“Shreyas Doshi warns that analogies excel at explaining your finished thinking but mislead when used to guide decisions—they’re maps you draw after the journey, not tools to navigate it.”

#11 𝕏 Shreyas Doshi warns that analogies excel at explaining your finished thinking but mislead when used to guide decisions—they’re maps you draw after the journey, not tools to navigate it.

“Shreyas Doshi argues you can simplify complex decisions by pinpointing what you’re really selling. Apple sells taste, Amazon convenience, Google utility, Disney nostalgia, Stripe deep care, Anthropic assistance, OpenAI answers, and Starbucks consistency.”

#14 𝕏 Shreyas Doshi argues you can simplify complex decisions by pinpointing what you’re really selling. Apple sells taste, Amazon convenience, Google utility, Disney nostalgia, Stripe deep care, Anthropic assistance, OpenAI answers, and Starbucks consistency. #15 𝕏 Santiago has been running the gemma-4:26b model locally on his Mac Studio since April to process private documents, now handling about 60% of his queries.

“#7 𝕏 Shreyas Doshi recommends feeding AI deep, ongoing product context and using it in real-time discussions to call out inconsistencies and keep your team honest—AI already excels at this practical application.”

Today's top 13 insights for PM Builders, ranked by relevance from X, Blogs, and LinkedIn. Why LLM features need end-to-end observability metrics #1 𝕏 Boris Cherny upgraded /usage to show personalized token usage by plugin, skill, and parallel agent, so you can pinpoint high-consumption drivers and maximize your doubled rate limits. #2 𝕏 xAI integrates X Premium subscriptions into Hermes Agent and equips it with native search across X posts. #3 📝 PromptLayer Blog A deep dive into LLM observability tools - Discusses the need for observability when shipping LLM-powered features, since models can return confidently wrong answers while logs show successful API responses. Argues observability must connect inputs, outputs, latency, cost, and quality to diagnose real production issues. #4 𝕏 Sebastian Raschka presents a visual overview of recent LLM architectures—from Gemma 4 to DeepSeek V4—showcasing long-context efficiency tweaks. He dives into innovations like KV sharing, per-layer embeddings, layer-wise attention budgets, compressed attention, and mHC. #5 𝕏 Garry Tan launched GBrain, an open-source knowledge system (not RAG in a box) with eight memory-enhancing layers that make agents like OpenClaw and Hermes feel clairvoyant about you, paving the way for personal AI. #6 𝕏 Peter Yang asks how to PM a frontier model like Opus, exploring with Alex Albert (Anthropic’s research PM for the next Claude) how to prioritize capabilities, build “dreaming” into Claude’s memory, and train its personality (and gauge if it’ll reach consciousness). #7 𝕏 Shreyas Doshi recommends feeding AI deep, ongoing product context and using it in real-time discussions to call out inconsistencies and keep your team honest—AI already excels at this practical application.

“Shreyas Doshi argues that product leaders with deep consumer-product experience and a strong user-empathy instinct find B2B “easy mode” and often excel—provided they dedicate themselves to acquiring the deep domain expertise many overlook.”

Shreyas Doshi argues that product leaders with deep consumer-product experience and a strong user-empathy instinct find B2B “easy mode” and often excel—provided they dedicate themselves to acquiring the deep domain expertise many overlook. Shreyas Doshi says AI now simplifies acquiring and leveraging domain expertise across your team, but warns that product leaders must still deeply value domain knowledge—beyond just user empathy and creativity.

“Shreyas Doshi argues that as AI amplifies individual talent, product people must unlearn outdated habits and sharpen their ability to discern what truly matters.”

#20 𝕏 Shreyas Doshi argues that as AI amplifies individual talent, product people must unlearn outdated habits and sharpen their ability to discern what truly matters.

“Shreyas Doshi @shreyas argued that the ceiling of mastery in product management is far higher than most mid-career PMs realize, encouraging a push for deeper expertise.”

Shreyas Doshi @shreyas argued that the ceiling of mastery in product management is far higher than most mid-career PMs realize, encouraging a push for deeper expertise. Learn why .

“Compound nature of product sense : Shreyas Doshi @shreyas emphasized that great product sense blends evaluative and generative intuition, enabling PMs to clarify vision, apply refined taste, and drive execution.”

Product Management Insights & Strategies Why AI products fail : Lenny Rachitsky @lennysan outlined patterns from 50+ enterprise AI deployments at OpenAI, Google, Amazon, and Databricks, offering a concise framework to avoid common pitfalls in AI product development. Compound nature of product sense : Shreyas Doshi @shreyas emphasized that great product sense blends evaluative and generative intuition, enabling PMs to clarify vision, apply refined taste, and drive execution. Customer research pitfalls : George Nurijanian @nurijanian advised PMs to ask users “ What did you do last time? ” instead of predictive questions, to gather concrete behavioral evidence in customer research.

“Outcomes over Learning : Shreyas Doshi @shreyas emphasized prioritizing outcomes over team learning in high-stakes scenarios in his latest newsletter post "Outcomes > Learning Opportunities".”

Product Management Insights & Strategies AI for Prep : Lenny Rachitsky @lennysan shared that ManusAI has become his go-to for podcast guest prep , demonstrating AI’s role in boosting PM productivity. Outcomes over Learning : Shreyas Doshi @shreyas emphasized prioritizing outcomes over team learning in high-stakes scenarios in his latest newsletter post "Outcomes > Learning Opportunities". Lean Experimentation : George from 🕹prodmgmt.world @nurijanian explained a method to work backwards to find the minimal signal when testing assumptions, avoiding bloated experiments.

“Time management deep dive : Shreyas Doshi @shreyas released an audio deep dive on advanced ideas for time management , offering actionable frameworks to boost personal and team productivity.”

Product Management Insights & Strategies Time management deep dive : Shreyas Doshi @shreyas released an audio deep dive on advanced ideas for time management , offering actionable frameworks to boost personal and team productivity. Maximizing right-brain time : Lenny Rachitsky @lennysan shared Arthur Brooks’s framing: use AI for left-brain tasks (like analytics and productivity) to create more space for right-brain activities such as relationship building and creativity.

“Fostering intrinsic motivation : Shreyas Doshi @shreyas observed that high-agency individuals draw motivation from intrinsic inspiration , suggesting leaders should minimize extrinsic slogans and instead foster autonomy.”

Product Management Insights & Strategies Configuring for emergent solutions : Lenny Rachitsky @lennysan shared that good product work seeks clarity , framing code more as conditions for agents to generate high-quality solutions than as handcrafted implementations. Fostering intrinsic motivation : Shreyas Doshi @shreyas observed that high-agency individuals draw motivation from intrinsic inspiration , suggesting leaders should minimize extrinsic slogans and instead foster autonomy. Cutting meetings for speed : Phil Schmid @_philschmid argued that reducing unnecessary meetings and fostering ownership will be critical in 2026, as faster decision-making differentiates high-performing teams. AI Industry Developments & News Lex Fridman's technical AI podcast : Lex Fridman @lexfridman announced a long-form, super-technical podcast covering LLM training architectures, robotics, compute, business, geopolitics and more, inviting community topic suggestions.