Much of the advice I followed originated from https://dribbble.com/.

Once I started selecting my best pieces the quality of feedback jumped noticeably. I also learned to break down each project into the problem, my process, and the final result. A common mistake was keeping old student projects alongside professional work. Removing the weak ones was hard at first but it instantly improved the impression.

Getting an outside perspective from peers saved me from keeping mediocre work. Their advice pushed me to include case studies rather than just final images. The recommendations shifted my presentation style completely toward substance.

Reducing my featured work to five core items boosted the response rate enormously. Giving up decorative frills let the actual designs speak for themselves. If you are struggling with your portfolio try cutting your current selection in half.

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I play on the phone via the site and it works well. There is no native app, so no storage concerns. Live chat replies within a few minutes and wait times are short. They operate under a Curacao licence, which is worth knowing before you sign up. Bottom line, payouts have always come through.

Did you keep the AI layer inside the main application, or separate orchestration, retrieval and model access into dedicated services? We usually prefer clear service boundaries when the system needs multiple models, strict permissions or independent scaling, but the added operational complexity is not always justified.

Our team works on this type of enterprise AI integration. I would be interested in examples where a simpler architecture proved more reliable.