2026-02-25 21:04:23
Jesse Genet is a homeschooling parent and entrepreneur who runs her household with five specialized OpenClaw agents. She layers them on top of her Obsidian “second brain,” deploys each on its own Mac Mini, and assigns every agent a distinct role—homeschool, finance, scheduling, development, and operations—so each one operates with clear scope and responsibility.
Listen or watch on YouTube, Spotify, or Apple Podcasts
How Jesse set up five OpenClaw agents, each with its own role, persona, SOUL.md file, and dedicated Mac Mini
The workflow for photographing an entire curriculum book and having an agent generate formatted, ready-to-teach lesson plans from the images
Using a coding agent to build a custom kids’ TV app from scratch and ship it to a real television in four days (with zero prior terminal experience)
Why Jesse treats agent onboarding like employee onboarding
The “decision file” trick and other incantations for managing agents that actually stick
Where multi-agent collaboration breaks down, and why no current messaging platform handles agent-to-agent handoffs well
How photographing every toy, book, and supply in the house lets the AI recommend real physical materials during lesson planning
The hands-free printing loop that took Jesse from scan → upload → email → print to “Sylvie, print this” in 30 seconds flat
Optimizely—Your AI agent orchestration platform for marketing and digital teams
(00:00) Meet Jesse and her “after Claw” life
(02:30) Layering OpenClaw on top of Obsidian
(04:44) Logging homeschool lessons automatically
(07:12) Turning books into a structured curriculum
(13:09) Using SOUL.md files to give each agent a personality
(14:39) Running multiple specialized AI agents
(16:43) Agent collaboration
(18:19) Partitioning data across Mac Minis
(27:00) Building a custom YouTube app with AI
(37:00) Creating a physical inventory from cupboard photos
(41:00) Printing from voice: reducing friction
(44:00) Managing agent memory and decision files
• OpenClaw: https://openclaw.ai/
• Obsidian: https://obsidian.md
• Slack: https://slack.com
• QuickBooks: https://quickbooks.intuit.com
• Google Gemini: https://gemini.google.com/
• Mac Mini: https://www.apple.com/mac-mini/
• Claude Code for product managers: research, writing, context libraries, custom to-do system, and more | Teresa Torres: https://www.lennysnewsletter.com/p/claude-code-for-product-managers
LinkedIn: https://www.linkedin.com/in/jessegenet/
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].
2026-02-24 21:45:06
👋 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
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One of the most frequent questions I’ve seen bubbling up in this community is how AI is impacting the interview process, both for interviewees and for hiring managers.
To find out, my Community Research Lead, Noam Segal, interviewed dozens of current and recent job seekers as well as hiring managers to learn how AI is transforming both sides of the hiring process.
Part 1 of the results from this research (below) focuses on job seekers—and the approach Noam took here is quite extraordinary. When he started analyzing what he’d learned, he realized the findings didn’t condense into tidy advice or tips. The best candidates had built interconnected systems to arm themselves for every step of the interview process. So Noam did something unique with this post: he encoded the results of his research—the successful techniques from over 30 participants—into a Claude Code–based coach you can plug-and-play into your interview process today.
Once you give it a go, if you have any feedback or suggestions to make this even more useful to you, feel free to email Noam at [email protected] (or ping him in our community Slack, @Noam Segal).
Let’s get into it.
For more from Noam, find him on LinkedIn. You can listen to this post in convenient podcast form: Spotify / Apple / YouTube.
Logan hadn’t interviewed for a new job in eight years. He’d been at one of the hottest companies in San Francisco, been promoted several times, and never felt the need to look elsewhere. When he decided to pursue a senior architect role at Anthropic, he hit a wall experienced engineers know well: interviewing is its own skill. Day-to-day, Logan solved architecture problems with full context and ample time. Interviews required him to grind LeetCode, whiteboard system designs on the spot, and compress years of expertise into rehearsed stories that fit a rubric.
Normally, preparing for senior engineering loops takes months. Logan had two weeks.
