The challenges of scaling AI solutions We recently built a small AI prototype in our team that actually worked pretty well in tests, but once we tried to move it closer to production, everything slowed down. Data pipelines weren’t ready, edge cases kept popping up, and suddenly the excitement faded. It made me wonder if this is a common wall teams hit or if we just underestimated the gap between a demo and a real product. Curious how others deal with that messy middle stage.
I’ve been messing around with a couple of AI clothes remover tools lately, mostly out of curiosity, and something I keep wondering about is batch processing. Doing one image at a time is fine when you’re testing things, but once you have a whole folder you want to experiment on, it becomes painfully slow https://goloveai.com/gpt-girlfriend . Has anyone here actually tried batch mode with any of these tools? I’m curious whether it works smoothly or just ends up glitching half the results.
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I’m redoing my bathroom and got stuck on the tile part because there are too many options. I keep going back and forth between matte porcelain and something with a bit of texture, mostly because I’m worried about slipping when the floor is wet. If anyone has gone through a full remodel recently, how did you decide on the right tile and what ended up mattering more in real use—style, safety, or durability?
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