We all have that one repetitive thing. Takes 15 30 minutes. Feels like it could be automated. But you just keep doing it by hand. Every Friday, I manually copy data from three different dashboards into a Google Sheet, reformat the dates, and email it to my team. Takes 25 minutes. I've told myself "I'll automate this" for six months. Still doing it by hand.
If I ever need to open up some space, I just delete some apps temporarly. But even if it's just for a second, I would never uninstall Google Keep. What is the one app you'd never delete?
We re building an enterprise product for medical-device companies, and one thing I m learning is that validation doesn t come from just one person.
End users can tell us whether the product fits their workflow, but they may not control the budget. Buyers look at whether the problem is worth paying to solve. Then InfoSec and compliance may raise a completely different set of concerns. In our space, sales cycles are long and security reviews can become a major part of the process.
My current view is to start with user pain, bring buyers in early, and not wait until the end to involve security. But for an early-stage team, it s difficult to validate with everyone simultaneously.
For those building enterprise B2B products, what sequence has worked for you? Have you seen strong user interest still stall during the buying or security review?
These cute and furry creatures can sometimes tire people a lot. What are the issues that you have the most difficulty with regarding pet care and that you think there should be a solution?
I am a hardcore music lover and love to listen to it while working. But sometimes, I do feel like, listening to music impacts my productivity. Has anyone experienced the same? Do you have any tips to share, to me and to the community in general?
Lately, I ve been looking closely at how independent builders and small teams are managing AI knowledge bases. It feels like the default "industry standard" is to immediately reach for a complex RAG pipeline and a heavy, paid Vector Database.
But I'm starting to wonder if we are over-engineering this for 90% of standard use cases.
Vector DBs are incredibly powerful for massive scale, but for smaller or non-massive datasets, they can be expensive, complex to query, and act as complete black boxes. If a search returns a weird chunk, diagnosing it is often a nightmare.
