Best reads for PMs & Designers
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First seen 4 February 2026
Lobstersseangoedecke.com · 20 September 2026 · not counted as a party
Being good at building and being good at shipping are two separate skills. In the short term, they’re actually countervailing: if you have a gift for building, you’re likely to be worse at shipping. Ira Glass has a classic quote about this. All of us who do creative work, we get into it because we have good taste. But there is this gap. For the first couple years you make stuff, it’s just not that good. It’s trying to be good, it has potential, but it’s not. But your taste, the thing that got you into the game, is still killer. And your taste is why your work disappoints you. The only way around this is to grit your teeth and ship it. You have to force yourself to publish things you’ve made even when you think they’re crap. Programming Gifted programmers have a nearly pathological desire to build elegant, correct, neat systems. That’s what motivates them to learn the arcane details of their languages, or to spend time polishing and refactoring over and over again. But it’s also what makes them reluctant to ship. Any flaws in the software bother them on an emotional level. If they ship with those flaws, they feel like people will think they weren’t payi
Nielsen Norman Groupnngroup.com · 11 September 2026
Summary: AI tools make it feasible to build fully interactive prototypes of complex interfaces so you can test them with users earlier in the design process. Complex interfaces like filters, dashboards, and conversational AI have many possible states and can be tedious to prototype by hand. Before AI tools were available, most teams would test a simple prototype that covered only a few happy paths and wait until developers built the product to see how users interacted with it. With AI tools, teams can now build realistic prototypes and test them before deployment. For less complex interfaces, static prototypes are still useful for answering many research questions. However, when an interface is complex, AI prototyping tools make it easier to build high-fidelity, interactive prototypes that closely resemble the real product. Interactive Prototypes, Fast How to Prototype Complex Interfaces with AI Integrating AI Prototyping into Your Process Interactive Prototypes, Fast AI tools bring speed to the design process, enabling designers to create a realistic working prototype in a day. Incorporating realistic participant
Dive Club (Ridd)rss.com · 9 September 2026
Ed Bayes - Designing 6 months int...Ed Bayes - Designing 6 months into the future at OpenAIDive Club 🤿 by RiddEpisode notesThis week's episode is with Ed Bayes and we're doing a deep dive into how designers at OpenAI prototype the future of Codex/ChatGPT Work.Some highlights:- How prototyping at OpenAI has shifted recently- The design challenges of bringing Codex into ChatGPT- How Ed thinks about the spectrum of dynamic interfaces- How Codex thought about their information architecture bets- Why Ed structures design teams around personas not features- The traits of the designers making the biggest impact at OpenAI- + a lot moreThe Bitter Lesson (http://www.incompleteideas.net/IncIdeas/BitterLesson.html) — ML essay he referenced on scaffolding and model releasesKeywordsdesignproduct designux designui design
Paweł Hurynproductcompass.pm · 7 September 2026 · 2 posts
Hey, Pawel here. Welcome to the Product Compass Newsletter. It’s #1 most hands-on AI PM newsletter. I strategize, prototype, experiment, and build with AI every day. And every week I share actionable tips, templates, and step-by-step guides for PMs. Here’s what you might have missed: AI Prototyping in 2026: The PM Field Guide The Ultimate AI PM Learning Roadmap 2026 What Is Product Discovery? The Ultimate Guide for PMs (2026 Edition) How to Create an AI Product Strategy: The AI Strategic Lens Framework Plus, 20+ recordings available for our premium members here: https://go.productcompass.pm/events Consider subscribing and updating your account for the full experience. Subscribe now Today, we continue Product Engineering for PMs inspired by the Gartner report: Source: @housecor The roles are coming closer together. My take: you don’t have to code, but as a PM you have a much stronger profile if you develop an understanding of engineering with AI. In Part 1, we started building AskOne, an alternative to Slido (Q&A and polling platform). We implemented rooms, anonymous questions, Google authentication, Supabase stora
Product Growthnews.aakashg.com · 27 August 2026
The path to becoming an AI PM in today’s market is “synthetic experience.” Here’s how to build it.
