Lenny's Newsletter

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

New: A free year of Cursor, Google AI Pro, Notion, Supabase, Gumloop, and Fin for subscribers

Lenny Rachitsky — Tue, 21 Apr 2026 15:03:33 GMT

👋 Hey there, I’m Lenny. Each week, I answer reader questions about building product, driving growth, and accelerating your career.

My mission with this newsletter (and podcast) is to give you the advice, community, and tools you need to build a world-class product and career. Because things are changing so fast, there’s a growing divide—in terms of career opportunities, financial outcomes, and how much you enjoy your work—between the people who benefit from the transformation we’re living through and those who get left behind. I want to make sure you’re in the first group.

To further that mission, I’m thrilled to announce that Lenny’s Product Pass now includes a free year of six additional incredible premium products:

Cursor* ($240 value): The best way to build software with AI.
Google AI* ($240 value): Higher access to Gemini, NotebookLM, Antigravity, Nano Banana, Flow, 5 TB of cloud storage, and more.
Notion ($240 value): Collaborative AI workspace where teams and agents work together.
Supabase* ($300 value): An all-in-one, easy-to-use, open-source managed Database, Auth, Storage, Edge Functions, Realtime, and Vector search.
Fin + Intercom (5 seats and $100/month in AI outcome credits, $7,140 value): Deliver perfect customer experiences with the #1 AI customer service agent.
Gumloop ($444 value): Build agents across all your apps and share them with your team. No engineering required.

→ Already a paid subscriber? Grab your free products here.
→ Not yet a subscriber? Subscribe and then grab your free products here.

I’m also super-excited to welcome back Replit and Gamma* to the Product Pass!

This is on top of the 17 premium products already available to subscribers: Canva Pro*, ChatPRD, ElevenLabs*, Factory, Framer, Granola, Linear, Lovable*, Magic Patterns, Manus, Mobbin, n8n*, PostHog, Railway, Stripe Atlas*, Warp, and Wispr Flow.

* These products are available for Insider-tier subscribers only

The total value of the Product Pass is now over $30,000. That’s 25 of the hottest, most important, and most beautifully crafted products in the world, free with your Lenny’s Newsletter subscription.

This all sounds way too good to be true, but it isn’t.

Many readers expense this subscription out of their workplace’s learning and development budget. Here’s an email you can send to your manager.

How to redeem these offers:

Step 1: Get an Annual or Insider subscription to Lenny’s Newsletter.
Step 2: Redeem your codes here.
Step 3: Have fun!

A note about how I hope you’ll approach these offers

The Product Pass is a community-powered collaboration. Many of these companies are brand-new and very excited for our community to try their products. Companies aren’t paying to be in the Product Pass, and I only include tools that I love and recommend. Similarly, my (very small) team is working hard to make this a seamless experience. Things will break, codes will run out, and I promise we’ll do our very best to resolve issues as fast as we can. Ultimately, my core offering is this newsletter, the podcast, and the community. These deals are the cherry on top.

What are the new products?

1. Cursor Pro* ($240 value)

What it is: The best way to build software with AI.

How people use it:

Building a personal OS (while building AI product sense)
Structuring AI workflows to build and ship real products (even as a non-technical builder)
Accelerating PR velocity and code quality at scale

2. Google AI Pro** ($240 value)

What it is: Get higher access to the newest and most powerful features in Gemini, Flow, and NotebookLM, as well as AI-powered assistance from Gemini directly in the Google apps you know and use. Plus, you get 5 TB of cloud storage to keep photos and files backed up.

How people use it:

Coding beautiful 3D pixel art based on a single image prompt
Building a physics-consistent ocean waves simulator
Using Nano Banana 2 to create video game backgrounds, UI design, home renovations, and highly detailed imagery

3. Notion Business ($240 value)

What it is: Collaborative AI workspace where teams and agents work together.

How people use it:

Running a Weekly Briefing agent that generates a summary of key updates, what’s coming up, and what needs attention that week
Building out an AI operating system for work, giving everyone at the company a personal Notion agent and cutting productivity-tool costs at Ramp
Using Notion as a lean “OS” where AI answers questions instantly, new hires onboard themselves, and every team builds exactly what they need at Cursor

Building a Scheduling Assistant agent that does meeting prep—automatically creating meeting notes, linking them to a CRM, and updating calendar events with links

4. Supabase Pro* ($300 value)

What it is: An all-in-one, easy-to-use, open-source managed Database, Auth, Storage, Edge Functions, Realtime, and Vector search.

How people use it:

Building an app to learn English
Crafting a tool to upscale images and generate videos
Hosting the database and analytics for a bulk URL shortener
Creating an open source app for finding a home for your food scraps

5. Fin AI Agent + Intercom Advanced (5 seats and $100/mo credits in AI outcomes, $7,140 value)

What it is: Deliver perfect customer experiences with an AI Customer Service Agent.

How people use it:

Transforming support operations for Anthropic
Supporting 50M users with 20 agents and Fin for Gamma
Involving Fin in 90% of support queries and resolving 50% over email, chat, and Slack for Clay

6. Gumloop Pro ($444 value)

What it is: An AI agent-building platform that makes it effortless for anyone on your team (not just engineers) to collaborate on and deploy agents. Describe exactly what tasks you want the agent to perform, when, and with which apps, and the agents will build themselves.

How people use it:

Using Gumloop agents to coach sales reps, prepare for renewal meetings, and monitor for clients at risk of churning at Gusto
Instantly answering data analytics questions and making visualizations with an agent that any team member can @ in Slack
Building a sales agent that can update your CRM and research and schedule meetings with customers
Creating an automated personal assistant agent integrated with Granola MCP that can draft its own emails, Slack messages, and Linear tickets
Running an entire global support operation by giving two humans a team of interconnected, specialized AI agents

Important offer details (read before subscribing):

For each product, you must be a new customer to take advantage of the free year. If you’ve already paid for one of the products yourself (monthly or yearly) or redeemed a code from one of our previous bundles, you won’t be able to get a free additional year of that specific product.
You must get an Annual or Insider subscription to Lenny’s Newsletter to be eligible for this offer. Monthly subscribers aren’t eligible.
Both existing and new subscribers of Lenny’s Newsletter are eligible for this deal. Existing subscribers can redeem the offer here.
We may run out of codes for a product at any time. Your code is only secured once you claim it. You need to claim each code individually, so hop on the products you are most excited about.
Your access to the Product Pass is contingent on your subscription remaining active. If you cancel or don’t renew your paid subscription at any time, your codes and active trials may be deactivated.
Your one free year of the product begins when you redeem the deal on the partner’s website, not when you purchase this newsletter subscription or claim your code.
All sales are final once you redeem your first code. We won’t issue refunds if a couple of products are out by the time you claim them. The deal is already so good, even if a couple of products are missing. If you request a chargeback for your subscription, all of your codes will be deactivated. We need to do this to avoid fraud.

To summarize, your paid subscription now includes:

A deeply researched newsletter and podcast with a 5+ year back catalog of evergreen content
A 30,000+ members-only global Slack community with local meetups, AMAs, mentorship programs, book clubs, and more
This growing collection of premium tools

It’s everything you need to build a world-class product and career.

X: https://x.com/brian_scanlan

* These products are available for Insider-tier subscribers only
** Google AI Pro’s $239.88 value is based on monthly recurring subscription price. Redeem the offer by December 31, 2026, at 11:59 p.m. PT. Cancel anytime. Gemini in Google AI Pro and Gemini for Gmail, Docs, and more are only available for ages 18+. Gemini for Gmail, Docs, and more is available in select languages. Existing Google One users can redeem this offer, but this free trial period cannot be added to an existing free trial. Users can only use one trial at a time, and promotion lengths are not stackable. If a user has an existing trial and redeems a new trial, their existing trial or subscription plan will be revoked and their new trial will begin, regardless of how much time they have left on their existing offer. The offer is not available to: Google One users subscribed to an add-on, Google One users subscribed to a higher-tier plan than the one being offered, Google One users on a family plan who are not the plan manager, Google One users who have a Google One subscription through a third party or affiliate. See full terms.

If you’ve already got a subscription, consider gifting a subscription to a colleague or purchasing a group subscription for your team.