But Logan got the job. When I asked what mattered most, he pointed to his AI workflows as the primary reason he pulled it off.
He’s not alone. I interviewed over 30 tech professionals about how they use AI throughout the interview process. What I found went far beyond polishing resumes. People had built entire systems tailor-made for their own situations: ways to get feedback on what they actually said in interviews, methods to predict questions before walking in, workflows to surface stories they didn’t know they had. Each person I spoke with had figured out how to use AI for one or two pieces of the interviewing puzzle.
I started pulling together a research report from these conversations, but I quickly realized that most people on the job market are stressed and anxious enough. The best value I could offer wasn’t a list of tips but, instead, a way to plug-and-play the hard work these participants have already done. So I changed direction and took every interview AI technique that worked for these participants. Then I added a layer of professional coaching techniques and built a Claude Code–based coach that guides you through how to prepare for job interviews and reach your peak performance.
The Interview Coach I’m offering in this post will give you the critical feedback and real-world reps you need to confidently walk into your next interview room—and succeed.
But first, let’s talk about what’s broken about interview prep today, and how AI solves it.
Interview prep hasn’t changed much in the past decade. You rehearse your stories, maybe run through a mock interview with a friend, and walk into the real thing, hoping everything clicks. But afterward, we’re left with nothing more than a vague sense of how it went, guessing at what to fix. Companies don’t tell you why they passed. Your friends and mentors don’t know what your interviewers were looking for. You’re prepping blind, and the post-interview experience is mostly confusing. There’s simply no usable feedback loop in the interview process.
Of the issues that participants raised in my conversations, three stood out (and all stem from a lack of feedback):
The impostor spiral. Lindsey, a senior PM who’d been at the same company for 10 years, sent out applications and heard nothing. “I started having creeping impostor syndrome,” she told me. “Am I actually a good PM? What’s going on?” Without feedback, you can’t tell if it’s your resume, your approach, your skills, or just bad luck.
The blind grind. Charles spent hours tailoring each resume, hoping to highlight the right experiences. Joan researched companies thoroughly but had no way to know if she was focusing on what interviewers actually cared about. Neither had any signal that their effort was pointed in the right direction.
The practice gap. You can’t get better at something you only do once every few years. And the common advice—to interview at companies you don’t care about to get your reps in—is bad for everyone.
Participants who landed roles at top companies all closed the feedback loop themselves by building AI tools.
Greg fed his interview transcripts to Claude, trained it on best practices, and got line-by-line feedback on answers he thought went well but didn’t. Ella built a workflow where she’d paste a job description alongside her resume and have ChatGPT surface the exact gaps a hiring manager would flag and then help her close them before the review. Sean stopped guessing which experiences to highlight. He’d simulate the interview beforehand, test which stories landed, and refine them before the real thing.
Some participants’ systems overlapped; some didn’t. All took real work to build. The problem: assembling those puzzle pieces for yourself would take weeks, and turning them into a successful system is a challenge most people can’t or won’t take on.
The tool you’ll find below pulls all that research together into an AI job interview coach that also leverages best-practice coaching techniques—self-reflection before feedback, powerful questions over prescriptions, co-creation over telling.
Until now, this level of coaching was reserved for those of us who could afford $300-an-hour career coaches. But even a great coach has limits: they can’t analyze a full interview transcript in minutes, track your weak spots across every session, or be there at 11 p.m. when you’re anxious about tomorrow. AI has no such limits, and it’s essentially free.
An AI job interview coach:
Is always on. An AI coach is free, instant, and omnipresent, whenever you need it.
Remembers everything. AI tracks your patterns across sessions, from your weak spots to your standout strengths.
Keeps it real. You can feed call transcripts of your interviews and get feedback on what you said, rather than what you think you said.
So let’s get you set up with an AI interview coach and help you land your next role.
The Interview Coach is a Claude Code project: a set of instruction files that turn Claude into a rigorous interview coach. You run it by opening the project folder, and it takes over from there.