Noah Daviswebdesignerdepot.com · 24 August 2026
Figma is the New Dreamweaver: How Modern Prototyping Tools are Trapping Us in 2018
The Product Compassproductcompass.pm · 19 August 2026 · 3 posts
I built the same CRM four times in just over an hour, live. The field guide: which tool for which job, the context prompt that beats a spec, the security check before you share a link, and the pixel-perfect handoff
Stack Overflow blogstackoverflow.blog · 18 August 2026
Ryan welcomes Suneet Malhotra, Senior Manager of Test Engineering at Motorola Solutions, to chat about building end-to-end agentic SDLC pipelines using MCPs, using Cohen’s kappa to evaluate multiple LLMs-as-judges, and how you can improve requirements by shifting QA left through a specification enrichment stage immediately after the design phase.
Christine Zhux.com · 27 July 2026
Software is designed, but models are grown, not designed. As such, AI products are organic. PM’ing AI products has an organic nature, akin to growing a garden. It’s more about creating the system for your plants to grow in rather than specifying exactly how an individual plant will look like. (see Alex Komoroske’s Gardening Platforms about platform PM'ing, the same concept applies for all PMs now.) This now makes systems thinking tablestakes for PMs. systems thinking truths Donella Meadows’ Thinking in Systems spelled out a few truths for applying systems thinking in building AI/agents in existing products. I only understood these better after living through building agent systems in a platform PM role. Truth 1. Behavior arise from the interactions between the parts, not the individual parts. A system’s output is the result of how the components connect and interact. You can perfect every individual part and still get a broken whole. The classic PM role (let’s call it the domain PM), especially at a large company, owns a specific scope/product area. By org design (and Conway’s law), every domain PM optimizes for their part. Bu
Magnus919magnus919.com · 13 July 2026
I wrote about the Dark Factory earlier this year, covering StrongDM’s Level 5 autonomous software factory where three engineers ship production code with no human touching the implementation. In that article I mapped the five levels of AI coding autonomy, a framework that has become a useful shorthand for where organizations sit on the spectrum from human-driven coding to full autonomy:Level 1: AI-Assisted. Human drives everything; AI is a faster keyboardLevel 2: AI-Generated + Human Review. AI drafts, human approves every PRLevel 3: AI-Generated + Automated Gates. AI writes, tests review, humans on failuresLevel 4: Mostly Autonomous + Escalation. AI handles the full loop, humans on novel issuesLevel 5: Full Dark Factory. AI runs end-to-end, humans define goals onlyMost organizations today are somewhere between Level 2 and Level 3. They have some form of AI coding assist: GitHub Copilot, Cursor, Claude Code, an internal agent pipeline. And they are measuring the productivity gains in pull request velocity. Stripe’s Minions ship 1,300 PRs per week at Level 2-3. The numbers are impressive and real.But her
Christine Vallaureuxdesign.cc · 3 July 2026
A beginner’s guide through the AI pipeline fog. If you put a room of designers together and asked how their work actually gets from Figma into real code with AI, you would get a few seconds of silence and then a lot of different answers, none of them confident. That is not because you missed a memo. There simply is no memo yet. So let me walk through it in plain words. The demos are real. Your confusion is also real. You have seen the videos. Someone connects Figma to an AI tool, types a sentence, and a working screen appears in seconds. It looks like the whole problem is solved. Then you try it on your own real project, and it falls apart, and you assume you did something wrong. You did not. The demos show you one clean layer working under perfect conditions. Your actual work needs three or four layers stacked together, and nobody shows you the stack, because the stack is where it gets messy and half-solved. The confusion comes from not knowing they are separate things at different stages that need different skills. The design-to-code stack, one layer at a time Layer 1: The MCP. The connection. MCP is one of those
Claire Volennysnewsletter.com · 29 June 2026
