AI Tools for UX

https://www.linkedin.com/learning/using-ai-tools-for-ux-design/ai-tools-for-ux-designers
https://www.msn.com/en-us/money/careersandeducation/openai-ceo-sam-altman-says-ai-agents-are-like-a-team-of-junior-employees/ar-AA1G1xIt?ocid=msedgntp&pc=U531&cvid=6fde0dc95f6843fdc9f716d369920d45&ei=13 
>> Linked in Post >> 
I automated 90% of my startup empire.

41 AI agents & tools for coding, marketing, seo, research, design, sales, accounting, legal, paid ads, data entry, scraping, and everything else:

1. Suna.so is an Open Source generalist AI Agent.
demo: https://lnkd.in/dKQup2br

2. seobotai.com AI Agent for Blog SEO
demo: https://lnkd.in/dE68GswA

3. coderabbitai can do the code review.
demo: https://lnkd.in/dtTsp4Zn

4. Spark AI - an agent that can generate slides, icons, images, videos and even make calls.
demo: https://lnkd.in/d2bKiVDk

5. Tinyadz.com AI Agent to connect ad buyers with ad publishers.

6. firecrawl.dev cost-efficient web action agent.

7. Agent swarms from Lindy AI
demo: https://lnkd.in/dHm_HTEV

8. Zara - AI Agent for HR
demo: https://lnkd.in/dY35wsKq

9. listingbott.com - AI for manual form filling and signups.
demo: https://lnkd.in/dzRnuXgR

10. AI Agent that can use computer and !!! also can use the smartphone.
demo: https://lnkd.in/d2dyfz6Q

11. Marko - AI Agent for visual marketing.
demo: https://lnkd.in/d3W2VXzR

12. unicornplatform.com - AI to build landing pages, waitlist, personal page, directories & static websites
demo: https://lnkd.in/dBzHFTrk

13. Opera browser operator,
demo: https://lnkd.in/dv8WJT9h

14. ARI: The world's first professional-grade deep research agent
demo: https://lnkd.in/d9qpUwxD

15. Proxy - a very capable web-browsing agent.
demo: https://lnkd.in/d4ftSuHV

16. OpenAI operator agent is using Replit code gen agent to build an app.
demo: https://lnkd.in/dqbnZugf

17. Jave - Email agent
demo: https://lnkd.in/dvDu4BpP

18. Stack AI - a platform to build your own AI Agents
demo: https://lnkd.in/da7-NnV5

19. Postman's AI Agent builder.
demo: https://lnkd.in/dxQKrfgC

20. Open AI - Operator.
demo: https://lnkd.in/dYjuyD33

21. SkyReels - text-to-film AI Agent.
demo: https://lnkd.in/dJZeQMQh

22. Higgsfield - a multi-agent platform that uses the best creative AI models to adapt story ideas into ready-to-watch videos.
demo: https://lnkd.in/dN6_XDbi

23. AI Agents from Google
demo: https://lnkd.in/dSGxaQz2


>> See email <<

Make product management fun again with AI agents
A guide to AI agents for product managers
Apr 29
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Preview
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Guest post







 Welcome to a  subscriber-only edition  of my weekly newsletter. Each week I tackle reader questions about building product, driving growth, and accelerating your career. For more: Lennybot | Podcast | Courses | Hiring | Swag
But first: Introducing Lenny’s Reads—an audio podcast edition of Lenny’s Newsletter 

Many of you have told me that you’d prefer to listen to this newsletter instead of read it. I’m excited to share that now you can.
Introducing Lenny’s Reads: an audio podcast edition of Lenny’s Newsletter.
Every newsletter post will now be transformed into an audio podcast, read to you by the soothing voice of Lennybot. You can subscribe and check out the first few episodes (including this very post below!) here: Spotify, Apple, YouTube.
We’ll ship an audio version of each post within 48 hours after it goes live. Paid subscribers will hear the full post, and just like with the newsletter, free subscribers will get everything up to the paywall.
For a limited time, however, the first five episodes of this new podcast will be completely unpaywalled and available to all listeners. Check out the first few episodes (and subscribe) here: Spotify, Apple, YouTube.
I’m excited to hear what you think.
Now, here’s today’s post. . .
Tal Raviv’s last guest post on working “unfairly” as a PM is my fourth most popular of all time, and his podcast episode is a huge fan favorite. Now he’s back with a guide to using AI agents to make PMing fun again, which I predict will be in the top 5 most popular posts of all time. Tal was an early PM at Patreon, Riverside, Wix, and AppsFlyer and now teaches one of the fastest-growing AI PM courses. Check out his upcoming free 45-minute lightning lesson, “How AI PMs Slice Open Great AI Products,” on May 13th. You can find Tal on LinkedIn and X.

