... users said they wanted. Feature requests. Sales calls. "It would be great if you added X." We built X. Nobody used it. So we stopped trusting what people said and started tracking what they actually did. The dataset We pulled 12 months of usage data from Rankfender. 1,247 active users. 27 distinct features across 8 modules. We looked at three metrics for each feature: Adoption rate % of users who tried it at least once Retention rate % who used ... ... every day? We tracked the top 10% of accounts by engagement. Their most-used feature was the Workflow Engine. Not because they requested it. Because it saved them time. They built automation for content publishing, citation alerts, and competitor

... months ago, we ran an experiment with our own data. At Rankfender, we tracked 5 of our own competitors across 8 AI systems. We log their share of voice, citation velocity, content gaps, platform variance. Months of raw numbers sitting in a dashboard. I pulled

... with Platform X." Estimated: eight weeks. Claude's analysis: "What outcome does this drive? No stated goal. No success metric. No evidence customers are asking for this. Without these, this is a cost center disguised as a feature." We pulled our customer call transcripts from the past six months. 147 calls. Not one mention of Platform X. We pulled

... recently noticed a disturbing pattern with one of our most promising Intern/Junior Engineers. Month 1: They were shipping features daily. Fast. Hungry. Month 2: The velocity slowed down. Month 3: Silence began.. They hadn't opened a Pull Request in 2/3 days, even though their tasks were marked 'In Progress'. I assumed the worst: Burnout? Disengagement? Maybe they are overemployed? I pulled

... building an app together, or couples joining their forces to start a business from scratch. That s actually how NiceJourney started. I m a copywriter, my partner s a designer, and we decided to combine our skills. Then we pulled in a few close friends from marketing and communication to form a small, trusted team. The pros are many: We share the same values, so we naturally pull

... seeing going deep into Answer Engine Optimization (AEO) for @Meet-Ting . I spend a lot of my day in GPT and Gemini, and over the last few months I started getting more curious about how their recommendations are actually pulled from the web. I'm not an expert, listen to the podcast, but I am actively tackling it so have some tactical learnings. I mainly came across AEO by looking at competitors

... want to send a monthly email to my customers with their usage statistics of my platform. This means being able to pull data from my back-end, and put it in the email. I've been looking at Brevo but I don't think it can do it, so I turn to you, product folks => do you know a mailing tool where I can create template with custom data points being pulled

... transparency and focus on building for developers first. Your approach to multi-product strategy is inspiring doubling down on what works rather than trying to reinvent the wheel. Curious, as you scale, how do you balance shipping new products vs. refining the core experience for existing users? Comment from Sree Chintala(@sreechintala): @james_hawkins3  That makes sense! 100K ARR seems like a good point to start these reviews. How do you usually tell if a team is under-resourced ... ... look at backlog size, velocity, or something else? Comment from steve beyatte(@steveb): Huge fan of what you guys do. I feel like you do enterprise software but play the enterprise game completely different than your competitors