Lenny’s Podcast116 min
Kevin Weil · Apr 10, 2025
Key learnings- Build products at the edge of current model capabilities, since models improve every few months and a barely-working product can…
- Treat evals as unit tests for models: define hero use cases, write evals for them, and hill-climb on those evals to know whether…
- Expect AI behavior to be fuzzy: a model right 60%, 95%, or 99.5% of the time implies very different product designs, so study…
Lenny’s Newsletter♥ 578
Feb 3, 2026 · 34 min
Subscriber post — summary onlyTal and Aman argue that the fastest way to build intuition for AI products is to use coding agents like Cursor for non-technical product work rather than consumer chat tools. The post walks readers through setup, model…
Lenny’s Podcast128 min
Nick Turley · Aug 9, 2025
Key learnings- Ship early to learn what people actually want; with AI, the valuable behaviors are emergent, so you won't know what to polish…
- Treat the model itself as a product: iterate on it using real user use cases, personality/vibe checks, and new capabilities like…
- Set the team's pace deliberately by asking whether work is 'maximally accelerated', separating fast product iteration from…
Lenny’s Newsletter · Free post♥ 724
Jul 22, 2025 · 27 min
“One of my favorite collaborators, Tal Raviv, is back with another incredible piece that will change how many of you operate at work. If you’re looking for the promised AI productivity gains everyone’s talking about, start here. For more from Tal, check out his 63 free video tutorials on using AI agents at work, his other guest post, “PM is an unfair role. So work unfairly”, and his episode on Lenny’s Podcast. You can also book Tal for…”
Lenny’s Newsletter · Free post♥ 517
Feb 20, 2024 · 15 min
Lenny Rachitsky argues that product and business teams should start experimenting with custom GPTs now, since AI tools are often dismissed as a fad or too complex. He explains what GPTs are, how to build one by…
Lenny’s Podcast93 min
Mike Krieger · Jun 5, 2025
Key learnings- Use AI as an independent strategy critic: ask it to be brutal or to challenge your thinking, since default prompts tend to…
- As AI writes most code, bottlenecks shift upstream to deciding what to build and aligning people, and downstream to merge queues…
- Give teams a minimum viable strategy so people feel empowered to build and explore at the edge of model capabilities without…
Lenny’s Podcast153 min
Marc Andreessen · Jan 29, 2026
Key learnings- Expect AI to make already-skilled people far more capable; the gains are largest for those who pair deep expertise with AI tools.
- Rather than fearing job loss, focus on task loss: jobs persist while the individual tasks composing them shift, which is how…
- Combine at least two or three domains deeply (e.g., coding, product, design); the combination of skills creates far more value…
Lenny’s Podcast128 min
Dan Shipper · Jul 17, 2025
Key learnings- Non-programmers can use Claude Code-style command-line agents with local files to process large text sets, like meeting notes or…
- Hire or assign someone to an AI operations role who continuously turns repetitive team tasks into prompts and workflows, and make…
- Apply 'compounding engineering': spend a bit of effort turning each recurring task (like writing PRDs) into a reusable prompt or…
Lenny’s Podcast109 min
Benedict Evans · May 31, 2026
Key learnings- Treat AI as roughly as big as the internet or mobile, and recognize we are early, like 1997, with most applications not yet built…
- Expect adoption to be very uneven; survey data shows many people are not using AI at all, so don't assume the people in your…
- Ask whether a job is a task that can be automated or a deeper outcome; consultants and professionals are hired for judgment…
Lenny’s Newsletter♥ 579
Apr 8, 2025 · 15 min
Subscriber post — summary onlyAman Khan argues that writing evaluations is becoming a defining skill for AI product managers, since evals measure how each part of a system affects quality. The post explains what evals are, compares human, code-based…
Lenny’s Podcast89 min
Michael Truell · May 1, 2025
Key learnings- Build the product you and your team actually use every day (dogfooding) so you never ship anything that isn't genuinely useful…
- When working with AI coding tools, chop tasks into small specify-generate-review loops rather than handing over one giant…
- Spend time on side projects deliberately pushing AI to its limits to build a calibrated gut feeling for what current models can…
Lenny’s Podcast111 min
Bret Taylor · Jul 31, 2025
Key learnings- Don't just digitize what existed before; rebuild the experience natively for the new platform so customers have a reason to…
- Ask yourself what the most impactful thing is today, and reflect on whether you're choosing it out of comfort or because it's…
- Be skeptical of your own storytelling: customer reasons given to salespeople can mask deeper product problems, so seek…
Lenny’s Podcast64 min
Marily Nika · Feb 5, 2023
Key learnings- Avoid the shiny object trap: only add AI when there is a real, well-defined pain point worth solving, and identify the problem…
- Don't use AI for an MVP; fake the AI behavior with a clickable prototype and test desirability with users first.