What’s beautiful about product management is that everything is our job.
What’s maddening about product management is that everything is our job.
But while we’re busy with draining-yet-essential tasks (penning updates, wrangling meetings, syncing sources of truth, or acting as mission control), we’re displacing critical time to get up to speed on new tech, immerse ourselves in customer conversations, analyze data, build trust, and be thoughtful about the future: the important parts of our job.
Productivity hacks and cultivating self-reliant teams can help, but with tech orgs flattening, more of these types of repetitive but necessary tasks are falling on fewer product managers.
Enter AI “agents.” Unlike chat-based LLMs, agents can listen to the real world, make basic decisions, and take action. In other words, they’re becoming talented enough to take on our least favorite, least impactful—but still necessary to do—PM tasks.
If you’re like me, you’ve heard the promises and proclamations about how agents will reshape productivity, but your workday hasn’t changed at all yet. It’s not you—operationalizing AI agents for product work is hard. Where to start? What tools? What about security? Costs? Risks? And why is there such a #$@% learning curve?
After interviewing founders of AI agent platforms, running numerous usability sessions with PMs building their first agents, and gathering insights from a hands-on workshop for over 5,000 product managers, I’ve compiled their collective wisdom. This post shares their insights on what works—and what doesn’t—in the real world. We’re first going to learn how to build an AI agent, hands-on. Then I’ll share a unified framework for any PM to plan their second (and third) agent. We’ll cover best practices, pitfalls, powers, and constraints.
While AI agents aren’t magic genies, they can take a lot of repetitive, energy-draining PM tasks off our plate, let us focus on the most important work, and even make our jobs a bit more fun.
What is an AI agent?

The term “AI agent” is admittedly fuzzy. Instead of debating names, it’s more useful to identify behaviors. Think of the term “agent” as a spectrum, where AI systems become “agentic” the more of the following behaviors they exhibit:
Acts proactively, as opposed to waiting to be prompted.
Makes a plan, as opposed to being given instructions.
Leverages context, accessing an internal knowledge base about your company and team, pulling the most up-to-date information regularly.
Draws on live data such as a web search or a support queue (as opposed to relying on static training or manually uploading a file).
Takes real-world action. Updates a CRM, runs code, or comments on a ticket, as opposed to only making recommendations.
Creates its own feedback loop. Watches its own output and iterates without human assistance.
With new startups launching weekly, this framework helps me appreciate each product’s place in the landscape. Each column here is a product category, and each row is a useful behavior.

“Takes real-world action” is the criterion with the biggest variance for two reasons: (1) there is a wide variance in available software integrations, and (2) the trust level a company already has in this tool to allow the integrations needed to take real-world action. Currently, I believe that Zapier has the advantage across both, though I’m curious to see how this changes with new protocols.
Notice how each row checks some, but not all, boxes. AI systems are still very early, and each category approaches the opportunity from a different starting point.
Of all the flavors and approaches of agents today, the category I refer to as “AI automations” is currently the most practical for helping product managers with monotonous busywork. This includes tools like Zapier, Lindy AI, Relay App, Gumloop, Cassidy AI, and so on. In this post, we’ll focus on this category of agents, but however you define AI agents, the important thing is that they can help us spend more time with our customers, give more attention to our teammates, build better products, and have more fun.
Launch your first AI agent right now
Let’s quickly launch an AI agent that preps you for a customer call, and let it run in the background while you read the rest of this article.
These instructions are for Zapier Agents (note: I have no affiliation with Zapier, I’m just a fan of this tool) and will take just 10 quick steps.
Ingredients
Zapier Agents
Google Calendar (or Outlook)
Slack (or MS Teams)
Instructions
1. Create a Zapier account and navigate to “Zapier Agents.”
Note that Zapier Agents is a new, separate product from the classic “Zaps” you may be familiar with.

2. Select “Create a custom agent,” give it a name, and click “Start from scratch.”

3. Click “Configure” at the top and “Create behavior.”

4. Set our automation to run every day at 8 a.m.

5. Paste the following prompt into the instructions field:
Look at all my calendar events for today [CALENDAR FIND MULTIPLE EVENTS], find all the external participants (people who have a different email address domain than mine), and do a web search for each of those email addresses. Summarize who these people are (where they work, what title they have, how long they’ve been in that role, information on past roles, and anything else you deem relevant) and send me a message in Slack.
[SEND DIRECT MESSAGE] for each meeting with external participants (skip meetings with only internal participants) and tell me:
- Name of meeting
- Time of meeting
- Name and relevant information of each external participant (formatted on their own line)

6. Delete the placeholder text “[CALENDAR FIND MULTIPLE EVENTS]” and, with your cursor still in that spot, click “Insert tools.”

