Drawer 05 · 298 notes · 90 people · 2014–2026

Building AI products

AI-native products, evals, agents, and how AI changes product work

Q:
Answers from Building AI products notes, cited
Lenny’s Podcast105 min

Sherwin Wu V2

Sherwin Wu V2 · Feb 12, 2026
Key learnings
  • Build for where models are going rather than where they are today, since products that are almost-working now can become…
  • Treat fast-changing AI scaffolding skeptically: models often absorb tooling like vector stores and agent frameworks, so avoid…
  • Don't blindly follow customer feature requests in AI; customers may anchor on local maxima while the underlying models are…
Lenny’s Podcast54 min

TiboSottiaux

TiboSottiaux
Key learnings
  • Expect most actions on the internet to be taken by agents, so build products that can serve agent traffic at scale and handle the…
  • Design for the capabilities you expect in roughly a year (about 10x better than today), not just what current models can do, to…
  • Favor systems that learn from your goals and feedback over hand-tuned agent loops and workflows, which Tibo thinks will be…
Lenny’s Podcast142 min

An AI state of the union: We’ve passed the inflection point, dark factories are coming, and automation timelines

Simon Willison · Apr 2, 2026
Key learnings
  • Coding agents crossed a reliability threshold around November 2025, so they now usually do what you ask, which changed how…
  • Writing code is now cheap, so the bottlenecks move to ideation, prototyping, testing, and process; build several quick prototypes…
  • Use test-driven development with agents: have them write tests, run them, and watch them fail first (Red/Green TDD) to keep…
SVPG · Marty CaganJun 9, 2023

Preparing For The Future

Jun 9, 2023 · 17 min

Marty Cagan argues that generative AI will reshape how product teams are built and how they work, drawing on his decades of experience through successive tech disruptions like the PC, the internet, and mobile. He is…

Lenny’s Newsletter · Free post♥ 335

How to use Perplexity in your PM work

Jun 11, 2024 · 6 min

Lenny Rachitsky surveyed hundreds of product managers, then ran follow-up calls, to collect concrete ways PMs use Perplexity, the AI-powered search engine, in their daily work. The post groups 27 real prompts into six…

Lenny’s Podcast93 min

Lessons in product leadership and AI strategy from Glean, Google, Amazon, and Slack

Tamar Yehoshua · Sep 26, 2024
Key learnings
  • Excel in your current role before chasing the next one; advancement follows demonstrated impact, not just hitting goals or…
  • Evaluate a potential employer's engineering partner before joining, since great ideas that can't be built lead nowhere.
  • Don't assume a company must be well run to succeed; hyper-growth firms often run chaotically, but prioritize companies whose…
@lissijean · Melissa Perri on X♥ 8
Most teams make the mistake of waiting for data to magically appear before building their AI product. They get stuck in the classic chicken-and-egg problem: you need data to build good AI, but you need users to generate that data. So they wait. And wait. And wait some more. /1