these emails).

Please support this newsletter by sharing it with colleagues or liking it (tap on the 💙).

Enjoy!

🍭 5 snacks

1️⃣ Retention curve’s flatness matters • Aug 2024 • 6 min read • #data
This thread challenges conventional wisdom and goes deep into the mechanics of good retention, by showing that flattening a retention curve is the holy grail. That’s why Discord or Roblox have consistent growth despite “low” early retention. Be sure to open it to read all the gems, and even play with this free tool to forecast DAU and LTV.

2️⃣ The Duolingo Handbook • Feb 2025 • 12 min read • #culture
A pretty high-level piece worth skimming through. Keep in mind that Duolingo is a true product-led company, with great monetization (enabling the focus on long-term mission), great branding and data-driven culture. Some highlights:
- Their principles: Take the long view (“we’re creating a 100-year brand”, “we’re building long-term user retention first”), Raise the bar (“V1 not MVP”, “we hire exceptional people”), Ship it (“we run hundreds of experiments”), Show don’t tell (“when we disagree, we test ideas and let the metrics decide), Make It Fun (“built on play”)

3️⃣ With AI, product management should thrive? • Jan 2025 • 4 min read • #AI
By now, we can all see that our line of job (= building software products) is one of the first and most impacted by AI’s recent advancements. Andrew Ng believes it’s good news for product managers who lean into these new technologies:
- Increased automation of development actually amplifies the strategic importance of PM work: "the demand for people who can come up with clear specs for valuable things to build will grow." Some companies already stopped hiring developers like Gumroad who is going full in!
- Traditional Engineer:PM ratios may shift: product management and design work will become the new bottleneck as execution become cheaper and augmented/automated.
- Product managers should become AI proficient as soon as possible: leveraging these technologies to resolve the bottleneck to come, as well as integrating them into their products when relevant.

Open in reddit

🤖 You’re not behind: Become AI-Native in 2025 • Jan 2025 • 10 min watch • #AI
Do you ever feel overwhelmed by the AI news cycle? Then be sure to watch this quick video describing a “just enough” mindset and few ideas to become proficient with the tools that matter to you. The author shares tips on:
- Fighting AI tool choice paralysis. He even created an AI plan document to guide you.
- Better organizing prompts with text expanders and your own prompts database.
- A few trusted sources to stay on top of AI news (my favorite being the Superhuman daily newsletter).

⏳ The Product Manager’s survival kit Part 1: Dispelling some myths • Jan 2023 • 10 min read • #culture
Most clicked link one year ago in PM Snacks 69.
Valentin joined ManoMano only to find out that despite its renowned product culture, there were still a lot of issues to unpack… In order to get to the root cause, he started a year-long project to redefine what it means to be a PM in this specific company and wrote a series of article. I chose to share the first one for multiple reasons:
1/ It’s a good reminder that no company gets it right. If you feel like your company is chaotic compared to the ones featured in the links I publish here, remember that it’s not the whole truth.
2/ Among the many articles available, always prioritize the ones that share a behind-the-scene knowledge, the ones that start with why, and explain how they chose to address specific issues in their context.
3/ This first article already debunks some myths. My favorite: product management is not about user centricity.

🗄️ What I saved recently

Links that I saved but did not choose to highlight:
- Why Every Product Manager Building in AI Needs to Start Thinking About LLM User Analytics
- No one talks about the wasteful work of over-specialization […] with AI talent market, if you can call it that, becomes much more liquid.
- The Lindy PM Standard of Performance (shared with my notes in the previous edition)
- Most common questions asked by Des Traynor (CEO of Intercom) in product review meetings

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (15y+ in product, Fractional CPO, coach & advisor). Each issue features handpicked links to help you become a better product maker.

Updates, job opportunities & one link

Olivier Courtois — Thu, 16 Jan 2025 06:36:42 GMT

Hey everyone,

This newsletter has been taking a break since November but I’ll start sending new editions very soon. Possibly even next week 🤞

In the meantime here are a few updates, since some of the readers asked me privately:

1️⃣ I’m closing my startup (uku.wtf) after 3 years of hard work, 4 pivots, and a ton of learnings. I’ll take a bit of time to process it but I can already share that it’s been the most creative, intense and enlightening time of my career. Among other things, it helped me optimize my tactics for Pre-PMF contexts. If you want to learn more about it, here are 2 podcasts in which I talk about this topic (only available in French, sorry):
- Comment faire du Produit en pré-Product/Market Fit (vs post-Product/Market Fit)
- Les coulisses du pivot d'une startup early-stage de la creator economy