The coach handles everything that the participants I interviewed were doing (and much more):
Scores your interview responses and tells you exactly what to fix, based on what you said, not what you think you said
Runs a quick company research brief (culture, interview style, and whether the role is a fit) before you even have an interview scheduled
Generates company-specific prep with predicted questions, story mapping against your story bank, interview format guidance, and likely concerns about your background
Runs full mock interviews (4-6 questions, no feedback until the end) and targeted drills that push back, interrupt you, and force you to think on your feet
Mines your experience for stories you didn’t know you had
Coaches you on standing out so you don’t sound like every other candidate who prepped with AI
Helps you strengthen weak stories and retire ones that aren’t landing
Debriefs rejections so you learn something from every no
Rewrites your weakest answers at interview-winning quality, side by side with the original, so the improvement is concrete—not just “add more detail”
Tracks your self-assessment accuracy over time—the gap between how you think you did and how you actually did
Coaches post-offer salary negotiation with exact scripts and fallback language
We’ll use the Claude desktop app to run our AI interviewing coach.
Here’s how to get started:
Install the Claude desktop app.
Download the project from GitHub (click Code → Download ZIP and unzip, or Git clone if you prefer).
Rename SKILL.md to CLAUDE.md (just right-click → Rename in your file browser).
Open the Claude desktop app. If you’re already using Claude or Claude Cowork on desktop, you’ve probably encountered Claude Code. Select the “Code” tab.
Open the relevant folder.
Type kickoff and your coach will load.
Claude walks you through the rest, one question at a time.
The whole setup takes about five minutes, and Claude writes a coaching_state.md file that tracks everything across sessions: your stories, scores, patterns, and progress.
Need help using the coach? Type help and you’ll get this:
Once you’ve run kickoff, everything else works through simple commands. Here’s how to use the coach for an upcoming interview.
Quick Prep vs. Full System: Decide if you want to quickly use it for an upcoming interview or want to spend a bit more time setting it up as a comprehensive interview system.
Role selection: Tell it what kind of job you’re looking for.
Feedback directness: Tell it how direct you want feedback to be (1-5 scale, default: 5).
Interview timeline: Share where you’re at in the interview process.
Interview history: Share whether you’ve been interviewing already, how many interviews you’ve done, and how they’ve gone.
Biggest concern: Finally, brain dump what’s stressing you out right now.
At this point, you’re done with the configuration, and Claude Code will ask to learn about your candidate context. Specifically:
Once you provide these details, Claude Code will write an update to its memory and share a summary:
Interested in a company but don’t have an interview yet? Type research [company name]. Claude pulls together a quick brief on the company’s culture, interview reputation, and how your background maps to what they typically look for. It’s a lighter version of prep, useful when you’re still deciding where to apply or when you want a read on fit before investing in full prep.
In the same conversation where you ran kickoff (or a new one):
Type: prep [company name]
You’ll be prompted to attach a job description if there is one and be asked some follow-ups.
Claude Code may request additional information from you. Insider intel is always helpful.
Claude generates a one-page prep brief with what this company optimizes for (based on the JD and their values), your unique positioning for this specific role, 7 to 10 predicted questions tagged by competency with story mapping (which of your stories to use for each, and where the gaps are), likely concerns about your background with one-sentence counters, a culture read on what this company rewards in interviews, and questions for you to ask them.
During prep, you can optionally share LinkedIn profile URLs for your interviewers. The coach needs actual LinkedIn URLs; names alone aren’t reliable enough due to false-match risk. You can include the URLs up front or provide them when the coach asks.
For each interviewer, the coach produces an “Interviewer Intelligence” card covering: their functional lens, career path signals, recent public interests, what you have in common, predicted focus areas, rapport hooks, and watch-for signals (likely interviewing style based on seniority and function). Each card includes a confidence rating so you know how much to rely on it.
If you have a story bank, the coach also maps specific stories to each interviewer—which story to deploy for which person and why. This interviewer intel then flows into other commands: mock calibrates its persona to match the interviewer, hype references their likely focus area, thankyou personalizes your notes, and questions tailors rapport-building questions.
