AI

AI as a Mini-Product Manager

Hacker News

AI as a Mini-Product Manager

Hey folks, I'm Yuval. I run a tiny startup called Glitter AI. It's just me full-time here, with a couple of freelances to help here and there. A couple of months ago, I went from managing zero requests to hundreds -- overnight (won Product of the day on Product Hunt). As someone who gets VERY easily distracted (maybe you relate), I had to find some sort of way of handling all the chaos if I didn't want to burn out. I came up with a pretty cool automation flow that I thought folks on HN here may be interested in reading about :) So here goes: Most of my interactions come through Intercom. After the product hunt launch, I got waves of bug reports and feature requests. So I link every incoming message to a webhook. I do manually trigger this step through Intercom after replying to the customer (so there's context for the AI - more on that in a bit), but then everything from there on out is automatic. Next, Make.com grabs the conversation details and runs them through prompts that guide OpenAI to "act like a savvy product manager." The task is to act like a customer-support-rep-meets-PM: parse and summarize conversations into actionable points—be it a bug report, feature request, or feedback (I'll include a link to a post with the full prompt below). But summarizing wasn’t enough. I needed this flow to live somewhere, and to categorize everything. I chose Notion, but it could have just as easily been Airtable or the likes (or Excel, honestly). I call the space in Notion my "Second Brain." Once in Notion, every request is linked back to its original Intercom conversation, which helps me quickly see patterns like repeated feature requests or pesky recurring bugs. It’s like having a high-level map of what users need. Then in Notion I do manually quickly go through the summary and tags to ensure everything is in place. I then attach those clean summaries to existing pages using a variation of the RICE framework to prioritize my action items. Every page counts the number of requests linking to it, so I can keep track of recurring bug reports / feature requests / product feedback etc and prioritize accordingly. I can't include screenshots here, so I'm linking to my original post. It's mostly the same content as here, but with the prompt and a bit more detail, if you're interested: https://www.glitter.io/blog/how-i-use-ai-and-automation-to-r...

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Actual performance

6points
3comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, context, openai · Missing: mac, agents, macos
98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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