2026-09-02 20:03:39
I’m running about 30 active agents at any given moment, and in this episode I break down my full Grok Bot setup: what it is, how it compares to OpenClaw, and the nine bots I’ve built for work and my personal life. We go deep on Chief (my chief-of-staff bot sweeping six inboxes and multiple Slack workspaces), TradBot (the family agent that prints a kitchen-table newspaper for my kids), two engineering bots handling my PR queue and SOC 2 compliance monitoring, Holly Helpdesk, and a handful of personal bots I didn’t expect to actually love. I also walk through how I migrated everything from OpenClaw, including the script I used to export and transplant each agent’s identity and schedule.
Listen or watch on YouTube, Spotify, or Apple Podcasts
The three primitives Grok Bot is built on, and why one of them changes what agents can actually do
How Chief, my general-purpose chief of staff, handles a scope I didn’t think a single bot could manage
The writing quirk I noticed immediately with the Grok model, and what I did about it before letting it near my inbox
Why I created a family agent, what it produces every morning, and the design principle I used that has nothing to do with a screen
The two engineering bots doing work I used to do myself, and how one of them handles compliance in a way that surprised me
How Holly Helpdesk started getting five-star reviews from customers who had no idea they were talking to a bot
The personal bots I built mostly on a whim, and the one I now look forward to every Monday morning
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Hyperagent—Deploy fleets of agents that handle real work
(00:00) Why I migrated from OpenClaw to Grok Bot
(02:10) Grok Bot overview: the three core primitives
(07:41) Chief: my chief-of-staff bot
(11:51) OpenClaw vs. Grok Bot
(12:41) TradBot: my family agent
(19:51) LGTM the PR Closer
(22:03) Lockdown: SOC 2 control monitoring bot
(24:00) Holly Helpdesk: customer support
(26:58) Penny Pincher: subscription audit, insurance negotiation, Rolex shopping
(29:37) ShopZilla and Sylvie Style: personal shopping and wardrobe bots
(32:54) How to migrate your OpenClaws
(34:26) Final take
• Grok Bot (SpaceXAI multi-agent platform): https://x.ai/news/introducing-grok-bot
• OpenClaw (previous agent platform): https://openclaw.ai/
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
2026-09-01 20:45:59
👋 Hey there, I’m Lenny. Each week, I share deeply researched product, growth, and career advice. For more: Lenny’s Jobs | Lenny’s Podcast | Lennybot | How I AI | Become an AI-Native Builder and my other favorite AI/PM courses
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I’d always thought AI was bad at design. But after reading this mind-blowing post by Anshu Chimala, I realize I was just doing it wrong. Anshu led software engineering and design teams at Apple for 12 years, focusing on research and prototyping for future AI products. He regularly shares design tutorials and demos on X (he’s one of my favorite follows). For deeper dives into crafting distinctive experiences with AI, check out his Substack and connect with him on LinkedIn.
Let’s get into it.
A conversational calorie tracker, built in three prompts with Claude Fable 5:
A space exploration game, built in two prompts with Claude Opus 5:
A dynamic landing page, built in three prompts with Claude Opus 5 + GPT-5.6 Sol:
I often post AI design demos like these on X. Every time I do, someone inevitably asks, “Why does the model create all this incredible stuff for you, but when I try, I only get generic slop? It’s like you’re using a completely different model.”
I’m not using a different model, but I am getting more out of the models I work with. Most people only see 1% of AI’s creative potential. I want to show you how to tap into the other 99%.
AI models are capable of amazing creativity, but that creativity gets stifled by how they’re trained. Large language models are next-token predictors: at each step, they look at a sequence of text and predict what comes next based on millions of examples. The results may be rated by humans, and those ratings fed back into the model. This teaches the model to make consistent, safe choices that fit everyone’s preferences.
This makes typical LLMs great at most tasks but poor designers. To create a design, an LLM has to build it out token by token. Whenever it needs to make a design decision—what colors to use, or how to arrange elements—the model fills in the tokens it thinks are most likely to please everyone. As a result, the design usually ends up being repetitive and bland. It’s like the ultimate case of design-by-committee.