Record your interviews using a tool like Granola (available as part of Lenny’s Product Pass) or built-in transcription within Zoom or Google Meet. Then:
Paste your transcript into the chat
Type: analyze
Claude starts by asking how you think it went. Which answers felt strong? Which felt off? Then it scores each answer on five dimensions: substance, structure, relevance, credibility, and differentiation, on a 1-5 scale.
After scoring, it triages: identifies your primary bottleneck, diagnoses the root cause (narrative hoarding? conflict avoidance? status anxiety?), and branches the coaching accordingly. You get a delta sheet with what’s working, what to fix, and which stories to sharpen or retire. If you want, it’ll also give you a side-by-side rewrite of your weakest answer, bringing it up to a quality rating of 4-5.
Then it asks which growth area feels most within your control to change by the next interview. You pick what to work on. This builds the kind of self-awareness that actually shows up in the room.
Over time, it tracks the gap between your self-ratings and the coach’s scores. If you consistently rate your structure higher than it actually is, the coach names that pattern. This calibration—knowing where your blind spots are—is often more valuable than any individual score.
The commands above handle one interview at a time. The commands below, which also come with the coach, build a system across your entire job search.
Build your story bank. Type stories. Claude uses reflective prompts to surface career stories you might overlook. Questions like “When were you at your best at work? What made it different?” and “What’s a decision you’d make differently with hindsight?” It indexes each story by skill, impact, and strength. Most people discover they have more usable material than they thought. Once you have at least 8 stories, you can run a rapid-retrieval drill: Claude throws 10 interview questions at you in rapid succession, and you have 10 seconds per question to name the right story and deliver your opening line. A well-organized story bank is useless if you can’t access it under pressure.
Practice under pressure. Type practice. Choose from eight drill types in progression order: constraint drills at 30 seconds, 60 seconds, 90 seconds, and 3 minutes (practice ladder), handle skepticism and “so what?” pressure (practice pushback), redirect when a question doesn’t match your prep (practice pivot), handle “I don’t have an example for that” moments (practice gap), face role-specific specialist scrutiny (practice role), manage multiple interviewer personas (practice panel), high-pressure simulation (practice stress), or rapid-fire story matching under time pressure (practice retrieval). Drills are gated: you advance when you hit scoring thresholds, not just when you feel ready. After each round, Claude asks what you’d rate yourself before sharing your scores—strengths first, then gaps, then one specific change for the next round.
Run mock interviews. Type mock behavioral Stripe (or whatever format and company you’re prepping for). Unlike drills, which build individual skills, mocks simulate a complete 4- to 6-question interview. Claude delivers questions one at a time with no feedback between them. At least one question targets a known gap in your story bank.
Afterward, you get holistic arc feedback: how your energy shifted across questions, whether you reused stories, how you adapted to interviewer cues, and where you were strongest and weakest across the full session. Individual drills build skills. Mocks test the whole package.
Please note that this is the most limited feature of the coach. Interview processes differ across companies, and interviews are changing. There’s no way to support all interview types. Therefore, the coach elicits as much information as possible from you and tries to be helpful, with clear caveats.
If you’re in product, I highly recommend reading Ben Erez’s product sense and analytical thinking articles, or check out his PM interview copilot.
Stand out. Type stories. Beyond building your story bank, Claude coaches you on differentiation so you don’t sound like every other candidate who prepped with AI. It extracts “earned secrets” from your experience (insights you can only claim because you lived them), sharpens your point of view, and helps you retire stories that aren’t landing.
Anticipate concerns. Type concerns. Claude asks what concerns you expect the interviewer to have, validates the ones you got right, and surfaces the ones you missed—each with counter-evidence and the best story from your story bank to deploy.
Prepare questions to ask. Type questions. Claude generates 5 tailored, non-generic questions—each with why it’s strong, who to ask it to, what the interviewer might ask back, and a prepared response for the reversal.
