The hottest Product Management Substack posts right now

And their main takeaways
Category
Top Business Topics
Inside Data by Mikkel Dengsøe • 183 HN points • 21 Feb 24
  1. Data may not reveal the best ideas, so trust your intuition and explore beyond the obvious data points.
  2. Focus on solving the big problems first, as they have a more significant impact than smaller issues.
  3. Think in small bets and iterations to make progress in the right direction, even if data may not provide immediate clarity.
Product Managers at Work • 2 implied HN points • 28 Feb 24
  1. Being a B2B Product Manager comes with unique challenges compared to B2C, like blending limited data with qualitative feedback for decision-making.
  2. It's crucial for B2B Product Managers to gather direct feedback from users through feedback portals to avoid bias and make informed decisions.
  3. Contextualizing and acting on user feedback effectively, based on target segments and feature usage data, can help prioritize product improvements for B2B success.
Askwhy: UX Research, Product Management, Design & Careers • 16 implied HN points • 27 Feb 24
  1. Avoid unnecessary research by understanding when it's not appropriate, like for problem-solving issues instead of validation.
  2. Prevent overcommitting by scoping projects well, building in time buffers, and looking at historical data to manage workload effectively.
  3. Enhance visibility for your UX research work by selecting the right method for your audience, sharing updates in product meetings, and knowing your stakeholders.
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TheSequence • 994 implied HN points • 19 Jan 24
  1. You may not need ML engineers for Generative AI projects due to the availability of pre-trained models like GPT-4.
  2. Prompt engineering, the clear articulation of needs in natural language, is a crucial skill for AI application development.
  3. Product managers and domain experts play a significant role in shaping AI products through prompt engineering, reducing the need for technical experts.
Channels of Growth • 687 implied HN points • 19 Jan 24
  1. The book 'Channels of Growth' focuses on a Growth Marketing Framework for dominating channels and building better products.
  2. All users come from channels when it comes to growth, emphasizing the importance of understanding and optimizing these channels.
  3. The book aims to provide a personal Growth Marketing framework based on lessons from over $100M+ spent on growing products.
The Product Channel By Sid Saladi • 10 implied HN points • 25 Feb 24
  1. Artificial Intelligence (AI) is a pivotal force in reshaping industries, offering product managers opportunities to enhance their development lifecycle.
  2. Integrating AI into product development leads to reduced time-to-market, increased efficiency, and better resonance with users.
  3. AI helps in enhancing ideation by analyzing customer feedback, conducting market research, and generating innovative concepts to uncover promising opportunities.
Untrapping Product Teams • 157 implied HN points • 07 Feb 24
  1. Product management is like cooking: it's not just about the recipe, but also about how you do it and the ingredients you use.
  2. Recognize the fundamental ingredients for thriving in product management, such as product vision, strategy, goals, and testing assumptions.
  3. Having a strong product vision is crucial for inspiring and motivating teams to progress in the right direction.
Alex Ewerlöf Notes • 353 implied HN points • 25 Jan 24
  1. Tech gamble is about paying the price of hypothetical future tech debt upfront without proper data or insight, leading to waste and friction for the product.
  2. Symptoms of tech gamble include complex technical solutions for simple problems, big bang improvement projects cancelled mid-execution, and rewriting systems without clear pragmatic checkpoints.
  3. Tech debt is reactive, while tech gamble is proactive, with tech debt giving engineers a bad conscience and tech gamble representing naive ambition or malice.
Department of Product • 275 implied HN points • 25 Jan 24
  1. Instagram is implementing a new safety feature to deter teens from night-time app use.
  2. Many companies struggle to train employees on GenAI tools, but new visual AI workflow tools like VectorShift aim to simplify the process for product teams.
  3. Netflix's success in ad-funded plans raises questions on whether this model will extend to other product categories like SaaS.
An Innovator's Sketchbook • 98 implied HN points • 04 Feb 24
  1. Transitioning from feature to product teams involves empowering cross-functional teams focused on outcomes and value.
  2. The localization industry is evolving with AI, leading to job destruction but also creating new business opportunities.
  3. Feedback is important for team growth, and using the 'Situation-Behavior-Impact' framework can lead to effective and powerful feedback.
The Product Channel By Sid Saladi • 13 implied HN points • 18 Feb 24
  1. Large Language Models (LLMs) trained on Private Data are becoming popular for creating AI assistants that can engage customers, answer questions, assist employees, and automate tasks.
