niko

Lenny's archive is genuinely one of the best concentrated sources of product wisdom that exists. Doing this made that even clearer. @lennyrachitsky — thank you for building it.

This is the foundation for 'product builders' — a community I'm building for AI-era PMs. The library isn't a replacement for Lenny's work. It's a structured entry point into it. Every blueprint points back to the source.

I downloaded Lenny's entire newsletter + podcast archive (640 files) and fed it to an AI. Here's what came out the other side 🧵

The failure database alone is worth it. 46 specific product failures — with the exact lesson from each. Sourced directly from the people who lived them, via Lenny's interviews.

What it includes: - 27 competency blueprints (Feature Spec, Growth, Pricing, AI PM, PMF, Experimentation...) - 12 company playbooks (Linear, Figma, Duolingo, Shopify, Perplexity, Ramp...) - 5 situation playbooks (First 90 Days, IC to Manager, 0-to-1...) - 6 reference databases (100 quotes, 46 failure stories, 114 frameworks, 130+ metrics...)

Okay I think I finally found a good use for @_chenglou 's Pretext. A resume builder that always fits on one page with realtime rendering.

just finished a 3-hour /office-hours session using the prompt from the article: "Interview me until you have 95% confidence about what I actually want, not what I think I should want." it peeled back the last layer of what i actually needed. i thought i knew what i wanted. turns out i only knew the surface. the plan that came out after 3 hours was completely different from what i walked in with. if you take one thing from the article, try this prompt + /office-hours. want the full three-layer development stack? here's the order i actually run: 1. 95% confidence prompt 2. /office-hours → /plan-ceo-review → /plan-eng-review (gstack) 3. /ce:brainstorm → /ce:plan → /ce:work (CE) 4. /ce:review + /qa (CE + gstack) 5. /ce:compound (CE) 6. ship it. next time step 3 already knows everything you learned this time. 1-2 make sure you build the right thing. 3-4 make sure you build it well. 5 makes sure next time is faster.

This is how date pickers should feel. Smoothness just hits.

Some people have been contemplating an idea for years, maybe decades. Obsessing, attempting, discarding, agonizing, retrying. Some of these ideas are unpopular, niche, impractical. Not obviously capitalizable. They live on in the inventor's mind. In 2026, millions of these ideas will come to life thanks to superintelligent coding agents. AI doesn't get tired. It amplifies the individual, and for better and sometimes for worse, it always takes you seriously. "Great idea. Splendid. Wow. You're absolutely right." A world of digital wonders awaits us. This world will disproportionally favor the boldest ideas. Software that once seemed impossible will be one hyperlink away. I can't wait to see it.

Called it.

My dear front-end developers (and anyone who’s interested in the future of interfaces): I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept): Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow

This is the pattern every D2C and service business should be watching. AI agents doing the prospecting → satellite data + insurance likelihood + warm lead scoring → and feeding qualified leads directly to humans who close. 3 weeks in and a roofing company is already using it. Imagine what D2C brands do with this → AI agents that find your ideal customer, qualify them, and serve them a personalized offer before a human ever gets involved. The businesses that adopt agent workflows first are going to dominate their verticals.

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

You need to start autoresearch maxxing. Here’s your guide

POV: you’re the only person at work who doesn’t know how to build an AI agent.

The real comparison is OpenClaw vs Claude Cowork. Claude is reactive. You open the browser, you type, it answers, you close the tab, it dies. 98% of people know this. Cowork is closer. It has skills, file access, task execution. But Cowork still requires you to point it at things. You invoke it. You give it direction. You check on it. OpenClaw runs as a continuous daemon. It makes decisions based on inferences from what you told it, not just direct instructions. It watches Slack channels you never mentioned because it figured out those channels are relevant to your role. It appends questions to an FAQ document because it noticed a pattern in what engineers were asking. Naman called it consciousness. That's a loaded word, but the functional difference is real. OpenClaw acts on its own judgment. Cowork acts on your instructions. The tradeoff is control. Cowork will never WhatsApp your mom. OpenClaw might. The autonomy that makes it powerful is the same autonomy that makes it dangerous without guardrails. For PMs who want maximum leverage and are willing to configure security properly, OpenClaw is the higher-ceiling tool. For PMs who want power without babysitting risk, Cowork is the safer bet.