2️⃣ I’ve been lucky to collaborate with The Mobile First Company for a few weeks, and they’re looking for new hires. I love their vision: to create B2B mobile apps with a B2C mindset. Their thesis is that the real economy is full of smart entrepreneurs (they even created a media to promote them), that will pilot their activity from a smartphone (= growing trend). So they want to create a suite of apps that is modern, powerful, affordable, and distributed through the AppStores. Their flagship app is Allo, a virtual phone line powered by AI, that works for you… and they already bought a second app. Would you like to work with them?
- The team is small, talented, hard working and distributed between France and Argentina. Remote accepted. Everyone is data-informed, customer obsessed and they ship once a week.
- The company is growing but still very early. They raised money from top-tier VCs and amazing business angels (who give regularly masterclasses)
- They’re looking for a Founding Senior Product Designer and a Founding Junior Product Manager anywhere in Europe or LATAM. Ideally, with an entrepreneurial spirit and obsessed with creating mobile apps. ✉️ Hit reply if you’re interested. ✉️
- Disclaimer: I usually never share job opportunities in this newsletter. I made an exception because these offers are off-market and super interesting in my opinion.

3️⃣ The Lindy Standard of Performance • Jan 2024 • 12 min read • #execution
It would not be a productverse edition without at least one interesting link. Here’s the clearest articulation I’ve read on the mindsets, and tactics that drive outstanding execution. For example, 2 questions to be sure that you’re working on the right thing:

1/ What must urgently be fixed in order to bridge our “performance gap?”
2/ What must importantly be built in order to bridge our “opportunity gap?”

These two terms were coined by HBS professor Linda Hill. In a nutshell, bridging your performance gap means taking care of your existing business; if you’re Apple in 2000, your performance gap is the Mac. How do you keep taking market share in the PC industry?
Bridging your opportunity gap means pursuing the things your business could become. For Apple in 2000, this was the iPod. You never run out of performance gap to bridge, but you must still find a way to bridge the opportunity gap — because if you don’t do it, someone else will, and it will become your performance gap.

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (10y+ in product, startup co-founder, coach & advisor). Each issue features handpicked links to help you become a better product maker.

🏔️ Move fast / PM Snacks 85

Olivier Courtois — Thu, 14 Nov 2024 06:04:54 GMT

Hey everyone,

Sorry for the recent lack of newsletters, but I started to collaborate on an ambitious project that takes most of my time.

Don’t worry, I still found a cute place to work from, and spare time to share some new reads. Keep on reading 👇

Please support this newsletter by sharing it with colleagues or liking it (tap on the 💙).

Enjoy!

1️⃣ The magic of small engineering teams • Jul 2024 • 8 min read • #hiring
Posthog shares how they operate and move so fast (their product really evolves at an impressive pace). In a nutshell:
1. Small, Empowered Teams: Small, autonomous teams make fast, effective decisions without heavy oversight, driving agility. Each team has one identified leader.
2. Direct User Engagement: Teams interact directly with users, ensuring product updates align closely with real needs.
3. Concrete Goals: Clear, deliverable-based goals keep teams focused on tangible value rather than abstract metrics like increase conversion by 10%.

2️⃣ How data sharpened the focus of product teams • Feb 2023 • 7 min read • #data
Duolingo growth was stagnating in 2019, so the growth team went beyond the regular DAU metric, and created a model that segment active users in several categories:

By tracking the probabilities of users moving between different activity states, such as new users becoming current users or at-risk users reactivating, Duolingo could identify areas needing attention. The main lessons being: 1/ they’re cracked at data-driven development, 2/ we need to break down broad user metrics into actionable segments, that we can experiment on.

3️⃣ 0-1 Path to Product-Market-Fit • Jul 2024 • 6 min read • #execution
A long-time reader, Robin 👋, shared with me this slidedeck about ways to exit the “pain cave”, and embrace a pragmatic approach to product development.

The most interesting advice is to sell to learn. This “economic” interaction is the only way to reveal if the product genuinely meets customer needs, and gather accurate feedback. Rather than relying on broad customer segments, start by deeply understanding and working with a real customer. Sell once. And repeat.

🤖 Stop Writing All Your AI Prompts from Scratch • Oct 2024 • 4min read • #AI
The article suggests to use AI to write precise and targeted prompts. For activities you do regularly, you should create a “Blueprint” prompt that hold a lot of the necessary knowledge that you have about the task. Take a peek at the example they wrote for educators. Then, try to create Blueprints for product management activities like: simulating a stakeholder meeting where everything goes wrong, preparing for a product reviews by brainstorming tough questions, and more.