Track your progress. Type progress. Claude asks what patterns you’re noticing before showing you the data: which competencies are improving, where you’re stagnant, and what to drill next. It closes by asking what you’ve learned about yourself as a communicator, because that self-knowledge is what transfers to the interview room.
Before you walk in. Type hype. Claude generates a 60-second hype “reel” (your biggest wins and strongest metrics, formatted to read aloud), a 3x3 sheet (3 likely concerns with counters, plus 3 questions to ask), a 10-minute physical and mental warmup routine, and mid-interview recovery scripts for when you bomb an answer or get a question you have no story for.
Send a strong follow-up. Type thankyou after an interview. Give it context on what you discussed, and it drafts a thank-you note that reinforces your strongest moments from the conversation.
Negotiate your offer. Type negotiate after you get an offer. Share the details, your ideal outcome, and your walk-away point. Claude helps you interpret the market data you bring (from sources like Levels.fyi and Glassdoor), identifies the most negotiable components, and gives you exact scripts—not just strategy, but the specific words to say, including fallback language for when they push back.
After a rejection. Type debrief. Tell Claude what happened, and it runs a structured debrief to extract what you can learn and carry forward, including what proof points to reinforce and what questions to ask the recruiter.
Done with your job search? Type reflect. Whether you accepted an offer or decided to stay put, Claude runs a retrospective on your full journey: what improved, what patterns persisted, what you learned about yourself as a communicator, and what to carry into your next role when the time comes.
2026-02-24 21:03:23
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Add the private feed to your podcast app at add.lennysreads.com
What if you could get line-by-line feedback on every interview answer you gave, predict questions before you walk in, and surface stories you didn't know you had? Noam Segal, Lenny's Community Research Lead, interviewed over 30 tech professionals about how they use AI throughout the interview process, and used the findings to build a free Claude Code-based interview coach. His coach encodes every successful interview technique he uncovered (and then some), making it an invaluable tool you can plug into your job search today.
In this episode, you’ll learn:
Why interview prep is broken (and why there’s no usable feedback loop)
How the most successful candidates built AI systems to close the gap
What the free AI Interview Coach does and how to set it up
How to stop sounding like every other candidate who prepped with AI
The action checklist for before, during, and a…
2026-02-24 00:02:26
Brought to you by:
Brian Lovin, a product designer at Notion, built a shared AI-powered “prototype playground” that lets the entire design team turn Figma designs into working code using Claude Code. Instead of staying in static mockups, the team prototypes directly in a shared Next.js environment connected to real AI models—so they can test ideas in the browser, catch edge cases early, and design for what’s actually possible. In this episode, Brian breaks down how the system works, how he uses plan mode, slash commands, and custom Claude Skills to automate repetitive tasks, and why his core rule for working with AI is simple: when Claude asks you to do something, teach it to do that thing itself.
• How Notion Designs with AI: Brian Lovin’s Prototype Playground and Claude Code Workflows: https://www.chatprd.ai/how-i-ai/how-notion-designs-with-ai-brian-lovins-prototype-playground-and-claude-code-workflows
• Automate Your Git and Deployment Workflow with a Custom AI Command: https://www.chatprd.ai/how-i-ai/workflows/automate-your-git-and-deployment-workflow-with-a-custom-ai-command
• Build an AI Workflow to Convert Figma Designs to Code with a Self-Correction Loop: https://www.chatprd.ai/how-i-ai/workflows/build-an-ai-workflow-to-convert-figma-designs-to-code-with-a-self-correction-loop
• Use Claude Code to Rapidly Build Interactive Prototypes from Ideas: https://www.chatprd.ai/how-i-ai/workflows/use-claude-code-to-rapidly-build-interactive-prototypes-from-ideas
Designs are shifting to code-first prototyping. While Brian still spends 60% to 70% of his time in Figma, he believes designers increasingly need to understand what AI models can actually do. This requires working with real models in code to “design something that’s plausible and possible.”
Encounter reality as early as possible in the design process. Brian’s philosophy is to move designs from “napkin sketches” toward production code as quickly as possible. When you try designs in a browser instead of Figma, you immediately notice problems with loading states, screen sizes, and interactions that static designs hide.
