Product feedback
Get product feedback from people who will actually try it.
Friends lie to protect your feelings. Strangers who have no stake in your success will tell you the truth. Tamkar is built to connect founders with the second group—people who try new products, notice what works, and give feedback you can act on.
The difference between useful feedback and noise
Useful product feedback is specific, grounded in a real experience, and connected to a decision you can make. Noise is vague praise, uncalibrated criticism, or advice that ignores what you are actually building.
- 'The onboarding confused me at step three' is useful.
- 'This is interesting, keep going' is not.
- 'I expected X but got Y' is the most useful sentence an early user can give you.
How Tamkar structures feedback collection
Every Tamkar product launch includes one focused question chosen by the founder. Visitors try the product, then respond to that question directly. The question filters the response toward what the founder needs to learn—not what is easiest to say.
Who gives feedback on Tamkar
The Tamkar community includes other founders who understand the early-stage product experience, early adopters who try new tools regularly, and curious people who bring a fresh perspective without prior assumptions about your product.
Use feedback to make a real decision
Feedback that does not change anything is wasted. After each launch round, commit to one decision it informs: rewrite a landing page, fix an onboarding step, change a pricing tier, or cut a feature nobody understood. Then come back for the next question.
Common questions
How do I get product feedback from real users?
Give users a clear product context and ask about one decision at a time. Tamkar helps founders share that context and collect focused reactions.
What is a good product feedback question?
Ask something answerable from experience, such as what was confusing, whether the value was clear, or which workflow felt most useful.
How do I turn feedback into product decisions?
Look for repeated patterns, separate observations from requests, and choose the changes that reduce the biggest uncertainty or friction.