Great design, on the other hand, starts with feeling and aims to create an emotional response. It bends the rules and delights users with memorable, unexpected choices. Great design is exactly the opposite of what an LLM does naturally, which is to make the most predictable choice at every step.
However, if we can get the model to reach beyond the most predictable choices, we can access a vast landscape of creative ideas that most people miss out on.
This is a lesson I learned from managing human designers, before I was managing AI ones. For most of my career at Apple, I led an R&D team designing exploratory future AI products. Early on, our preconceived notions about how user interfaces should work limited our creativity and kept us returning to the same old ideas. Through rigor and new processes, we learned to stop re-creating what’s comfortable and instead look to the fringes of what’s possible, to generate something new. We became experts at polishing the little details to an Apple level of quality.
Since my time at Apple, I’ve been working on applying that same process to my work with AI. In the past couple years, AI agents have become extremely capable. They can do in hours what used to take my team weeks. And with the right guidance, they can create designs that look completely unlike anything else.
Loosely inspired by the Double Diamond design process, I’ve reimagined the design process for a team of AI agents instead of human designers:
Discover new ideas beyond the average slop by exploring a variety of directions and creating bold, ambitious design briefs.
Define an individual design identity by pushing AI beyond its familiar patterns and chaining models together to fully realize the design’s potential.
Deliver a stunning final result by polishing away the sloppy rough edges and focusing on the key elements.
By following these stages and applying the techniques within each one, you can create an incredible design remarkably quickly—and make people ask, “Why does AI create magic for you (and not me)?”
The hardest part of the design process is looking at a blank screen with infinite possibilities. The best way to tackle that moment is to start by going broad before going deep. AI is an excellent tool to explore a wide variety of potential directions.
As we know, though, models tend to overrely on familiar patterns and make conservative choices. To explore the full potential design space, we want to coax a model to do the opposite: be bold, be varied, and take risks. Below are two ways to push it out of its comfort zone.
The idea here is to get the model to find a new source of inspiration for designs, rather than relying on the defaults it learned from training. If you’ve tried to prompt a model to design a website or app, you’ve probably already seen what that default looks like.
As a simple example, I gave four instances of Claude Code the same prompt:
Prompt:
Build me a landing page for my productivity app.
Claude Opus 5:
Almost every time, we get a purplish gradient, text on the left, graphic on the right, and the exact same structure. It looks like every AI-designed website ever.
We didn’t ask the model to do anything unique or varied, so it makes sense that it keeps falling back on the same patterns it knows well. But just asking for variety doesn’t work:
Prompt:
Build me a landing page for my productivity app. Give me something totally unique. Make every design decision completely at random.
Claude Opus 5:
The results are different from before, but they’re still not varied. The model always uses the same color scheme, structure, and even the same awkward pottery metaphors. It’s predicting tokens that sound random but aren’t actually random.
The problem is that the model can’t inherently act randomly. It can only predict the most likely token. If we want variety, we have to bring it from outside the model. One technique for this is String Seed of Thought, published by Sakana AI. We make the AI generate a random string and use it as design inspiration. That way, the model is truly making different decisions each time.
Prompt:
I want you to build me a landing page for my productivity app.
Follow this procedure:
Generate a long, random alphanumeric string using a shell script.
Define the creative direction (color scheme, layout, typography, etc.) based on the string. Look beyond the surface for subpatterns, special numbers, anything that inspires you.
Use your judgment to bring this direction to life and make it look great.
Don’t reveal the string in the design. It’s only for your inspiration.
Claude Opus 5:
Suddenly the outputs are much more varied! Now we’re seeing different color schemes, fonts, and new ideas. The previous designs were ones that any Claude user could get. These designs are one-of-a-kind; no two runs ever produce the same result.