  2. The Retrieval-Augmented Generation (RAG) framework enhances the capabilities of LLMs by incorporating external, real-time information into AI responses, revolutionizing the accuracy and relevance of generated content.
  3. Implementing RAG in enterprises through steps like choosing a foundational LLM, preparing a knowledge base, encoding text into embeddings, implementing semantic search, composing final prompts, and generating responses can transform business operations by empowering employees, enhancing customer engagement, streamlining decision-making, driving innovation, and optimizing content strategy.
The Product Channel By Sid Saladi • 20 implied HN points • 11 Feb 24
  1. Building a competitive moat in AI involves strategic navigation of the generative AI value chain to create unique advantages.
  2. For AI startups, it's crucial to focus on acquiring proprietary data, integrating AI into comprehensive workflows, and specializing models through incremental training techniques.
  3. Companies like Anthropic, Landing AI, and Stability AI showcase effective moat-building strategies in AI by emphasizing ethical development, democratizing technology, and niche specialization.
An Innovator's Sketchbook • 78 implied HN points • 21 Jan 24
  1. Focus on understanding consumer goals and their hierarchy for successful product development.
  2. Differentiate between needs, goals, and ToDos as part of the goal hierarchy.
  3. Recognize the importance of designing products or services that effectively remove obstacles or facilitate faster achievement of consumer goals.
Suzan's Fieldnotes • 98 implied HN points • 15 Jan 24
  1. María de Antón transitioned from Head of Customer Success to Product Manager by following her instincts and overcoming imposter syndrome.
  2. Transitioning from customer success to product management involved a shift from talking with customers to creating joyful product experiences.
  3. As a leader considering switching functions, trust your cross-functional skills, seek necessary training, and have confidence in yourself.
Three Data Point Thursday • 39 implied HN points • 01 Feb 24
  1. Netflix transformed into a data company by focusing on leverage and transitions in their value chain.
  2. To create a good data strategy, consider mapping out your value chain, playing Perfect World scenarios, and performing pipeline analysis.
  3. Use data and algorithms to increase the bottleneck in your value pipelines for impactful data strategies.
First 1000 • 1513 implied HN points • 13 Jul 23
  1. In UX design, smart defaults can be very powerful.
  2. Sometimes, a design that looks slick and communicates well may not perform as well as another in tests.
  3. Don't underestimate the impact of smart defaults in design choices.
Harmony • 19 implied HN points • 07 Feb 24
  1. Premortem involves imagining project failure and analyzing potential reasons for it.
  2. Analyzing career directions like consulting, coaching, and product management can help in setting robust goals.
  3. Considering possible reasons for failure in different workstreams can aid in planning and making improvements.
The Beautiful Mess • 1254 implied HN points • 18 Jun 23
  1. A strategy should be a clear description of challenges, decisions on what to address, and specific actions to impact those challenges.
  2. Translating strategy into tangible goals and budgets is crucial for making it meaningful to people in the organization.
  3. Creating a safe space for hard discussions, dedicating time for strategy development, and focusing on deployment are essential for making a strategy effective.
The Product Channel By Sid Saladi • 16 implied HN points • 04 Feb 24
  1. AI product managers bridge business and technology in the development of AI-powered products
  2. Key responsibilities of AI product managers include research, strategy, development, execution, product launch, and growth
  3. Necessary skills for AI product managers include AI and data literacy, technical acumen, business savviness, strategic thinking, stakeholder alignment, and user empathy
High Growth Engineer • 465 implied HN points • 27 Aug 23
  1. Collaboration with product managers and designers can be challenging due to differing priorities and project impacts.
  2. Engineers often face the dilemma of balancing what they can do, what the PM wants, and what the PM thinks they can do.
  3. Maintaining a good relationship and meeting deadlines are key aspects of being a favored engineer among product managers and designers.
Product Power by Samet Ozkale • 78 implied HN points • 21 Dec 23
  1. Product roadmapping is like conducting a symphony with prioritization setting the rhythm.
  2. Key elements of a product roadmap include vision, strategy, roadmap, prioritization, and backlog.
  3. Balancing stakeholder needs in roadmapping, aligning short-term goals with long-term vision, and agile adaptation are crucial for successful product development.
An Innovator's Sketchbook • 19 implied HN points • 28 Jan 24
  1. Leverage AI to boost personal productivity in product management through planning, execution, and user feedback analysis.
  2. Use large language models (LLMs) in product strategy for idea generation, evaluation, and decision-making.
  3. Optimize day-to-day efficiency by using AI to break down goals into manageable tasks and plan daily schedules.