- Use ChatGPT as a sounding board for sharpening mission statements and generating user segments, personas, and motivations, while…
Lenny’s Newsletter♥ 763
Jan 7, 2025 · 19 min
Subscriber post — summary onlyA practical guide to AI prototyping for product managers, covering the main categories of tools (chatbots, cloud development environments, local developer assistants) and when to use each. It walks through turning Figma…
@ttorres · Teresa Torres on X♥ 97
"The only way to know if our AI products and workflows are any good is with evals." 💡 If you're using AI to write PRDs, analyze customer feedback, or build customer-facing AI products, you need to understand evals. This guide breaks down what evals actually are and why product teams should be…
@lennysan · Lenny Rachitsky on X♥ 968
My conversation with Head of ChatGPT and Codex, @thsottiaux We discuss: 🔸 Why the model picker is likely going away 🔸 Why loops and graphs are a passing phase 🔸 Why most actions on the internet will soon be taken by agents 🔸 OpenAI’s approach to AI safety Listen now 👇
Lenny’s Newsletter♥ 451
Dec 23, 2025 · 24 min
Subscriber post — summary onlyLenny Rachitsky and Noam Segal report on a 1,750-respondent survey of tech workers about AI productivity. The core argument is that AI is overdelivering for most people, with the biggest gains in founders and PMs, while…
Lenny’s Podcast79 min
Anton Osika · Mar 9, 2025
Key learnings- Be precise and explicit when prompting an AI builder: say exactly what you expect and which parts are not working, rather than…
- Use the agent's chat mode to ask how something works or why it isn't doing what you want; this builds understanding while you…
- Expect to spend a full week taking one real problem from idea to a working product that people use; Anton suggests that alone…
Lenny’s Newsletter♥ 293
Oct 29, 2024 · 15 min
Subscriber post — summary onlyLenny Rachitsky shares a guest post by prompt engineer Mike Taylor covering eight prompting techniques, from role-playing and few-shot examples to chain-of-thought, retrieval, and using an LLM as a judge. Each tactic…
Lenny’s Podcast100 min
Jenny Wen · Mar 1, 2026
Key learnings- Classic linear design process (research, diverge, converge) is breaking down as engineers ship prototypes quickly; designers…
- Design work is splitting into two modes: supporting implementation and setting a vision, where the vision now often spans three…
- Designers can use coding tools to do last-mile polish and prototype in real code, rather than waiting on engineers for every…
Lenny’s Podcast117 min
Sander Schulhoff · Jun 19, 2025
Key learnings- Prompt engineering remains valuable: Sander argues that bad prompts can drop accuracy near zero on a task while good prompts can…
- Few-shot prompting is the highest-impact basic technique: give the model several examples of what good output looks like, using a…
- Ask the model to decompose a problem by first listing the subproblems it needs to solve, then solving each one before answering…
@ttorres · Teresa Torres on X♥ 18
"I'm going to tell you my story because I think it's an amazing story of continuous improvement, of how teeny-tiny steps compound over time." Over the past year, my work has transformed completely—and it all started with a broken ankle and a willingness to get curious about AI. In this article, I…
Lenny’s Podcast134 min
Claire Vo · Mar 29, 2026
Key learnings- Run several purpose-built agents instead of one general agent; splitting tasks keeps each agent's context focused and reduces…
- Install OpenClaw on a separate clean machine such as an old laptop or Mac Mini, with its own local admin account, rather than…
- Onboard the agent like a new employee: give it its own email and calendar, share access selectively, and expand trust…
Lenny’s Newsletter♥ 839
Oct 14, 2025 · 20 min
Subscriber post — summary onlyLenny Rachitsky argues that non-technical people should use Claude Code, which he frames as a local-feeling AI agent that can act on files and tools on a computer. The post walks through installation steps, shows five…