And it can be challenging to keep up! How do you supercharge your community growth? I don't understand how some communities really explode overnight suddenly. If you've cracked the code on building a thriving community, share your tips and tricks.
Naming is always a pain (even while coding). How do you proceed when you have a new product but can't find the right name? I've used Namelix but that doesn't really help For my previous startup we spent literally several weeks on this. What're your tips to get a good name?
I've spent a lot of time thinking about the difference between testing a product and validating it.
When you're building software, there are so many things you can measure. Does the functionality work? Are the calculations correct? Can users complete the workflow? Are the security controls working as intended?
Those are important questions, and you can spend months getting them right.
But there's another question that technical testing can't fully answer:
This year I purchased my first mouse with customisable keys - Logi M650, and I was mind-blown. I've always had a bad time remembering keyboard shortcuts and this mouse was a real life saver for me - as I could customise all my keyboard shortcuts on my mouse. it's the best gadget I purchased this year. What was your best purchase of 2022 that improved your productivity?
As the title suggests, I want to discuss strategies that SaaS companies have implemented or hope to implement on converting their free users into a free trial, and hopefully a paid plan. What have your most successful campaigns been vs some of your worst to get these conversions? Hearing the details of both successful & non-successful campaigns could hopefully streamline the process for other startups and help increase paid users!
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.
