Lenny’s Podcast131 min
Hamel Husain & Shreya Shankar · Sep 25, 2025
Key learnings- Evals are systematic ways to measure and improve an AI application, essentially data analytics on LLM behavior, replacing…
- Start with error analysis by manually reviewing around 100 sampled traces and writing short notes on the first upstream failure…
- Product people with domain expertise should lead open coding; appoint one 'benevolent dictator' whose judgment you trust instead…
Lenny’s Podcast87 min
Elizabeth Stone · Jul 19, 2026
Key learnings- Expect a 'storming' phase with new technology; don't abandon AI, but be deliberate about capturing benefits while limiting costs…
- Let product, design and data people prototype early against a clearly defined business problem, while still working with…
- Invest in source-of-truth data, guardrails for shipping and testing, and review processes where AI output needs verification, and…
Lenny’s Newsletter♥ 459
Dec 16, 2025 · 16 min
Subscriber post — summary onlyAmir Klein, writing via Lenny's newsletter, explains how he built an AI 'second brain' using ChatGPT Projects while launching monday.com's first AI agent. He covers giving the project a defined personality, feeding it…
The Beautiful Mess · John Cutler♥ 83
Jun 7, 2026
The piece argues that organizations asking their teams to find ways to use AI for company benefit are undermining that goal by stripping away the agency people need to experiment and decide. The core claim is that…
Lenny’s Podcast100 min
Zevi Arnovitz · Jan 18, 2026
Key learnings- Start slowly with a ChatGPT project acting as a skeptical 'CTO' before moving to coding tools, gradually easing into code to…
- Capture ideas quickly as Linear issues using reusable slash commands, so you can pick them up later with context already gathered.
- Separate the workflow into explore, plan, execute, review, and document phases, and have the AI ask clarifying questions before…
Lenny’s Podcast108 min
Cat Wu · Apr 23, 2026
Key learnings- Shorten the path from idea to user: AI-era timelines have shrunk from six months to days or a week, so PMs should focus on…
- Ship features as clearly labeled research previews to lower the commitment of launching and get fast user feedback that can be…
- Set a clear, specific goal for who the key user is and what success means, which rules out many approaches and speeds decisions.
Lenny’s Newsletter · Free post♥ 268
Jul 9, 2024 · 31 min
Lenny Rachitsky and prompt engineer Mike Taylor ran a blind test to see how close AI comes to doing core product manager work. They argue that most headlines saying AI 'can't do X' rely on weak models and basic prompts…
Lenny’s Podcast78 min
Dr. Fei Fei Li · Nov 16, 2025
Key learnings- Large labeled datasets were the missing ingredient that unlocked modern AI; big data, neural networks, and GPUs together formed…
- Pursue a north-star problem and commit to it for years, as ImageNet's object recognition focus did for her lab.
- Be willing to take intellectual and career risks, such as leaving tenure track or joining new ventures, when the mission and…
Lenny’s Podcast97 min
Edwin Chen · Dec 7, 2025
Key learnings- Define quality in rich, specific terms for each domain (e.g., what makes a poem Nobel-worthy, not just whether it has eight…
- Gather thousands of signals on each worker and task, from background and expertise to performance, and use them like an ML…
- Don't trust benchmarks at face value: many contain wrong answers, and optimizing for them can make models worse at messy…
Lenny’s Newsletter♥ 1,017
Jul 8, 2025 · 20 min
Subscriber post — summary onlyLenny Rachitsky collected over a thousand reader stories about personal tools they built through vibe coding, then curated 50+ examples spanning health, parenting, work, music and fun. His core argument is that…
Lenny’s Podcast102 min
Chip Huyen · Oct 23, 2025
Key learnings- Focus on what actually improves AI apps, such as talking to users, improving data, writing better prompts, and optimizing…
- Be cautious about committing to newly released technologies that have not been widely tested, since switching away from them…
- Fine-tuning and post-training can shape model behavior a lot, and many teams now focus effort there because base pre-training…
Lenny’s Newsletter♥ 384
Nov 19, 2024 · 17 min
Subscriber post — summary onlyHilary Gridley, a WHOOP product director, argues that AI can help managers become "supermanagers" by scaling their coaching and feedback. She shares five practical strategies, from building custom GPTs that replicate…
Lenny’s Podcast123 min
Lazar Jovanovic · Feb 8, 2026
Key learnings- Start a project with several parallel attempts (brain dump, typed prompt, design reference, code template) to find the strongest…
- Spend most of your time planning and chatting with the AI rather than executing, since clarity matters more than raw build speed.
