When I was at university, one professor of electrical engineering would refuse to answer any student who asked whether their answer to a circuit theory problem was correct.
When asked why he would refuse to answer, he would say: "If you aren't confident that your answer is correct, then I would like to mark you as wrong even though you may be right. You are the expert. You should be telling me that your answer is right, not asking me whether it is right."
He would also say that another acceptable answer occasionally might be "Your question is wrong" (along with an explanation as to why it is wrong).
This professor's attitude bothered me at first when I was on the receiving end of this advice, but I have come to regard it as some of the best advice I have received.
Especially in a professional context, I have found that it pays to convince myself that my recommended solution is a good solution, perhaps by confirming it using a few independent methods, and also by anticipating the responses of detractors, and coming prepared with answers to likely objections that others might offer.
Yeah the road to confidence involves some intermediary steps where you begin to understand that your answers are correct, and walking that path brings you to confidence imo.
I don't know why this antididactical bullshit is tolerated in STEM or even praised. Every single research in the field show us that feedback is one if not the most important part of learning, that's why self teaching is so difficult, but problem sets without solutions, no guidance on how to approach problem solving and, my favourite, fear of memorization and firm knowledge are the rule in that world. They were just annoying, frustrating, full of themselves and generally poor teachers.
To get over the fear of falling over backwards, you must learn to turn your tree trunk like fall into a collapse to a ball and then a forward roll. If you have a bit of muscle on your back and shoulders rolling even on a hard floor should not be too uncomfortable. (A crash mat is still a good idea though.)
You think it's the balance, but consider that the more strength you have, the less balance you need. For example with more forearm strength you will be able to lean further onto your fingertips. With more shoulder, arm, and abdominal strength, starting from a handstand position you will be able to pivot at your shoulders and bend all the way down to a planche position. So work on your strength, and handstands will become a lot easier.
As far as I can tell, all handstand to planche, and frankly all planche videos are CGI, because this move is impossible. /s
Interesting thing about this one is that he doesn't put is palms down but balances on the fingers and thumbs. I put the weight on my palms, most youtube handstand explainers say that's ok.
I developed this F1 statistics site using a low-code database app creation tool that I also developed (flatpackapps.com). Constructive feedback is welcome.
I've created a Formula 1 statistics database using a freely available data set.
To create the F1 online database, I used a tool that I developed called Flatpack Apps (flatpackapps.com) That tool will be the subject of a future 'Show HN' submission.
F1 fans might be particularly interested in the 'teammate battles' section. By using this feature, one can get a very good idea of how a driver has progressed throughout his career relative to his teammates.
For example, with Sebastian Vettel having performed poorly last season, many people discount his four world championships (saying his success was mostly due to a dominant car), and discount even further the rating of his teammate at the time Mark Webber, who struggled in those years relative to Vettel.
However, browsing the teammate battles of Mark Webber over his career [1], it is clear that before he was teamed with Seb Vettel, Mark Webber was a stellar performer. He dominated over each one of his seven other teammates in qualifying, including Nico Rosberg and David Coulthard.
I plan to add charts to the teammate battles feature, and have some ideas on how to algorithmically find the best driver of all time using the teammate battles data (which has already been done very well here [2]), but will always be a fun exercise.