Sincerely,

Lenny 👋

🎙️ This week on How I AI: How Intercom 2x’d their engineering velocity with Claude Code

Lenny Rachitsky — Mon, 20 Apr 2026 15:33:11 GMT

How Intercom 2x’d their engineering velocity in 9 months with Claude Code | Brian Scanlan

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Celigo—Intelligent automation built for AI
Cursor—The best way to code with AI

Brian Scanlan, Senior Principal Engineer at Intercom, breaks down how the company doubled engineering throughput in just nine months by going all-in on Claude Code. He shares how custom skills, deep telemetry, and a culture of permission turned AI into a true force multiplier.

Biggest takeaways:

Treat your engineering org like a product, and instrument everything. Intercom tracks skill invocations in Honeycomb, stores anonymized Claude Code sessions in S3, and built custom dashboards that show engineers how they compare to peers. This isn’t surveillance—it’s the same product thinking you’d apply to customer-facing features. You can’t improve what you don’t measure, and you can’t scale AI adoption without visibility into what’s working and what’s breaking.
The 2x velocity gain is real, but only if you prepare your foundation. Intercom doubled their merged PRs per R&D employee in nine months, but they already had mature CI/CD, comprehensive test coverage, and a high-trust culture. AI magnifies your strengths and weaknesses—if your deployment pipeline is broken or your code review process is manual chaos, AI will just help you ship broken code faster. Fix your fundamentals first, then pour gasoline on the fire.
Custom skills with hooks enforce quality at the point of creation, not after the fact. Intercom’s “Create PR” skill blocks Claude Code from using the GitHub CLI directly and forces it to write context-rich PR descriptions instead of just regurgitating code. Build guardrails that make the golden path the only path.
Code quality improves when you ship twice as fast because you finally have capacity for tech debt. Intercom’s partnership with Stanford researchers shows their code quality metrics are going up, not down. When the cost of fixing flaky tests, improving developer experience, and tackling technical debt compresses to near-zero, you can actually do those things instead of just talking about them in retros. The business constraint on internal projects disappears when agents can execute them in hours instead of quarters.
The most important job of technical leadership in the AI era is giving permission and taking accountability. Brian’s framework is simple: Tell people they can do things, and if anything goes wrong, blame me. Engineers don’t need more tutorials or documentation; they need permission to connect Claude Code to Snowflake, to ship code from their phone on the subway, to build a CLI that bypasses email verification. The activation energy for experimentation is cultural, not technical.
Make your product agent-friendly or watch customers build it themselves. Brian built an Intercom CLI that can autonomously sign up for Fin, verify email addresses by accessing Gmail, and complete installation without human intervention. If you don’t build this, your customers’ agents will just brute-force your website, burn more tokens, get frustrated, and eventually press escape and build it themselves. The switching cost is literally one keystroke. Your conversion funnel is now invisible, and your drop-off point is “forget it, let’s do this a different way.”
All work will become agent-first, and you should set a deadline for it. Brian’s vision is that by the end of any given month, the first response to an alarm, a planning meeting, or a customer question should be an agent doing the basic work. This isn’t aspirational—it’s a realistic expectation given the current state of models and harnesses. The bottleneck isn’t the technology; it’s organizational willingness to reimagine workflows from first principles and give people permission to experiment.

Detailed workflow walkthroughs from this episode:

How Intercom Doubled Engineering Output: Brian Scanlan’s 4 AI Workflows for Claude Code: https://www.chatprd.ai/how-i-ai/how-intercom-doubled-engineering-output-brian-scanlan-ai-workflows-for-claude-code
Design an Agent-Friendly CLI to Automate SaaS Product Onboarding: https://www.chatprd.ai/how-i-ai/workflows/design-an-agent-friendly-cli-to-automate-saas-product-onboarding
Build a Self-Improving AI Agent to Automatically Fix Flaky Tests: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-agent-to-automatically-fix-flaky-tests
Automate High-Quality Pull Request Descriptions with a Custom AI Skill: https://www.chatprd.ai/how-i-ai/workflows/automate-high-quality-pull-request-descriptions-with-a-custom-ai-skill

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

How Intercom 2x’d their engineering velocity in 9 months with Claude Code | Brian Scanlan

Claire Vo — Mon, 20 Apr 2026 12:03:25 GMT

Brian Scanlan is a senior principal engineer at Intercom, where he’s led the company’s transformation to AI-first engineering. In just nine months, Intercom doubled their R&D throughput while maintaining code quality, with 100% of engineers—plus designers, PMs, and TPMs—now shipping code via Claude Code.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

How Intercom doubled their merged PRs per R&D employee in just nine months using Claude Code
The telemetry infrastructure they built to measure AI adoption and quality across hundreds of engineers
Why they built a skills repository with hooks that enforce engineering standards automatically
How they’re preparing their product for an agent-first world with CLIs, MCPs, and ephemeral APIs
The permission and accountability framework that enabled rapid AI adoption
Why backlog zero is now achievable and what that means for engineering culture

Brought to you by:

Celigo—Intelligent automation built for AI

Cursor—The best way to code with AI

In this episode, we cover:

(00:00) Introduction to Brian Scanlan

(02:40) Why Intercom went all-in on AI for both product and engineering

(05:01) The breakthrough moment with Opus 4.6 and Christmas break 2025

(07:02) Demo: Intercom’s merged PRs per R&D head

(12:50) Agent-first work as a fundamental reimagining of technical workflows

(14:27) The cost tradeoff: treating AI spend as an investment

(16:47) Measuring quality

(21:22) Demo: Shipping a redirect in the Rails monolith with Claude Code

(24:03) Creating a custom PR skill

(26:33) Building a software factory with predictable quality standards

(30:15) Telemetry infrastructure: Honeycomb for skill usage tracking

(32:10) Session data collection and personalized usage insights

(36:08) Quick overview

(39:20) Walking through Intercom’s skills repository

(42:16) Deep dive: The flaky spec skill and how it reached 100x capability

(46:44) The “and then” workflow for building comprehensive skills

(52:31) The live website and overview of workflows

(53:32) How internal AI experience informs customer product decisions

(56:18) Making SaaS products agent-friendly with CLIs and helpful hints

(01:03:49) Why conversion drop-off is invisible in agent-driven workflows

(01:05:28) Lightning round and final thoughts

Tools referenced:

• Claude Code: https://claude.ai/code

• Cursor: https://cursor.com/

• Honeycomb: https://www.honeycomb.io/

• Snowflake: https://www.snowflake.com/

• Fin AI: https://www.intercom.com/fin

• Vercel: https://vercel.com/

Other references:

• Intercom GitHub Repo: https://github.com/intercom

• Google API Go Client Repo: https://github.com/googleapis/google-api-go-client

Where to find Brian Scanlan:

LinkedIn: https://www.linkedin.com/in/scanlanb/

Company: https://www.intercom.com

Where to find Claire Vo:

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google)

Lenny Rachitsky — Sun, 19 Apr 2026 12:31:44 GMT

Nikhyl Singhal is the founder of The Skip, a community for senior product leaders; a former product exec at Meta, Google, and Credit Karma; and a many-time founder. He’s also one of the most honest, unfiltered voices on what’s actually happening in product management right now.

Listen on YouTube, Spotify, and Apple Podcasts

In our in-depth conversation, we discuss:

Why the next two years will be the most chaotic period in product management history
Why half of current product managers are at risk, and what separates those who’ll do well
Why you need to find your “moments of joy” with AI
The “smiling exhaustion” he’s seeing across the product community
The psychological barriers that prevent people from reinventing themselves
Why your resume’s fancy logos matter less than ever, and what matters now
His prediction that companies will shed 30,000 people and rehire 8,000—all AI-first

Brought to you by:

WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs
Vanta—Automate compliance, manage risk, and accelerate trust with AI

Where to find Nikhyl Singhal:

• LinkedIn: https://www.linkedin.com/in/nikhyl

• X: https://x.com/nikhyl

• Podcast & Newsletter: https://skip.show

• Skip Community: https://skip.community

• Skip Coach: https://skip.coach

• Skip.help: https://skip.help

Referenced:

• Building a long and meaningful career | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/building-a-long-and-meaningful-career

• COBOL: https://en.wikipedia.org/wiki/COBOL

• United Airlines: https://www.united.com

• State of the product job market in early 2026: https://www.lennysnewsletter.com/p/state-of-the-product-job-market-in-ee9

• Head of Growth (Anthropic): “Claude is growing itself at this point” | Amol Avasare: https://www.lennysnewsletter.com/p/anthropics-1b-to-19b-growth-run