Another approach to giving a model a strong push is to get more specific and wild with your prompts. This gives the model a clear vision to base its decisions on, rather than letting it make them up on the fly. The best way to find a unique idea is by bringing your own taste into the equation. You first imagine the inspiration—a video game, an interior design trend, an art installation—and describe how you’d like that inspiration to influence the AI’s outputs. Here are some examples:
“Build me a landing page for my productivity app, with a bold pixel art theme and stunning graphics. Each section should feel like a still from a video game, yet somehow it should all function as a landing page.”
“Build me a landing page for my productivity app, set in an isometric living 3D city, where different features are somehow represented by neighborhoods or buildings.”
“Build me a landing page for my productivity app, with a radically asymmetric layout, dissonant colors and typography, and uncomfortable negative space. Break all the rules but still make it look good.”
Of course, the hard part is coming up with original ideas to ask for. AI can help with this too, but if you simply ask it for ideas, you’ll get the same average ones everyone else gets. Here’s a system I use to find unique prompt ideas with AI:
I want to come up with a bold, unique design language for my product. Can you list as many ideas as you can, with short, high-level descriptions? Go broad, not deep.
Industrial Control Panel:
I’m imagining something tactile. Clicky, satisfying buttons, nice sounds.
Initially I pictured something cartoony or skeuomorphic, but this feels tacky to me. Avoid that.
Instead, want consistent components and little touches that land this look without going overboard.
Gray gradients would look boring. Need more texture. Maybe we can incorporate some color, while retaining the control panel feel?
Can you sharpen this one based on my tastes?
Can you write a concise prompt that an AI agent could use to build an initial POC page with this?
If you just paste AI-generated ideas back into AI, it’s hard to get something unique. After all, anyone else could have done the same thing. However, when you actively steer the design direction, you end up with something only you could have created.
Don’t be afraid to try ideas that sound terrible. If you find yourself thinking, “There’s no way this will work,” you’re on the right track. Often, your agent will surprise you, and you’ll realize you were underestimating it. If not, just throw away those results and try something else. But save the prompts that don’t work, and test them again when newer models come out. That way, you’ll know you’re taking full advantage of what the latest models can do.
So far, we’ve looked at how to explore a broad set of ideas and hopefully land on a promising initial design. No matter how we prompt, though, our initial AI-generated designs will usually still feel generic.
For example, look at the designs we came up with using seed strings:
These have promise, but they’re still relying heavily on the same stale patterns: text on the left with a CTA button below, nav bar up top, graphic on the right.
Our next goal is to give each design an individual personality through distinct design choices. Below are my favorite techniques to do that.
We need to iterate on our designs to improve them. But simply asking our agent to look at the design and improve it won’t work, because the agent isn’t objective: it reviews its own code, past decisions, and previous rationale. AI can’t easily zoom out, look at the big picture, and “think different.”
To solve this, instead of letting the coding agent decide when the design is good enough, have it ask another agent—a “design critic.” The critic’s job is to look at screenshots of the current design and provide feedback. It doesn’t care how the current design is implemented or how much effort went into it, only if it actually hits the quality bar.
This approach has an extra benefit: we can use a big, expensive model for the critic without breaking the bank, because we’ll only use it for executive decisions. A cheap, fast model can do the grunt work, while the strong critic model provides taste.
Let’s try this on our previous designs, using Claude Fable 5 as the critic:
Prompt:
I want you to improve this design. To figure out what to focus on, use a Fable 5 subagent as a design critic.
Follow this procedure at each iteration:
Capture a screenshot of the current design
Invoke the critic in a fresh context, with just the screenshot, not the code, implementation details, or earlier iterations/critiques
Ask it to evaluate the aesthetic that the design is going for, imagine how a top design studio would execute this aesthetic, then outline the biggest gaps
Lastly, it should provide a score out of 10 indicating how close the current design is to that studio-level quality bar
Provide this guidance to the critic in its prompt:
It should think high-level about the overall structure and composition as well as look at the fine details
It should watch out for patterns that feel overdone, excessive, or otherwise obviously AI-generated, and penalize them
It should provide tight, specific feedback, not vague prose
It should be bold and opinionated, not rely on what’s safe or easy
Your work is only complete when the critic independently deems it 9/10 or higher. Do not put that criterion in the critic prompt; keep it objective in its scoring. Use the same critic prompt each time.
