Original Summary

my users' sales through my app passed $300k this weekend. i built the app alone. the first sale was in march, and there are now 1,000+ users. $74k+ has gone through it in the last 30 days. it's a chat tool for creators, so that's their sales, not my revenue. ai does most of the daily work now, but i got three things pretty badly wrong before figuring out what actually worked. give the ai a memory most of what i do with ai repeats. the same workflows come up every day, and not only in development, also in marketing and content. i use claude code, and it starts every session knowing nothing, so at first i explained the same things again every day and the results came out a bit different each time. so i gave it a memory: a folder of plain notes, one for each workflow and how it's done, one for each important decision and why, and a short daily log. the recent notes get loaded automatically when a session starts, and when a session ends the ai updates the notes that the day's work changed, so the workflows stay current as they evolve. two rules made the biggest difference: only write down things that are actually settled, and when the way you do something changes, update the note at the same time. the ai trusts what it reads, so a wrong note is worse than no note. i haven't had to re-explain how i work in months. don't let ai decide what "normal" means once an app has real users you need monitoring. i first had claude check the app every hour and decide whether things looked normal, and that was a mess. the same revenue baseline came out anywhere from $1,057 to $1,226 depending on the run, and 104 of 331 checks said critical. the better setup was boring sql. i set the thresholds myself, for example revenue in the last 24h under 40% of the previous week's daily average, or more than 10% of messages failing to send in the last hour. those checks run on a schedule, and the ai only gets involved when something goes from ok to not ok. then it investigates and tells me what happened. ai is good at explaining why something is wrong, but not at deciding what "normal" should be. keep doing support yourself for longer than feels necessary i'm at 1,000+ users and i still answer support manually. it's probably the least scalable thing i do, but it's where i learn the most. you hear what paying customers actually want, why they get stuck and why they leave, in their own words, and a dashboard doesn't show you that. i don't want to answer the same questions forever, so automating the repetitive ones is next, but i don't want to lose what i learn from doing it by hand. if you've automated support while still keeping the customer feedback loop, i'd be interested to hear how you did it and what you deliberately kept manual.   submitted by   /u/Crazy-Mountain6125 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/10/5 15:03:06