• Demis Hassabis on X: https://x.com/demishassabis

• Sam Altman on X: https://x.com/sama

• Dario Amodei on X: https://x.com/DarioAmodei

• Cross on Prime Video: https://www.amazon.com/Cross-Season-1/dp/B0D6X7ZZHC

• Jack Ryan on Prime Video: https://www.amazon.com/Tom-Clancys-Jack-Ryan/dp/B0CNDCMN8R

• 24 on Prime Video: https://www.amazon.com/24-Season-1/dp/B000HPF85A

• Claude Code: https://code.claude.com

• Codex: https://chatgpt.com/codex

• Lovable: https://lovable.dev

• Sonos: https://www.sonos.com

• “There are only four jobs” on X: https://x.com/yrechtman/status/2039012253341495462

• Paradise on Hulu: https://www.hulu.com/series/paradise-2b4b8988-50c9-4097-bf93-bc34a99a5b4f

• Lioness on Paramount+: https://www.paramountplus.com/shows/lioness

• Tesla: https://www.tesla.com

• Albert Einstein’s quote: https://www.goodreads.com/quotes/115696-genius-is-1-talent-and-99-percent-hard-work

Recommended books:

• James: https://www.amazon.com/James-Novel-Percival-Everett/dp/0385550367

• The Adventures of Huckleberry Finn: https://www.amazon.com/Adventures-Huckleberry-Finn-Unabridged-Uncensored/dp/195483943X

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

🧠 Community Wisdom: Sharing Claude Code skills across the org, Why did lennysjobs.com shut down, opening an LLC for side work, AI for SMB sales, and more

Kiyani — Sat, 18 Apr 2026 18:38:18 GMT

👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

Listen: Not all AI agents are created equal

Lenny Rachitsky — Wed, 15 Apr 2026 03:45:04 GMT

If you’re a premium subscriber

Add the private feed to your podcast app at add.lennysreads.com

In this episode, you’ll learn:

Why prioritizing AI agent initiatives is so hard, and why familiar planning tools like impact-effort matrices break down
The three architectural categories every agent falls into
How to choose the right platform for each category
Success metrics and ROI frameworks tailored to each architectural type
How to quickly course-correct when you’ve picked the wrong architecture

Listen now: YouTube | Apple | Spotify

Read the post

Not all AI agents are created equal

Hamza Farooq — Tue, 14 Apr 2026 12:46:08 GMT

👋 Hey there, I’m Lenny. Each week, I answer reader questions about building product, driving growth, and accelerating your career. For more: Lenny’s Podcast | Lennybot | How I AI | My favorite AI/PM courses, public speaking course, and interview prep copilot

X: https://twitter.com/jjenglert

P.S. Get a full free year of Lovable, Manus, Replit, Gamma, n8n, Canva, ElevenLabs, Amp, Factory, Devin, Bolt, Wispr Flow, Linear, PostHog, Framer, Railway, Granola, Warp, Perplexity, Magic Patterns, Mobbin, ChatPRD, and Stripe Atlas by becoming an Insider subscriber. Yes, this is for real.

Agents are so hot right now. Every other day, someone’s launching a new one or a new tool to manage them. I bet your team has a half-dozen agent ideas on your backlog right now. None of this means you actually need to build an agent today. But it does mean that you need to understand how agents fit into your broader strategy, and what the right investment looks like.

Hamza Farooq and Jaya Rajwani teach two of the most highly rated and well-respected courses on building AI agents (Agent Engineering Bootcamp and Agentic AI for PMs) and spent over 50 hours putting this guide together. By the time you finish reading this post, you’ll understand the three types of agents, how to decide which initiatives to prioritize, and how to avoid common pitfalls—with specific recommended tools and platforms and tons of real-life examples.

Let’s get into it.

P.S. You can listen to this post in convenient podcast form: Spotify / Apple / YouTube.

Over the past year, we’ve had the same conversation at least 30 times. An AI leader pulls up their roadmap, usually 5 to 10 “agent” initiatives, and says, “Help us figure out which one to build first.”

The list usually includes a PM assistant, a RAG copilot, a customer support system, a code review agent, and a voice-enabled shopping assistant

If you’re reading this, you probably have a similar list. Your team is energized, investors are asking about it, competitors are announcing agent launches. You need to pick something and ship it.

That’s where most teams get stuck. The problem isn’t that they lack ideas; it’s that they try to prioritize fundamentally different kinds of systems as if they were the same thing. The usual approach is to reach for familiar planning tools. Teams open an impact-vs.-effort matrix and try to compare ideas side by side.

But with AI agents, that quickly falls apart. One “agent” might take six weeks to build. Another might take six months. One can be assembled by a product manager using n8n. Another requires a dedicated ML engineering team. One costs $500 per month to operate. Another could generate a six-figure annual LLM bill.

A customer support assistant and a voice-enabled shopping agent may both be called agents, but they demand different architectures, different teams, different infrastructure, and different timelines. Until you recognize those differences, any attempt to compare “effort” or “impact” is essentially guesswork.

Treating architecturally different products as if they’re in the same category makes effective prioritization nearly impossible. Prioritization breaks not because teams are bad at planning but because they’re comparing apples, oranges, and jet engines on the same spreadsheet.

The missing step is hierarchy

Before you can decide which agent to build first, you need to answer a more basic question: What type of agent is each idea actually proposing?

This will determine almost everything that matters for planning:

How complex it will be to build
What skills and infrastructure are required
How long it is likely to take
How expensive it will be to operate
How you should measure success

In other words, categorization isn’t just a technical exercise. It’s the foundation for smart prioritization.

This post gives you a decision framework you can start using today with your current roadmap.

We developed this framework from patterns we’ve seen while helping organizations turn agent ideas into real production systems. Working with enterprise teams across Fortune 500 companies such as Jack in the Box, Tripadvisor, and The Home Depot, we found that grouping ideas by their underlying architecture unlocks prioritization and significantly speeds up the development and launch process. These distinctions also mirror how the broader industry is beginning to classify AI agents, from automation workflows to reasoning systems and multi-agent networks (like the Levels of Autonomy for AI Agents paper and Types of AI agents by IBM). These are also the foundations of how massively popular tools like OpenClaw and Claude Code are actually architected.

If you’re staring at a backlog of agent ideas trying to figure out what to build first, here’s what you’ll have by the end of this post:

A 5-minute triage process to categorize every agent idea into one of three architectural types
A guide to picking the right tool/platform for your project (i.e. when to use n8n vs. LangGraph vs. ADK)
Success metrics and ROI frameworks tailored to each architectural type
Warning signs that you’ve picked the wrong path (and how to fix it)

You’ll be able to look at your backlog and know which ideas can ship in six weeks for quick ROI, which need three months but will drive significant revenue growth, and which are a six-month bet that only makes sense with the right resourcing and expectation setting.

All by first recognizing that “agent” is an umbrella term for very different kinds of systems.

The three agent categories

Every “agent” idea falls into one of three architectural categories.

Category 1: Deterministic automation
You define the entire flow. AI handles content at specific steps. Think: n8n or Zapier workflows with LLM nodes. This is where the majority of agent opportunities belong and where most teams should start. These projects are fastest to launch and deliver measurable ROI quickly.

Category 2: Reasoning and acting agents
AI decides what to do next, using available tools. Think: Cursor, Lovable, or agents built with LangGraph, CrewAI, Google ADK, etc. These initiatives typically come after Category 1, when higher-value problems require flexibility and dynamic decision-making that workflows alone can’t handle.

Category 3: Multi-agent networks
Multiple specialized agents coordinate with each other. Think: enterprise systems built with ADK or AutoGen. These projects are typically reserved for later stages, when multiple teams must coordinate across domains, and should almost never be the starting point on a roadmap.

Some examples of “agents” that fit into each category to help you understand the differences:

Organizations often try to build Category 1 problems with Category 2 frameworks—overengineering solutions that add unnecessary complexity and cost. Less frequently but with worse outcomes, they try to solve Category 2 problems with Category 1 tools, and it breaks in production because the tool is not robust enough.

Let’s take a deeper dive into each category, starting with the workhorse, Category 1.

Category 1: Deterministic automation

What this is

These are workflows where you define every step, every branch, every decision point. An LLM handles natural language understanding and generation at specific nodes, but you control the flow. Think of them as intelligent flowcharts where you design the path and AI handles the content.

Tools most commonly used for deterministic automation are n8n, Zapier, Make.com, OpenAI AgentKit, Lindy, and Gumloop. These tools are built around explicit triggers and predefined branching logic. You define the workflow, while LLMs are used only for classification, extraction, or drafting within those boundaries.