⏳ How we ship fast: our framework • Nov 2022 • 6 min read • #execution
Most clicked link one year ago in PM Snacks 66.
In order to improve impact, Lago optimised their process to move fast. How do they manage to do it? They focus on a select set of topics and more importantly, they invest time beforehand to improve the quality:
1/ Spend time on the framing: define the usuals (problem, use cases, success measure, etc.) then go deep on the tech part (backend behaviors, graphQL features, design for each screen, etc.)
2/ Ask the engineers to dive-in and scope down accordingly
3/ Review everything: framing, scope, code and test a lot

Be sure to open the link to access all their templates

—

Thank you for reading this far.
Until next time!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (10y+ in product, startup co-founder, coach & advisor). Each issue features handpicked links to help you become a better product maker.

🤓 My go-to content / PM Snacks 84

Olivier Courtois — Tue, 03 Sep 2024 08:05:47 GMT

Hey everyone,

I hope you had a wonderful summer!

Since you might be busy with “la rentrée”, instead of the usual format, here is a special edition of productverse. I’ll answer a question I often receive about my must-read resources on product building. Keep on reading.

Please support this newsletter by sharing it with colleagues or liking it (tap on the 💙).

Enjoy!
PS: if you want to ask me a question like Sylvain, hit reply!

Sylvain asked: Do you have any must-read, must-watch, must-listen (books, videos, podcasts)? How can I improve my product knowledge?

1️⃣ Use a “map” like the one I created back in comet. Auto-assess your current knowledge, then focus on what can be improved. If this map is too simple, be sure to check the Product Map (super exhaustive).

2️⃣ Break knowledge into mindsets, processes, and tools.

Source: this article on Medium (potentially paywalled)

3️⃣ Seek the best content available for each category 👇

General knowledge (= subscribe and follow, get this knowledge pushed to you on a regular basis).
- ✉️ Newsletters: Productverse 👋, ProductCompass, Lenny’s Newsletter, Reforge (notably artifacts), Product Manager HQ, Product Inbox (fr), Le Ticket (fr), Mozza Bytes (fr) …
- 🎙️ Podcasts: Lenny’s Podcast, Clef De Voute (fr), How’s work with Esther Perel, 20VC podcast, Dive.club (design) …
- 📢 Thought leaders: Sachin Rekhi, John Cutler, Shreyas Doshi, Marty Cagan, Teresa Torres, Melissa Perri, Roman Pichler, Gibson Biddle, Claire Vo …
- 🏭 Industry leaders: Andrew Chen (VC), Benedict Evans, a16z blog, Sequoia blog, First Round Review …
Cool tools (= test regularly tools, relevant to your product work or that can inform the way you think about your own product)
- Linear and their writing. What I like about them: opinionated tool, outstanding performance and to-the-point design.
- June and their writing. What I like about them: data but super actionable, use a lot of smart defaults. They compete in a crowded market by being different.
- Basecamp and their writing. What I like about them: clarity of thought, contrarian, deep knowledge of their users, dedicated to small businesses.
- The Browser Company and their writing. What I like about them: outstanding brand, craft lovers, optimizing for emotions and committed to building the future.
- Your next pick ;)

—

Thank you for reading this far.
Hit reply if you want me to answer one of your questions in a following edition!

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (10y+ in product, startup co-founder, coach & advisor). Each issue features handpicked links to help you become a better product maker.

Better job interviews / PM Snacks 83

Olivier Courtois — Wed, 10 Jul 2024 05:29:52 GMT

Hey everyone,

I started working on a side-project to help more product people become 10x contributors by leveraging genAI in their day-to-day tasks. Stay tuned for more.

🚨 You can help me out by jumping in a user-research call with me, just hit reply to let me know you’re interested 🚨. It’s going be useful for you:
- If you’ve been wanting to start using AI technologies but you’re not sure how
- If you’ve been feeling like ChatGPT and others are not that useful to you
- If you’re skeptical that these technologies can help at all (then you should read Lenny’s latest article to challenge your view or write to me)

Quick reminder: to support this newsletter, consider sharing it with colleagues or liking it (tap on the 💙). Enjoy!

1️⃣ Decoding Behavioral Interviewing: Strategies for Success • Apr 2024 • 8 min read • #hiring
If you’ve had a few job interviews, you might have a go-to answer to most questions. Read this article to level-up your game, and make sure you show off the right skill at the right time. Learn how to have a top-down approach (ask yourself what the company wants to assess and precisely answer it), tease out the noteworthy bits and add some color to your answer.

