Product Development
Curated experts and resources for product development.
Recommended Experts (15)
Mahesh Ram
1206 messages
Community member active in community, gtm strategy, and sales operations.
Observed Signal
Hitesh Shah
53 messages
Community member active in product development, community, and india ops.
Observed Signal
Aditya Rohit
172 messages
Community member active in product development, engineering, and b2b saas.
Reena Gupta
166 messages
Community member active in ai ml, product development, and startup ops.
Protik
121 messages
Community member active in product development, engineering, and ai ml.
Vinay Chandrasekaran
53 messages
Community member active in community, startup ops, and sales operations.
Ravi Kumar Chivukula
43 messages
Community member active in ai ml, community, and enterprise sales.
Sophia Narrato
42 messages
Community member active in marketing, community, and product development.
Prasad Pillai
41 messages
Community member active in ai ml, community, and india ops.
Kshitij
31 messages
Community member active in ai ml, startup ops, and product development.
Shreesha Ramdas
390 messages
Community member active in community, startup ops, and fundraising.
Mani Iyer
290 messages
Community member active in gtm strategy, sales operations, and marketing.
Piyanka Jain
254 messages
Community member active in ai ml, product development, and fundraising.
Debu
163 messages
Community member active in ai ml, compliance, and enterprise sales.
Anuraag Nallapati
151 messages
Community member active in fundraising, gtm strategy, and hiring recruiting.
Resources (6)
How Tech Giants Harvest Data for AI
NYT analysis of how major tech companies are leveraging proprietary data as a strategic asset in the AI era. The article discusses data as a key acquisition driver and competitive moat. Community members noted this highlights why 'data being so critical and potentially a key reason to buy the company.'
Rec For:Founders considering data strategy, M&A opportunities, or building AI products where proprietary data creates defensibility.
Zoom's Federated AI Approach for Quality and Performance
Technical deep-dive by Zoom's CTO Xuedong Huang (co-authored with Mahesh) explaining Zoom's federated AI architecture. The post covers how Zoom balances quality, performance, and affordability by using multiple models for different features rather than a one-size-fits-all approach. Provides insights into building production AI systems at scale.
Rec For:AI product leaders and CTOs building multi-model systems who need to optimize for both quality and cost at enterprise scale.
How Zoom's Federated AI Maximizes Performance, Quality, and Affordability
Continuation of Zoom's federated AI series, detailing the architecture and decision framework for model selection across different use cases. Explains how federated approach enables cost optimization while maintaining quality standards. Written by Zoom leadership to share enterprise AI implementation patterns.
Rec For:Technical leaders and product managers implementing AI features who need to balance cost, quality, and performance trade-offs.
How Duolingo Uses Streaks and Notifications to Build Product Stickiness
WSJ case study on Duolingo's engagement mechanics, including streak features, notification strategies, and gamification tactics that drive daily active usage. Community members highlighted this as an 'excellent read for those looking to build stickiness in your product' with lessons applicable to B2B despite being a B2C example.
Rec For:Product managers and founders working on retention and engagement strategies, especially for products requiring habitual usage.
How to Escape Competition: Building Enduring Value with LLMs
Strategic framework by Benchmark's Sarah Tavel on building defensible AI products. Discusses application-level value creation beyond model capabilities and how to avoid commoditization in the LLM era. Shared in discussion about moats and whether 'solving a real problem well' is sufficient for building sustainable businesses.
Rec For:AI startup founders concerned about defensibility and building lasting competitive advantages in a rapidly commoditizing space.
The LLM Reasoning Debate Heats Up
Analysis of the ongoing debate around whether large language models truly 'reason' or simply pattern-match. Examines recent research and industry perspectives on LLM capabilities, limitations, and implications for product development. Referenced by community members discussing 'frontier models perform competently even on novel tasks.'
Rec For:AI product builders and technical leaders who need to understand current LLM capabilities and limitations for product planning.