If you’re using Google Calendar, choose “Find Multiple Events.”

7. Link your calendar software, and select your personal calendar.

You should see the calendar block inline with your prompt:

8. Delete the placeholder text “[SEND DIRECT MESSAGE]” and, with your cursor still in that spot, use the “Insert tools” menu to connect your favorite messaging service.

I’m using Slack, and I’ll set it to be able to send me a direct message.

Constraining the agent to only DM frees us for worry-free experimentation.
9. Woo! We’re ready to run. Click “Save instructions & test.”

You’ll get a message like this:

10. If you’re happy with this, go ahead and turn it on. (Don’t sweat this decision, since the only action it can take is privately DMing you.)

Congratulations. You’ve set up your first PM AI agent, now running in the background while you sit back and read Lenny’s Newsletter.
Plan your second AI agent
With our new agent running in the background, let’s unpack what we did . . . by planning our second AI agent.
The first step is deciding what problem we want to solve. For this exercise, we’ll focus on opportunities that pre-AI automations couldn’t address.
Ask yourself: What ongoing work requires some judgment and writing abilities—but not your full expertise and intuition? Put another way, if my company assigned me a junior intern, what would I have them do?
Below are examples of use cases where product managers have gotten a lot of value from AI agents. (If any of them jump out at you, feel free to copy.)

Note: Try to phrase this goal in one or two sentences, exactly the way you would in a Slack message to a junior intern.
I recommend choosing ongoing tasks that arrive continually. AI automations shine in one-at-a-time, repetitive tasks.
In contrast, I don’t recommend designing for a big, one-time “batch” task (e.g. sifting through dozens of emails that already arrived). For batch tasks, consider working directly from an AI system:
Export the data and manually upload to Claude, Gemini, or ChatGPT.
Use a built-in tool like Slack AI, Notion AI, Gemini for Workspace, or Microsoft Copilot.
Connect directly to software apps with MCP integrations.
Design how your agent is going to work
Now that we’ve chosen what to delegate, let’s design how it’s going to work. Below is a checklist for planning an agent, regardless of what platform we’ll choose. Keep your chosen task in mind as you work through the list:
 Do I understand this task?
 Could I start even smaller?
 Can I keep the downside low?
 Am I giving enough context?
 Am I staying close to raw customer signals?
1. Do I understand this task?
Just as when delegating to people, the fanciest AI will perform only as well as the instructions it’s given.
Are you clear on how you would accomplish this task manually, with mouse, keyboard, and coffee? Do you know where the key information lives? Can you clearly put it in words? The first step is looking inward.
As Max Brodeur-Urbas, the CEO of Gumloop, put it, “Understanding a problem should be the only prerequisite to solving it.”
The best way to gain this clarity is to do the task once or twice. That way, you can provide examples of what success looks like. In our customer prep call example, think of the last time you prepared for a customer call. What sources did you intuitively consult? What information were you primarily looking for? (And what wasn’t relevant?)
And if you’ve already been doing this task manually for a while, you’ve got this step covered. For example, when I watched a colleague set up a “weekly updates” agent, he already had a channel full of examples he could copy and paste as templates.
2. Could I start even smaller?
Since AI agents evoke the image of a magical genie, it’s tempting to ask for all of our wishes at once.
It’s more realistic to approach our agent like a new product, or a new process. As PMs, we know that to make both successful, we need to start small and cut scope. (Ironically, as PMs, it’s hard to cut scope when it’s for ourselves.)
Ask yourself, what’s the worst part? The step you dread the most? Let’s start by delegating only that. We’ll do the rest of the steps manually first.
If your dream is to monitor five competitor websites, first launch with one.
In our customer prep call example, it would have been tempting to have it scan the web, our Slack, Gong, Zendesk, Mixpanel, and HubSpot. However, we launched it with one data source to keep it simple to start, which allows us to build from there.
3. Can I keep the downside low?
Murphy’s Law is as true for AI as it is for people: Anything that can go wrong, will go wrong. To sleep well at night, let’s ensure that any mistakes don’t really cost anything.
Don’t try to predict how much a model will hallucinate (it will) or if your workflow will get it right the first time (it won’t). Instead, design your agent in a way that gives you all the upside and caps the downside.
Examples of keeping a low downside, with high benefit:
Instead of pinging a Slack channel → Send me a DM that I can copy-paste
Instead of sending an email → Create a draft and star the thread for my review
Instead of making a decision → Make a recommendation
Instead of modifying a document → Append suggestions at the bottom
Keeping a low downside is also a matter of physically restricting access with permissions (and even more granular). This is where agent platforms that have hard access constraints can really shine, because those physically limit the AI system’s behavior.

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