Claude Opus 5:
Instead of the same cookie-cutter layout over and over, each design now has its own identity—but still maintains its original high-level aesthetic.
Notably, in each case, Fable accounted for less than 10% of output tokens. Asking Fable to redesign the page directly would have cost twice as much and taken much longer.
The way you set these loops up matters a lot. Here are some tips:
Make sure the criteria for the critic are as clear and objective as possible.
Bad: “Judge if our design looks beautiful, not AI-generated.” This is too subjective, and the results will vary wildly from run to run.
OK: “Review the aesthetic we’re going for, visualize how a top design studio would execute it, then judge our design’s quality against that bar.” The prompt is still mushy, but it provides a consistent framework and quality bar.
Great: “Here are 5 designs: 4 professional examples and 1 screenshot of our product. Rank them by polish and taste level.” This instruction is concrete and objective, and gives a visual baseline for judgment.
Provide example images to demonstrate the target quality bar. You can use comparable screenshots or designs you like, or even AI-generated concept art. Instruct the critic to treat these as a baseline or a moodboard, not a target. You don’t want it to copy other designs outright.
Set the stopping criteria carefully. Otherwise, the critic may never consider the design good enough, and your agent will helplessly burn tokens trying to please it. Prompt it to do one or two iterations first, and see if it’s converging before adding more.
Choose the right model for each job. Consider bigger models for the critic role, since more parameters generally translate to better design sense and a wider distribution of ideas. Small models can be effective as the implementer, but don’t go too small. You still need a model that’s capable of executing a design direction well.
Coding agents love to write code, but they usually don’t incorporate images. Instead, they tend to use the easy code-based alternatives: gradients, shapes, and basic patterns. Those are all strong giveaways of an AI-generated design.
Some agents have image tools built in, but they underutilize them. Others don’t have image tools out of the box but can easily use the OpenAI or Gemini APIs to generate images with an API key.
Let’s try this on the designs from the last step:
Prompt:
The design is pretty plain. Add more personality using image generation. Consider shaders or 3D effects in combination with images to create more interesting visuals.
For image generation, use this OpenAI API key (only use it locally, do not store it in the code or product): sk-a1b2c3d4…
Verify that your work looks right frame-by-frame in the browser.
Claude Opus 5 (before and after):
Images and effects like these can quickly add a lot of personality and make a design less obviously AI-generated, since they demonstrate more than surface-level effort.
Depending on your setup, there are different ways to connect your agent to image generation tools:
If you use Codex, Antigravity, or Grok Build:
Tell your agent to use its built-in image generation. The agent already knows how to do this but rarely does so until instructed.
If you use Claude Code or another agent but also have a ChatGPT subscription:
Tell your agent, “Use the Codex CLI to generate images. Help me install it if it isn’t already present. Make sure it’s billing my subscription, not an API key.” This lets you use your ChatGPT subscription for image generation without extra costs.
If you only use Claude, or any other tool:
The simplest path is to give your agent an OpenAI or Gemini API key to generate images. I recommend creating a separate API key with a tight spend limit, just for your agent. That way, your costs are controlled even if the key gets out or the agent misuses it, and you can easily revoke the key without disrupting other work.
If you find yourself pasting keys into chats frequently, put them in a file instead, and point your agent to it in your project. Tell your agent: “Create a gitignored file called .env.agents, store this API key in it, and note to yourself in AGENTS.md/CLAUDE.md that these keys are for you to use during development (but must not ship with the product).”
Video generation models are incredibly powerful these days, but most people think of them as tools for generating UGC ads or clips of Will Smith eating spaghetti. They can work wonders for everyday design work too.