The “prototype playground” is a shared Next.js app that centralizes all design prototypes. Instead of designers working in isolated repositories with different setups, this shared environment makes it easy to discover what others are working on and reuse code. The repository organizes prototypes by designer name and provides shared components for Notion-style UI elements.
Brian found it impossible to design good AI experiences in Figma: “You can design what the chat input looks like ... but what you can’t design in Figma is what it actually will feel like to use that thing.” Code prototypes connected to real AI models are essential for understanding edge cases and failure modes.
When Claude asks you to do something, teach it to do that thing itself. Brian’s most important rule for working with AI is to avoid manual intervention. For example, instead of manually checking if a prototype works in the browser, teach Claude to launch Chrome, test the functionality, and verify the results.
Claude Skills can solve specific recurring problems. When AI consistently hallucinated icon names (using “search” instead of “magnifying glass”), Brian created a skill that programmatically searches for icons and their synonyms across thousands of files. This demonstrates how AI can be taught to overcome its own limitations.
Custom slash commands dramatically simplify complex workflows. Brian created commands like “/figma” that handle everything from checking if MCPs are installed to extracting designs, implementing them as code, and verifying the results through multiple iterations. This makes advanced AI techniques accessible to less-technical team members.
▶️ Listen now on YouTube | Spotify | Apple Podcasts
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.
2026-02-23 21:03:17
Brian Lovin is a designer at Notion AI who has transformed how the design team builds prototypes, by creating a shared code environment powered by Claude Code. Instead of designers working in isolated repositories or limited to static Figma designs, Brian built a collaborative “prototype playground” where the entire team can create, share, and iterate on functional prototypes. In this episode, Brian demonstrates how AI-assisted coding has dramatically accelerated the design process and why code-based prototyping is essential for building AI-powered products.
Listen or watch on YouTube, Spotify, or Apple Podcasts
How Brian built a shared Next.js app that serves as a collaborative prototyping environment for Notion’s design team
Why encountering “reality” early in the design process leads to better products
How to use Claude Code’s “plan mode” to get better results when prototyping
The power of custom Claude slash commands and skills to automate repetitive tasks
How to transform Figma designs into working code with a single prompt
Why AI-powered products can’t be effectively designed in static tools like Figma
Brian’s rule for working with AI: “When Claude asks you to do something, teach it to do that thing itself”
WorkOS—Make your app enterprise-ready today
Orkes—The enterprise platform for reliable applications and agentic workflows
(00:00) Introduction to Brian
(02:36) Building for B2B SaaS
(04:42) Notion’s prototype playground: what it is and how it works
(08:01) The technical background of designers using the playground
(10:52) Demo: building a podcast player prototype
(16:00) Actionable tips for better Claude Code results
(20:16) Analyzing the result
(20:30) Creating slash commands to simplify the workflow
(23:03) Turning Figma designs into production-ready code
(25:06) MCP frustrations and tips
(30:54) Demo: creating a custom “find icon” skill
(35:03) Demo: Creating a deploy command to simplify GitHub workflows
(41:09) Quick recap
(41:59) How code-based prototyping is changing design at Notion
(46:48) Brian’s tool preferences
(48:42) Prompting techniques when AI is not listening
• Claude Code: https://claude.ai/
• Cursor: https://cursor.sh/
• Next.js: https://nextjs.org/
• Figma: https://figma.com/
• Monologue: https://www.monologue.to/
• GitHub: https://github.com/
• GitHub Desktop: https://desktop.github.com/
• Tailwind CSS: https://tailwindcss.com/
• Bun: https://bun.sh/
• Claude Skills explained: How to create reusable AI workflows: https://www.lennysnewsletter.com/p/claude-skills-explained
Website: https://brianlovin.com/
LinkedIn: linkedin.com/in/brianlovin
X: https://twitter.com/brian_lovin
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].
2026-02-22 01:08:16
👋 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.
