I stayed away from reviews and forums for a while. When I went back to read and watch videos about some equipment I realized that people complain so much about the equipments imperfections. So i started to think: why not see the good things instead of the bad things? If the bad things do not make the equipment impossible to use, so why not accept that and be happy? Why not deal with the limitations?
Of course we all want the best equipment, but deal with imperfections and limitations is important to make things happens with the available money...
I know that the manufacturers release new features in new models in a planed obsolescense. And best equipment are higher price. It is about money and capitalism. They need profit. So i decided to live with what i can pay and be happy.
The best example i found is the G95. Many positive points: cheap, v-log, headphones output, 4k without aliasing, good cleaning with neatvideo, ibis, continous af face/eye detection, good codec performance, no significant overheat and people complain about lots of things... oohh!!! The 4k crop is heavy, oohh!!! There is a minimal aliasing in 1080p, oohh!!! There is no xlr audio input, oohh!!! It is plastic and fragile, oohh!!! There is noise in video, oohh!!! The battery life is short, oohh!!! autofocus is not perfect, oohh!!! There is no 4k60p, oohh!!! There is no 10bit 422, oohh!!! The codec is only 20/28/100Mbps, oohh!!! I want an Arri, a Red, a Sony FX in the price of G95!!!
But there is a positive point about complain. I believe the manufacturers follows the internet reaction to their equipments and the improvements probably happens because they see a market for a better product.
Maybe the reason for complain is because most people do not have money to buy high price top pro equipment. So they keep complain wanting pro features and top quality in cheap equipment.
I can tell you big secret.
People complain because they are set apart from production and design.
Idea of dedicated product design team. market research and production is outdated.
But replacement is not ready yet. Both production and neural networks are not so mature.
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