Hi everyone

We re launching Needle AI Agents tomorrow!

Our goal is to help teams automate processes and build AI Agents with context of their own data sources (Google Drive, Notion, Slack, etc.).

It's fascinating how a book's impact can hinge on the timing of when you read it. The right message at the right moment can be a genuine game-changer, while the same book at a different time might not resonate as strongly.

For me, "Deep Work" by Cal Newport was that book. I received it from my former VP during an incredibly busy period, and it completely transformed my approach to work. Now, I find myself referencing the "brain penalty" at least once a month. It's a powerful concept that explains the high cost of switching between tasks after being interrupted a lesson that has stuck with me and profoundly changed the way I work.
What is the best lesson you learned from a book and which one would you recommend reading to anyone starting the founding journey?

Today we launch our private beta on Product Hunt.

Ellie is a language learning app, that is built to end one-size-fits-all learning paths. We aim that our learners finally learn what they want to learn: the things they actually use!

It won't be a perfect version, but we can use all support with just a simple upvote to continue building this product.
Get a free invite for our private beta via the page!

https://www.producthunt.com/post...

My environment consists of many solo founders.

However, when I see someone is already starting to hire an employee, the question crosses my mind:
What profit has that person achieved when they can afford to pay another extra person?

I'm seeing a growing trend of B2C products actively advertising their AI features as a USP, claiming AI being the prime solution.

However, being back in my hometown for a weekend, I've heard a lot of apprehension around data privacy and a general lack of understanding "what happens in that blackbox". Nothing I hear very often back in Berlin, so demographic differences are clearly playing a big role in user receptiveness.

Transparency is crucial, no doubt. Advertising AI on platforms like producthunt or in decks for investors makes a lot of sense - that's the right audience.
But are we far enough along the AI adoption curve for "AI-powered" to be a major selling point on the customer-facing side? Or are we scaring off potential users with concerns about data usage and complexity?
Let's discuss!

• Have you seen AI transparency hurt or help your user acquisition efforts?

• How are you addressing user concerns about AI?

From testing new products, I've come to several conclusions and possibly also identified mistakes that product creators make when launching them.

Whenever you want to attract your first testers (especially those who are well-known), try first to build up your credibility.