After outsourcing for three years, it will be discarded after it is done, and only software will be installed in the end.
For young people, they need work, but also life.
For young people, they need to work hard, and they need "bread".
For young people, planning and development are needed.
As for outsourcing, although it was not completely abandoned, in those three years, it was almost three years of business trips, three years of playing games, and three years of neglect. In those three years, the technical level was almost zero growth. To be honest, the only one who has the age to grow up, the understanding of the machine vision industry, and the hours of staying up late and working overtime, his body is almost useless. The others didn't grow at all. Most importantly, I lost interest in women.
The analysis that machine vision engineers cannot do outsourcing is as follows?
The first point: mechanical, repetitive "screw turning"
Everyone who has just graduated or worked in the field of machine vision for a few years enters this industry with a learning attitude. There is no need to lower your own requirements, but you can choose to enter an outsourcing company with a grievance. In order to learn higher technology, you should choose an excellent company platform carefully.
The second point: the body is tired, and the heart is even more tired.
No one is leading the team, it's all on your own. Only by staying up late and working overtime can the project progress be accelerated. Working overtime desperately does not necessarily have a good result. Facing customers directly, I don’t have much experience in communicating with customers. Extremely exhausted physically and mentally. There is only one last straw left to crush you, which is to leave the job after all.
The third point: meager wages
There is no overtime pay for overtime work, and there is no holiday every week. If you ask the leader to take a break, you may be forced to resign. The reason for leadership is that if others don't rest, why should you rest. Looking at the salary, after all, he is resigning.
The fourth point: the good learning technology, it turned out to be parameter adjustment,
The company is full of standardized software, which is not used in the market at all, and the detection results are poor and muddled, and there is no way to solve it in research and development. I will use the application to solve it on weekends. see what other people use