How to prioritize Category 1 products

If your backlog includes a mix of agent ideas, Category 1 projects are almost always the smartest place to begin. These initiatives tend to be the simplest to plan and the lowest-risk to execute.

They’re best suited to situations where the process is already well-defined and the goal is to automate repetitive, high-volume work. If you need quick, measurable ROI, have limited AI engineering capacity, or are under pressure to deliver results in weeks rather than months, Category 1 projects are almost always the right starting point.

Most initiatives in this category share a similar profile across certain criteria:

That combination of fast timelines, modest resources, and clear business impact is what makes Category 1 initiatives such powerful early wins. They generate near-term value while building organizational confidence for more advanced efforts later.

What types of products fall in this category

If you can map the entire process as a flowchart with clear decision points, a product belongs in Category 1. Here are some more traits of a Category 1 product:

Execution paths are finite and predictable (fewer than 15 to 20 branches)
Task completion needs to happen in seconds to minutes
The value is in automating a known process, not discovering new approaches

In our experience with customers, this covers 60% to 70% of agent opportunities. Revisiting the typical list of opportunities I mentioned above, here is a great example of a Category 1 product: “We need an AI agent to handle incoming customer emails, read them, understand what they’re asking, pull relevant information from our docs, draft replies, and route to our team for approval.”

At first, this sounds like it needs sophisticated reasoning. But when you map out what actually needs to happen, it’s remarkably deterministic:

Every step is predictable. The “intelligence” is in understanding the email and generating a good response, not in figuring out what to do next. This is Category 1.

There are a ton of great examples of automation agents; here’s one built by me.

I love Airbnb, but I hate spending long hours finding the best ones, so I built an agent that will take my exact request for, e.g., “Modern apartment in Paris near train stations from 20th March to 26th March. Great for a couple” (more than 10,000 users have used it) and run a search. Here’s how you can build your own.

Other examples of Category 1 “agents”:

a travel planning agent
a voice-enabled book companion agent
a content automation agent (YouTube → LinkedIn)
a knowledge base of your organization with internet search (Perplexity clone)
an agent that generates deeply researched blogs, based on a given topic
a highly personalized calorie counter app that allows you to upload images of your meals to keep track of your daily caloric intake and recommends better dietary choices and exercises

How to evaluate Category 1 products

The metrics below are designed to answer a simple question: Did this agent automate the right process, or should this idea be reconsidered or re-scoped?

A deterministic agent built for the email automation process can be evaluated as follows:

Workflow completion rate: % of executions that finish successfully
Automation rate: % of requests handled without human intervention
Accuracy: correctness of intent classification, data extraction, and routing decisions
Latency: time from trigger to final output (P50/P95 if relevant)
Cost per workflow: total LLM and API cost per completed run
Error rate: % of runs failing due to tool, integration, or system errors
Human review rate: % of runs requiring manual approval or intervention

Here are workflow completion rate metrics from a real-life example of a Category 1 product, an email support agent built by a SaaS company we worked with:

Week 1: 52% completion rate (lots of edge cases discovered)
Week 4: 78% completion rate (refined classification logic)
Week 8: 87% completion rate (stable, production-ready)
Result: 3,000 support emails/month automated, 2.5 FTE hours/day freed, $18K/month savings

When these metrics stabilize and cost trends downward, the workflow is doing what it should. If completion remains low or manual intervention stays high, the problem may not be deterministic enough for this category.

How to know you’ve outgrown Category 1

You’ll know you need a different architecture when:

Your flowchart has 30+ nodes and you’re adding new branches every week
Customers phrase things in ways you can’t anticipate, and mapping all variations is impossible
The agent needs to decide which API or knowledge source to use based on context, not follow a predetermined path
Breaking down ambiguous requests requires exploration and adaptation, not predefined decomposition
The highest-value opportunities can no longer be expressed as predictable workflows
Most quick-win processes are already automated

If several of these signals are present at once, the problem is no longer a good fit for a deterministic workflow, and you should consider Category 2.

Category 2: Reasoning and acting agents (ReAct)

What this is

Instead of defining the flow, you define the available tools, and an LLM autonomously decides what to do next. The agent operates in a loop: observe → reason → act → observe result → repeat.

The key characteristic: you control the tools; the LLM controls the reasoning.

Tools most commonly used for building ReAct agents include LangGraph, CrewAI, AutoGen, and other agent orchestration libraries that support tool use, memory, and dynamic planning.

How to prioritize Category 2 products

Category 2 is for situations where user requests are ambiguous, workflows cannot be mapped in advance, and real value comes from flexible, contextual decision-making. If you need agents that can reason across multiple tools, handle conversational interactions, or adapt dynamically to new inputs, that’s a Category 2 product.

Category 2 products are more complex to plan and carry higher execution risk than Category 1. Most initiatives in this category share a similar profile:

The combination of longer timelines, specialized expertise, and higher costs is what makes Category 2 initiatives powerful but more demanding than Category 1. If your backlog includes problems that truly require reasoning and dynamic behavior, prioritizing Category 2 projects becomes essential. They unlock use cases that deterministic automation cannot handle and enable more advanced, high-impact agent experiences.

What types of products fall in this category

A product belongs in Category 2 if the same user request can trigger different action sequences every time. That means that you don’t determine the path; the LLM does. That’s the key difference from Category 1. Here are some more traits of a Category 2 product:

The same high-level task requires different sequences of actions depending on input
You have 5-15+ distinct capabilities and the right one depends on context
User intent is ambiguous and needs clarification through interaction
Multiple input modalities (voice, image, text) need to be understood contextually
Breaking down complex requests into sub-tasks is part of the value

In our work with customers, this is the right choice for 25% to 30% of agent opportunities. For an example of this type of product, let’s return to the voice-enabled shopping assistant opportunity from the start of this post.

Customers should be able to search products by voice, upload images to find similar items, check order status, update preferences, and initiate returns, all through conversation.

At first, this sounds like Category 1. Just map out the intents and route accordingly, right? But in practice, real conversations don’t follow fixed paths. To see why, let’s walk through one interaction.

A customer uploads a photo of shoes and says:
“These are too small. I need a size up, and I want them delivered by Thursday.”

Here’s what happens under the hood:

Observe. The agent receives mixed input: an image + voice request.
Reason. It determines:
- First, identify the product in the image
- Then, find available size variants
- Then, check delivery dates
- Finally, confirm the order with the user
Act. The agent dynamically selects tools:
- visual_search() → identify product
- check_inventory() → find size-up availability
- get_delivery_options() → verify Thursday delivery
- place_order() → after confirmation
Observe result → reason again. Each tool response updates the agent’s state and influences the next step.

This sequence cannot be pre-defined.

If the item is out of stock, the agent may suggest alternatives.
If Thursday delivery isn’t available, it may propose pickup.
If the image can’t be recognized, it asks a clarifying question.

The same user request triggers different action sequences based on reasoned considerations.

Other examples of Category 2 “agents”:

a conversational customer support agent
a code assistant that modifies repositories, e.g. Claude Code
an intelligent personal shopping assistant
an IT troubleshooting agent
a sales copilot that researches accounts and drafts outreach
a multimodal assistant combining voice, image, and text

How to evaluate Category 2 products

Reasoning agents should be evaluated on whether they help users achieve their goals across variable paths, while remaining efficient enough to justify their cost.

These metrics answer the question: Was dynamic reasoning necessary, or should the problem be simplified to a lower category?

Task completion rate: % of sessions where users achieve their intended goal
Reasoning accuracy: correctness of task decomposition, tool selection, and decision ordering
Conversation length: average turns to resolution
Multimodal accuracy: correctness of image, voice, or structured input interpretation (if applicable)
Tool call efficiency: average number of tool calls per successful session
Latency: time per turn and end-to-end session duration
Cost per session: total LLM and API cost per completed interaction
User satisfaction: post-interaction CSAT or equivalent signal
Business impact: lift in conversion, retention, or task success versus baseline

Here are some metrics from a real-life example, a voice + image shopping assistant for a home goods retailer we built:

Month 1: 71% task completion, longer conversations, higher tool usage, $0.12 cost per session
Month 4: 86% task completion, shorter conversations, fewer tool calls, $0.08 cost per session

Result: Image identification accuracy improved from 76% to 91%, conversion lift increased from +8% to +22%, and CSAT rose from 4.0 to 4.5.

When task completion improves while conversation length, tool usage, and cost per session decline, the agent’s reasoning loop is adding value. If performance stalls while costs remain high, the problem may be over-scoped or better served by the deterministic approach of Category 1 tools.