There are many video models out there, and the best ones change frequently, so I like to use an aggregator platform like fal.ai. This way, we can give our agent a single API key and let it evaluate different options and choose the best one without needing multiple integrations.
Here are two ways I love to use video models in my designs:
The trick is to generate a looping clip with a solid color background, then either chroma key it out (like a green screen) or, in more complex cases, use a video matting model to remove the background. This gives you an animation that you can layer anywhere in your UI without it looking like a video.
For example, I took one of our previous designs and ran this prompt:
Prompt:
Can you replace the image on this page with a looping video clip that does something more interesting? Have the crystal splinter apart and slowly spin around. It should have awesome glassy effects that refract the page background and cast shadows and light around it.
To get convincing glass refraction effects, render the video of the glass over the page background colors first (so it bakes in the refraction effects), then remove the background with a video matting model.
Use this fal.ai API key: sk-a1b2c3d4…
Find appropriate recent models for video generation and background removal.
GPT-5.6 Sol (before and after):
This is a much richer effect than you can get with code: interesting caustic reflections, glassy refraction effects, and complex physical motion.
This is a really underrated use case for video models. In addition to generating video from text, many video models can interpolate between keyframe images. This lets you take two product stills and create a transition clip between them. You can play the clip when the user takes an action (like navigating to another screen of your app) or scrub through it frame-by-frame in response to a gesture (like scrolling or swiping).
Here’s a demo page showing off a scroll effect. I built it with a single prompt using GPT-5.6 Sol in Codex:
Prompt:
Build a demo page for a suitcase that uses a video model to create interactive transitions between a couple of screens. Each screen should show the suitcase in a different state, with vertical motion that feels appropriate for scrolling:
Initially, have the suitcase floating high up in the air
Then have it land on the floor and pop open
Finally, have its contents neatly land into it from the top
Generate the initial frame using your image generation skill. Then, generate a video clip that starts from that frame and animates to the next state. Use the final frame of that video to seed the next transition so that it continues seamlessly. Scrub through the transitions one by one as the user scrolls.
Use this fal.ai API key: sk-a1b2c3d4…
Use a video model with strong physics and consistency, like Seedance 2.5.
GPT-5.6 Sol:
The transitions between pages scrub fluidly with the user’s scrolling and are fun to play with. Design like this makes the user want to keep scrolling and reading more about your product. And it only took one prompt!
Once we’ve gotten to a unique, standout design, the final step is to clean up the details and get it ready for production use. AI can build amazing, striking visuals, but your judgment will be key to making sure the design makes sense, flows well, and serves its practical purpose for your users.
AI loves to add more, but it rarely takes away. One of the biggest signs that a design is AI-generated is that it overexplains everything or contains elements that don’t serve any practical purpose. By contrast, a design that exercises restraint immediately looks premium and tasteful.
When polishing AI designs, most of my effort goes into removing things. For example, when I was building my calorie tracking app, this was my initial design from Claude:
I’d described the app’s functionality and specifically asked for a “clean, minimalist design.” The results weren’t bad, and were certainly impressive for being fully AI-generated. However, despite my asking for minimalism, a lot in the design wasn’t adding value:
Pink glowy effects in the background and on the progress bar
Random colors and highlights on text
Extra labels and empty space when displaying all the foods for a day, when the images already communicate this
Custom buttons and text fields that look worse than built-in iOS components
I asked Claude to dial things back:
Simplify the layout into an image-centric grid
Get rid of gradients, glows, and unnecessary containers
Aim for a truly minimalist aesthetic that feels Apple-native
This was the result:
To my trained eye, the result is much better. It’s opinionated and allows the visuals to speak for themselves. It uses native iOS components, and the excessive colors and gradients are gone. The text is smaller, simpler, and tighter. This is good design.
Today’s AI models would never think to make these choices on their own. Remember, AI doesn’t like to take risks, and it’s risky to strip down a design and delete code. The model needs a push from you. Look over your design and ask yourself what really needs to be there. Often, putting less on the screen communicates more, because you can hold your users’ attention without overwhelming them with clutter.