- Write a set of source-of-truth docs (master plan, implementation plan, design guidelines, user journeys, tasks.md) so the agent…
Lenny’s Newsletter♥ 497
Apr 11, 2023 · 4 min
Subscriber post — summary onlyLenny Rachitsky argues that learning to work alongside AI tools like ChatGPT is quickly becoming a baseline skill for product managers, not something that replaces them. The post surveys a dozen concrete ways PMs…
Lenny’s Podcast102 min
Dianne Penn · Jul 26, 2026
Key learnings- Treat evals as the new PRDs: turn vague user complaints into concrete, reproducible test sets that researchers can act on and…
- Dig into user transcripts, not just pixels, to find where the model actually failed, such as wrong tool calls, bad retrieval, or…
- Keep strong conviction about a theme or area while staying loose about the specific prototype, and revisit bets across one to two…
Lenny’s Podcast102 min
Paul Adams · Oct 26, 2023
Key learnings- Before redesigning a product for AI, start from the core premise: why people use it and what problem it solves, then map what AI…
- Treat AI as a strategic shift rather than a feature; Intercom ripped up its strategy and rebuilt from first principles after the…
- Build real machine learning depth on the team, but spread AI skills across product teams instead of isolating AI work in a…
Lenny’s Podcast148 min
Amol Avasare · Apr 5, 2026
Key learnings- Expect most growth time to go to 'success disasters' once growth is rapid; firefighting breaking systems matters as much as new…
- In AI-first products, skew the growth portfolio toward larger swings, since future product value could be orders of magnitude…
- Treat activation as a top lever in AI products: identify user traits that predict which feature fits them, then guide them there.
@joulee · Julie Zhuo on X♥ 228
Too many teams are doing AI transformation poorly, so I wrote down everything I have learned in my journey and in chatting with many other leaders. A long read, so get your coffees.
Lenny’s Newsletter♥ 888
Mar 31, 2026 · 15 min
Subscriber post — summary onlyClaire Vo presents a step-by-step guide to installing, configuring, and using OpenClaw, an open-source personal AI agent that runs locally and takes instructions through everyday chat apps. The post covers hardware and…
Lenny’s Newsletter♥ 528
Jun 24, 2025 · 14 min
Subscriber post — summary onlyA glossary explaining 20+ common AI terms in simple language, from models and LLMs to transformers, training, RLHF, RAG, evals, agents, and hallucinations. It aims to help readers follow AI conversations in meetings and…
Lenny’s Podcast73 min
Asha Sharma · Aug 28, 2025
Key learnings- Treat AI products as living systems: build the loop of collecting usage signals, defining rewards, A/B testing, and fine-tuning…
- Invest in post-training and fine-tuning on your own data, not just off-the-shelf models; once models are large, adapting them to…
- Watch for a shift from GUIs toward composable, code-native interfaces, since text streams and composability connect better with…
@joulee · Julie Zhuo on X♥ 284
4 tests any personal AI needs to pass to go big: 1. The mom test 2. The spare key test 3. The putting-it-off test 4. The bored-in-line test ...and my review of how @Muse does across each of them! Buckle up with a coffee and a snack, cuz this one goes deep.
Lenny’s Podcast100 min
Dhanji R. Prasanna · Oct 26, 2025
Key learnings- Track AI impact with self-reported time savings plus validation metrics such as PR throughput and feature delivery, which Block…
- Expect the current AI value baseline to keep rising; adopt tools continuously and re-evaluate where they add value as…
- Non-technical staff building their own small internal tools with agents can compress weeks of waiting on engineering queues into…
Lenny’s Podcast118 min
Dan Shipper · May 24, 2026
Key learnings- Keep using the newest models and agent tools for your real work, and experiment on your own time if your employer restricts…
- Expect most company AI to start as one shared agent that everyone can talk to, likely in Slack, rather than many personal agents…
- Design products so agents can be users too: make them easy to drive via CLI and browser-friendly, because the customer's own AI…