How to know you’ve outgrown Category 2

You’ll know you need a different architecture when:

Your single agent is trying to handle too many domains (customer service + inventory + logistics + finance) and performance is degrading
You need agents to delegate tasks to each other, not just call stateless APIs. Example: A shopping agent needs to ask an inventory agent, “Can you check all warehouses and suggest alternatives?”
Tasks take hours or days to complete (like the automated eval agent analyzing 10,000 conversations overnight)
You need hundreds of agent instances running in parallel, coordinating work among them
Different teams want to own their specialized agents, but they need to work together

If you’re hitting two to three or more of these, it’s time to consider Category 3 tools and approaches.

Category 3: Multi-agent network

What this is

Instead of one agent calling tools, you have multiple specialized agents that coordinate with each other. Each agent is owned by a different team, handles its own domain, and can request help from other agents.

🎙️ This week on How I AI: Claude Cowork tutorial for non-engineers + Build your own Slack inbox (for $0)

Lenny Rachitsky — Mon, 13 Apr 2026 15:07:46 GMT

I built a custom Slack inbox. It was easier than you think. | Yash Tekriwal (Clay)

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Guru—The AI layer of truth
ThoughtSpot—Build AI-powered analytics into your product

In this episode, Claire talks with Yash Tekriwal, the head of education at Clay, about how he built a custom AI-powered Slack inbox that turns 150 daily notifications into around 30 that actually matter. Instead of reacting to messages, Yash designed a system that categorizes, prioritizes, and routes everything into a clean, actionable workflow. They walk through exactly how he built it using Perplexity Computer and OpenClaw, why most people are using AI wrong (doing tasks instead of building systems), and how anyone—technical or not—can start creating their own personalized software stack.

Biggest takeaways:

Yash receives 100 to 150 Slack notifications daily, but only 30 to 40 require real action. By categorizing messages into DMs, group mentions, threads, and channel mentions, then sub-categorizing into action-required, need-to-read, and FYI, he transformed anxiety-inducing notification overload into a manageable workflow.
Use AI to build deterministic tools, not just to do tasks. There’s a crucial difference between asking AI to categorize things repeatedly versus using it to build code that handles structured data through APIs. Yash used OpenClaw to build a Slack digest that pulls notifications via API endpoints—AI built the tool once, but the categorization runs on deterministic code (except for the final action/read/FYI sorting).
Perplexity Computer’s multi-model orchestration removes you from the loop. Unlike Claude Code or Codex, Perplexity automatically uses different models for different subtasks: Sonnet for fetching data, Gemini for planning and coding, Opus for complex builds. This eliminates the frustrating back-and-forth of “good try, but it doesn’t work; try again” that plagues single-model coding agents.
Perplexity Computer’s cloud deployment and native connectors are game changers. Unlike local coding agents, Computer runs in the cloud with pre-authenticated connectors to Gmail, Slack, Notion, Asana, and more. Apps are automatically deployed and shareable via URL—no GitHub repos, no Vercel deployment, no technical overhead. This makes it accessible to non-technical builders.
The anti-to-do list framework: spend an hour daily automating what you never want to do again. Instead of a to-do list, maintain an anti-to-do list of tasks you despise (manually deleting email spam, entering meeting action items into Asana, triaging unread Slack messages). Dedicate time to systematically eliminate these tasks through automation.
SaaS isn’t dying; it’s about to explode into micro-software. Yash says the narrative that AI will kill SaaS is backward. Instead, we’ll see a Cambrian explosion of specialized software built on top of existing platforms. Yash would happily pay $15 a month for someone to maintain his Slack digest as a product—and thinks thousands of similar micro-businesses will emerge serving narrow use cases that were never venture-scale before.
The future of productivity is personalized software. Every knowledge worker will eventually have custom apps optimized for their specific mental model and workflow. These won’t replace SaaS platforms but extend them, filling the gap between what Slack/Notion/Asana provide and what each individual actually needs to work at their best.

Detailed workflow walkthroughs from this episode:

How I AI: Yash Tekriwal on Taming Slack with a Custom AI-Built Dashboard: https://www.chatprd.ai/how-i-ai/taming-slack-with-a-custom-ai-built-dashboard
How to Turn a Text Digest into an Interactive Kanban Dashboard: https://www.chatprd.ai/how-i-ai/workflows/how-to-turn-a-text-digest-into-an-interactive-kanban-dashboard
How to Prototype a Website Redesign Using an AI with Browser Access: https://www.chatprd.ai/how-i-ai/workflows/how-to-prototype-a-website-redesign-using-an-ai-with-browser-access
How to Build a Custom AI-Powered Text Digest for Slack Notifications: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-custom-ai-powered-text-digest-for-slack-notifications

Claude Cowork 101: How to automate your workday without touching code | JJ Englert (Tenex)

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Tines—Start building intelligent workflows today
Cursor—The best way to code with AI

In this episode, Claire talks with JJ Englert, the enablement and community lead at Tenex, about how he uses Claude Cowork to build a fully automated daily operating system that drafts emails, reviews his work, plans his day, and coordinates across his tools. They walk step-by-step through how to set it up—from creating a “brain” file that teaches AI how you think, to building reusable skills that write exactly like you, to running daily briefings that prepare your entire day before you even open your laptop.

Biggest takeaways:

Cowork is the first real bridge for non-technical knowledge workers to move beyond chat and actually have AI do things for them. While chat tools like ChatGPT tell you what to do, Cowork actually does it: drafting emails, organizing files, managing your calendar, and more. It’s the middle ground between passive chat and full coding in Claude Code, and it’s designed for people who want AI to be productive without learning terminal commands.
Start every project with a “brain” file that tells Claude who you are, how you work, and who you work with. JJ’s secret weapon is a Markdown file that contains detailed instructions about his working preferences, team members, communication style, and more. Every time he starts a new task in that project, Claude reads this file and immediately understands his context. This eliminates the need to re-explain yourself every time and ensures consistent, personalized results. It’s like having a coworker who already knows everything about you.
Always include good examples and bad examples when building skills. AI doesn’t know what success looks like for you unless you show it. When you get an output you love, save it as a good example. When you get something off-brand or low-quality, save it as a bad example with a note: “Don’t do this again.” Over time, Claude learns your subjective preferences and delivers increasingly consistent results.
Build an anti-to-do list—things you never want to do again—and then automate them with skills. JJ recommends thinking about the repetitive tasks that drain your energy: first-drafting emails, preparing for meetings, writing social posts, organizing files. Each of those becomes a skill. Over time, you build a library of skills that handle the mundane work, freeing you to focus on creative and strategic decisions. The goal isn’t to eliminate your job; it’s to eliminate the parts of your job you hate.
Cowork teaches you skills that transfer directly to Claude Code. JJ uses Cowork for business productivity and Claude Code for building. But Cowork is a great on-ramp because it introduces concepts like projects, skills, and file management without requiring you to use the terminal. As you get comfortable orchestrating agents and managing context in Cowork, you’re learning the mental models that make you effective in Code.
Projects don’t have to be work-related—use them for house maintenance, wedding planning, recruiting, or anything else. You can create a house maintenance project with reminders to change air filters, seasonal checklists, and remodel plans. Whether a wedding planning project that finds vendors, drafts emails, and manages timelines, or a recruiting project with job descriptions, interview guides, and onboarding docs, the pattern is the same: organize context in a folder, connect it to Cowork, build skills, and let AI do the work.