2️⃣ Get started on your KPI Tree • Oct 2023 • 9 min read • #data
I’m a big believer in KPI Trees. It’s a great way to articulate your operating model into a few metrics that are exhaustive and easy to understand, in order to spot opportunities. Read on to discover how to implement it, Louisa does a great job explaining it.

3️⃣ Family values rundown • Jul 2024 • 8 min read • #craft #design
An exceptional rundown on the user experience of the “Family” crypto wallet app. It’s a must-read if you value simplicity, fluidity and delight in products. They achieve an outstanding level of craft through motion and a context-preserving tray system.

We aim for a sense of dimensionality throughout the app. Our goal is to create a coherent journey that users are able to follow easily. Every movement feels like a logical step forward to resemble what we experience in physical spaces
[…]
My definition of a fluid interface is akin to moving through water — you float rather than walk through it. In practice, this means creating visible links between various screens, components, and features in a way that feels natural.

🤖 Hard truths bout the future of product mgmt • Jul 2024 • 23 min watch • #AI
Bit of context: Claire Vo is the definition of the super IC, she’s both the CPO of LaunchDarkly and creator of ChatPRD (a chatbot that helps you write product requirements docs).

There is a lot to unpack from this talk. She’s definitely onto something when it comes to the trend for the next decade:
- teams should become smaller (optimisations will be done by machines, each contributor will have 10x more impact)
- teams should be filled with generalists profiles “collapsing the talent stack”, aka. the super IC (see next article, shared a year ago)
- taste, decisiveness and ability to drive bold bets (w/ huge upsides) should become the basic expectation for our profession

Scary yet? If you don’t want to fall behind, consider jumping into a user research call with me (see the intro)

⏳ Collapsing the Talent Stack… & Designing Organizations for the Future • Jul 2023 • 7 min read • #productculture #leadership
Most clicked link one year ago in PM Snacks 74.
Okay, by now, you must have heard that Airbnb is “getting rid of the PM function”. Or at least, that’s how many people seem to have interpreted it.
1/ You should watch this 5 min video by their CEO to better understand their reasoning (it’s worth the watch!).
2/ According to me, the main highlight was not what they are doing to PMs but that they chose to put designers back in charge (around 2:45). Airbnb, much like Apple, is and will be a Design-led company.
3/ I’ve a theory: for each business, you need to understand your edge and how to win the market. Some companies compete by shipping the best experience/story (Airbnb, Apple, Nike), others compete on the flawless execution/speed (Amazon, Google), etc. With that knowledge, you need to implement an organisation that empowers the right teams to have the most impact. At Airbnb, it means, designers should be in charge. At Amazon or Google, engineers should be. And as a result, the other functions should adapt, especially product mgmt.
4/ If we go by the assumption that Airbnb will win by delivering the best experience and story, it makes sense to elevate designers to be product people equals. Designers solve problems by eliminating parts of the system and creating the best experience, Product people start by crafting the story and marketing for these experiences to shine, aka. the reason why they are blending traditional PM functions with PPM ones.
5/ Getting back to today’s article and the unfair advantage of “collapsing the talent stack”. Basically, to deliver faster and increase clarity, it’s better to seek generalists, people with extreme talents that can do several things at once. Teams where the design leader is also the product leader, or the front-end developer is also the designer, will generally craft better products and win. Actually most startups begin this way because of their limited resources, only to then grow, and turn into a place full of specialists. I encourage you to read the rest of Scott Belsky’s ideas on why organisations should change, it’s enlightening.

—

Thank you for reading this far.
Have a great summer break!

Olivier

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About → Productverse is curated by Olivier Courtois (10y+ in product, startup co-founder, coach & advisor). Each issue features handpicked links to help you become a better product maker.

🤌 Product Sense / PM Snacks 82

Olivier Courtois — Thu, 13 Jun 2024 05:36:53 GMT

Hey everyone, I’m back from my first holiday in two years. While it was great, can’t wait to get back to work.

In this edition, we will focus on the limits of data-informed decisions and how to build “product sense”. It’s a difficult skill to grasp, but honing it will enable you to move faster and more accurately. We could even argue that in a world where “software is ending” (see article #4), taste and intuition will become the most important skills.

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1️⃣ Why data-driven product decisions are hard (sometimes impossible) • May 2024 • 8 min read • #culture
Andrew Chen has been a long-time advocate for data-driven methods (his blog almost single-handedly started the growth movement when he was still at Uber), so it’s refreshing to read his thoughts on the limits of this mindset. He now argues that

This dominant culture is one of data, of optimization, of taking small concrete steps to move metrics that are big and easy to understand […] You can’t race their race. If you are data-driven in the same way they are, have the same dominant PM culture, and make decisions as slow as them, you will lose. […] Instead, you may have to do the opposite to compete:
mostly intuition rather than waiting for data
super fast decision-making rather than incremental decisions
big moves rather than small ones

I’m a believer in creating strong product sense in the team, in order to progress faster and be more audacious. You should always be working on one big bold bet at any point in time (where data is usually lacking and intuition is your best guide).