Eddie Kim is the co-founder and CTO of the payroll and HR platform Gusto, which just crossed $1 billion in revenue and serves more than 500,000 small businesses. Recently he did something most CTOs don’t: he went back to writing code. With three other engineers and one designer, Eddie built Gusto Cofounder, a net-new AI product, from zero code to a tier-one launch in 10 weeks. He walks through how that team actually worked, why they threw out nearly every process, and how anyone can copy the approach. Listen or watch on YouTube, Spotify, or Apple Podcasts What you’ll learn: The trash-can method: how to write, review, and delete a full PR as a product decision instead of a planning doc The two-tool agent stack behind Gusto Cofounder The exact “perma-Zoom” setup that replaced standups, retros, and Slack threads for 10 weeks How a designer with no engineering background hit the 94th percentile for shipping code The eval-first workflow Eddie uses to fix real customer bugs with Claude Code How a non-technical leader can prototype an idea to win buy-in, then carry it all the way to productio
Aakash Guptanews.aakashg.com · 27 June 2026
Last month, your Claude setup worked perfectly. This month, it feels like the AI is actively gaslighting you. It ignores your skills details and writes docs so lazy you end up rewriting them yourself. If you’re like me, you find yourself scrolling through past chats, desperately trying to find that one version of a prompt that actually worked a few months back. Where did it go? What skill version was it? Did I accidentally overwrite it when I updated my CLAUDE.md yesterday? This is the exhausting reality of AI workspace drift. When you don’t track your AI infrastructure, a single word shift can completely break your OS’s behavior. Top product managers are stopping this bleeding by moving their workspaces to GitHub. So to build a truly bulletproof engineering manual for PM workspaces, I needed to partner with someone who done that himself. Introducing Shubham Saboo Shubham is a Senior AI Product Manager at Google and the mastermind behind Awesome LLM Apps - an open-source powerhouse with over 120 production-ready templates covering everything from multi-agent teams and MCP configurations to complex agent skills. If his
slack.designslack.design · 10 June 2026
The biggest shift AI has brought to design isn’t that designers can now write production code. It’s that we can finally generate evidence before committing to anything. There’s a lot of conversation about AI affecting production: designers pushing code and shipping something closer to the final product. That matters. But the bigger opportunity is even earlier: exploring a design space before anything gets built. Not prototypes that demonstrate a decision, but prototypes that stress-test one, built to find where a solution breaks. From Theory to Practice In data visualization, building parametrized tools to explore a design space is standard practice. In product design, it rarely happens. Static mocks show what something looks like, not how it behaves across a distribution of real inputs. While engineers could build a throwaway prototype, they usually don’t. Their incentives are toward scale and production quality, not disposable experiments. AI-assisted coding is changing this. Before, exploration like this was possible in theory but rarely happened in practice. Now it only takes a couple of days, so it actually gets d
Itamar Giladitamargilad.com · 3 June 2026
There’s a certain type of meme, often called “how to create a minimal viable product”, that’s been making the rounds in the product web for years. I must have seen at least 5 original versions and they all disagree with each other. Not very originally, I felt that they all missed the mark and decided to create my own version with the help of OpenAI’s powerful image generator. Rows 1 and 2 show two common “wrong” versions. In row 3 I tried to illustrate product discovery: a series of validation steps — customer interviews, fake door test, early-adopter/alpha — to test the key assumptions in the idea before launching it in full. Naturally this is a massive oversimplification for a complex product like a car, but all these memes are not really about cars — they’re about software. Content with my contribution to product meme culture I posted the illustration on my LinkedIn feed and mostly forgot about it. The post got a bunch of thumbs up and smiley faces, but there was also a subgroup of people who clearly were not amused (if LinkedIn had a dislike button I’m pretty sure they would violently press it). Some took the time to exp