Detailed workflow walkthroughs from this episode:

How I AI: JJ Englert’s Guide to a ‘Daily Operating System’ with Claude Cowork: https://www.chatprd.ai/how-i-ai/jj-englerts-guide-to-a-daily-operating-system-with-claude-cowork
Build a Multi-Persona ‘Sub-Advisory Board’ for Instant Feedback: https://www.chatprd.ai/how-i-ai/workflows/build-a-multi-persona-sub-advisory-board-for-instant-feedback
Train Claude Cowork to Write Emails in Your Personal Style: https://www.chatprd.ai/how-i-ai/workflows/train-claude-cowork-to-write-emails-in-your-personal-style
How to Set Up a ‘Daily Operating System’ in Claude Cowork: https://www.chatprd.ai/how-i-ai/workflows/how-to-set-up-a-daily-operating-system-in-claude-cowork

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

Claude Cowork 101: How to automate your workday without touching code | JJ Englert (Tenex)

Claire Vo — Mon, 13 Apr 2026 12:03:52 GMT

JJ Englert leads community enablement at Tenex. In this episode, JJ provides a complete zero-to-one tutorial on Claude Cowork, Anthropic’s desktop tool that sits between simple chat and full terminal-based coding.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

How to create your first Claude Cowork project by connecting a folder on your computer and building context over time
The “brain” file strategy: how to create a preferences document that Claude reads every time to understand who you are and how you work
Why one-click connectors to Gmail, Slack, Notion, and Google Calendar unlock AI that actually does work instead of just suggesting it
How to analyze your sent emails to build a writing skill that perfectly matches your tone and style
The sub-advisory-board technique: spinning up three AI agents with different personas to review your work from multiple perspectives
How to set permissions for each connector so Claude only drafts (never sends) or always asks before taking action
The scheduled-task workflow that creates a morning debrief by reading your email, Slack, and calendar every day at 7:30 a.m.
Why projects with shared memory beat individual chat threads for consistent, high-quality AI outputs

Brought to you by:

Tines—Start building intelligent workflows today

Cursor—The best way to code with AI

In this episode, we cover:

(00:00) Introduction to JJ Englert

(02:48) What Cowork is and who it’s for

(05:49) Getting started: Opening the Cowork tab in Claude Desktop

(07:04) Understanding projects as folders on your computer

(07:54) Creating your “brain” file, with working preferences and context

(10:24) Demo: Building a daily operating system project from scratch

(12:18) How to prompt Cowork when starting a new project

(14:54) Understanding the project interface and shared memory

(18:37) Setting up connectors to Gmail, Slack, Google Calendar, and other tools

(21:00) Using connectors to analyze your emails and build personalized writing skills

(24:21) Creating a thinking-partner skill for decision support

(26:18) Cowork vs. OpenClaw

(27:18) Building a sub-advisory skill with multiple AI personas for feedback

(34:03) Advanced skill example: Multi-step newsletter creation with research and evaluation

(36:08) Setting up scheduled tasks for morning debriefs

(37:57) Going beyond one-off tasks with AI

(41:00) Progressive trust and the tradeoff of information for productivity

(44:08) Different use cases beyond work productivity

(46:08) Lightning round

Tools referenced:

• Claude Code: https://claude.ai/code

• Wispr Flow: https://whisperflow.ai/

• Monologue: https://www.monologue.to/

• Domo: https://www.domo.com/

• Pencil.dev: https://pencil.dev/

• Remotion: https://www.remotion.dev/

• Obsidian: https://obsidian.md/

• OpenClaw: https://openclaw.ai/

• Notion: https://notion.so/

Other references:

• Get Started with Claude Cowork: https://support.claude.com/en/articles/13345190-get-started-with-cowork

Where to find JJ Englert:

YouTube: https://www.youtube.com/channel/UCv2ovDhYVtlJw4QMidLFP8Q

LinkedIn: https://www.linkedin.com/in/jj-englert-a08836a6/

Where to find Claire Vo:

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Hard truths about building in the AI era | Keith Rabois (Khosla Ventures)

Lenny Rachitsky — Sun, 12 Apr 2026 12:31:41 GMT

Keith Rabois was an early executive at PayPal (part of the famous PayPal Mafia), COO at Square, VP of Corporate Development at LinkedIn, and an early investor in Stripe, DoorDash, Airbnb, YouTube, Ramp, and Palantir. Currently he’s managing director at Khosla Ventures. Also, he hasn’t touched a computer since September 2010 (he does everything from an iPad).

Listen on YouTube, Spotify, and Apple Podcasts

In our in-depth conversation, Keith shares:

The barrels vs. ammunition hiring framework (and how to spot barrels)
Why talking to customers is actively harmful for consumer products
How to identify undiscovered talent
Why the PM role is dying
The three traits of the best-performing companies right now
The specific interview question he asks every senior candidate
Why CMOs (not engineers) are becoming the #1 consumer of tokens

Brought to you by:

WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs
Vanta—automate compliance, manage risk, and accelerate trust with AI

Where to find Keith Rabois:

• X: https://x.com/rabois

• LinkedIn: linkedin.com/in/keith

• Website: https://www.khoslaventures.com

Referenced:

• Square: https://squareup.com

• Jack Dorsey on X: https://x.com/jack

• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens

• Simon Willison’s Weblog: https://simonwillison.net

• Vinod Khosla on X: https://x.com/vkhosla

• Peter Thiel on X: https://x.com/peterthiel

• Max Levchin on X: https://x.com/mlevchin

• David Sacks on LinkedIn: https://www.linkedin.com/in/davidoliversacks

• Tony Xu on X: https://x.com/t_xu

• David Sze on X: https://x.com/davidsze

• Faire: https://www.faire.com

• Max Rhodes on X: https://x.com/MaxRhodesOK

• Jeffrey Kolovson on LinkedIn: https://www.linkedin.com/in/jeffreykolovson

• Uncapped | Comparative Advantages w/ Keith Rabois: https://www.khoslaventures.com/posts/uncapped-comparative-advantages-w-keith-rabois

• Lattice: https://lattice.com

• Taylor Francis on LinkedIn: https://www.linkedin.com/in/taylor-francis-4ba49640

• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein

• The art of hiring: insights from Khosla Ventures, Airbnb, Ramp and Traba: https://ramp.com/velocity/the-art-of-hiring-insights

• Eric Glyman: Seek out super individual contributors (ICs): https://ramp.com/velocity/the-art-of-hiring-insights#Eric-Glyman:-Seek-out-super-individual-contributors-(ICs)

• Eric Glyman on X: https://x.com/eglyman

• Mike Moore on LinkedIn: https://www.linkedin.com/in/mike-moore-802223177

• Brian Chesky’s new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach

• Why you should work much harder RIGHT NOW: https://marginalrevolution.com/marginalrevolution/2026/03/why-you-should-work-much-harder-right-now.html

• Opendoor: https://www.opendoor.com

• The Craft of Early Stage Venture | Peter Fenton, General Partner at Benchmark | Uncapped with Jack Altman: https://www.youtube.com/watch?v=vRiblwiXt-Q

• Lovable: https://lovable.dev

• The rise of the professional vibe coder (a new AI-era job) | Lazar Jovanovic (Professional Vibe Coder): https://www.lennysnewsletter.com/p/getting-paid-to-vibe-code

• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika

• Marc Andreessen: The real AI boom hasn’t even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom

• Jeremy Stoppelman on X: https://x.com/jeremys

• The design process is dead. Here’s what’s replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead

• Andy Warhol: https://en.wikipedia.org/wiki/Andy_Warhol

• Curation and Algorithms: https://stratechery.com/2015/curation-and-algorithms

• Ernest Hemingway: https://en.wikipedia.org/wiki/Ernest_Hemingway

• William Shakespeare: https://en.wikipedia.org/wiki/William_Shakespeare

• Evan Moore on X: https://x.com/evancharles

• Andrew Mason on X: https://x.com/andrewmason

• Read Taylor Swift’s Full Viral Speech After Record-Breaking Awards Sweep: https://www.newsweek.com/entertainment/read-taylor-swift-full-acceptance-speech-record-breaking-awards-sweep-11745941

• The Chainsmokers: Stories Behind the Songs, AI’s Impact on Music, and Venture Investing | Uncapped with Jack Altman: https://www.youtube.com/watch?v=9GMSC-2pYnw&list=PLtpH7YnTL8ihy0nR2BV32n5VkRtqlDAS1&index=16

• How to spot a top 1% startup early: https://www.lennysnewsletter.com/p/how-to-spot-a-top-1-startup-early

• David Weiden on LinkedIn: https://www.linkedin.com/in/davidweiden

• Alfred Lin on LinkedIn: https://www.linkedin.com/in/linalfred

• Keith’s post about vertical integration on X: https://x.com/rabois/status/870673635375104000

• Jon Chu on X: https://x.com/jonchu

• Kanu Gulati on X: https://x.com/KanuGulati

• Rogo: https://rogo.ai

• Profound: https://www.tryprofound.com

• Basis: https://www.getbasis.ai

• Spellbook: https://www.spellbook.legal

• Roelof Botha on X: https://x.com/roelofbotha

• Delian Asparouhov on LinkedIn: https://www.linkedin.com/in/delian-asparouhov-87447742

• Lessons From Keith Rabois, Essay 1: How to become a Venture Capitalist: https://delian.io/lessons-1

• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp

• Nuremberg on AppleTV+: https://tv.apple.com/us/movie/nuremberg/umc.cmc.3sg4y0382byupy76bfy7307k4

• Eight Sleep: https://www.eightsleep.com

• “NO DAYS OFF”—Bill Belichick on X: https://x.com/SNFonNBC/status/829036279069364224

Recommended books:

• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Overcoming-Unseen-Inspiration/dp/0812993012

• The Jordan Rules: The Inside Story of One Turbulent Season with Michael Jordan and the Chicago Bulls: https://www.amazon.com/Jordan-Rules-Sam-Smith/dp/0671796666

• The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It: https://www.amazon.com/Upside-Stress-Why-Good-You/dp/1101982934

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

🧠 Community Wisdom: Vibe coding as a team sport, teaching interviewers to surface real signal, what’s missing from leadership offsites, and more

Kiyani — Sat, 11 Apr 2026 19:36:51 GMT

I built a custom Slack inbox. It was easier than you’d think. | Yash Tekriwal (Clay)

Claire Vo — Wed, 08 Apr 2026 12:03:47 GMT

Yash Tekriwal is the head of education at Clay. A self-described hyper-optimizer, Yash has built multiple custom productivity applications using Perplexity Computer and OpenClaw to manage his overwhelming daily workflow—including a Slack digest system that categorizes over 150 daily notifications into actionable priorities, and a consolidated news/email/Slack dashboard that serves as his personal command center.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

How Yash built a custom Slack digest that categorizes 150+ daily notifications into action-required, need-to-read, and FYI buckets
Why Perplexity Computer beats Claude Code and Codex for building personal productivity apps
His “anti-to-do list” framework: spending an hour daily automating tasks you never want to do again
How to use AI for deterministic tasks (APIs, structured data) vs. subjective tasks (categorization, summarization)
Why the SaaS apocalypse narrative is wrong—and why we’re about to see an explosion of micro-software
How his team uses Perplexity Computer to prototype design systems and communicate with cross-functional partners

Brought to you by:

Guru—The AI layer of truth

ThoughtSpot—Build AI-powered analytics into your product

In this episode, we cover:

(00:00) Introduction to Yash

(02:38) The burden of 150 daily Slack notifications

(05:45) When to use AI for tasks vs. building deterministic code

(06:38) Building the Slack digest with OpenClaw

(11:33) Introducing Perplexity Computer and the visual dashboard

(14:28) Three reasons Perplexity Computer beats Claude Code

(16:14) Using connectors to automate meeting follow-ups across Notion and Asana

(18:21) The Kanban-style Slack dashboard

(20:15) The long tail of customer requests and the future of micro-software

(24:09) The anti-to-do list framework

(26:21) Building a consolidated news, email, and Slack digest

(29:48) How Perplexity Computer handles authentication and deployment

(31:46) Team use case: Prototyping persona-based learning journeys for Clay University

(35:49) Lightning round and final thoughts

Tools referenced:

• Perplexity Computer: https://www.perplexity.ai/computer/new

• OpenClaw: https://openclaw.ai/

• Discord: https://discord.com/

• Claude Code: https://claude.ai/code

• Codex: https://openai.com/codex/

• Asana: https://asana.com/

• Airtable: https://airtable.com/

• Figma: https://www.figma.com/

• Vercel: https://vercel.com/

• ChatGPT: https://chat.openai.com/

Other references:

• Slack: https://slack.com/

• Notion: https://www.notion.so/

• Superhuman: https://superhuman.com/

• Clay University: https://www.clay.com/university

• Kanban boards: https://en.wikipedia.org/wiki/Kanban_board

Where to find Yash Tekriwal:

LinkedIn: https://www.linkedin.com/in/yashtekriwal/

X: https://x.com/yash_tek

Company: https://www.clay.com/

Where to find Claire Vo:

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Listen: A visual guide to getting out of a creative slump

Lenny Rachitsky — Wed, 08 Apr 2026 02:51:13 GMT

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Add the private feed to your podcast app at add.lennysreads.com

In this episode, my wife Michelle Rial, cartoonist and author, celebrates the launch of her new book Charts for Babies by sharing a pep talk for anyone stuck in a creative rut. She walks through 12 tried-and-tested steps designed to lift you out of a creative block and get back to making things that matter.

Listen now: YouTube | Apple | Spotify

In this episode, you’ll learn:

Why it’s necessary to create work that might feel embarrassing
The simple mindset shift that stops stalling and gets you to just start
How to reframe scorn as a positive signal
The thought process that can help you move out of a state of rumination
How to treat failed ideas as seeds for future breakthroughs

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Read the full article

A visual guide to getting out of a creative slump

Michelle Rial — Tue, 07 Apr 2026 12:45:49 GMT

I’m so, so excited to share a guest post by Michelle Rial, my wife! Her first children’s book, Charts for Babies, comes out TODAY, and we thought: what better way to celebrate than collaborating on a post? Below, you get a glimpse into her brilliant mind. This visual guide is both inspiring and useful, and is for anyone trying to create something. Read it right now or bookmark it for the next time you get stuck.

P.S. Michelle’s book is truly great, and makes an excellent gift—but you don’t have to just take my word for it. Renowned child psychologist and parenting coach Dr. Becky said, “Charts for Babies says to your child, ‘I believe you can understand the world’—and that message matters from the very start.” And Booklist said, “Rial’s understanding of her audience and her ability to distill information clearly are gifts, and the whimsical hand-drawn illustrations and text will delight readers.” Grab a copy (or two) here.

Are you feeling what I’m feeling? Blink twice with your heavy human eyelids if you are starting to feel like there is no point to creating things anymore. That if robots can do the creative work we spend hours, days, months, or years pulling our hair out over, why should we even try?

Or maybe you’re just in a regular creative drought. The paralysis of worrying that your work isn’t worth doing anymore or that someone (or something) is already doing it better.

Well, it’s time to lift yourself by your Blundstone loops and climb right out of that rut. I’ve made you a visual pep talk.

Each of the following 12 charts I’m about to share was crafted from the inside of one of these hollows. Making them felt like peeking out from underwater before heaving myself out of the sea like a slippery, blubbery walrus, gripping the land and steadying myself with my number 2 pencil-tusks.

I hope that this guide will help you, too. Maybe you’ll be able to hoist your heavy artist soul out of the deep dark ocean, onto a sunny platform where we can all warm our insides together and enjoy some ceviche.

And if any robots are listening, please get back to work on helping me find the cause of my eczema.

1. Be OK with being embarrassing

As a sensitive human, putting work out into the world to be judged can feel excruciating. What if I look “thirsty”? Or worse, what if Gen Z thinks I’m cringe? What if, what if, what IF? It makes me want to pack up my infinity notebooks and quit.

But the people who always end up with something really good? They don’t stop. Maybe they pause or take a rest, but they always come back and keep going. Nobody remembers the project that didn’t get any traction, the one that may have seemed a bit embarrassing at the time. People remember the successes and forget all the attempts.

2. Ignore the algorithm

Are you making work to please the algorithm like it’s your never-satisfied immigrant parent? (Just me?) Well, the algorithm will change, and then you’ll look back and be like: what the hell was that? So instead, try to make things that make YOU laugh, make you tear up, make you spit out your iced matcha latte that is apparently causing your anemia.

3. Use your caffeine wisely

Caffeine can be amazing for creativity . . . until it isn’t. Try not to fall into the trap of squandering your mental alertness on a pile of emails that only needed a simple “sounds good!” What happens then is we don’t make any progress on the juicy stuff, we feel defeated, and we reach for another cup. And then . . .

4. Overwhelmed? Take some breaths.

You might think it’s too early in this process to be overwhelmed, but sometimes staring at a blank page (or at a very full page that you just realized is trash) can feel incredibly overwhelming. And the overwhelm paralyzes you before you can even get going. Especially if you just flew past your caffeine limit.

I highly recommend having a meditation or breathwork practice. The times I’ve been consistent with it have been my most steady and productive times. Unfortunately, today I’m right here with you, panicking that I need to finish this post.

5. Stop thinking about what you’re going to work on and just get started

Yes, your ergonomics should be solid; yes, you need to find quiet or a playlist that helps you focus. But after that, stop stalling and just get started. If you get distracted, start again. Butt in chair.

6. Keep going

And going, and going, and going. The love of the work is the reason you got into this in the first place. Remember that the work is the point, and the struggle is what makes your story interesting. Imagine succeeding at every attempt—you’d need a whole forearm full of mal de ojo bracelets.