2️⃣ Product Sense Demystified • Oct 2022 • 4 min read • #culture
So what is Product Sense anyway? Marty Cagan explains that it’s just deep product knowledge:

developing it requires immersing ourselves in a specific product space
it’s not innate nor a skill you can apply to every product/industry. Success in one product space does not imply accurate product sense in other spaces.
it should not be seen as a substitute for testing product ideas or avoiding thinking from first principles. Some of the best innovations come from people willing to question dogma, & bringing fresh perspectives
it should lead to more accurate predictions, higher-quality ideas and faster decision making

3️⃣ How do I develop Product Sense? • Mar 2022 • 12 min read • #culture

Here are four key practices to develop your Product Sense:
1/ Observe people interacting with products to discover user needs and behaviors (= build empathy).
2/ Deconstruct everyday (or competitor) products to understand design decisions and user experiences (= build empathy).
3/ Learn from great product thinkers to understand their mental models and principles (= improve creativity). Yes, obviously it means you should keep reading this newsletter ;)
4/ Be curious about changes in technology and your domain to identify new opportunities (= improve creativity).

🤖 The End of Software • Jun 2024 • 4 min read • #strategy #AI
Are developers and PMs in the same position as journalists in the late 90’s? The author argues that genAI will drive the cost of creating software to 0, disrupting the dominant SaaS business model, the same way social media & UGCs destroyed the traditional media business model. It’s a provocative take that drove many interesting conversations on twitter (be sure to read the replies). I prefer Jens-Fabian opinion on this.

⏳ Permission To Fail • Dec 2015 • 6 min read • #mentalhealth
Most clicked link one year ago in PM Snacks 73.
We hear on a daily basis about budget cuts, failed fund-raising attempts, or even layoffs. Navigating through uncertain times can be frightening. So I wanted to share one of the most liberating things I read on giving yourself the permission to fail.

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A warm welcome to the 35 new subscribers, we are now 3524 readers strong! Thank you for reading this far.

Olivier

Share → Please help me promote this newsletter by sharing it with colleagues (tap the refer button) or consider liking it (tap on the 💙, it helps me a lot)!

About → Productverse is curated by Olivier Courtois (10y+ in product, startup co-founder, coach & advisor). Each issue features handpicked links to help you become a better product maker.

Productverse

⚡ Better Products, Not Just Faster Building / PM Snacks 95

🍭 Snacks

#1

#2

#3

#4

#5

🗄️ Recently saved

🧠 The AI Product Workspace / AI Bites 05

The AI Product Workspace (5 files to improve your agent’s quality)

Why your AI keeps forgetting

🛫 The 5-File Scaffold

1/ Company ID (company-id.md)

2/ Current Bet (current-bet.md)

3/ Users, Competitors, Guidelines

What NOT to put in your workspace

🎙️ Create the 5 files by talking

Enrich with Deep Research & existing files

🔄 Keeping it alive

Where automation helps

Level 1: Manual update loop

🪦 Bonus: Premortem

🪄 Show, Don't Tell / PM Snacks 94

🍭 Snacks

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🗄️ Recently saved

🗞️ Automating docs with Claude Code / AI Bites 04

How I automated my product documentation with Claude Code (and why you should too)

The problem: product docs always lagging behind

🛫 Maxime’s Daily Workflow

Using Claude Code as a documentation partner

Max-Doc-AI: automating docs end-to-end

How it works under the hood (without getting too technical)

How to use it (quick start)

What’s next

Three lessons learned building this tool

🧠 The Thinking Trap / PM Snacks 93

🍭 Snacks

#1

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AI Copilot with MCP / AI Bites 03

What is an MCP Server?

Which AI should you use as your copilot?

🛫 Backlog Autopilot

Improving a specific ticket

Turning a PRD into independent tickets

📣 Communicate Progress

Backlog Progress

Project Progress (Backlog & Meetings)

📈 Impact Era / PM Snacks 92

🍭 Snacks

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Ticket writer that really works / AI Bites 02

🤖 Prompter project (GPT-5 updates)

🎟️ Ticket Writer

How I use this prompt

Current prompt

💰 Money talks / PM Snacks 91

🍭 Snacks

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👶 Useful Prompts / AI Bites 01

1/ Company ID (`company-id.md`)

2/ Current Bet (`current-bet.md`)