2026-08-31 23:01:58
Listen now on YouTube • Spotify • Apple Podcasts
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Daniel Blum is a product manager at Melio who has built a self-improving AI system that now handles 70% to 80% of his workday. In this episode, he breaks down how Claude and Cowork manage his Notion board, prepare him for the week, scan Slack and email for important context, and learn from his edits without waiting for explicit feedback. He explains how he turned the system into a 15-minute onboarding experience for other Melio employees, why the first few weeks of building with AI can feel painfully slow, and how the payoff eventually helped him accomplish a week’s worth of PM work in a single day.
The architecture matters more than the AI tool itself. Daniel believes a system becomes genuinely powerful when it can update its own core files and connect to the tools someone already uses. Once those pieces are in place, the system can improve and become more useful over time, whether it is built in Cowork, Codex, ChatGPT, or something else. He created a transformative setup using the tools Melio had already licensed, proving that the underlying architecture matters more than choosing the perfect platform.
Context isn’t something you set up once; it requires an ongoing system. Daniel spent months giving Claude voice memos, links, decks, and verbal brain dumps to build detailed context files for every area of his work. He then created recurring updates that refresh those files every few weeks. This keeps the gap between what Claude knows and what is actually happening inside the company as small as possible.
The most impressive part of Daniel’s morning brief is that it identifies what it does not know. Each day, Claude reviews his Slack, email, and notes for unfamiliar terms, projects, or goals that do not appear in its context files. It then asks Daniel targeted questions to fill those gaps. When it encountered the phrase “settlement cap,” for example, it had already read the relevant thread and understood the general idea. It only needed Daniel to confirm the meaning before saving it.
The value of a personalized AI system builds slowly, then becomes enormous. Daniel is candid about how frustrating the first few weeks can feel. The system does not know enough yet, its work is slightly off, and nearly everything requires a second look. But once someone pushes through the work of centralizing information and building context, the payoff can be difficult to overstate. He can now accomplish in one focused day what previously took him an entire week.
Self-improvement loops learn from the difference between what the AI drafted and what the person actually sent. Daniel built a weekly skill that compares Claude’s original drafts with his final versions, then uses those differences to improve future work. It resembles Alex Lieberman’s “write like me” loop, but it relies less on explicit feedback. Instead, it observes Daniel’s actual behavior and learns from the small edits he makes instinctively.
Feedback telemetry turns personal AI workflows into products that can improve themselves. Every one of Daniel’s skills captures moments of friction. If he says something is not working or requests a correction during a session, the system logs that signal. Once a week, his improvement loop identifies the most common problems and recommends updates. It is essentially analytics for his internal tools, and it gives him a structured way to refine the system based on how it performs in real life.
The Workstation plugin addresses one of the biggest barriers to adopting AI inside a company: personalization. Daniel watched several product managers struggle with Spectacular, his spec-writing gem, because it had been designed entirely around his own working style. He responded by building an onboarding flow that connects each employee’s tools, maps their colleagues, and learns their voice in about 15 minutes. Instead of starting from scratch with a generic system, every Melio employee now begins with a strong shared foundation personalized to their needs.
The biggest remaining limitation of today’s AI systems is persistence, not intelligence. Daniel estimates that Claude already handles 70% to 80% of his workday. What it still cannot reliably do is continue working in the cloud while his computer is off. He is already preparing for that future by teaching Claude how to recognize the “closed state” of different tasks. If a drafted Slack message is no longer saved, for example, the system can infer that it was probably sent. When truly autonomous operation becomes available, Daniel’s system will already understand what completion looks like.
Claude Cowork for PMs: My Self-Improving Productivity System: https://www.chatprd.ai/how-i-ai/claude-cowork-for-pms-my-self-improving-productivity-system
↳ Create a Meta-Workflow to Continuously Improve Your AI Assistant’s Performance: https://www.chatprd.ai/how-i-ai/workflows/create-a-meta-workflow-to-continuously-improve-your-ai-assistant-s-performance
↳ Build a Self-Improving AI Morning Brief to Capture Action Items and Learn Company Jargon: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-morning-brief-to-capture-action-items-and-learn-company-jargon
↳ Automate Your Weekly Planning with an AI-Powered PM Assistant: https://www.chatprd.ai/how-i-ai/workflows/automate-your-weekly-planning-with-an-ai-powered-pm-assistant
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.
2026-08-31 20:04:18
Daniel Blum is a product manager at Melio, a B2B payments company, and one of the most systematic thinkers I’ve had on the show when it comes to personal AI infrastructure. He’s spent the past year building a Claude- and Cowork-based productivity system that manages his Notion board, processes his Slack and email, and runs self-improvement loops every week without needing to be prompted. Beyond his own workflow, Daniel built and scaled a “Workstation” onboarding plugin that gets any Melio employee up and running with a personalized Claude setup in about 15 minutes.
Listen or watch on YouTube, Spotify, or Apple Podcasts
Why Daniel says the two rules that make any AI system powerful aren’t about the tool you pick
How his weekly prep automation fills an entire Notion board from scratch every Sunday, without his touching it
The morning brief feature that teaches Claude new internal terms on its own, so company jargon never slows it down
Why he describes Notion as “read-only” now, and what that says about how PM workflows are changing
The self-improvement loop that watches Daniel’s edits, spots recurring friction, and suggests new skills to build
How he uses a skill called “Improve” to filter the endless flood of AI tips without drowning in them
What he built to scale his personal system to every PM at Melio, and the UX lesson he learned the hard way
The capability gap that’s still keeping him from running 100% of his work through Claude
Optimizely—Your AI agent orchestration platform for marketing and digital teams
Jira AI SDLC—Get your tokens’ worth with Jira
(00:00) Daniel’s background and the PM overhead problem he needed to solve
(03:30) His AI stack at Melio
(05:00) The two rules that make any AI system genuinely powerful
(06:00) The Notion board Cowork built for him (and manages on his behalf)
(07:30) How he contextualizes Claude with voice memos, links, and recurring updates
(09:00) His weekly prep automation
(11:00) His morning brief
(15:00) How Claude flags unknown internal terms and saves them to context
(17:30) Running 70% to 80% of his workday through Cowork
(19:00) Chrome connector vs. MCPs for tools without integrations
(20:00) The real ROI question: why the early weeks feel slow, and why you push through anyway
(25:00) Scaling the system to the team with the Workstation plugin
(26:30) The self-improvement loop
(31:00) How the Improve skill separates actually useful AI tips from the hype
(32:00) The Workstation onboarding flow, and the UX lesson from distributing “Spectacular”
(38:00) The 20% Claude still can’t do, and what changes when it can
(41:00) What Daniel spends his reclaimed time on
(42:30) Claude rage
• Claude: https://claude.ai
• Notion: https://notion.so
• From a $6.90 newsletter to $3M API: How a non-coder built Memelord | Jason Levin: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how?utm_source=publication-search
• How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman: https://www.lennysnewsletter.com/p/how-the-founder-of-morning-brew-built?utm_source=publication-search
LinkedIn: https://www.linkedin.com/in/blumd/
Website: https://www.imdanielblum.com
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
2026-08-30 20:31:30
Tara Seshan leads product for ChatGPT Work at OpenAI (alongside previous podcast guest Andrew Ambrosino, who’s her engineering manager). Before OpenAI, Tara spent over six years at Stripe, where she joined as one of the first five product managers. She went on to lead product for Watershed, which Time magazine named one of the best inventions of 2022, and she is also a founder and Thiel Fellow. Most personally meaningful to me: Tara is one of the three inaugural Lenny’s Newsletter Fellows, a program I ran a couple of years ago to spotlight the most exciting up-and-coming product leaders.
Listen on YouTube, Spotify, and Apple Podcasts
The shift from “rowing” to “steering,” and why human judgment and ambition will become differentiators as AI takes on execution
How OpenAI thinks about building for model capabilities two to three months out
OpenAI’s best internal memes, such as “Is this maximally accelerated?” and “Are you mainlining it yet?”
Why ambition is the new bottleneck for companies, and why elevating others’ ambitions is now the key part of the PM job
Writing as thinking vs. writing as reporting
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Mercury—Radically different banking, now with Command
• LinkedIn: https://www.linkedin.com/in/tarstarr
• Newsletter: https://substack.com/@taraseshan
• Codex: https://chatgpt.com/codex
• ChatGPT Work: https://openai.com/chatgpt-work
• Stripe: https://stripe.com
• Watershed: https://watershed.com
• Thiel Fellowship: https://thielfellowship.org
• Meet your Lenny’s Newsletter Fellows: https://www.lennysnewsletter.com/p/meet-your-lennys-newsletter-fellows
• The rituals of great teams | Shishir Mehrotra of Coda, YouTube, Microsoft: https://www.lennysnewsletter.com/p/the-rituals-of-great-teams-shishir
• The nature of product | Marty Cagan, Silicon Valley Product Group: https://www.lennysnewsletter.com/p/the-nature-of-product-marty-cagan
• Product management theater | Marty Cagan (Silicon Valley Product Group): https://www.lennysnewsletter.com/p/product-management-theater-marty
• Patrick Collison’s examples of fast projects: https://patrickcollison.com/fast
• Inside ChatGPT: The fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley
• Andrew Ambrosino on X: https://x.com/ajambrosino
• Tyler Cowen’s website: https://tylercowen.com
• OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai
• “Chop wood, carry water” quote: https://buddhism.stackexchange.com/questions/15921/what-is-the-meaning-of-the-zen-quote-before-enlightenment-chop-wood-carry-wat
• 4 questions Shreyas Doshi wishes he’d asked himself sooner | Former PM leader at Stripe, Twitter, Google: https://www.lennysnewsletter.com/p/shreyas-doshi-live
• Alan Kay: https://en.wikipedia.org/wiki/Alan_Kay
• Brie Wolfson on X: https://x.com/zebriez
• The playbook for building high-talent-density teams | Adam Ward, Head of Talent at Cursor: https://www.lennysnewsletter.com/p/the-playbook-for-building-high-talent
• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein
• Sutter Hill Ventures: https://shv.com
• Snowflake: https://www.snowflake.com
• Mike Speiser on LinkedIn: https://www.linkedin.com/in/mikespeiser
• Footnotes and Tangents: https://footnotesandtangents.substack.com
• The Power Broker Book Club: https://www.robertcaro.org/copy-of-six-books-six-ny-times-book
• The Odyssey: https://www.imdb.com/title/tt33764258
• Rashomon: https://www.imdb.com/title/tt0042876
• Akira Kurosawa: https://en.wikipedia.org/wiki/Akira_Kurosawa
• Kevin Kwok on LinkedIn: https://www.linkedin.com/in/kevinakwok
• The Work You Do, the Person You Are: https://www.newyorker.com/magazine/2017/06/05/toni-morrison-the-work-you-do-the-person-you-are
• Ari Weinstein on X: https://x.com/AriX
• Sky: https://sky.app
• Dylan Field live at Config: Intuition, simplicity, and the future of design: https://www.lennysnewsletter.com/p/dylan-field-live-at-config
• Barbarian Days: A Surfing Life: https://www.amazon.com/dp/0143109391
• Anna Karenina: https://www.amazon.com/Anna-Karenina-LEO-TOLSTOY/dp/8175993421
• The Power Broker: https://www.amazon.com/dp/0394720245
• War and Peace: https://www.amazon.com/War-Peace-Leo-Tolstoy/dp/8175992832
• Wolf Hall: https://www.amazon.com/dp/0312429983
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Lenny may be an investor in the companies discussed.
2026-08-30 02:59:48
👋 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.