7. You can’t control how the work will be perceived

Sure, you can throw your best effort into sharing and promoting your thing. Some people will hate it. Some people hate you and thus hate it. If people hate it, congrats, it’s popular enough to attract scorn. Maybe nobody sees it? Congrats, it’s a hidden gem. Be proud of what you’ve done, share as best you can and as often as you can stomach, and then keep on making and progressing.

8. Rumination is a waste of time

Something I know about the writer of this newsletter is he doesn’t ruminate or get caught up in regret, which may be how he is so mind-blowingly productive. His thought loops? They move forward in a straight line. The only time he looks back is to note how he can do something better in the future. I might try that.

9. Each failed idea creates the potential for new growth

Don’t discount ideas just because they didn’t work right away. Let them grow and sprout in new directions. Put them in your bad-idea pile and let that pile grow and grow until your dining table is covered in bad ideas and you have only a tiny sliver of your table to actually eat on. Come back to those ideas, and see what blossoms into something new and what’s still a goner.

And then clean your dining table, for the love of god!! People LIVE HERE.

10. You’re already further along than you think

If you’re blocked and feeling like you’ve been at something forever and still have nothing, go back and look at your draft pile. I bet you’ll spot some progress.

11. It’s never too late to start

Be the adult leaving your piano lesson as a first grader is arriving. Be the youngest person at your rec center’s water aerobics class. It’s never too late (or too early). You might discover you’re great at something you didn’t know about. Or even better, you might have a blast. The juicy new neural pathways in your brain will thank you.

12. Still feeling it all? “Let it be.”

Did you enjoy this in any small, tiny, pie-chart fraction of a way?

If so, please check out my new children’s book, Charts for Babies! It is OUT TODAY and available wherever you buy your books (it’s also at places like MoMA Design Store and SFMOMA Museum Store!).

It’s my way of introducing the language of math at a very early age, when the brain is at its spongiest.

You can request it from your local library or bookstore, gift it to a new parent (unless they sternly specified NO GIFTS), or get it for the future mathlete in your life!

Thanks, Michelle! Have a fulfilling and productive week 🙏

If you’re finding this newsletter valuable, share it with a friend, and consider subscribing if you haven’t already. There are group discounts, gift options, and referral bonuses available.

Sincerely,

Lenny 👋

🎙️ This week on How I AI: I gave Claude Code our entire codebase. Our customers noticed.

Lenny Rachitsky — Mon, 06 Apr 2026 15:03:07 GMT

I gave Claude Code our entire codebase. Our customers noticed. | Al Chen (Galileo)

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Orkes—The enterprise platform for reliable applications and agentic workflows
Tines—Start building intelligent workflows today

Al Chen is a field engineer at Galileo who works directly with enterprise customers asking highly technical questions. In this episode, Claire talks with Al about how he uses Claude Code to query Galileo’s entire codebase across 15 repositories, combine it with Confluence and Slack, and deliver accurate, real-time answers without relying on documentation or constantly pinging engineering. They cover why code is often a better source of truth than docs, how to turn customer questions into scalable knowledge, and how AI is changing what customer-facing teams can do.

Biggest takeaways:

Write a script to pull the latest code from all repos daily—and let Claude Code write it. Al uses a 16-line script (written entirely by Claude Code) that pulls the latest main branch from all 15 repositories every morning. This ensures that he’s always querying current code instead of outdated information, solving the “docs are stale” problem that plagues most technical support teams.
Maintain a “customer quirks” page to make AI answers customer-specific. Al keeps a Confluence page listing each enterprise customer’s unique deployment requirements: how they handle secrets, namespaces, encryption, air-gapped environments. His Claude Code custom commands reference this page first, generating highly tailored deployment instructions instead of generic answers anyone could Google.
Combine code repositories with Confluence MCP for maximum context. Al’s custom Claude Code commands first check Confluence for deployment documentation, then query the code repositories if needed. This multi-source approach means Claude Code pulls from official docs, tribal knowledge, and actual implementation—delivering answers no single source could provide.
Your code is better documentation than your docs. Al realized public documentation couldn’t answer his enterprise customers’ detailed technical questions. By pulling all 15 of Galileo’s repositories into VS Code and querying them with Claude Code, he can now answer questions about how services cascade together, how features actually work, and deployment specifics that aren’t captured anywhere else.
Turn Slack support threads into knowledge base articles automatically. Using Pylon, Al converts detailed customer conversations into abstracted help articles with one click. These articles are more in-depth and current than official docs because they’re based on real customer questions and don’t require the overhead of PR reviews and approval processes.
Reduce engineering interruptions to near-zero by self-serving answers. Before this system, Al constantly pinged engineering with customer questions, creating frustration on both sides. Now he queries the code directly, only reaching out to validate answers or when Claude Code can’t find information (usually because it exists only in meeting notes or hallway conversations).
The human value-add is making AI answers sound human, and knowing when to validate. Al doesn’t blindly copy-paste Claude Code responses. He proofreads everything, removes telltale AI phrases like “in summary,” condenses verbose answers to what customers actually need, and validates complex technical answers with engineering when he doesn’t fully understand the implementation.
Compete on customer experience, not just product velocity. Everyone uses AI to ship faster products. Al uses AI to show up differently in customer relationships—delivering custom deployment documentation that accounts for each customer’s specific security requirements and infrastructure constraints. This differentiation in service quality is harder to replicate than product features.

Detailed workflow walkthroughs from this episode:

How Al Chen Uses Claude Code and 15 Repos to Answer Any Customer Question: https://www.chatprd.ai/how-i-ai/claude-code-and-repos-to-answer-any-customer-question
Automatically Create a Knowledge Base from Slack Support Threads: https://www.chatprd.ai/how-i-ai/workflows/automatically-create-a-knowledge-base-from-slack-support-threads
How to Use AI to Answer Customer Questions from Your Entire Codebase: https://www.chatprd.ai/how-i-ai/workflows/how-to-use-ai-to-answer-customer-questions-from-your-entire-codebase

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

I gave Claude Code our entire codebase. Our customers noticed. | Al Chen (Galileo)

Claire Vo — Mon, 06 Apr 2026 12:03:35 GMT

Al Chen is a field engineer at Galileo, an observability platform for AI applications, where he works on the front lines with enterprise customers asking highly technical questions. Despite never having held an engineering role, Al has built a system using Claude Code to query Galileo’s 15 separate repositories, combine that with Confluence documentation and customer-specific quirks, and deliver hyper-personalized answers that would otherwise require constant engineering support.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

How to use Claude Code to query multiple repositories simultaneously for customer support
Why code is often a better source of truth than documentation
How to combine repository context with Confluence and Slack using MCPs
The “customer quirks” system that creates hyper-personalized deployment guides
How to build virtuous loops that turn single customer questions into scalable knowledge
Why information organization matters less in the AI era
A simple 16-line script (written by Claude Code) that pulls the latest main branch across all your repositories to keep your context current
How to reduce engineering interruptions to near-zero by empowering customer-facing teams to query the codebase directly

Brought to you by:

Orkes—The enterprise platform for reliable applications and agentic workflows

Tines—Start building intelligent workflows today

In this episode, we cover:

(00:00) Introduction to Al Chen

(02:50) The problem: documentation wasn’t enough

(04:23) Pulling 15 repos into VS Code

(06:03) How Claude Code queries the entire codebase

(08:00) Why current code beats documentation

(08:31) The pull script that keeps everything updated

(09:54) Opening projects at the multi-repo level

(11:40) Live demo: answering deployment questions

(13:25) The customer quirks system

(15:00) Living in chaos: why organization matters less now

(17:03) Competing on customer experience, not just product

(18:20) Should customers be able to query the code directly?

(20:05) Where humans still add value

(25:46) Using AI for reactive Slack support

(29:16) The “and then” workflow discovery

(32:07) Scaling processes across the team

(34:07) Lightning round and final thoughts

Tools referenced:

• Claude Code: https://claude.ai/code

• VS Code: https://code.visualstudio.com/

• Pylon: https://usepylon.com/

• Confluence: https://www.atlassian.com/software/confluence

Other references:

• Slack: https://slack.com/

• Kubernetes: https://kubernetes.io/

• Stack Overflow: https://stackoverflow.com/

• Intercom: https://www.intercom.com/

Where to find Al Chen:

LinkedIn: https://www.linkedin.com/in/thealchen/

Company: https://www.rungalileo.io

Where to find Claire Vo: