For example: https://twitter.com/LadyAshBorg/status/1378237638671364099But the actual reality is closer to this: https://huyenchip.com/2021/02/27/why-not-join-a-startup.htmlThe same founders will lay you off in a heartbeat if the situation demands, and they keep on preaching to naive employees about trust, loyalty, and shit!PS: To be clear, I'm not totally against working at a startup, if you...
What is the best way to build a personal website in 2020? I'm unable to decide between netlify vs zeit now and next.js vs gatsby.js?Though I really like next.js and it has an added advantage that it can sometimes be used professionally also to build full featured application unlike gatsby.js.Which platform between netlify and zeit now is best for deployment? my concerns are privacy and...
I'm all for replacing all the network application with dapps, but in my opinion the bottleneck for wide acceptance of dapps is that you have to always keep your identity and secret key safe (in Blockstacks's case) and if you lose that, you lose everything in the app. This is not convenient for older people or for much younger people who could easily lose those information or anyone who is not...
I really wanted to know about how much time you all spend learning new things in your field or a different field after a full-time day job on weekdays and weekends?And How is your learning time distributed between MOOCs and Books?I just want to calibrate the general theme towards learning habits. For me, I spend 2 hours on weekdays and 5-6 hours on weekends learning new things in my field,...
What do you think is more valuable in learning new subjects? What should one focus on?Personally, I learn more easily with MOOCs but retain more with books. Just want to know what is your preferred medium and why? Is there any research about it?
Especially with twitter, consuming information feels like the red queen effect, it just feels like chasing, chasing, chasing...How do you deal with it?
As an example, I'm looking for a data scientist job and as you might guess, it is a very broad field. So, I thought I would learn the basics of machine learning, deep learning, required maths and programming and then I would search for the job and in whatever field I'll get the job like Natural language processing, computer vision, supply chain analytics, time series analysis, etc. , I would learn that and specialize in that field. But when I started looking for job every start-up wanted that I know current state-of-the-art techniques of their field, which is simply not possible as each start-up specialize in a different set of techniques, for example, some want deep learning, some statistics, some time series analysis, some big data processing frameworks like Apache Spark, some recommendation system and so on and so forth. As a result, I'm still searching for a job and somewhat have lost passion for data science.
So, the only learning that I got from going through this process is whatever field you choose, you have to specialize in that field to have a great career.
It's okay to learn Algorithms and Data structure once and practise questions to be good at it but once you leave practising you forget almost all the details and when time comes to interview for next company, you start to practise all over again to pass the interview which is quite inefficient and tedious.What strategies do you use to keep up with these interview questions every 2-3 years...